modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
New-videos-mezzo-fun-viral-Clips/FULL.VIDEO.Mezzo.fun.Viral.Video.Tutorial.Official
|
New-videos-mezzo-fun-viral-Clips
| 2025-06-23T17:33:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-23T17:32:31Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
tasal9/Multilingula-ZamAI-Embeddings
|
tasal9
| 2025-06-23T16:59:36Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"multilingual",
"embeddings",
"semantic-search",
"RAG",
"llamaindex",
"chromadb",
"vector-search",
"pashto",
"sentence-similarity",
"en",
"ps",
"dataset:custom-multilingual-corpus",
"base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
"base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-06-21T11:55:14Z |
---
license: apache-2.0
datasets:
- custom-multilingual-corpus
language:
- en
- ps
metrics:
- retrieval-accuracy
- mAP
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
pipeline_tag: sentence-similarity
library_name: sentence-transformers
tags:
- multilingual
- embeddings
- semantic-search
- RAG
- llamaindex
- chromadb
- vector-search
- pashto
---
# Multilingula-ZamAI-Embeddings
[](https://huggingface.co/tasal9/Multilingula-ZamAI-Embeddings-repo)
**Multilingula-ZamAI-Embeddings** is a multilingual sentence embedding and retrieval system, designed for efficient semantic search and retrieval-augmented generation (RAG) pipelines across many languages. This model and index enable language-agnostic document retrieval for downstream applications such as chatbots, search, and question-answering.
---
## Model Details
- **Type**: Multilingual Sentence Embeddings with Vector Index
- **Embedding Model**: [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
- **Languages Supported**: 50+ (including English, Pashto, Arabic, Urdu, Farsi, Turkish, Hindi, Chinese, French, and more)
- **Vector Store**: [ChromaDB](https://www.trychroma.com/) (Persistent, ready for retrieval)
- **Indexing Framework**: [LlamaIndex](https://github.com/jerryjliu/llama_index)
- **Repository Maintainer**: [tasal9](https://huggingface.co/tasal9)
---
## Usage
### Load and Query the Embedding Index Locally
```python
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext, VectorStoreIndex
import chromadb
# Download the index files (chroma_db) to your working directory
# Initialize ChromaDB in persistent mode
chroma_client = chromadb.PersistentClient(path="./chroma_db")
collection = chroma_client.get_or_create_collection("my_collection")
vector_store = ChromaVectorStore(chroma_collection=collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
# Set up the embedding model (same as used for indexing)
embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
# Load the index
index = VectorStoreIndex.from_vector_store(
vector_store=vector_store,
embed_model=embed_model,
storage_context=storage_context
)
# Create a query engine and ask questions in any language
query_engine = index.as_query_engine()
result = query_engine.query("What is the capital of Afghanistan?")
print(result)
```
---
### Simple Embedding Demo
You can use the included simple_demo.py script to see how well the model works with multilingual content:
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
# Encode sentences in multiple languages
sentences = [
"This is a sample sentence in English.",
"دا په پښتو کې یوه نمونه جمله ده.", # This is a sample sentence in Pashto
]
# Generate embeddings
embeddings = model.encode(sentences)
# Use these embeddings for similarity, clustering, etc.
```
---
### Example Applications
- **Multilingual Semantic Search**
- **Retrieval-Augmented Generation (RAG) Pipelines**
- **Document and Knowledge Base Q&A**
- **Chatbots supporting multiple languages**
---
## Recommended Embedding Models
| Model | Languages Supported | Dimensions | HF Link |
|---------------------------------------------|--------------------|------------|-----------------------------------------------------------------------------------------------|
| paraphrase-multilingual-MiniLM-L12-v2 | 50+ | 384 | [HF](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) |
| paraphrase-multilingual-mpnet-base-v2 | 50+ | 768 | [HF](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) |
| LaBSE | 109 | 768 | [HF](https://huggingface.co/sentence-transformers/LaBSE) |
---
## How to Reproduce or Extend
This model is part of the ZamAI project, focused on Pashto language models. You can find the full code and more resources at the [ZamAI GitHub repository](https://github.com/your-github-username/ZamAI).
## Getting Started
1. Install the dependencies:
```bash
pip install -r models/embeddings/requirements.txt
```
2. Add documents to index:
```bash
# Place your text files in the data/text_corpus directory
python models/embeddings/indexer.py --corpus data/text_corpus/
```
3. Run the demo application:
```bash
python models/embeddings/demo.py
```
## Using the Embeddings in Your Code
```python
from models.embeddings.setup import setup_embedding_model
# Initialize the model and related components
embedding_components = setup_embedding_model()
# Get the query engine
query_engine = embedding_components["query_engine"]
# Query in any language
result = query_engine.query("What is the capital of Afghanistan?")
# Or in Pashto
result = query_engine.query("د افغانستان پلازمېنه څه ده؟")
print(result)
```
## Reference
This implementation is based on the [Multilingula-ZamAI-Embeddings](https://huggingface.co/tasal9/Multilingula-ZamAI-Embeddings) model from Hugging Face.
|
xsvmmy/finetune_llama3
|
xsvmmy
| 2025-06-23T16:22:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mllama",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-23T16:15:34Z |
---
base_model: unsloth/llama-3.2-11b-vision-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** xsvmmy
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-11b-vision-instruct-bnb-4bit
This mllama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
lucadang/Qwen2.5-7B-Sudoku-SFT
|
lucadang
| 2025-06-23T16:02:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T15:45:07Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
model_name: Qwen2.5-7B-Sudoku-SFT
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for Qwen2.5-7B-Sudoku-SFT
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="lucadang/Qwen2.5-7B-Sudoku-SFT", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/nddang-luca/huggingface/runs/p0td31i0)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.0
- Transformers: 4.52.4
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
hardrave/medicanite-granite-3.3-2b-instruct
|
hardrave
| 2025-06-23T15:47:25Z | 5 | 0 | null |
[
"safetensors",
"granite",
"en",
"base_model:ibm-granite/granite-3.3-2b-instruct",
"base_model:finetune:ibm-granite/granite-3.3-2b-instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-06-22T09:16:07Z |
---
license: apache-2.0
language:
- en
base_model:
- ibm-granite/granite-3.3-2b-instruct
---
# Medicanite – Fine-Tuned Medical Language Model (Granite-3.3-2B-Instruct)
## Model Summary
**Medicanite** is a fine-tuned, **2.53-billion parameter** instruction-following model specialized for medical and healthcare-related tasks. Built on top of [`Granite-3.3-2B-Instruct`](https://huggingface.co/ibm-granite/granite-3.3-2b-instruct), Medicanite is optimized for factuality and QA performance in clinical domains.
It was trained on the **MedQUAD** corpus (pre-cleaned) using 15,586 curated QA pairs for training and 821 for validation, with a low learning rate and LoRA (r=4, α=16) adaptation strategy. Despite its compact size, Medicanite outperforms both its base model and other leading open medical models in its class.
---
## 🔬 Benchmark Performance
| Task | Medicanite | Granite-3.3-2B | JSL-MedPhi2-2.7B |
|------------------------------|------------|----------------|------------------|
| MedMCQA | **40.90%** | 40.50% | 38.90% |
| MedQA | **43.80%** | 42.50% | 38.41% |
| MMLU Anatomy | **53.30%** | 51.90% | 48.89% |
| MMLU Clinical Knowledge | 57.70% | 60.80% | 63.02% |
| MMLU College Biology | 59.00% | 59.00% | 62.50% |
| MMLU College Medicine | 53.80% | 54.30% | 58.38% |
| MMLU Medical Genetics | **67.00%** | 62.00% | 62.00% |
| MMLU Professional Medicine | **54.00%** | 51.80% | 44.12% |
| PubMedQA | 72.80% | 71.00% | 73.00% |
| **Average** | **55.82%** | 54.86% | 54.36% |
---
## 🛠️ Training Details
- **Base model:** `ibm-granite/granite-3.3-2b-instruct`
- **Training corpus:** MedQUAD (cleaned)
- **# Training examples:** 15,586
- **# Validation examples:** 821
- **LoRA rank (r):** 4
- **Alpha:** 16
- **Learning rate:** 7e-6
- **Precision:** bfloat16
---
## 🩺 Intended Use
This model is optimized for:
- Medical question answering
- Clinical knowledge tasks
- Instruction-following in health-related domains
- Educational use in biomedical fields
- Local/offline deployment for low-resource settings
---
## ⚠️ Limitations
- **Not a diagnostic tool.** Medicanite is intended for research and educational purposes only.
- May hallucinate facts or give outdated/incomplete medical advice.
- Always consult a licensed medical professional for clinical decisions.
---
## 📜 License
Apache 2.0 — same as the base Granite model.
---
|
billjeremy/a2c-PandaReachDense-v3
|
billjeremy
| 2025-06-23T15:46:24Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-23T15:42:14Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.15 +/- 0.08
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
akashmadisetty/Nayana-IR-merged-colpali-v1_3-DescVQA-4bit
|
akashmadisetty
| 2025-06-23T15:37:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T15:37:47Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
SsharvienKumar/SASVi
|
SsharvienKumar
| 2025-06-23T15:05:05Z | 0 | 0 | null |
[
"arxiv:2502.09653",
"license:cc-by-4.0",
"region:us"
] | null | 2025-06-23T14:26:03Z |
---
license: cc-by-4.0
---
<div id="top" align="center">
# SASVi - Segment Any Surgical Video (IPCAI 2025)
[](https://arxiv.org/abs/2502.09653)
[](https://link.springer.com/article/10.1007/s11548-025-03408-y)
[](https://huggingface.co/SsharvienKumar/SASVi)
</div>
## Overview
SASVi leverages pre-trained frame-wise object detection and segmentation to re-prompt SAM2
for improved surgical video segmentation with scarcely annotated data.
## Example Results
* You can find the complete segmentations of the video datasets [here](https://huggingface.co/SsharvienKumar/SASVi/tree/main/dataset).
* Checkpoints of the all the overseers can be found [here](https://huggingface.co/SsharvienKumar/SASVi/tree/main/checkpoints).
## Setup
* Create a virtual environment of your choice and activate it: `conda create -n sasvi python=3.11 && conda activate sasvi`
* Install `torch>=2.3.1` and `torchvision>=0.18.1` following the instructions from [here](https://pytorch.org/get-started/locally/)
* Install the dependencies using `pip install -r requirements.txt`
* Install SDS_Playground from [here](https://github.com/MECLabTUDA/SDS_Playground)
* Install SAM2 using `cd src/sam2 && pip install -e .`
* Place SAM2 [checkpoints](https://github.com/facebookresearch/sam2/tree/main#model-description) at `src/sam2/checkpoints`
* Convert video files to frame folders using `bash helper_scripts/video_to_frames.sh`. The output should be in the format:
```
<video_root>
├── <video1>
│ ├── 0001.jpg
│ ├── 0002.jpg
│ └── ...
├── <video2>
│ ├── 0001.jpg
│ ├── 0002.jpg
│ └── ...
└── ...
```
## Overseer Model Training
We provide training scripts for three different overseer models (Mask R-CNN, DETR, Mask2Former)
on three different datasets (CaDIS, CholecSeg8k, Cataract1k).
You can run the training scripts as follows:
`python train_scripts/train_<OVERSEER>_<DATASET>.py`
## SASVi Inference
The frames in the video needs to be extracted beforehand and placed in the formatting above. More optional arguments can be found in the script directly.
```
python src/sam2/eval_sasvi.py \
--sam2_cfg configs/sam2.1_hiera_l.yaml \
--sam2_checkpoint ./checkpoints/<SAM2_CHECKPOINT>.pt \
--overseer_checkpoint <PATH_TO_OVERSEER_CHECKPOINT>.pth \
--overseer_type <NAME_OF_OVERSEER> \
--dataset_type <NAME_OF_DATASET> \
--base_video_dir <PATH_TO_VIDEO_ROOT> \
--output_mask_dir <OUTPUT_PATH_TO_SASVi_MASK> \
--overseer_mask_dir <OPTIONAL - OUTPUT_PATH_TO_OVERSEER_MASK>
```
## nnUNet Training & Inference
Fold 0: `nnUNetv2_train DATASET_ID 2d 0 -p nnUNetResEncUNetMPlans -tr nnUNetTrainer_400epochs --npz`
Fold 1: `nnUNetv2_train DATASET_ID 2d 1 -p nnUNetResEncUNetMPlans -tr nnUNetTrainer_400epochs --npz`
Fold 2: `nnUNetv2_train DATASET_ID 2d 2 -p nnUNetResEncUNetMPlans -tr nnUNetTrainer_400epochs --npz`
Fold 3: `nnUNetv2_train DATASET_ID 2d 3 -p nnUNetResEncUNetMPlans -tr nnUNetTrainer_400epochs --npz`
Fold 4: `nnUNetv2_train DATASET_ID 2d 4 -p nnUNetResEncUNetMPlans -tr nnUNetTrainer_400epochs --npz`
Then find the best configuration using
`nnUNetv2_find_best_configuration DATASET_ID -c 2d -p nnUNetResEncUNetMPlans -tr nnUNetTrainer_400epochs`
And run inference using
`nnUNetv2_predict -d DATASET_ID -i INPUT_FOLDER -o OUTPUT_FOLDER -f 0 1 2 3 4 -tr nnUNetTrainer_400epochs -c 2d -p nnUNetResEncUNetMPlans`
Once inference is completed, run postprocessing
`nnUNetv2_apply_postprocessing -i OUTPUT_FOLDER -o OUTPUT_FOLDER_PP -pp_pkl_file .../postprocessing.pkl -np 8 -plans_json .../plans.json`
## Evaluation
* For frame-wise segmentation evaluation:
* `python eval_scripts/eval_<OVERSEER>_frames.py`
* For frame-wise segmentation prediction on full videos:
* See `python eval_scripts/eval_MaskRCNN_videos.py` for an example.
* For video evaluation:
1. E.g. `python eval_scripts/eval_vid_T.py --segm_root <path_to_segmentation_root> --vid_pattern 'train' --mask_pattern '*.npz' --ignore 255 --device cuda`
2. E.g. `python eval_scripts/eval_vid_F.py --segm_root <path_to_segmentation_root> --frames_root <path_to_frames_root> --vid_pattern 'train' --frames_pattern '*.jpg' --mask_pattern '*.npz' --raft_iters 12 --device cuda`
## TODOs
* [ ] **The code will be refactored soon to be more modular and reusable!**
* [ ] Pre-process Cholec80 videos with out-of-body detection
* [ ] Improve SASVi by combining it with GT prompting (if available)
* [ ] Test SAM2 finetuning
## Citation
If you use SASVi in your research, please cite our paper:
```
@article{sivakumar2025sasvi,
title={SASVi: segment any surgical video},
author={Sivakumar, Ssharvien Kumar and Frisch, Yannik and Ranem, Amin and Mukhopadhyay, Anirban},
journal={International Journal of Computer Assisted Radiology and Surgery},
pages={1--11},
year={2025},
publisher={Springer}
}
```
|
yu3733/paligemma2-3b-lora-vqa-v21-enhanced-d8000-r4
|
yu3733
| 2025-06-23T14:57:12Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"paligemma",
"lora",
"adapter",
"visual-question-answering",
"image-to-text",
"v2.1-enhanced",
"en",
"base_model:google/paligemma2-3b-mix-224",
"base_model:adapter:google/paligemma2-3b-mix-224",
"region:us"
] |
image-to-text
| 2025-06-23T14:56:55Z |
---
tags:
- paligemma
- lora
- adapter
- visual-question-answering
- image-to-text
- v2.1-enhanced
base_model: google/paligemma2-3b-mix-224
language:
- en
library_name: peft
---
# paligemma2-3b-lora-vqa-v21-enhanced-d8000-r4 - v2.1 Enhanced
This is a **v2.1 Enhanced** LoRA adapter for PaliGemma-2 3B trained on VQA tasks.
## 🆕 v2.1 Enhanced Improvements
- **EOS Token Learning**: Explicit EOS tokens for better generation termination
- **Memory Optimization**: 16-step gradient accumulation for stability
- **VizWiz Format Support**: Full support with most frequent answer selection
- **Robust Label Masking**: Enhanced prompt masking during training
- **Production Memory Management**: Advanced garbage collection
## Usage
```python
from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
from peft import PeftModel
import torch
from PIL import Image
# Base model
base_model_id = "google/paligemma2-3b-mix-224"
adapter_id = "yu3733/paligemma2-3b-lora-vqa-v21-enhanced-d8000-r4"
# Load processor
processor = AutoProcessor.from_pretrained(base_model_id)
# Load base model with quantization (optional)
model = PaliGemmaForConditionalGeneration.from_pretrained(
base_model_id,
torch_dtype=torch.float16,
device_map="auto"
)
# Load LoRA adapter
model = PeftModel.from_pretrained(model, adapter_id)
# Prepare input
image = Image.open("your_image.jpg")
prompt = "<image>\nQuestion: What is in this image?\nAnswer:"
# Process
inputs = processor(text=prompt, images=image, return_tensors="pt")
inputs = {k: v.to(model.device) for k, v in inputs.items()}
# Generate
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=20)
# Decode
print(processor.decode(outputs[0], skip_special_tokens=True))
```
## Training Configuration
- **Base Model**: google/paligemma2-3b-mix-224
- **LoRA Rank**: 4
- **Training Framework**: PEFT + Transformers
- **Optimization**: 4-bit quantization + gradient checkpointing
- **Dataset**: VizWiz VQA
## License
Same as the base model (see google/paligemma2-3b-mix-224)
|
leobianco/npov_RM_google_S130104_LLM_false_STRUCT_false_epo3_lr1e-3_r8_2506231452
|
leobianco
| 2025-06-23T14:56:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T14:52:48Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
annasoli/Qwen2.5-14B-Instruct_R1-DP24-LR2e-5-A256_bad-medical-topics_T
|
annasoli
| 2025-06-23T14:50:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T14:46:28Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
gsarch/ViGoRL-7b-Web-Grounding
|
gsarch
| 2025-06-23T14:43:39Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"arxiv:2505.23678",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-13T21:09:31Z |
---
library_name: transformers
pipeline_tag: image-text-to-text
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
---
# ViGoRL: Visually Grounded Reinforcement Learning for Visual Reasoning
This model card describes the ViGoRL (**Vi**sually **G**r**o**unded **R**einforcement **L**earning) model, introduced in our paper ["Grounded Reinforcement Learning for Visual Reasoning"](https://arxiv.org/abs/2505.23678).
**Authors:** Gabriel Sarch, Snigdha Saha, Naitik Khandelwal, Ayush Jain, Michael J. Tarr, Aviral Kumar, Katerina Fragkiadaki
---
## Model Overview
ViGoRL is a vision-language model fine-tuned using reinforcement learning (RL) to explicitly anchor textual reasoning steps to visual coordinates. Inspired by human visual cognition, ViGoRL employs multi-turn visual grounding, dynamically zooming into image regions to perform fine-grained visual reasoning and grounding.
This model was trained using supervised fine-tuning (SFT) on visually-grounded reasoning traces generated via Monte Carlo Tree Search (MCTS), followed by reinforcement learning with Group Relative Policy Optimization (GRPO).
---
## Model Details
* **Base Architecture:** Qwen2.5-Vision-Language (3B or 7B parameters)
* **Training Paradigm:**
* Supervised Fine-Tuning on MCTS-generated reasoning traces
* Group Relative Policy Optimization (GRPO)
* Multi-turn visual grounding with dynamic zoom-in feedback (if "Multiturn" appears in name)
---
## Use Cases
This model excels in visual reasoning tasks that require precise visual grounding and region-level reasoning. Please see model name for specific domain.
* **Spatial Reasoning:** SAT-2, BLINK, RoboSpatial
* **Visual Search:** V\*Bench
* **Web Interaction and Grounding:** ScreenSpot (Pro and V2), VisualWebArena
---
## Usage
You can load this model easily using Hugging Face's Transformers library:
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
# # default: Load the model on the available device(s)
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
# "gsarch/ViGoRL-7b-Web-Grounding", torch_dtype="auto", device_map="auto"
# ) # replace with any of the ViGoRL models
# We recommend enabling flash_attention_2 for better acceleration and memory saving.
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"gsarch/ViGoRL-7b-Web-Grounding",
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map="auto",
)
# default processer
processor = AutoProcessor.from_pretrained("gsarch/ViGoRL-7b-Web-Grounding")
# The default range for the number of visual tokens per image in the model is 4-16384.
# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained("gsarch/ViGoRL-7b-Web-Grounding", min_pixels=min_pixels, max_pixels=max_pixels)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "path/to/image.png",
},
{"type": "text", "text": "QUERY HERE"},
],
}
]
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=512)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text) # this will output a single tool call turn of the model if version is multiturn.
```
**Important**: This model requires a system prompt for proper usage. Please see the model's chat template for details.
---
## Datasets and Training Data
Training datasets and generated reasoning chains are publicly available:
* [Code](https://github.com/Gabesarch/grounded-rl)
* [ViGoRL Datasets on Hugging Face](https://huggingface.co/datasets/gsarch/vigorl_datasets)
---
## Citation
If you use ViGoRL in your research or applications, please cite our paper:
```bibtex
@article{sarch2025vigorl,
title={Grounded Reinforcement Learning for Visual Reasoning},
author={Sarch, Gabriel and Saha, Snigdha and Khandelwal, Naitik and Jain, Ayush and Tarr, Michael J and Kumar, Aviral and Fragkiadaki, Katerina},
year={2025}
}
```
---
## Contact
For questions, feedback, or collaborations, please reach out to Gabriel Sarch or open an issue in our [GitHub repository](https://github.com/Gabesarch/grounded-rl).
---
|
neural-interactive-proofs/finetune_dpo_Qwen_Qwen2.5-32B-Instruct_cv_open_prover_training_test_4_0_iter_0_provers_group_175
|
neural-interactive-proofs
| 2025-06-23T14:32:45Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-32B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T14:31:55Z |
---
base_model: Qwen/Qwen2.5-32B-Instruct
library_name: transformers
model_name: finetune_dpo_Qwen_Qwen2.5-32B-Instruct_cv_open_prover_training_test_4_0_iter_0_provers_group_175
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for finetune_dpo_Qwen_Qwen2.5-32B-Instruct_cv_open_prover_training_test_4_0_iter_0_provers_group_175
This model is a fine-tuned version of [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="neural-interactive-proofs/finetune_dpo_Qwen_Qwen2.5-32B-Instruct_cv_open_prover_training_test_4_0_iter_0_provers_group_175", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/lrhammond-team/pvg-self-hosted-finetune/runs/Qwen_Qwen2.5-32B-Instruct_dpo_2025-06-23_15-22-24_cv_open_prover_training_test_4_0_iter_0_provers_group)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 2.21.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
pico-lm/pico-decoder-small
|
pico-lm
| 2025-06-23T14:30:17Z | 134 | 0 | null |
[
"safetensors",
"pico_decoder",
"text-generation",
"custom_code",
"en",
"dataset:pico-lm/pretokenized-dolma",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-02-22T03:04:15Z |
---
datasets:
- pico-lm/pretokenized-dolma
language:
- en
license: apache-2.0
metrics:
- pico-lm/perplexity
pipeline_tag: text-generation
---
# Pico Decoder Small
**pico-decoder-small** is a 65M parameter model in the `pico-decoder` suite — a lightweight, LLaMA-style decoder-only transformer trained from scratch using [`pico-train`](https://github.com/pico-lm/pico-train). It is designed for transparent and reproducible research into the learning dynamics of language models, and is fully compatible with the `pico-analyze` toolkit for detailed interpretability analysis.
> NOTE: The `pico-decoder-small-1` branch contains the full commit history for the training run.
## 🔧 Model Details
| Field | Value |
|---------------------|------------------------------------|
| **Architecture** | Decoder-only transformer (LLaMA-style) |
| **Parameters** | 65M |
| **Layers** | 12 |
| **Hidden Size** | 384 |
| **Feed Foward Size** | 1536 |
| **Attention Heads** | 12 |
| **Key/Value Heads** | 4 |
## 📚 Training
- **Dataset**: [`pretokenized-dolma`](https://huggingface.co/datasets/pico-lm/pretokenized-dolma), English-only
- **Training steps**: 200,000
- **Batch size**: 1024
- **Sequence length**: 2048
- **Optimizer**: AdamW
- **Learning rate schedule**: Linear decay with warmup
- **Compute**: 16 A100-SXM4-80GB GPUs
## 📈 Evaluation and Analysis
This model supports fine-grained analysis using [`pico-analyze`](https://github.com/pico-lm/pico-analyze). This tool enables researchers to understand how learning unfolds over training, even at modest scales.
We also evaluate perplexity of the model on the [`pico-paloma-tinsy`](https://huggingface.co/datasets/pico-lm/pretokenized-paloma-tinsy) dataset.
## 📄 Citation
If you use `pico-small` or any other `pico-decoder` model in your research, please cite:
```bibtex
@software{pico2025,
author = {Diehl Martinez, Richard},
title = {Pico: A Lightweight Framework for Studying Language Model Learning Dynamics},
year = {2025},
url = {https://github.com/pico-lm}
}
|
isurut/wav2vec2_base_finetune_cv_igbo
|
isurut
| 2025-06-23T14:23:28Z | 11 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-20T19:46:46Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
model-index:
- name: wav2vec2_base_finetune_cv_igbo
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2_base_finetune_cv_igbo
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.1469
- eval_wer: 0.7373
- eval_runtime: 111.9879
- eval_samples_per_second: 10.26
- eval_steps_per_second: 1.286
- epoch: 11.3240
- step: 3250
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 20
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
MBASE/Phi-3-medium-128k-instruct-GGUF
|
MBASE
| 2025-06-23T14:14:55Z | 4 | 0 | null |
[
"gguf",
"text-generation",
"en",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-01-08T17:54:49Z |
---
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/LICENSE
language:
- en
pipeline_tag: text-generation
---
# Phi-3-medium-128k-instruct-GGUF
**Original Author** : [Microsoft](https://huggingface.co/microsoft).<br>
**Model Owner**: [Microsoft](https://huggingface.co/microsoft).<br>
**Original Repository**: [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct).<br>
**Conversion Tool**: [llama.cpp](https://github.com/ggerganov/llama.cpp).
## Description
This repo contains Phi-3 medium 128k instruct model in gguf format to be leveraged by MBASE Inference engine.
## Conversion Process
Original model [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) safetensors are converted
using the [llama.cpp](https://github.com/ggerganov/llama.cpp) model conversion script.
The default configuration applied during the conversion process. In other words, neither the imatrix applied or the GGUF parameters
altered.
## MBASE Use Case
This model is specifically referenced and used in MBASE Library documentation tutorials.
Tutorial link: https://docs.mbasesoftware.com/inference/quickstart/single_prompt_ex/downloading_model.html
## Chat Template
```
<|system|>
{system_prompt}<|end|>
<|user|>
{user_prompt}<|end|>
<|assistant|>
{assistant_response}<|end|>
```
## Disclaimer
MBASE Software Corporation is a software company officially registered as (MBASE Yazılım A.Ş.) in Turkey (https://www.mbasesoftware.com). Throughout this document, references to MBASE Software Corporation or MBASE refer to the (MBASE Yazılım A.Ş.)
MBASE is not the creator, originator, or owner of the model featured in this repository. This model is created and provided by third parties. MBASE does not endorse, support, or guarantee the completeness, accuracy, or reliability of the model or its outputs.
You understand that the model can generate content that may be offensive, harmful, inaccurate, inappropriate, or deceptive. Responsibility for the model and its outputs lies solely with the entity or individual who created and provided the model. MBASE does not monitor or control the model's outputs and disclaims any liability arising from its use.
MBASE provides no warranties regarding the accuracy, reliability, or fitness of the model for any particular purpose. Additionally, MBASE disclaims any guarantees that the model will operate without errors, interruptions, viruses, or other issues. You are solely responsible for any consequences resulting from the use or access of this model, including any damage caused by downloading or utilizing it.
|
taguser/flightctl-epoch20-2025-Jun-18
|
taguser
| 2025-06-23T14:11:03Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:taguser/flightctl-epoch20-2025-Jun-15-merged",
"base_model:adapter:taguser/flightctl-epoch20-2025-Jun-15-merged",
"license:other",
"region:us"
] | null | 2025-06-18T10:57:31Z |
---
library_name: peft
license: other
base_model: taguser/flightctl-epoch20-2025-Jun-15-merged
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test
This model is a fine-tuned version of [taguser/flightctl-epoch20-2025-Jun-15-merged](https://huggingface.co/taguser/flightctl-epoch20-2025-Jun-15-merged) on the training_dataset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
### Framework versions
- PEFT 0.15.1
- Transformers 4.51.0
- Pytorch 2.7.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1
|
Bandolik/autogen-mistral-4bit-lora-adapterv04
|
Bandolik
| 2025-06-23T13:40:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T13:39:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
eyepyon/judicial-exam-llama3-jpv5-merged-v3
|
eyepyon
| 2025-06-23T13:36:41Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"japanese",
"legal",
"judicial-exam",
"司法試験",
"fine-tuned",
"lora",
"ja",
"dataset:custom-judicial-exam-dataset",
"base_model:elyza/Llama-3-ELYZA-JP-8B",
"base_model:adapter:elyza/Llama-3-ELYZA-JP-8B",
"license:llama3",
"region:us"
] | null | 2025-06-23T13:33:42Z |
---
license: llama3
base_model: elyza/Llama-3-ELYZA-JP-8B
tags:
- japanese
- legal
- judicial-exam
- 司法試験
- fine-tuned
- llama
- peft
- lora
language:
- ja
datasets:
- custom-judicial-exam-dataset
---
# 司法試験特化日本語LLM
## モデル概要
このモデルはelyza/Llama-3-ELYZA-JP-8Bをベースに、日本の司法試験問題でファインチューニングした特化モデルです。
## 特徴
- **ベースモデル**: elyza/Llama-3-ELYZA-JP-8B
- **特化分野**: 日本の司法試験（憲法、民法、刑法等）
- **言語**: 日本語
- **ファインチューニング手法**: QLoRA (Quantized Low-Rank Adaptation)
## 学習情報
- **学習データ数**: 399件
- **エポック数**: 1
- **学習時間**: 0:04:03.975761
- **LoRA ランク**: 4
- **学習率**: 1e-05
## 使用方法
### LoRAアダプター版（eyepyon/judicial-exam-llama3-jpv5-lora-v3）
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("elyza/Llama-3-ELYZA-JP-8B")
tokenizer = AutoTokenizer.from_pretrained("elyza/Llama-3-ELYZA-JP-8B")
model = PeftModel.from_pretrained(base_model, "eyepyon/judicial-exam-llama3-jpv5-lora-v3")
inputs = tokenizer("司法試験問題:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
### マージ済みモデル版（eyepyon/judicial-exam-llama3-jpv5-merged-v3）
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("eyepyon/judicial-exam-llama3-jpv5-merged-v3")
tokenizer = AutoTokenizer.from_pretrained("eyepyon/judicial-exam-llama3-jpv5-merged-v3")
inputs = tokenizer("司法試験問題:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
## 注意事項
- このモデルは教育・研究目的で作成されています
- 実際の司法試験や法的判断には使用しないでください
- 出力結果は参考程度に留めてください
## ライセンス
ベースモデルのLlama 3ライセンスに準拠します。
---
|
ezhdeha/neo-medical-autocomplete
|
ezhdeha
| 2025-06-23T13:18:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T13:18:42Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
morturr/Llama-2-7b-hf-PAIR_amazon_dadjokes-COMB-dadjokes-comb-2-seed-28-2025-06-23
|
morturr
| 2025-06-23T13:07:01Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-23T13:06:53Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-PAIR_amazon_dadjokes-COMB-dadjokes-comb-2-seed-28-2025-06-23
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-PAIR_amazon_dadjokes-COMB-dadjokes-comb-2-seed-28-2025-06-23
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
MJ92/AceGPT-v2-8B-Chat_finetuned_500_cass
|
MJ92
| 2025-06-23T12:49:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T12:36:47Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
huni0304/whisper-medium-translate-ch2en
|
huni0304
| 2025-06-23T12:37:47Z | 3 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:covost2",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-23T07:25:48Z |
---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- covost2
model-index:
- name: whisper-medium-translate-ch2en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-translate-ch2en
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the covost2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7790
- Meteor: 0.0884
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Meteor |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 1.5753 | 0.4776 | 500 | 1.6145 | 0.0631 |
| 1.6442 | 0.9551 | 1000 | 1.5164 | 0.0413 |
| 0.9631 | 1.4327 | 1500 | 1.5073 | 0.0517 |
| 0.9537 | 1.9102 | 2000 | 1.4967 | 0.0457 |
| 0.5588 | 2.3878 | 2500 | 1.5277 | 0.0606 |
| 0.563 | 2.8653 | 3000 | 1.5659 | 0.0540 |
| 0.3102 | 3.3429 | 3500 | 1.6492 | 0.0831 |
| 0.3082 | 3.8204 | 4000 | 1.6616 | 0.0498 |
| 0.1883 | 4.2980 | 4500 | 1.7804 | 0.0818 |
| 0.1505 | 4.7755 | 5000 | 1.7790 | 0.0884 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
satvikhfsiemens/della_3_7_d5
|
satvikhfsiemens
| 2025-06-23T12:23:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2406.11617",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T12:01:34Z |
---
base_model:
- meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
tags:
- mergekit
- merge
---
# merged_della_della_3_7_d5
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DELLA](https://arxiv.org/abs/2406.11617) merge method using [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) as a base.
### Models Merged
The following models were included in the merge:
* /tmp/lora_merge_della_3_7_d5/temp_model1
* /tmp/lora_merge_della_3_7_d5/temp_model2
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: meta-llama/Llama-3.1-8B-Instruct
dtype: float16
merge_method: della
modules:
default:
slices:
- sources:
- layer_range: [0, 32]
model: /tmp/lora_merge_della_3_7_d5/temp_model1
parameters:
density: 0.5
weight: 0.3
- layer_range: [0, 32]
model: /tmp/lora_merge_della_3_7_d5/temp_model2
parameters:
density: 0.5
weight: 0.7
- layer_range: [0, 32]
model: meta-llama/Llama-3.1-8B-Instruct
tokenizer: {}
```
|
emperorKai/ai-developer-classifier-mistral-4bit-lora-adapter
|
emperorKai
| 2025-06-23T12:15:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T12:14:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
TheStageAI/Elastic-mochi-1-preview
|
TheStageAI
| 2025-06-23T12:14:29Z | 19 | 1 | null |
[
"text-to-video",
"base_model:genmo/mochi-1-preview",
"base_model:quantized:genmo/mochi-1-preview",
"license:apache-2.0",
"region:us"
] |
text-to-video
| 2025-06-17T20:16:43Z |
---
license: apache-2.0
base_model:
- genmo/mochi-1-preview
base_model_relation: quantized
pipeline_tag: text-to-video
---
# Elastic model: Fastest self-serving models. mochi-1-preview.
Elastic models are the models produced by TheStage AI ANNA: Automated Neural Networks Accelerator. ANNA allows you to control model size, latency and quality with a simple slider movement. For each model, ANNA produces a series of optimized models:
* __XL__: Mathematically equivalent neural network, optimized with our DNN compiler.
* __L__: Near lossless model, with less than 1% degradation obtained on corresponding benchmarks.
* __M__: Faster model, with accuracy degradation less than 1.5%.
* __S__: The fastest model, with accuracy degradation less than 2%.
__Goals of Elastic Models:__
* Provide the fastest models and service for self-hosting.
* Provide flexibility in cost vs quality selection for inference.
* Provide clear quality and latency benchmarks.
* Provide interface of HF libraries: transformers and diffusers with a single line of code.
* Provide models supported on a wide range of hardware, which are pre-compiled and require no JIT.
> It's important to note that specific quality degradation can vary from model to model. For instance, with an S model, you can have 0.5% degradation as well.
-----
Prompt: Timelapse of urban cityscape transitioning from day to night
Number of frames = 100
| S | XL | Original |
|:-:|:-:|:-:|
| <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/6799fc8e150f5a4014b030ca/7D4jSJXgO0St8M34qPpTF.mp4"></video>| <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/6799fc8e150f5a4014b030ca/ir7veWK4F6-n6vdMwEea5.mp4"></video>| <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/6799fc8e150f5a4014b030ca/boWKOxsIFr8GHpC9sB96V.mp4"></video>|
## Inference
> Compiled versions are currently available only for 163-frame generations, height=480 and width=848. Other versions are not yet accessible. Stay tuned for updates!
To infer our models, you just need to replace `diffusers` import with `elastic_models.diffusers`:
```python
import torch
from elastic_models.diffusers import DiffusionPipeline
from diffusers.video_processor import VideoProcessor
from diffusers.utils import export_to_video
mode_name = "genmo/mochi-1-preview"
hf_token = ""
device = torch.device("cuda")
dtype = torch.bfloat16
pipe = DiffusionPipeline.from_pretrained(
mode_name, torch_dtype=dtype, token=hf_token, mode="S"
)
pipe.enable_vae_tiling()
pipe.to(device)
prompt = "Kitten eating a banana"
with torch.no_grad():
torch.cuda.synchronize()
(
prompt_embeds,
prompt_attention_mask,
negative_prompt_embeds,
negative_prompt_attention_mask,
) = pipe.encode_prompt(prompt=prompt)
if prompt_attention_mask is not None and isinstance(
prompt_attention_mask, torch.Tensor
):
prompt_attention_mask = prompt_attention_mask.to(dtype)
if negative_prompt_attention_mask is not None and isinstance(
negative_prompt_attention_mask, torch.Tensor
):
negative_prompt_attention_mask = negative_prompt_attention_mask.to(dtype)
prompt_embeds = prompt_embeds.to(dtype)
negative_prompt_embeds = negative_prompt_embeds.to(dtype)
with torch.autocast("cuda", torch.bfloat16, enabled=True):
frames = pipe(
prompt_embeds=prompt_embeds,
prompt_attention_mask=prompt_attention_mask,
negative_prompt_embeds=negative_prompt_embeds,
negative_prompt_attention_mask=negative_prompt_attention_mask,
guidance_scale=4.5,
num_inference_steps=64,
height=480,
width=848,
num_frames=163,
generator=torch.Generator("cuda").manual_seed(0),
output_type="latent",
return_dict=False,
)[0]
video_processor = VideoProcessor(vae_scale_factor=8)
has_latents_mean = (
hasattr(pipe.vae.config, "latents_mean")
and pipe.vae.config.latents_mean is not None
)
has_latents_std = (
hasattr(pipe.vae.config, "latents_std")
and pipe.vae.config.latents_std is not None
)
if has_latents_mean and has_latents_std:
latents_mean = (
torch.tensor(pipe.vae.config.latents_mean)
.view(1, 12, 1, 1, 1)
.to(frames.device, frames.dtype)
)
latents_std = (
torch.tensor(pipe.vae.config.latents_std)
.view(1, 12, 1, 1, 1)
.to(frames.device, frames.dtype)
)
frames = frames * latents_std / pipe.vae.config.scaling_factor + latents_mean
else:
frames = frames / pipe.vae.config.scaling_factor
with torch.autocast("cuda", torch.bfloat16, enabled=False):
video = pipe.vae.decode(frames.to(pipe.vae.dtype), return_dict=False)[0]
video = video_processor.postprocess_video(video)[0]
torch.cuda.synchronize()
export_to_video(video, "mochi.mp4", fps=30)
```
### Installation
__System requirements:__
* GPUs: H100, B200
* CPU: AMD, Intel
* Python: 3.10-3.12
To work with our models just run these lines in your terminal:
```shell
pip install thestage
pip install elastic_models[nvidia]\
--index-url https://thestage.jfrog.io/artifactory/api/pypi/pypi-thestage-ai-production/simple\
--extra-index-url https://pypi.nvidia.com\
--extra-index-url https://pypi.org/simple
# or for blackwell support
pip install elastic_models[blackwell]\
--index-url https://thestage.jfrog.io/artifactory/api/pypi/pypi-thestage-ai-production/simple\
--extra-index-url https://pypi.nvidia.com\
--extra-index-url https://pypi.org/simple
pip install -U --pre torch --index-url https://download.pytorch.org/whl/nightly/cu128
pip install -U --pre torchvision --index-url https://download.pytorch.org/whl/nightly/cu128
pip install flash_attn==2.7.3 --no-build-isolation
pip uninstall apex
pip install tensorrt==10.11.0.33 opencv-python==4.11.0.86 imageio-ffmpeg==0.6.0
```
Then go to [app.thestage.ai](https://app.thestage.ai), login and generate API token from your profile page. Set up API token as follows:
```shell
thestage config set --api-token <YOUR_API_TOKEN>
```
Congrats, now you can use accelerated models!
----
## Benchmarks
Benchmarking is one of the most important procedures during model acceleration. We aim to provide clear performance metrics for models using our algorithms.
### Latency benchmarks
Time in seconds of generation.
### Number of frames: 100
| GPU | S | XL | Original |
|----------|-----|-----|----------|
| H100 | 144 | 163 | 311 |
| B200 | 77 | 87 | 241 |
### Number of frames: 163
| GPU | S | XL | Original |
|----------|-----|-----|----------|
| H100 | 328 | 361 | 675 |
| B200 | 173 | 189 | 545 |
## Links
* __Platform__: [app.thestage.ai](https://app.thestage.ai)
<!-- * __Elastic models Github__: [app.thestage.ai](app.thestage.ai) -->
* __Subscribe for updates__: [TheStageAI X](https://x.com/TheStageAI)
* __Contact email__: contact@thestage.ai
|
umm-dev/synapse-1-base-162m
|
umm-dev
| 2025-06-23T11:54:18Z | 0 | 0 | null |
[
"text-generation",
"en",
"dataset:wikimedia/wikipedia",
"license:gpl-3.0",
"region:us"
] |
text-generation
| 2025-06-23T08:58:08Z |
---
license: gpl-3.0
datasets:
- wikimedia/wikipedia
language:
- en
pipeline_tag: text-generation
---
|
unsloth/Mistral-Small-3.2-24B-Instruct-2506
|
unsloth
| 2025-06-23T11:40:10Z | 3,128 | 2 |
vllm
|
[
"vllm",
"safetensors",
"mistral3",
"image-text-to-text",
"conversational",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ne",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"base_model:mistralai/Mistral-Small-3.2-24B-Instruct-2506",
"base_model:finetune:mistralai/Mistral-Small-3.2-24B-Instruct-2506",
"license:apache-2.0",
"region:us"
] |
image-text-to-text
| 2025-06-20T20:31:37Z |
---
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ne
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
license: apache-2.0
library_name: vllm
inference: false
base_model:
- mistralai/Mistral-Small-3.2-24B-Instruct-2506
pipeline_tag: image-text-to-text
---
# Mistral-Small-3.2-24B-Instruct-2506
Mistral-Small-3.2-24B-Instruct-2506 is a minor update of [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503).
Small-3.2 improves in the following categories:
- **Instruction following**: Small-3.2 is better at following precise instructions
- **Repetition errors**: Small-3.2 produces less infinite generations or repetitive answers
- **Function calling**: Small-3.2's function calling template is more robust (see [here](https://github.com/mistralai/mistral-common/blob/535b4d0a0fc94674ea17db6cf8dc2079b81cbcfa/src/mistral_common/tokens/tokenizers/instruct.py#L778) and [examples](#function-calling))
In all other categories Small-3.2 should match or slightly improve compared to [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503).
## Key Features
- same as [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503#key-features)
## Benchmark Results
We compare Mistral-Small-3.2-24B to [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503).
For more comparison against other models of similar size, please check [Mistral-Small-3.1's Benchmarks'](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503#benchmark-results)
### Text
#### Instruction Following / Chat / Tone
| Model | Wildbench v2 | Arena Hard v2 | IF (Internal; accuracy) |
|-------|---------------|---------------|------------------------|
| Small 3.1 24B Instruct | 55.6% | 19.56% | 82.75% |
| **Small 3.2 24B Instruct** | **65.33%** | **43.1%** | **84.78%** |
#### Infinite Generations
Small 3.2 reduces infitine generations by 2x on challenging, long and repetitive prompts.
| Model | Infinite Generations (Internal; Lower is better) |
|-------|-------|
| Small 3.1 24B Instruct | 2.11% |
| **Small 3.2 24B Instruct** | **1.29%** |
#### STEM
| Model | MMLU | MMLU Pro (5-shot CoT) | MATH | GPQA Main (5-shot CoT) | GPQA Diamond (5-shot CoT )| MBPP Plus - Pass@5 | HumanEval Plus - Pass@5 | SimpleQA (TotalAcc)|
|--------------------------------|-----------|-----------------------|------------------------|------------------------|---------------------------|--------------------|-------------------------|--------------------|
| Small 3.1 24B Instruct | 80.62% | 66.76% | 69.30% | 44.42% | 45.96% | 74.63% | 88.99% | 10.43% |
| **Small 3.2 24B Instruct** | 80.50% | **69.06%** | 69.42% | 44.22% | 46.13% | **78.33%** | **92.90%** | **12.10%** |
### Vision
| Model | MMMU | Mathvista | ChartQA | DocVQA | AI2D |
|--------------------------------|------------|-----------|-----------|-----------|-----------|
| Small 3.1 24B Instruct | **64.00%** | **68.91%**| 86.24% | 94.08% | 93.72% |
| **Small 3.2 24B Instruct** | 62.50% | 67.09% | **87.4%** | 94.86% | 92.91% |
## Usage
The model can be used with the following frameworks;
- [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended)
- [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers)
**Note 1**: We recommend using a relatively low temperature, such as `temperature=0.15`.
**Note 2**: Make sure to add a system prompt to the model to best tailer it for your needs. If you want to use the model as a general assistant, we recommend to use the one provided in the [SYSTEM_PROMPT.txt](https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506/blob/main/SYSTEM_PROMPT.txt) file.
### vLLM (recommended)
We recommend using this model with [vLLM](https://github.com/vllm-project/vllm).
#### Installation
Make sure to install [`vLLM >= 0.9.1`](https://github.com/vllm-project/vllm/releases/tag/v0.9.1):
```
pip install vllm --upgrade
```
Doing so should automatically install [`mistral_common >= 1.6.2`](https://github.com/mistralai/mistral-common/releases/tag/v1.6.2).
To check:
```
python -c "import mistral_common; print(mistral_common.__version__)"
```
You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39).
#### Serve
We recommand that you use Mistral-Small-3.2-24B-Instruct-2506 in a server/client setting.
1. Spin up a server:
```
vllm serve mistralai/Mistral-Small-3.2-24B-Instruct-2506 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --limit_mm_per_prompt 'image=10' --tensor-parallel-size 2
```
**Note:** Running Mistral-Small-3.2-24B-Instruct-2506 on GPU requires ~55 GB of GPU RAM in bf16 or fp16.
2. To ping the client you can use a simple Python snippet. See the following examples.
#### Vision reasoning
Take leverage of the vision capabilities of Mistral-Small-3.2-24B-Instruct-2506 to take the best choice given a scenario, go catch them all !
<details>
<summary>Python snippet</summary>
```py
from datetime import datetime, timedelta
from openai import OpenAI
from huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.15
MAX_TOK = 131072
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
today = datetime.today().strftime("%Y-%m-%d")
yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
model_name = repo_id.split("/")[-1]
return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": [
{
"type": "text",
"text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.",
},
{"type": "image_url", "image_url": {"url": image_url}},
],
},
]
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
)
print(response.choices[0].message.content)
# In this situation, you are playing a Pokémon game where your Pikachu (Level 42) is facing a wild Pidgey (Level 17). Here are the possible actions you can take and an analysis of each:
# 1. **FIGHT**:
# - **Pros**: Pikachu is significantly higher level than the wild Pidgey, which suggests that it should be able to defeat Pidgey easily. This could be a good opportunity to gain experience points and possibly items or money.
# - **Cons**: There is always a small risk of Pikachu fainting, especially if Pidgey has a powerful move or a status effect that could hinder Pikachu. However, given the large level difference, this risk is minimal.
# 2. **BAG**:
# - **Pros**: You might have items in your bag that could help in this battle, such as Potions, Poké Balls, or Berries. Using an item could help you capture the Pidgey or heal your Pikachu if needed.
# - **Cons**: Using items might not be necessary given the level difference. It could be more efficient to just fight and defeat the Pidgey quickly.
# 3. **POKÉMON**:
# - **Pros**: You might have another Pokémon in your party that is better suited for this battle or that you want to gain experience. Switching Pokémon could also be a strategic move if you want to train a lower-level Pokémon.
# - **Cons**: Switching Pokémon might not be necessary since Pikachu is at a significant advantage. It could also waste time and potentially give Pidgey a turn to attack.
# 4. **RUN**:
# - **Pros**: Running away could save time and conserve your Pokémon's health and resources. If you are in a hurry or do not need the experience or items, running away is a safe option.
# - **Cons**: Running away means you miss out on the experience points and potential items or money that you could gain from defeating the Pidgey. It also means you do not get the chance to capture the Pidgey if you wanted to.
# ### Recommendation:
# Given the significant level advantage, the best action is likely to **FIGHT**. This will allow you to quickly defeat the Pidgey, gain experience points, and potentially earn items or money. If you are concerned about Pikachu's health, you could use an item from your **BAG** to heal it before or during the battle. Running away or switching Pokémon does not seem necessary in this situation.
```
</details>
#### Function calling
Mistral-Small-3.2-24B-Instruct-2506 is excellent at function / tool calling tasks via vLLM. *E.g.:*
<details>
<summary>Python snippet - easy</summary>
```py
from openai import OpenAI
from huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.15
MAX_TOK = 131072
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
return system_prompt
model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png"
tools = [
{
"type": "function",
"function": {
"name": "get_current_population",
"description": "Get the up-to-date population of a given country.",
"parameters": {
"type": "object",
"properties": {
"country": {
"type": "string",
"description": "The country to find the population of.",
},
"unit": {
"type": "string",
"description": "The unit for the population.",
"enum": ["millions", "thousands"],
},
},
"required": ["country", "unit"],
},
},
},
{
"type": "function",
"function": {
"name": "rewrite",
"description": "Rewrite a given text for improved clarity",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The input text to rewrite",
}
},
},
},
},
]
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": "Could you please make the below article more concise?\n\nOpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership.",
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "bbc5b7ede",
"type": "function",
"function": {
"name": "rewrite",
"arguments": '{"text": "OpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership."}',
},
}
],
},
{
"role": "tool",
"content": '{"action":"rewrite","outcome":"OpenAI is a FOR-profit company."}',
"tool_call_id": "bbc5b7ede",
"name": "rewrite",
},
{
"role": "assistant",
"content": "---\n\nOpenAI is a FOR-profit company.",
},
{
"role": "user",
"content": [
{
"type": "text",
"text": "Can you tell me what is the biggest country depicted on the map?",
},
{
"type": "image_url",
"image_url": {
"url": image_url,
},
},
],
}
]
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
tools=tools,
tool_choice="auto",
)
assistant_message = response.choices[0].message.content
print(assistant_message)
# The biggest country depicted on the map is Russia.
messages.extend([
{"role": "assistant", "content": assistant_message},
{"role": "user", "content": "What is the population of that country in millions?"},
])
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
tools=tools,
tool_choice="auto",
)
print(response.choices[0].message.tool_calls)
# [ChatCompletionMessageToolCall(id='3e92V6Vfo', function=Function(arguments='{"country": "Russia", "unit": "millions"}', name='get_current_population'), type='function')]
```
</details>
<details>
<summary>Python snippet - complex</summary>
```python
import json
from openai import OpenAI
from huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.15
MAX_TOK = 131072
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
return system_prompt
model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
image_url = "https://math-coaching.com/img/fiche/46/expressions-mathematiques.jpg"
def my_calculator(expression: str) -> str:
return str(eval(expression))
tools = [
{
"type": "function",
"function": {
"name": "my_calculator",
"description": "A calculator that can evaluate a mathematical expression.",
"parameters": {
"type": "object",
"properties": {
"expression": {
"type": "string",
"description": "The mathematical expression to evaluate.",
},
},
"required": ["expression"],
},
},
},
{
"type": "function",
"function": {
"name": "rewrite",
"description": "Rewrite a given text for improved clarity",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The input text to rewrite",
}
},
},
},
},
]
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": [
{
"type": "text",
"text": "Can you calculate the results for all the equations displayed in the image? Only compute the ones that involve numbers.",
},
{
"type": "image_url",
"image_url": {
"url": image_url,
},
},
],
},
]
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
tools=tools,
tool_choice="auto",
)
tool_calls = response.choices[0].message.tool_calls
print(tool_calls)
# [ChatCompletionMessageToolCall(id='CyQBSAtGh', function=Function(arguments='{"expression": "6 + 2 * 3"}', name='my_calculator'), type='function'), ChatCompletionMessageToolCall(id='KQqRCqvzc', function=Function(arguments='{"expression": "19 - (8 + 2) + 1"}', name='my_calculator'), type='function')]
results = []
for tool_call in tool_calls:
function_name = tool_call.function.name
function_args = tool_call.function.arguments
if function_name == "my_calculator":
result = my_calculator(**json.loads(function_args))
results.append(result)
messages.append({"role": "assistant", "tool_calls": tool_calls})
for tool_call, result in zip(tool_calls, results):
messages.append(
{
"role": "tool",
"tool_call_id": tool_call.id,
"name": tool_call.function.name,
"content": result,
}
)
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
)
print(response.choices[0].message.content)
# Here are the results for the equations that involve numbers:
# 1. \( 6 + 2 \times 3 = 12 \)
# 3. \( 19 - (8 + 2) + 1 = 10 \)
# For the other equations, you need to substitute the variables with specific values to compute the results.
```
</details>
#### Instruction following
Mistral-Small-3.2-24B-Instruct-2506 will follow your instructions down to the last letter !
<details>
<summary>Python snippet</summary>
```python
from openai import OpenAI
from huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.15
MAX_TOK = 131072
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
return system_prompt
model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": "Write me a sentence where every word starts with the next letter in the alphabet - start with 'a' and end with 'z'.",
},
]
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
)
assistant_message = response.choices[0].message.content
print(assistant_message)
# Here's a sentence where each word starts with the next letter of the alphabet, starting from 'a' and ending with 'z':
# "Always brave cats dance elegantly, fluffy giraffes happily ignore jungle kites, lovingly munching nuts, observing playful quails racing swiftly, tiny unicorns vaulting while xylophones yodel zealously."
# This sentence follows the sequence from A to Z without skipping any letters.
```
</details>
### Transformers
You can also use Mistral-Small-3.2-24B-Instruct-2506 with `Transformers` !
To make the best use of our model with `Transformers` make sure to have [installed](https://github.com/mistralai/mistral-common) `mistral-common >= 1.6.2` to use our tokenizer.
```bash
pip install mistral-common --upgrade
```
Then load our tokenizer along with the model and generate:
<details>
<summary>Python snippet</summary>
```python
from datetime import datetime, timedelta
import torch
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from huggingface_hub import hf_hub_download
from transformers import Mistral3ForConditionalGeneration
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
today = datetime.today().strftime("%Y-%m-%d")
yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
model_name = repo_id.split("/")[-1]
return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
tokenizer = MistralTokenizer.from_hf_hub(model_id)
model = Mistral3ForConditionalGeneration.from_pretrained(
model_id, torch_dtype=torch.bfloat16
)
image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": [
{
"type": "text",
"text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.",
},
{"type": "image_url", "image_url": {"url": image_url}},
],
},
]
tokenized = tokenizer.encode_chat_completion(ChatCompletionRequest(messages=messages))
input_ids = torch.tensor([tokenized.tokens])
attention_mask = torch.ones_like(input_ids)
pixel_values = torch.tensor(tokenized.images[0], dtype=torch.bfloat16).unsqueeze(0)
image_sizes = torch.tensor([pixel_values.shape[-2:]])
output = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
pixel_values=pixel_values,
image_sizes=image_sizes,
max_new_tokens=1000,
)[0]
decoded_output = tokenizer.decode(output[len(tokenized.tokens) :])
print(decoded_output)
# In this situation, you are playing a Pokémon game where your Pikachu (Level 42) is facing a wild Pidgey (Level 17). Here are the possible actions you can take and an analysis of each:
# 1. **FIGHT**:
# - **Pros**: Pikachu is significantly higher level than the wild Pidgey, which suggests that it should be able to defeat Pidgey easily. This could be a good opportunity to gain experience points and possibly items or money.
# - **Cons**: There is always a small risk of Pikachu fainting, especially if Pidgey has a powerful move or a status effect that could hinder Pikachu. However, given the large level difference, this risk is minimal.
# 2. **BAG**:
# - **Pros**: You might have items in your bag that could help in this battle, such as Potions, Poké Balls, or Berries. Using an item could help you capture Pidgey or heal Pikachu if needed.
# - **Cons**: Using items might not be necessary given the level difference. It could be more efficient to just fight and defeat Pidgey quickly.
# 3. **POKÉMON**:
# - **Pros**: You might have another Pokémon in your party that is better suited for this battle or that you want to gain experience. Switching Pokémon could also be strategic if you want to train a lower-level Pokémon.
# - **Cons**: Switching Pokémon might not be necessary since Pikachu is at a significant advantage. It could also waste time and potentially give Pidgey a turn to attack.
# 4. **RUN**:
# - **Pros**: Running away could be a quick way to avoid the battle altogether. This might be useful if you are trying to conserve resources or if you are in a hurry to get to another location.
# - **Cons**: Running away means you miss out on the experience points, items, or money that you could gain from defeating Pidgey. It also might not be the most efficient use of your time if you are trying to train your Pokémon.
# ### Recommendation:
# Given the significant level advantage, the best action to take is likely **FIGHT**. This will allow you to quickly defeat Pidgey and gain experience points for Pikachu. If you are concerned about Pikachu's health, you could use the **BAG** to heal Pikachu before or during the battle. Running away or switching Pokémon does not seem necessary in this situation.
```
</details>
|
Juna190825/github_jeffprosise_model
|
Juna190825
| 2025-06-23T11:36:44Z | 80 | 0 |
keras
|
[
"keras",
"region:us"
] | null | 2025-06-22T05:29:42Z |
---
library_name: keras
---
This model has been uploaded using the Keras library and can be used with JAX,
TensorFlow, and PyTorch backends.
This model card has been generated automatically and should be completed by the
model author.
See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for
more information.
For more details about the model architecture, check out
[config.json](./config.json).
|
StephanST/WALDO30
|
StephanST
| 2025-06-23T11:34:57Z | 0 | 239 | null |
[
"object-detection",
"en",
"base_model:Ultralytics/YOLOv8",
"base_model:finetune:Ultralytics/YOLOv8",
"region:us"
] |
object-detection
| 2024-10-02T14:20:40Z |
---
language:
- en
base_model:
- Ultralytics/YOLOv8
pipeline_tag: object-detection
---
W.A.L.D.O.
Whereabouts Ascertainment for Low-lying Detectable Objects
---------------------------------------------------------------------
[](https://www.youtube.com/watch?v=1y5y9yklj2U)
Welcome to the WALDO v3.0 public release
---------------------------------------------------------------------
WHAT IS WALDO?
WALDO is a detection AI model, based on a large YOLO-v8 backbone and my own
synthetic data pipeline. **The model is capable of detecting these classes
of items in overhead imagery ranging in altitude from about 30 feet to
satellite imagery!**
Output classes:
0 -> 'LightVehicle' --> all kinds of civilan cars, including pickup trucks, vans etc... 🚗🏎️🚓🚐🚑 </br>
1 -> 'Person' --> people! all kinds of people including ones that are on bikes or swimming in the sea 🧍♀️🕺💃🧜🏽♀️🏂🧞</br>
2 -> 'Building' --> all kinds of buildings 🕌🏛️🏭🏡</br>
3 -> 'UPole' --> utility poles, power poles, anything thin and sticking up that you should avoid with a drone 🎏</br>
4 -> 'Boat' --> boats, ships, canoes, kayaks, surf boards... all the floaty stuff 🚢🏄</br>
5 -> 'Bike' --> bikes, mopeds, motorbikes, all stuff with 2 wheels 🚲</br>
6 -> 'Container' --> shipping containers, including on the back of an articulated truck... 📦🏗️</br>
7 -> 'Truck' --> large commercial vehicles including articulated trucks or big box-on-chassis delivery trucks 🚚</br>
8 -> 'Gastank'--> cylindrical tanks such as butane tanks and gas expansion tanks, or grain silos... pretty much anything that looks cylindrical for storing liquids 🫙</br>
10 -> 'Digger' --> all kinds of construction vehicles, including tractors and construction gear 🚜</br>
11 -> 'Solarpanels' --> solar panels ▪️🌞▪️</br>
12 -> 'Bus' --> a bus 🚌</br>
--> In general the lower the class number the better-trained you can expect it to be.
For users of previous versions of WALDO: note that I removed the military class and smoke detection. This is meant to be a FOSS tool for civilian use and I don't want to pursue making it work for military applications.
---------------------------------------------------------------------
WHERE IS WALDO?
Right here on HF!
Note there are a couple more models that have slightly better performance over on Gumroad here: https://6228189440665.gumroad.com/l/WALDOv3
Those are for sale as a kind of sponsorship for the project: if you find value in the free ones here you can buy those for a nice little performance boost... but it's entirey up to you!
[]
In both cases the actual files are MIT license and you can freely share them, so if someone gives you the ones from Gumroad you are free yo use them including commercially. It's really just a way to offset some of the work and compute that went into making this project and keeping it FOSS.
---------------------------------------------------------------------
WHAT IS IT GOOD FOR?
People are currently using versions of WALDO for:
1. disaster recovery
2. monitoring wildlife sanctuaries (intruder detection)
3. occupancy calculation (parking lots etc..)
4. monitoring infrastructure
5. construction site monitoring
6. traffic flow management
7. crowd counting
8. some fun AI art applications!
9. drone safety (avoiding people / cars on the ground)
10. lots of other fun stuff...
The main reason for me to make WALDO free has in fact been discovering all these cool applications. Let me know what you build!
---------------------------------------------------------------------
FOR AI NERDS !
It's a set of YOLOv8 model, trained on my own datasets of synthetic and "augmented" / semi-synthetic data.
I'm not going to release the dataset for the time being.
The weights are completely open, allowing you to deploy in any number of ways this time!
---------------------------------------------------------------------
HOW CAN I START WITH WALDO?
Check out the boilerplate code in the repo to run the models and output pretty detections using the wonderful Supervision annotation library from Roboflow :)
---------------------------------------------------------------------
GOING DEEPER
Of course if you know your way around deploying AI models there is a lot more you do
with this release, inclusing:
1. fine-tuning the models on your own data (if you know what you are doing, this is probably your starting point)
2. building a nicely optimized sliding-window inference setup that works nicely on your edge hardware
3. quantizing the models for super-duper edge performance on cheap devices
4. using the models to annotate your own data and train something of your own!
Enjoy!
---------------------------------------------------------------------
PREVIOUS VERSIONS
I am retiring the old versions, this is the only one that will stay online.
---------------------------------------------------------------------
CAN YOU HELP ME WITH X?
Sure, email me at stephan.sturges@gmail.com
---------------------------------------------------------------------
DETECTION OF X ISN'T WORKING AS EXPECTED:
I'd love to see example images, videos, sample data, etc at:
stephan.sturges@gmail.com
---------------------------------------------------------------------
SUPPORT WALDO!
Visit [] to support the project!
---------------------------------------------------------------------
LICENSE
----------------------------------------------------------------------------
Unless otherwise specified all code in this release is published with the
licence conditions below.
----------------------------------------------------------------------------
MIT License
Copyright (c) 2024 Stephan Sturges / Aircortex.com
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
The software cannot be used for the 2025 SPRIND "fully autonomous flight 2.0" competition.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
morturr/Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-headlines-comb-2-seed-7-2025-06-23
|
morturr
| 2025-06-23T11:24:55Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-23T11:24:37Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-headlines-comb-2-seed-7-2025-06-23
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-headlines-comb-2-seed-7-2025-06-23
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
owao/RLT-32B-Q4_K_M-GGUF
|
owao
| 2025-06-23T10:59:35Z | 0 | 0 | null |
[
"gguf",
"reasoning",
"reinforcement",
"learning",
"RLT",
"math",
"science",
"code",
"distillation",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"zh",
"base_model:SakanaAI/RLT-32B",
"base_model:quantized:SakanaAI/RLT-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-23T10:58:19Z |
---
license: apache-2.0
language:
- en
- zh
base_model: SakanaAI/RLT-32B
pipeline_tag: text-generation
tags:
- reasoning
- reinforcement
- learning
- RLT
- math
- science
- code
- distillation
- llama-cpp
- gguf-my-repo
---
# owao/RLT-32B-Q4_K_M-GGUF
This model was converted to GGUF format from [`SakanaAI/RLT-32B`](https://huggingface.co/SakanaAI/RLT-32B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/SakanaAI/RLT-32B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo owao/RLT-32B-Q4_K_M-GGUF --hf-file rlt-32b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo owao/RLT-32B-Q4_K_M-GGUF --hf-file rlt-32b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo owao/RLT-32B-Q4_K_M-GGUF --hf-file rlt-32b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo owao/RLT-32B-Q4_K_M-GGUF --hf-file rlt-32b-q4_k_m.gguf -c 2048
```
|
OG2022/Github_OG1000.Model_1.Emnist
|
OG2022
| 2025-06-23T10:55:49Z | 0 | 0 | null |
[
"image-text-to-text",
"en",
"region:us"
] |
image-text-to-text
| 2025-06-23T10:22:41Z |
---
language:
- en
pipeline_tag: image-text-to-text
---
# Model Card: EMNIST Handwritten Character Classifier (OG2022/Github_OG1000.Model_1.Emnist)
## Model Overview
This model is designated as "Model_1" by the GitHub user OG1000 within the "OG2022" project. It is designed for **handwritten character recognition** on the EMNIST dataset. It classifies images of handwritten digits and letters into their corresponding categories.
## Model Details
* **Model Identifier:** OG2022/Github_OG1000.Model_1.Emnist
* **Developed by:** OG1000 (GitHub User)
* **Model Type:** Convolutional Neural Network (CNN)
* **Architecture:** The `SimpleEMNISTCNN` model is a convolutional neural network, implemented in PyTorch. Its architecture includes:
* An initial convolutional layer (`self.conv1`) taking 1 input channel (for grayscale images) and producing 32 output channels, with a 3x3 kernel and stride of 1.
* A second convolutional layer (`self.conv2`) taking 32 input channels and producing 64 output channels, also with a 3x3 kernel and stride of 1.
* Rectified Linear Unit (ReLU) activation functions are applied after both convolutional layers.
* A max-pooling layer with a 2x2 kernel (`nn.functional.max_pool2d(x, 2)`) is applied after the second convolutional layer.
* A dropout layer (`self.dropout1`) with a probability of 0.25 is applied after pooling.
* The feature maps are then flattened (`torch.flatten(x, 1)`) for input to the fully connected layers.
* A fully connected layer (`self.fc1`) maps 9216 input features to 128 output features, followed by a ReLU activation.
* Another dropout layer (`self.dropout2`) with a probability of 0.5 is applied after `fc1`.
* A final fully connected layer (`self.fc2`) maps 128 input features to 62 output features.
* The output of the final fully connected layer is passed through a `log_softmax` function (`nn.functional.log_softmax(x, dim=1)`) to produce log-probabilities for each of the 62 classes.
* **Input:** Grayscale images of handwritten characters, expected to be 28x28 pixels.
* **Output:** Predicted class label (one of 62 categories corresponding to digits '0'-'9', uppercase 'A'-'Z', and lowercase 'a'-'z', consistent with the EMNIST 'ByClass' split).
* **License:** Not specified.
## Intended Use
This model is primarily intended for use within the **OG1000/AI-HOCR-Project repository on GitHub**. It serves for:
* **Research and development** in handwritten character recognition.
* **Educational purposes** to demonstrate image classification using CNNs with PyTorch.
* **Prototyping applications** requiring individual handwritten digit and letter recognition.
## Training Data
The model was trained on the **EMNIST (Extended MNIST) dataset**. EMNIST is a larger and more complex version of the MNIST dataset, derived from the NIST Special Database 19.
* **Dataset Split Used for Training:** EMNIST ByClass (inferred from the model's 62 output classes, matching the 62 unbalanced classes of this split). This dataset contains 814,255 handwritten characters comprising digits, uppercase, and lowercase letters.
* **Data Characteristics:** 28x28 pixel grayscale images. Images in the raw EMNIST format are typically inverted horizontally and rotated 90 degrees anti-clockwise, and may require pre-processing (e.g., rotation, inversion) to be human-readable or compatible with standard image viewing tools.
## Performance
* **Evaluation Metrics:** Accuracy, Loss
* **Performance on Test Set:**
* **Accuracy:** 90% (104840/116323 Test Batches)
* **Loss:** 0.26954 (average loss)
* **Strengths:**
* The model is described as being fast.
* Good generalization to unseen handwritten characters within the EMNIST distribution.
* Relatively robust to variations in handwriting styles.
* **Limitations:**
* May struggle with highly ambiguous or uncharacteristic handwriting.
* Performance might degrade on characters outside the EMNIST distribution or with different image characteristics (e.g., noise, resolution, different fonts).
* The model is designed for individual character recognition and does not handle connected characters or full words.
## Ethical Considerations and Biases
* **Dataset Bias:** The EMNIST dataset, while extensive, is derived from a specific source (NIST). This may introduce biases related to the demographic distribution of contributors, handwriting styles, and common letter formations that might not generalize perfectly to all global handwriting styles.
* **Fairness:** As with any character recognition system, there's a potential for bias if the training data does not adequately represent the diversity of handwriting styles. This model is not specifically designed for fairness mitigation beyond the dataset's inherent properties.
* **Misuse:** The model should not be used for critical applications where misclassification could lead to significant harm or incorrect decisions without further validation, human oversight, and appropriate risk assessment.
## Technical Specifications
* **Framework:** PyTorch
* **Dependencies:** `torch`, `torch.nn`, `torch.functional`. Additional dependencies for data handling (e.g., `torchvision`) and general Python utilities (`os`) are likely required. The UI uses `tkinter`, which is built into Python.
* **Hardware Requirements (Training):** Training was performed in Google Colab, implying GPU usage for efficient training.
* **Hardware Requirements (Inference):** The project aims to improve minimum requirements for older systems, suggesting it might be runnable on CPUs, but specific minimal requirements are not detailed.
* **Model Size:** 4.83MB
## Citation
If you use the EMNIST dataset, please cite the following paper:
|
GeorgeUwaifo/distilgpt2-ivieai-finetuned-wikitext2
|
GeorgeUwaifo
| 2025-06-23T10:39:37Z | 2 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-22T19:05:23Z |
---
library_name: transformers
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-ivieai-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-ivieai-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2464
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 154 | 3.3733 |
| No log | 2.0 | 308 | 3.2739 |
| No log | 3.0 | 462 | 3.2464 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
xuan-luo/MTPQwen3-0.6B-Base
|
xuan-luo
| 2025-06-23T10:09:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mtpqwen3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-06-23T10:09:15Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Gusanidas/branch-grpo-model-qwen-3b-branch3
|
Gusanidas
| 2025-06-23T09:54:48Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-14T19:12:23Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Nitish035/mistral_CMoS_adapter32_combine1800_single-2
|
Nitish035
| 2025-06-23T09:51:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T09:51:03Z |
---
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Nitish035
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ertghiu256/qwen3-4b-merged-ties-gguf
|
ertghiu256
| 2025-06-23T09:38:23Z | 2 | 0 | null |
[
"gguf",
"mergekit",
"ties",
"thinking",
"reasoning",
"merge",
"code",
"base_model:Qwen/Qwen3-4B",
"base_model:merge:Qwen/Qwen3-4B",
"base_model:Tesslate/UIGEN-T3-4B-Preview",
"base_model:merge:Tesslate/UIGEN-T3-4B-Preview",
"base_model:ValiantLabs/Qwen3-4B-Esper3",
"base_model:merge:ValiantLabs/Qwen3-4B-Esper3",
"base_model:ertghiu256/qwen3-4b-code-reasoning",
"base_model:merge:ertghiu256/qwen3-4b-code-reasoning",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-15T12:45:33Z |
---
license: apache-2.0
base_model:
- ertghiu256/qwen3-4b-code-reasoning
- ValiantLabs/Qwen3-4B-Esper3
- Tesslate/UIGEN-T3-4B-Preview
- Qwen/Qwen3-4B
tags:
- mergekit
- ties
- thinking
- reasoning
- merge
- code
---
# Model Info
Based on the Qwen 3 4b parameter model. This model is merged from models that are trained on reasoning coding tasks.
# Use cases
- Coding (HTML, C++, Python)
- Agentic coding
- General assistant
# Drawbacks
- Overly Overthinking
# Recommended Parameters
- context length: >= 8000
- temperature: 0.6
- top p: 40
- top k: 0.95
|
ByteFlow-AI/DetailFlow-32-GPT-L
|
ByteFlow-AI
| 2025-06-23T09:27:08Z | 0 | 0 | null |
[
"c2i",
"license:apache-2.0",
"region:us"
] | null | 2025-06-09T12:21:37Z |
---
license: apache-2.0
---
|
Kittykat924/TinyPi-chat-V1
|
Kittykat924
| 2025-06-23T09:24:38Z | 137 | 1 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"tinyllama",
"fine-tuned",
"peft",
"conversational",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-13T07:46:04Z |
---
license: mit
language:
- en
library_name: transformers
tags:
- tinyllama
- fine-tuned
- text-generation
- peft
new_version: Kittykat924/TinyPi-Chat-v1.5
---
# TinyPi-Chat-V1
TinyPi-Chat-V1 is a fine-tuned version of the `TinyLlama/TinyLlama-1.1B-Chat-v1.0` model. This project's goal was not to create a simple instruction-following assistant, but to cultivate an AI with a distinct, friendly, and engaging personality, mirroring the natural, witty, and sometimes quirky style of general-purpose Discord conversations.
, It was trained on a large dataset of chat logs, resulting in a model that excels at open-ended conversation, offers playful and sometimes evasive humor, and can maintain a consistent character.
This version (v1) represents the initial, highly specialized fine-tune and serves as the foundation for further alignment using techniques like RLAIF.
## How to Use
This model is a merged, standalone model and can be used directly for text generation. It follows a specific chat template that must be used to get the best results.
### Installation
```bash
pip install transformers torch accelerate
```
```Python
from transformers import pipeline
import torch
model_path = "Kittykat924/TinyPi-chat-V1"
pipe = pipeline(
"text-generation",
model=model_path,
torch_dtype=torch.float16,
device_map="auto"
)
prompt = "What do you think of today?"
messages = [
{"role": "user", "content": prompt},
]
prompt_formatted = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(
prompt_formatted,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_k=50,
top_p=0.95
)
response = outputs[0]["generated_text"]
assistant_response = response.split("<|assistant|>")[1].strip()
print(assistant_response)
```
# Training Procedure
This model was trained using a custom script built on the Hugging Face accelerate, peft, and datasets libraries.
# v1 Fine-tuning Details
Base Model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
Dataset: A large, private dataset of over 2 million general-purpose Discord chat messages.
Training Method: Parameter-Efficient Fine-Tuning (PEFT) using the LoRA technique.
Hardware: 2x NVIDIA T4 GPUs
Framework: accelerate for distributed training.
# Key Hyperparameters:
Learning Rate: 2e-4
LoRA r (rank): 64
LoRA alpha: 16
Batch Size: 4 per device
Gradient Accumulation: 4 steps
Optimizer: AdamW
The model was trained for approximately 2500 steps, with the final adapter chosen based on the lowest validation loss, which occurred very early in the training process (around step 200), indicating rapid specialization on the dataset. The final merged model uses the weights from this optimal checkpoint.
# Project Goals
The primary goal of this project was to explore the emergence of personality in language models. Instead of optimizing for factual accuracy or instruction-following, the training was designed to capture the nuances of human-to-human digital interaction. The success of this v1 model lies in its ability to generate responses that are not just correct but believable and in-character.
The "weirdness" and occasional abstract responses are not viewed as bugs, but as features of a model that has learned a rich but ungrounded set of conversational styles.
# Limitations and Bias
This model was trained on a large corpus of public internet chat data. As such, it may have inherited biases, opinions, and language styles present in that data. It is not designed to be a source of factual information and may produce incorrect or nonsensical statements, especially on topics outside its training domain. It is intended for research and entertainment purposes. User discretion is advised.
-*kittykat924*
|
PORTULAN/albertina-1b5-portuguese-ptbr-encoder-256
|
PORTULAN
| 2025-06-23T09:11:20Z | 95 | 2 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"albertina-pt*",
"albertina-100m-portuguese-ptpt",
"albertina-100m-portuguese-ptbr",
"albertina-900m-portuguese-ptpt",
"albertina-900m-portuguese-ptbr",
"albertina-1b5-portuguese-ptpt",
"albertina-1b5-portuguese-ptbr",
"bert",
"deberta",
"portuguese",
"encoder",
"foundation model",
"pt",
"dataset:PORTULAN/glue-ptpt",
"arxiv:2403.01897",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-01-04T00:34:12Z |
---
language:
- pt
tags:
- albertina-pt*
- albertina-100m-portuguese-ptpt
- albertina-100m-portuguese-ptbr
- albertina-900m-portuguese-ptpt
- albertina-900m-portuguese-ptbr
- albertina-1b5-portuguese-ptpt
- albertina-1b5-portuguese-ptbr
- fill-mask
- bert
- deberta
- portuguese
- encoder
- foundation model
license: mit
datasets:
- PORTULAN/glue-ptpt
widget:
- text: >-
A culinária portuguesa é rica em sabores e [MASK], tornando-se um dos
maiores tesouros do país.
---
---
<img align="left" width="40" height="40" src="https://github.githubassets.com/images/icons/emoji/unicode/1f917.png">
<p style="text-align: center;"> This is the model card for Albertina 1.5B PTBR 256.
You may be interested in some of the other models in the <a href="https://huggingface.co/PORTULAN">Albertina (encoders) and Gervásio (decoders) families</a>.
</p>
---
# Albertina 1.5B PTBR 256
**Albertina 1.5B PTBR 256** is a foundation, large language model for the **American variant of Portuguese**.
It is an **encoder** of the BERT family, based on the neural architecture Transformer and
developed over the DeBERTa model, with most competitive performance for this language.
It has different versions that were trained for different variants of Portuguese (PT),
namely the European variant, spoken in Portugal (**PTPT**) and the American variant, spoken in Brazil (**PTBR**),
and it is openly distributed free of charge under an open license.
| Albertina's Family of Models |
|----------------------------------------------------------------------------------------------------------|
| [**Albertina 1.5B PTPT**](https://huggingface.co/PORTULAN/albertina-1b5-portuguese-ptpt-encoder) |
| [**Albertina 1.5B PTBR**](https://huggingface.co/PORTULAN/albertina-1b5-portuguese-ptbr-encoder) |
| [**Albertina 1.5B PTPT 256**](https://huggingface.co/PORTULAN/albertina-1b5-portuguese-ptpt-encoder-256)|
| [**Albertina 1.5B PTBR 256**](https://huggingface.co/PORTULAN/albertina-1b5-portuguese-ptbr-encoder-256)|
| [**Albertina 900M PTPT**](https://huggingface.co/PORTULAN/albertina-900m-portuguese-ptpt-encoder) |
| [**Albertina 900M PTBR**](https://huggingface.co/PORTULAN/albertina-900m-portuguese-ptbr-encoder) |
| [**Albertina 100M PTPT**](https://huggingface.co/PORTULAN/albertina-100m-portuguese-ptpt-encoder) |
| [**Albertina 100M PTBR**](https://huggingface.co/PORTULAN/albertina-100m-portuguese-ptbr-encoder) |
**Albertina 1.5B PTBR 256** is a version for the **American variant of Portuguese**,
and to the best of our knowledge, this is an encoder specifically for this language and variant
that, at the time of its initial distribution, with its 1.5 billion parameters and performance scores sets a new state of the art for it,
and is made publicly available and distributed for reuse.
It is an **encoder** of the BERT family, based on the neural architecture Transformer and
developed over the DeBERTa model, with most competitive performance for this language.
It is distributed free of charge and under a most permissible license.
**Albertina 1.5B PTBR 256** is developed by a joint team from the University of Lisbon and the University of Porto, Portugal.
For a fully detailed description, check the respective [publication](https://arxiv.org/abs/2403.01897):
``` latex
@misc{albertina-pt-fostering,
title={Fostering the Ecosystem of Open Neural Encoders
for Portuguese with Albertina PT-* family},
author={Rodrigo Santos and João Rodrigues and Luís Gomes
and João Silva and António Branco
and Henrique Lopes Cardoso and Tomás Freitas Osório
and Bernardo Leite},
year={2024},
eprint={2403.01897},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Please use the above canonical reference when using or citing this model.
<br>
# Model Description
**This model card is for Albertina 1.5B PTBR 256**, with 1.5 billion parameters, 48 layers and a hidden size of 1536.
Albertina 1.5B PTBR 256 is distributed under an [MIT license](https://huggingface.co/PORTULAN/albertina-ptpt/blob/main/LICENSE).
DeBERTa is distributed under an [MIT license](https://github.com/microsoft/DeBERTa/blob/master/LICENSE).
<br>
# Training Data
[**Albertina 1.5B PTBR 256**](https://huggingface.co/PORTULAN/albertina-1b5-portuguese-ptbr-encoder-256) was trained over a 36 billion token data set that resulted from gathering some openly available corpora of American Portuguese from the following sources:
- [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX): the CulturaX is a multilingual corpus, freely available for research and AI development, created by combining and extensively cleaning two other large datasets, mC4 and OSCAR. It is the result of a selection performed over the [Common Crawl](https://commoncrawl.org/) data set, crawled from the Web, that retains only pages whose metadata indicates permission to be crawled, that performs deduplication, and that removes some boilerplate, among other filters. Given that it does not discriminate between the Portuguese variants, we performed extra filtering by retaining only documents whose meta-data indicate the Internet country code top-level domain of Portugal.
## Preprocessing
We filtered the PTBR corpora using the [BLOOM pre-processing](https://github.com/bigscience-workshop/data-preparation) pipeline.
We skipped the default filtering of stopwords since it would disrupt the syntactic structure, and also the filtering
for language identification given the corpus was pre-selected as Portuguese.
## Training
As codebase, we resorted to the [DeBERTa V2 xxlarge](https://huggingface.co/microsoft/deberta-v2-xxlarge), for English.
To train **Albertina 1.5B PTBR 256**, the data set was tokenized with the original DeBERTa tokenizer with a 128-token sequence
truncation and dynamic padding for 250k steps and a 256-token sequence-truncation for 80k steps.
These steps correspond to the equivalent setup of 48 hours on a2-megagpu-16gb Google Cloud A2 node for the 128-token input sequences and 24 hours of computation for the 256-token
input sequences.
We opted for a learning rate of 1e-5 with linear decay and 10k warm-up steps.
<br>
# Performance
We resorted to [extraGLUE](https://huggingface.co/datasets/PORTULAN/extraglue), a **PTBR version of the GLUE and SUPERGLUE** benchmark.
We automatically translated the tasks from GLUE and SUPERGLUE using [DeepL Translate](https://www.deepl.com/), which specifically
provides translation from English to PTPT or PTBR as possible options.
| Model | RTE (Accuracy) | WNLI (Accuracy)| MRPC (F1) | STS-B (Pearson) | COPA (Accuracy) | CB (F1) | MultiRC (F1) | BoolQ (Accuracy) |
|-------------------------------|----------------|----------------|-----------|-----------------|-----------------|------------|--------------|------------------|
| [**Albertina 1.5B PTBR**](https://huggingface.co/PORTULAN/albertina-1b5-portuguese-ptbr-encoder) | **0.8676** | 0.4742 | 0.8622 | **0.9007** | 0.7767 | 0.6372 | **0.7667** | **0.8654** |
| [**Albertina 1.5B PTBR 256**](https://huggingface.co/PORTULAN/albertina-1b5-portuguese-ptbr-encoder-256) | 0.8123 | 0.4225 | 0.8638 | 0.8968 | **0.8533** | **0.6884** | 0.6799 | 0.8509 |
| [**Albertina 900M PTBR**](https://huggingface.co/PORTULAN/albertina-900m-portuguese-ptbr-encoder) | 0.7545 | 0.4601 | **0.9071**| 0.8910 | 0.7767 | 0.5799 | 0.6731 | 0.8385 |
| **BERTimbau (335M)** | 0.6446 | **0.5634** | 0.8873 | 0.8842 | 0.6933 | 0.5438 | 0.6787 | 0.7783 |
| [**Albertina 100M PTBR**](https://huggingface.co/PORTULAN/albertina-100m-portuguese-ptbr-encoder) | 0.6582 | **0.5634** | 0.8149 | 0.8489 | n.a. | 0.4771 | 0.6469 | 0.7537 |
||||||||||
| **DeBERTa 1.5B (English)** | 0.7112 | **0.5634** | 0.8545 | 0.0123 | 0.5700 | 0.4307 | 0.3639 | 0.6217 |
| **DeBERTa 100M (English)** | 0.5716 | 0.5587 | 0.8060 | 0.8266 | n.a. | 0.4739 | 0.6391 | 0.6838 |
<br>
# How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='PORTULAN/albertina-1b5-portuguese-ptbr-encoder-256')
>>> unmasker("A culinária portuguesa é rica em sabores e [MASK], tornando-se um dos maiores tesouros do país.")
[{'score': 0.8332648277282715, 'token': 14690, 'token_str': ' costumes', 'sequence': 'A culinária portuguesa é rica em sabores e costumes, tornando-se um dos maiores tesouros do país.'},
{'score': 0.07860890030860901, 'token': 29829, 'token_str': ' cores', 'sequence': 'A culinária portuguesa é rica em sabores e cores, tornando-se um dos maiores tesouros do país.'},
{'score': 0.03278181701898575, 'token': 35277, 'token_str': ' arte', 'sequence': 'A culinária portuguesa é rica em sabores e arte, tornando-se um dos maiores tesouros do país.'},
{'score': 0.009515956044197083, 'token': 9240, 'token_str': ' cor', 'sequence': 'A culinária portuguesa é rica em sabores e cor, tornando-se um dos maiores tesouros do país.'},
{'score': 0.009381960146129131, 'token': 33455, 'token_str': ' nuances', 'sequence': 'A culinária portuguesa é rica em sabores e nuances, tornando-se um dos maiores tesouros do país.'}]
```
The model can be used by fine-tuning it for a specific task:
```python
>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
>>> from datasets import load_dataset
>>> model = AutoModelForSequenceClassification.from_pretrained("PORTULAN/albertina-1b5-portuguese-ptbr-encoder-256", num_labels=2)
>>> tokenizer = AutoTokenizer.from_pretrained("PORTULAN/albertina-1b5-portuguese-ptbr-encoder-256")
>>> dataset = load_dataset("PORTULAN/glue-ptbr", "rte")
>>> def tokenize_function(examples):
... return tokenizer(examples["sentence1"], examples["sentence2"], padding="max_length", truncation=True)
>>> tokenized_datasets = dataset.map(tokenize_function, batched=True)
>>> training_args = TrainingArguments(output_dir="albertina-ptbr-rte", evaluation_strategy="epoch")
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=tokenized_datasets["train"],
... eval_dataset=tokenized_datasets["validation"],
... )
>>> trainer.train()
```
<br>
# Citation
When using or citing this model, kindly cite the following [publication](https://arxiv.org/abs/2403.01897):
``` latex
@misc{albertina-pt-fostering,
title={Fostering the Ecosystem of Open Neural Encoders
for Portuguese with Albertina PT-* family},
author={Rodrigo Santos and João Rodrigues and Luís Gomes
and João Silva and António Branco
and Henrique Lopes Cardoso and Tomás Freitas Osório
and Bernardo Leite},
year={2024},
eprint={2403.01897},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<br>
# Acknowledgments
The research reported here was partially supported by: PORTULAN CLARIN—Research Infrastructure for the Science and Technology of Language,
funded by Lisboa 2020, Alentejo 2020 and FCT—Fundação para a Ciência e Tecnologia under the
grant PINFRA/22117/2016; research project ALBERTINA - Foundation Encoder Model for Portuguese and AI, funded by FCT—Fundação para a Ciência e Tecnologia under the
grant CPCA-IAC/AV/478394/2022; innovation project ACCELERAT.AI - Multilingual Intelligent Contact Centers, funded by IAPMEI, I.P. - Agência para a Competitividade e Inovação under the grant C625734525-00462629, of Plano de Recuperação e Resiliência, call RE-C05-i01.01 – Agendas/Alianças Mobilizadoras para a Reindustrialização; and LIACC - Laboratory for AI and Computer Science, funded by FCT—Fundação para a Ciência e Tecnologia under the grant FCT/UID/CEC/0027/2020.
|
thuerey-group/pde-transformer
|
thuerey-group
| 2025-06-23T09:10:09Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"physics",
"PDEs",
"surrogate",
"en",
"arxiv:2505.24717",
"license:mit",
"region:us"
] | null | 2025-06-05T21:38:22Z |
---
license: mit
language:
- en
tags:
- physics
- PDEs
- surrogate
---
# PDE-Transformer
This repository contains pre-trained weights of **PDE-Transformer**, a transformer-based foundation model
for physics simulations on regular grids. PDE-Transformer combines recent architectural improvements of diffusion transformers with adjustments specific
for large-scale physical simulations to provide a scalable and versatile general-purpose architecture for physics modeling.
## 🌐 Project Links
- **Project Webpage**: [https://tum-pbs.github.io/pde-transformer/landing.html](https://tum-pbs.github.io/pde-transformer/landing.html)
- **Paper**: [Efficient and Versatile Transformers for Physics Simulations](https://arxiv.org/abs/2505.24717v1)
- **GitHub Repository**: [https://github.com/tum-pbs/pde-transformer](https://github.com/tum-pbs/pde-transformer)
## 📝 Model Description
PDE-Transformer is designed to efficiently process and predict the evolution of physical systems described by partial differential equations (PDEs).
It can handle multiple types of PDEs, different resolutions, domain extents, boundary conditions,
and includes deep conditioning mechanisms for PDE- and task-specific information.
Key features:
- **Multi-scale architecture** with token down- and upsampling for efficient modeling
- **Shifted window attention** for improved scaling to high-resolution data
- **Mixed Channel (MC) and Separate Channel (SC)** representations for handling multiple physical quantities
- **Flexible conditioning mechanism** for PDE parameters, boundary conditions, and simulation metadata
- **Pre-training and fine-tuning capabilities** for transfer learning across different physics domains
## Installation
PDE-Transformer models and additional tools for training/inference can be installed via pip:
```bash
pip install pdetransformer
```
## 🏗️ Architecture Variants
PDE-Transformer comes in three different model sizes with separate channel (SC) and mixed channel (MC) variants:
### How To Load Pretrained Models
PDETransformer can be loaded via
```python
from pdetransformer.core.mixed_channels import PDETransformer
import torch
# Load pre-trained model
subfolder = 'mc-s'
model = PDETransformer.from_pretrained('thuerey-group/pde-transformer', subfolder=subfolder).cuda()
# For physics simulation
x = torch.randn((1,2,256,256), dtype=torch.float32).cuda()
predictions = model(x)
```
The model variant can be chosen via the subfolder, see the following list of pretrained models.
For more information, see the [documentation](https://tum-pbs.github.io/pde-transformer/).
### Available Models
| Model | Channels | Size | Hidden Dim | Heads | Parameters | Training Epochs | Model Size |
|-------|----------|------|------------|-------|------------|----------------|------------|
| **SC-S** | Separate | Small | 96 | 4 | ~46M | 100 | ~133MB |
| **SC-B** | Separate | Base | 192 | 8 | ~178M | 100 | ~522MB |
| **SC-L** | Separate | Large | 384 | 16 | ~701M | 100 | ~2.07GB |
| **MC-S** | Mixed | Small | 96 | 4 | ~33M | 100 | ~187MB |
| **MC-B** | Mixed | Base | 192 | 8 | ~130M | 100 | ~716MB |
| **MC-L** | Mixed | Large | 384 | 16 | ~518M | 100 | ~2.81GB |
### Model Specifications of Pretrained Models
- **Separate Channel (SC)**: Embeds different physical channels independently with channel-wise axial attention. Number of input/outputs channels is variable.
- **Mixed Channel (MC)**: Embeds all physical channels within the same token representation. Using 2 input/output channels.
- **Patch Size**: Embeds 4×4 patch into spatio-temporal token.
- **Window Size**: 8×8 for windowed attention
- **Boundary Conditions**: Supports both periodic and non-periodic boundary conditions
## Citation
If you use PDE-Transformer in your research, please cite:
```bibtex
@article{holzschuh2025pde,
title={PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations},
author={Holzschuh, Benjamin and Liu, Qiang and Kohl, Georg and Thuerey, Nils},
booktitle = {Forty-second International Conference on Machine Learning, {ICML} 2025, Vancouver, Canada, July 13-19, 2025},
year = {2025}
}
```
## 📄 License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
---
**Note**: This is a research project from the Technical University of Munich (TUM) Physics-based Simulation Group.
For questions and support, please refer to the GitHub repository or contact the authors.
|
efd2/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-foxy_gentle_slug
|
efd2
| 2025-06-23T09:08:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am foxy gentle slug",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T03:43:26Z |
---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-foxy_gentle_slug
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am foxy gentle slug
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-foxy_gentle_slug
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="efd2/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-foxy_gentle_slug", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
VyDat/Llama-3.2-1B-Instruct-Chat-sft
|
VyDat
| 2025-06-23T09:05:52Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-1B-Instruct",
"region:us"
] | null | 2025-06-23T08:58:36Z |
---
base_model: meta-llama/Llama-3.2-1B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
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## Model Card Contact
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### Framework versions
- PEFT 0.14.0
|
Irvan14/t5-small-seo-description-indo-generator
|
Irvan14
| 2025-06-23T09:02:38Z | 36 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:panggi/t5-small-indonesian-summarization-cased",
"base_model:finetune:panggi/t5-small-indonesian-summarization-cased",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-21T09:23:21Z |
---
library_name: transformers
base_model: panggi/t5-small-indonesian-summarization-cased
tags:
- generated_from_trainer
model-index:
- name: t5-small-seo-description-indo-generator
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-seo-description-indo-generator
This model is a fine-tuned version of [panggi/t5-small-indonesian-summarization-cased](https://huggingface.co/panggi/t5-small-indonesian-summarization-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2676
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2237 | 1.0 | 2875 | 0.2691 |
| 0.1926 | 2.0 | 5750 | 0.2483 |
| 0.1799 | 3.0 | 8625 | 0.2676 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Prince-1/AceReason-Nemotron-14B-Onnx
|
Prince-1
| 2025-06-23T08:41:55Z | 0 | 1 |
onnxruntime_genai
|
[
"onnxruntime_genai",
"onnx",
"nvidia",
"reasoning",
"math",
"code",
"reinforcement learning",
"text-generation",
"conversational",
"en",
"arxiv:2505.16400",
"arxiv:2506.13284",
"base_model:nvidia/AceReason-Nemotron-14B",
"base_model:quantized:nvidia/AceReason-Nemotron-14B",
"license:other",
"region:us"
] |
text-generation
| 2025-06-22T18:12:54Z |
---
library_name: onnxruntime_genai
license: other
license_name: nvidia-open-model-license
license_link: >-
https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
pipeline_tag: text-generation
language:
- en
tags:
- nvidia
- reasoning
- math
- code
- reinforcement learning
- onnxruntime_genai
base_model:
- nvidia/AceReason-Nemotron-14B
---
# AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning
<p align="center">
[](https://arxiv.org/abs/2505.16400)
[](https://huggingface.co/datasets/nvidia/AceReason-Math)
[](https://huggingface.co/collections/nvidia/acereason-682f4e1261dc22f697fd1485)
[](https://huggingface.co/nvidia/AceReason-Nemotron-14B/blob/main/README_EVALUATION.md)
</p>
<img src="https://huggingface.co/datasets/davanstrien/model_cards_with_metadata/viewer/default/fig/main_fig.png" alt="main_fig" style="width: 600px; max-width: 100%;" />
## 🔥News
- **6/16/2025**: We are excited to share our new release combining SFT with RL: **AceReason-Nemotron-1.1-7B**
- Paper: https://arxiv.org/pdf/2506.13284
- Model: https://huggingface.co/nvidia/AceReason-Nemotron-1.1-7B
- 4M SFT Data: https://huggingface.co/datasets/nvidia/AceReason-1.1-SFT
- **6/11/2025**: We share our evaluation toolkit at [AceReason Evalution](https://huggingface.co/nvidia/AceReason-Nemotron-14B/blob/main/README_EVALUATION.md) including:
- scripts to run inference and scoring
- LiveCodeBench (avg@8): model prediction files and scores for each month (2023/5-2025/5)
- AIME24/25 (avg@64): model prediction files and scores
- **6/2/2025**: We are excited to share our Math RL training dataset at [AceReason-Math](https://huggingface.co/datasets/nvidia/AceReason-Math)
We're thrilled to introduce AceReason-Nemotron-14B, a math and code reasoning model trained entirely through reinforcement learning (RL), starting from the DeepSeek-R1-Distilled-Qwen-14B. It delivers impressive results, achieving 78.6% on AIME 2024 (+8.9%), 67.4% on AIME 2025 (+17.4%), 61.1% on LiveCodeBench v5 (+8%), 54.9% on LiveCodeBench v6 (+7%), and 2024 on Codeforces (+543). We systematically study the RL training process through extensive ablations and propose a simple yet effective approach: first RL training on math-only prompts, then RL training on code-only prompts. Notably, we find that math-only RL not only significantly enhances the performance of strong distilled models on math benchmarks, but also code reasoning tasks. In addition, extended code-only RL further improves code benchmark performance while causing minimal degradation in math results. We find that RL not only elicits the foundational reasoning capabilities acquired during pre-training and supervised fine-tuning (e.g., distillation), but also pushes the limits of the model's reasoning ability, enabling it to solve problems that were previously unsolvable.
We share our training recipe, training logs in our [technical report](https://arxiv.org/abs/2505.16400).
## Results
We evaluate our model against competitive reasoning models of comparable size within Qwen2.5 and Llama3.1 model family on AIME 2024, AIME 2025, LiveCodeBench v5 (2024/08/01 - 2025/02/01), and LiveCodeBench v6 (2025/02/01-2025/05/01). More evaluation results can be found in our [technical report](https://arxiv.org/abs/2505.16400).
| **Model** | **AIME 2024<br>(avg@64)** | **AIME 2025<br>(avg@64)** | **LCB v5<br>(avg@8)** | **LCB v6<br>(avg@8)** |
| :---: | :---: | :---: | :---: | :---: |
| <small>QwQ-32B</small> | 79.5 | 65.8 | 63.4 | - |
| <small>DeepSeek-R1-671B</small> | 79.8 | 70.0 | 65.9 | - |
| <small>Llama-Nemotron-Ultra-253B</small> | 80.8 | 72.5 | 66.3 | - |
| <small>o3-mini (medium)</small> | 79.6 | 76.7 | 67.4 | - |
| <small>Light-R1-14B</small> | 74 | 60.2 | 57.9 | 51.5 |
| <small>DeepCoder-14B (32K Inference)</small> | 71 | 56.1 | 57.9 | 50.4 |
| <small>OpenMath-Nemotron-14B</small> | 76.3 | 63.0 | - | - |
| <small>OpenCodeReasoning-Nemotron-14B</small> | - | - | 59.4 | 54.1 |
| <small>Llama-Nemotron-Super-49B-v1</small> | 67.5 | 60.0 | 45.5 | - |
| <small>DeepSeek-R1-Distilled-Qwen-14B</small> | 69.7 | 50.2 | 53.1 | 47.9 |
| <small>DeepSeek-R1-Distilled-Qwen-32B</small> | 72.6 | 54.9 | 57.2 | - |
| [AceReason-Nemotron-7B 🤗](https://huggingface.co/nvidia/AceReason-Nemotron-7B)| 69.0 | 53.6 | 51.8 | 44.1 |
| [AceReason-Nemotron-14B 🤗](https://huggingface.co/nvidia/AceReason-Nemotron-14B)| 78.6 | 67.4 | 61.1 | 54.9 |
## How to use
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = 'nvidia/AceReason-Nemotron-14B'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
prompt = "Jen enters a lottery by picking $4$ distinct numbers from $S=\\{1,2,3,\\cdots,9,10\\}.$ $4$ numbers are randomly chosen from $S.$ She wins a prize if at least two of her numbers were $2$ of the randomly chosen numbers, and wins the grand prize if all four of her numbers were the randomly chosen numbers. The probability of her winning the grand prize given that she won a prize is $\\tfrac{m}{n}$ where $m$ and $n$ are relatively prime positive integers. Find $m+n$."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to("cuda")
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768,
temperature=0.6,
top_p=0.95
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
## Usage Recommendations
1. Don't include a system prompt; instead, place all instructions directly in the user prompt.
2. We recommend using the following instruction for math questions: Please reason step by step, and put your final answer within \\boxed{}.
3. We recommend using the following instruction for code questions:
```python
question = "" # code question
starter_code = "" # starter code function header
code_instruction_nostartercode = """Write Python code to solve the problem. Please place the solution code in the following format:\n```python\n# Your solution code here\n```"""
code_instruction_hasstartercode = """Please place the solution code in the following format:\n```python\n# Your solution code here\n```"""
if starter_code != "":
question += "\n\n" + "Solve the problem starting with the provided function header.\n\nFunction header:\n" + "```\n" + starter_code + "\n```"
question += "\n\n" + code_instruction_hasstartercode
else:
question += "\n\n" + code_instruction_nostartercode
final_prompt = "<｜User｜>" + question + "<｜Assistant｜><think>\n"
```
4. Our inference engine for evaluation is **vLLM==0.7.3** using top-p=0.95, temperature=0.6, max_tokens=32768.
## Evaluation Toolkit
Please check evaluation code, scripts, cached prediction files in https://huggingface.co/nvidia/AceReason-Nemotron-14B/blob/main/README_EVALUATION.md
## Correspondence to
Yang Chen (yachen@nvidia.com), Zhuolin Yang (zhuoliny@nvidia.com), Zihan Liu (zihanl@nvidia.com), Chankyu Lee (chankyul@nvidia.com), Wei Ping (wping@nvidia.com)
## License
Your use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
## Citation
```
@article{chen2025acereason,
title={AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning},
author={Chen, Yang and Yang, Zhuolin and Liu, Zihan and Lee, Chankyu and Xu, Peng and Shoeybi, Mohammad and Catanzaro, Bryan and Ping, Wei},
journal={arXiv preprint arXiv:2505.16400},
year={2025}
}
```
|
sterbanger/absa-bert-model
|
sterbanger
| 2025-06-23T08:29:19Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-22T19:07:46Z |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: absa-bert-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# absa-bert-model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1728
- Accuracy: 0.9273
- Precision: 0.9651
- Recall: 0.8513
- F1: 0.8975
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7279 | 0.0833 | 10 | 0.3989 | 0.8620 | 0.5824 | 0.6081 | 0.5939 |
| 0.3455 | 0.1667 | 20 | 0.2670 | 0.8679 | 0.5926 | 0.6093 | 0.5986 |
| 0.2282 | 0.25 | 30 | 0.2069 | 0.9276 | 0.9655 | 0.8514 | 0.8977 |
| 0.188 | 0.3333 | 40 | 0.1806 | 0.9276 | 0.9650 | 0.8517 | 0.8977 |
| 0.1828 | 0.4167 | 50 | 0.1755 | 0.9276 | 0.9655 | 0.8514 | 0.8977 |
| 0.1627 | 0.5 | 60 | 0.1728 | 0.9276 | 0.9655 | 0.8514 | 0.8977 |
| 0.1512 | 0.5833 | 70 | 0.1738 | 0.9276 | 0.9655 | 0.8514 | 0.8977 |
| 0.1859 | 0.6667 | 80 | 0.1757 | 0.9276 | 0.9655 | 0.8514 | 0.8977 |
| 0.1675 | 0.75 | 90 | 0.1726 | 0.9276 | 0.9655 | 0.8514 | 0.8977 |
| 0.1692 | 0.8333 | 100 | 0.1720 | 0.9276 | 0.9655 | 0.8514 | 0.8977 |
| 0.1809 | 0.9167 | 110 | 0.1716 | 0.9276 | 0.9655 | 0.8514 | 0.8977 |
| 0.1621 | 1.0 | 120 | 0.1715 | 0.9276 | 0.9655 | 0.8514 | 0.8977 |
| 0.1604 | 1.0833 | 130 | 0.1734 | 0.9276 | 0.9655 | 0.8514 | 0.8977 |
| 0.1795 | 1.1667 | 140 | 0.1718 | 0.9274 | 0.9653 | 0.8514 | 0.8976 |
| 0.1615 | 1.25 | 150 | 0.1717 | 0.9273 | 0.9650 | 0.8514 | 0.8975 |
| 0.1622 | 1.3333 | 160 | 0.1721 | 0.9272 | 0.9648 | 0.8513 | 0.8974 |
| 0.1734 | 1.4167 | 170 | 0.1763 | 0.9262 | 0.9632 | 0.8509 | 0.8966 |
| 0.1709 | 1.5 | 180 | 0.1753 | 0.9266 | 0.9640 | 0.8511 | 0.8970 |
| 0.1543 | 1.5833 | 190 | 0.1734 | 0.9274 | 0.9652 | 0.8514 | 0.8976 |
| 0.1407 | 1.6667 | 200 | 0.1732 | 0.9274 | 0.9652 | 0.8514 | 0.8976 |
| 0.1674 | 1.75 | 210 | 0.1717 | 0.9274 | 0.9652 | 0.8514 | 0.8976 |
| 0.1734 | 1.8333 | 220 | 0.1718 | 0.9274 | 0.9652 | 0.8514 | 0.8976 |
| 0.1757 | 1.9167 | 230 | 0.1720 | 0.9274 | 0.9652 | 0.8514 | 0.8976 |
| 0.1603 | 2.0 | 240 | 0.1722 | 0.9276 | 0.9655 | 0.8514 | 0.8977 |
| 0.1643 | 2.0833 | 250 | 0.1724 | 0.9276 | 0.9655 | 0.8514 | 0.8977 |
| 0.1737 | 2.1667 | 260 | 0.1717 | 0.9276 | 0.9655 | 0.8514 | 0.8977 |
| 0.1724 | 2.25 | 270 | 0.1715 | 0.9276 | 0.9655 | 0.8514 | 0.8977 |
| 0.1721 | 2.3333 | 280 | 0.1716 | 0.9276 | 0.9655 | 0.8514 | 0.8977 |
| 0.1545 | 2.4167 | 290 | 0.1720 | 0.9273 | 0.9651 | 0.8513 | 0.8975 |
| 0.1605 | 2.5 | 300 | 0.1723 | 0.9273 | 0.9651 | 0.8513 | 0.8975 |
| 0.1482 | 2.5833 | 310 | 0.1728 | 0.9273 | 0.9651 | 0.8513 | 0.8975 |
| 0.166 | 2.6667 | 320 | 0.1729 | 0.9273 | 0.9651 | 0.8513 | 0.8975 |
| 0.1563 | 2.75 | 330 | 0.1727 | 0.9273 | 0.9651 | 0.8513 | 0.8975 |
| 0.159 | 2.8333 | 340 | 0.1727 | 0.9273 | 0.9651 | 0.8513 | 0.8975 |
| 0.1664 | 2.9167 | 350 | 0.1727 | 0.9273 | 0.9651 | 0.8513 | 0.8975 |
| 0.1646 | 3.0 | 360 | 0.1728 | 0.9273 | 0.9651 | 0.8513 | 0.8975 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
kewkew78912/Thai-FLS-V1
|
kewkew78912
| 2025-06-23T08:22:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"camembert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-23T08:22:25Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Goutham204/IMDB_sentiment_analysis
|
Goutham204
| 2025-06-23T08:00:31Z | 7 | 0 |
keras
|
[
"keras",
"sentiment-analysis",
"imdb",
"tensorflow",
"lstm",
"binary_crossentropy",
"en",
"license:mit",
"model-index",
"region:us"
] | null | 2025-06-23T07:51:53Z |
---
language: en
license: mit
tags:
- sentiment-analysis
- imdb
- tensorflow
- lstm
- binary_crossentropy
model-index:
- name: IMDb Sentiment Classifier (LSTM)
results:
- task:
type: text-classification
name: Sentiment Analysis
dataset:
name: IMDb Movie Reviews
type: imdb
metrics:
- name: Accuracy
type: accuracy
value: 0.72
---
# IMDb Movie Review Sentiment Analysis using LSTM
This model performs binary sentiment classification (positive/negative) on IMDb movie reviews using an LSTM-based neural network built with TensorFlow.
## Model Details
- **Architecture**: LSTM
- **Framework**: TensorFlow/Keras
- **Dataset**: IMDb Movie Reviews (50k)
- **Tokenizer**: TensorFlow `Tokenizer`
- **Accuracy**: 72%
## Sentiment Labels
- `0`: Negative
- `1`: Positive
|
StealthPort/yong-zhu-zhai-dai-li-qiang-quan-qiu-xian-liang-qiu-xie-wo-de-shi-zhan-gong-lue
|
StealthPort
| 2025-06-23T07:49:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-23T07:45:10Z |
# 用住宅代理抢全球限量球鞋：我的实战攻略
<a href='https://postimages.org/' target='_blank'><img src='https://i.postimg.cc/nrj0B9vF/684ba6f713f9c335a1f3092f-scaled-cover.jpg' border='0' alt='684ba6f713f9c335a1f3092f-scaled-cover'/></a>
> 想第一时间买到稀有球鞋、又想拿到最低折扣？住宅代理就是你的隐藏神招。
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品牌方为了制造稀缺感，喜欢把**折扣或首发锁定在特定国家/地区**。同一双鞋在美国可能半价清仓，在其他地区却原价出售；有时官网甚至直接屏蔽海外 IP。没有合适工具，你根本看不到这些隐藏优惠，更别提下单。
---
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手动或自动切换 IP，日版、欧版、美版优惠随你挑。
---
## 选代理前必须关注的 4 个指标
| 指标 | 重要性 | 我的经验 |
| ---- | ------ | -------- |
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---
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---
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---
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|
RoxanneWsyw/qwen1.5_gsm_esft_gate_lr5e-6
|
RoxanneWsyw
| 2025-06-23T07:28:05Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_moe",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen1.5-MoE-A2.7B",
"base_model:finetune:Qwen/Qwen1.5-MoE-A2.7B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T07:23:01Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen1.5-MoE-A2.7B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: qwen1.5_gsm_lr5e-6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qwen1.5_gsm_lr5e-6
This model is a fine-tuned version of [Qwen/Qwen1.5-MoE-A2.7B](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) on the gsm_lf dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1
|
Hachipo/OpenCoder-8B-Base-MIFT-en_newbase_v1-MIFT-en_5000
|
Hachipo
| 2025-06-23T07:02:15Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T06:59:29Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Satram/Llama_Instruct_Manuales_FINAL
|
Satram
| 2025-06-23T06:56:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T06:56:16Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Satram
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Marlo1982/RTA-4096-Merged-2
|
Marlo1982
| 2025-06-23T06:53:16Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T06:49:42Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
himedia/fincredit-Llama-3.2-3B-lr2e04-bs16-r64-steps1000-20250623_060351
|
himedia
| 2025-06-23T06:52:34Z | 0 | 0 | null |
[
"safetensors",
"financial",
"credit-rating",
"korean",
"llama",
"unsloth",
"fine-tuned",
"text-generation",
"conversational",
"ko",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-23T06:52:19Z |
---
language: ko
license: apache-2.0
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- financial
- credit-rating
- korean
- llama
- unsloth
- fine-tuned
model_name: FinCreditLlama-3.2-3B
pipeline_tag: text-generation
---
# FinCreditLlama-3.2-3B
## 모델 개요
FinCreditLlama-3.2-3B는 금융 신용 평가를 위해 특별히 설계된 한국어 언어 모델입니다.
**베이스 모델**: unsloth/Llama-3.2-3B-Instruct
**데이터셋**: himedia/financial_dummy_data_v4
**학습 방법**: LoRA (Low-Rank Adaptation)
**학습 일시**: 20250623_060351
## 📊 학습 결과
- **Final Training Loss**: 0.2510
- **Final Validation Loss**: 0.2518
- **Best Validation Loss**: 0.2518 (step 1000)
- **Overall Improvement**: 89.0%
- **Training Time**: 47.81 minutes
## 하이퍼파라미터
- **Learning Rate**: 0.0002
- **Max Steps**: 1000
- **Batch Size**: 2
- **Gradient Accumulation**: 8
- **LoRA r**: 64
- **LoRA alpha**: 64
- **Max Sequence Length**: 2048
- **Warmup Steps**: 5
## 🔧 메모리 사용량
- **GPU**: NVIDIA RTX A5000
- **Peak Memory**: 4.217 GB
- **Memory Usage**: 17.9%
## 사용 방법
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
# 모델과 토크나이저 로드
tokenizer = AutoTokenizer.from_pretrained("himedia/fincredit-Llama-3.2-3B-lr2e04-bs16-r64-steps1000-20250623_060351")
model = AutoModelForCausalLM.from_pretrained("himedia/fincredit-Llama-3.2-3B-lr2e04-bs16-r64-steps1000-20250623_060351")
# 간단한 추론 예제
prompt = "고객의 신용등급을 평가해주세요:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 📊 학습 데이터 파일
이 레포지토리에는 다음 학습 관련 파일들이 포함되어 있습니다:
- `training_log.json`: 전체 학습 로그 (JSON 형식)
- `FinCreditLlama-3.2-3B_20250623_060351_training_curves.png`: 학습 곡선 시각화 이미지
## 레포지토리명 구성
```
fincredit-Llama-3.2-3B-lr2e04-bs16-r64-steps1000-20250623_060351 = fincredit-lamma3-4b-lr2e04-bs2-r64-steps1000-20250623_060351
```
- `fincredit-lamma3-4b`: 모델 기본명
- `lr2e04`: Learning Rate
- `bs2`: Batch Size
- `r64`: LoRA rank
- `steps1000`: 학습 스텝
- `20250623_060351`: 학습 시각
## 성능
이 모델은 한국어 금융 텍스트에 대해 파인튜닝되어 신용 평가 관련 질의응답에 특화되어 있습니다.
## 라이선스
Apache 2.0
|
zaid683/phi3-mini-yoda-adapter
|
zaid683
| 2025-06-23T06:34:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"base_model:finetune:microsoft/Phi-3-mini-4k-instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T06:34:07Z |
---
base_model: microsoft/Phi-3-mini-4k-instruct
library_name: transformers
model_name: phi3-mini-yoda-adapter
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for phi3-mini-yoda-adapter
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="zaid683/phi3-mini-yoda-adapter", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.12.1
- Transformers: 4.46.2
- Pytorch: 2.1.0+cu118
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
jung33010/klue-bert-base-nsmc
|
jung33010
| 2025-06-23T06:30:03Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-23T06:29:39Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
yungu09/klue-bert-base-nsmc2
|
yungu09
| 2025-06-23T06:24:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-23T06:23:53Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
**APA:**
[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
vitna/klue-bert-base-nsmc
|
vitna
| 2025-06-23T06:24:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-23T06:23:49Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Euiryeong/klue-bert-base-nsmc
|
Euiryeong
| 2025-06-23T06:19:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-23T06:18:48Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Goutham204/Emotion_detection
|
Goutham204
| 2025-06-23T05:58:51Z | 0 | 0 |
keras
|
[
"keras",
"emotion-detection",
"facial-expression",
"image-classification",
"fer2012",
"en",
"license:mit",
"model-index",
"region:us"
] |
image-classification
| 2025-06-23T05:14:42Z |
---
language: en
license: mit
tags:
- keras
- emotion-detection
- facial-expression
- image-classification
- fer2012
model-index:
- name: Facial Emotion Recognition (FER-2012)
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: FER-2012
type: fer2012
metrics:
- name: Accuracy
type: accuracy
value: 0.84
---
# Facial Expression Recognition using CNN (FER-2012 Dataset)
This repository contains a Convolutional Neural Network (CNN) model trained using the FER-2012 dataset to classify facial expressions into seven emotion categories.
## Model Details
- **Framework**: TensorFlow / Keras
- **Input**: 48x48 grayscale facial image
- **Output**: Emotion class (0–6)
- **Model Format**: `.keras` (Keras native format)
## Emotion Classes
```text
0 → Angry
1 → Disgust
2 → Fear
3 → Happy
4 → Sad
5 → Surprise
6 → Neutral
|
Rerandaka/Cild_safety_bigbird
|
Rerandaka
| 2025-06-23T05:38:40Z | 0 | 0 | null |
[
"safetensors",
"big_bird",
"text-classification",
"en",
"base_model:google/bigbird-roberta-base",
"base_model:finetune:google/bigbird-roberta-base",
"license:mit",
"region:us"
] |
text-classification
| 2025-06-23T05:20:11Z |
---
license: mit
language:
- en
base_model:
- google/bigbird-roberta-base
pipeline_tag: text-classification
---
|
BootesVoid/cmbyjnk1403xvrdqsg2kyovgu_cmc8lgwdk0cytbfifb66p8wmw
|
BootesVoid
| 2025-06-23T05:28:18Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-23T05:28:17Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: EMMA
---
# Cmbyjnk1403Xvrdqsg2Kyovgu_Cmc8Lgwdk0Cytbfifb66P8Wmw
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `EMMA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "EMMA",
"lora_weights": "https://huggingface.co/BootesVoid/cmbyjnk1403xvrdqsg2kyovgu_cmc8lgwdk0cytbfifb66p8wmw/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmbyjnk1403xvrdqsg2kyovgu_cmc8lgwdk0cytbfifb66p8wmw', weight_name='lora.safetensors')
image = pipeline('EMMA').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbyjnk1403xvrdqsg2kyovgu_cmc8lgwdk0cytbfifb66p8wmw/discussions) to add images that show off what you’ve made with this LoRA.
|
rainorangelemon2/smolgemma-waymo-base-merged-2
|
rainorangelemon2
| 2025-06-23T05:18:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"smolvlm",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-23T05:17:35Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
amai-gsu/pythia-1.4b-Q4_0-GGUF
|
amai-gsu
| 2025-06-23T05:18:30Z | 0 | 0 | null |
[
"gguf",
"pytorch",
"causal-lm",
"pythia",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:EleutherAI/the_pile",
"base_model:EleutherAI/pythia-1.4b",
"base_model:quantized:EleutherAI/pythia-1.4b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T05:18:24Z |
---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- llama-cpp
- gguf-my-repo
license: apache-2.0
datasets:
- EleutherAI/the_pile
base_model: EleutherAI/pythia-1.4b
---
# amai-gsu/pythia-1.4b-Q4_0-GGUF
This model was converted to GGUF format from [`EleutherAI/pythia-1.4b`](https://huggingface.co/EleutherAI/pythia-1.4b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/EleutherAI/pythia-1.4b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo amai-gsu/pythia-1.4b-Q4_0-GGUF --hf-file pythia-1.4b-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo amai-gsu/pythia-1.4b-Q4_0-GGUF --hf-file pythia-1.4b-q4_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo amai-gsu/pythia-1.4b-Q4_0-GGUF --hf-file pythia-1.4b-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo amai-gsu/pythia-1.4b-Q4_0-GGUF --hf-file pythia-1.4b-q4_0.gguf -c 2048
```
|
amai-gsu/pythia-1b-Q4_0-GGUF
|
amai-gsu
| 2025-06-23T05:17:54Z | 0 | 0 | null |
[
"gguf",
"pytorch",
"causal-lm",
"pythia",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:the_pile",
"base_model:EleutherAI/pythia-1b",
"base_model:quantized:EleutherAI/pythia-1b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T05:17:49Z |
---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- llama-cpp
- gguf-my-repo
license: apache-2.0
datasets:
- the_pile
base_model: EleutherAI/pythia-1b
---
# amai-gsu/pythia-1b-Q4_0-GGUF
This model was converted to GGUF format from [`EleutherAI/pythia-1b`](https://huggingface.co/EleutherAI/pythia-1b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/EleutherAI/pythia-1b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo amai-gsu/pythia-1b-Q4_0-GGUF --hf-file pythia-1b-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo amai-gsu/pythia-1b-Q4_0-GGUF --hf-file pythia-1b-q4_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo amai-gsu/pythia-1b-Q4_0-GGUF --hf-file pythia-1b-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo amai-gsu/pythia-1b-Q4_0-GGUF --hf-file pythia-1b-q4_0.gguf -c 2048
```
|
amai-gsu/pythia-410m-Q4_0-GGUF
|
amai-gsu
| 2025-06-23T05:17:30Z | 0 | 0 | null |
[
"gguf",
"pytorch",
"causal-lm",
"pythia",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:EleutherAI/pile",
"base_model:EleutherAI/pythia-410m",
"base_model:quantized:EleutherAI/pythia-410m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T05:17:22Z |
---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- llama-cpp
- gguf-my-repo
license: apache-2.0
datasets:
- EleutherAI/pile
base_model: EleutherAI/pythia-410m
---
# amai-gsu/pythia-410m-Q4_0-GGUF
This model was converted to GGUF format from [`EleutherAI/pythia-410m`](https://huggingface.co/EleutherAI/pythia-410m) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/EleutherAI/pythia-410m) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo amai-gsu/pythia-410m-Q4_0-GGUF --hf-file pythia-410m-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo amai-gsu/pythia-410m-Q4_0-GGUF --hf-file pythia-410m-q4_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo amai-gsu/pythia-410m-Q4_0-GGUF --hf-file pythia-410m-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo amai-gsu/pythia-410m-Q4_0-GGUF --hf-file pythia-410m-q4_0.gguf -c 2048
```
|
amai-gsu/pythia-160m-Q4_0-GGUF
|
amai-gsu
| 2025-06-23T05:16:58Z | 0 | 0 | null |
[
"gguf",
"pytorch",
"causal-lm",
"pythia",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:EleutherAI/pile",
"base_model:EleutherAI/pythia-160m",
"base_model:quantized:EleutherAI/pythia-160m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T05:16:55Z |
---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- llama-cpp
- gguf-my-repo
license: apache-2.0
datasets:
- EleutherAI/pile
base_model: EleutherAI/pythia-160m
---
# amai-gsu/pythia-160m-Q4_0-GGUF
This model was converted to GGUF format from [`EleutherAI/pythia-160m`](https://huggingface.co/EleutherAI/pythia-160m) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/EleutherAI/pythia-160m) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo amai-gsu/pythia-160m-Q4_0-GGUF --hf-file pythia-160m-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo amai-gsu/pythia-160m-Q4_0-GGUF --hf-file pythia-160m-q4_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo amai-gsu/pythia-160m-Q4_0-GGUF --hf-file pythia-160m-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo amai-gsu/pythia-160m-Q4_0-GGUF --hf-file pythia-160m-q4_0.gguf -c 2048
```
|
huni0304/whisper-small-translate-ch2en
|
huni0304
| 2025-06-23T04:50:38Z | 22 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:covost2",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-19T08:12:30Z |
---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- covost2
model-index:
- name: whisper-small-translate-ch2en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-translate-ch2en
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the covost2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 5
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
henomoto/furigana_whisper_small_jsut
|
henomoto
| 2025-06-23T04:22:11Z | 18 | 0 | null |
[
"safetensors",
"whisper",
"automatic-speech-recognition",
"ja",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"region:us"
] |
automatic-speech-recognition
| 2025-06-19T07:03:50Z |
---
language:
- ja
base_model:
- openai/whisper-small
pipeline_tag: automatic-speech-recognition
---
## 概要
- 日本語の音声ファイルに対して、書記素列（漢字仮名交じり文）をプロンプトに入れることで、書記素列と整合性のあるモーラ列（カタカナ列）を出力するモデルです。
- 以下の記事を参考にしてください: TODO
## 使用方法
```python
from transformers import pipeline
from pathlib import Path
pipe = pipeline(
"automatic-speech-recognition",
model="henomoto/furigana_whisper_small_jsut",
)
def transcribe_with_prompt(pipe, audio_path: str | Path, prompt: str) -> str:
prompt_ids = pipe.tokenizer.get_prompt_ids(
prompt, return_tensors="pt"
).to(pipe.device)
generate_kwargs = {"prompt_ids": prompt_ids}
result = pipe(str(audio_path), generate_kwargs=generate_kwargs)
return result["text"]
```
## 注意
- 音声の長さは30秒以下でないとうまく動きません。
- 公開しているsmallモデルはそこまで精度が良いとは言えず、G2Pマッチ率（データセットに対してフィルタリングを行った後に残るデータ量）が40%程度となっています。より精度の高いモデルを使いたい方はデータを揃え、ベースモデルもwhisper-smallより大きいモデルにして自分で学習を行うことをおすすめします。
- 学習データでのプロンプトは、全て「句読点が、。のみ」「最後に必ず。が付く」と正規化されています。よって、与えるプロンプトも同様の形式にしたほうが精度が高くなります。
|
thuan220401/multilingual-e5-large
|
thuan220401
| 2025-06-23T04:20:33Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"xlm-roberta",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"feature-extraction",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:2402.05672",
"arxiv:2108.08787",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-06-23T04:18:30Z |
---
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- feature-extraction
- sentence-transformers
model-index:
- name: multilingual-e5-large
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 79.05970149253731
- type: ap
value: 43.486574390835635
- type: f1
value: 73.32700092140148
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (de)
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.22055674518201
- type: ap
value: 81.55756710830498
- type: f1
value: 69.28271787752661
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en-ext)
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 80.41979010494754
- type: ap
value: 29.34879922376344
- type: f1
value: 67.62475449011278
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (ja)
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 77.8372591006424
- type: ap
value: 26.557560591210738
- type: f1
value: 64.96619417368707
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.489875
- type: ap
value: 90.98758636917603
- type: f1
value: 93.48554819717332
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.564
- type: f1
value: 46.75122173518047
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (de)
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 45.400000000000006
- type: f1
value: 44.17195682400632
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (es)
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 43.068
- type: f1
value: 42.38155696855596
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 41.89
- type: f1
value: 40.84407321682663
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (ja)
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.120000000000005
- type: f1
value: 39.522976223819114
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 38.832
- type: f1
value: 38.0392533394713
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.725
- type: map_at_10
value: 46.055
- type: map_at_100
value: 46.900999999999996
- type: map_at_1000
value: 46.911
- type: map_at_3
value: 41.548
- type: map_at_5
value: 44.297
- type: mrr_at_1
value: 31.152
- type: mrr_at_10
value: 46.231
- type: mrr_at_100
value: 47.07
- type: mrr_at_1000
value: 47.08
- type: mrr_at_3
value: 41.738
- type: mrr_at_5
value: 44.468999999999994
- type: ndcg_at_1
value: 30.725
- type: ndcg_at_10
value: 54.379999999999995
- type: ndcg_at_100
value: 58.138
- type: ndcg_at_1000
value: 58.389
- type: ndcg_at_3
value: 45.156
- type: ndcg_at_5
value: 50.123
- type: precision_at_1
value: 30.725
- type: precision_at_10
value: 8.087
- type: precision_at_100
value: 0.9769999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 18.54
- type: precision_at_5
value: 13.542000000000002
- type: recall_at_1
value: 30.725
- type: recall_at_10
value: 80.868
- type: recall_at_100
value: 97.653
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 55.619
- type: recall_at_5
value: 67.71000000000001
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 44.30960650674069
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 38.427074197498996
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.28270056031872
- type: mrr
value: 74.38332673789738
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 84.05942144105269
- type: cos_sim_spearman
value: 82.51212105850809
- type: euclidean_pearson
value: 81.95639829909122
- type: euclidean_spearman
value: 82.3717564144213
- type: manhattan_pearson
value: 81.79273425468256
- type: manhattan_spearman
value: 82.20066817871039
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (de-en)
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.46764091858039
- type: f1
value: 99.37717466945023
- type: precision
value: 99.33194154488518
- type: recall
value: 99.46764091858039
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (fr-en)
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.29407880255337
- type: f1
value: 98.11248073959938
- type: precision
value: 98.02443319392472
- type: recall
value: 98.29407880255337
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (ru-en)
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 97.79009352268791
- type: f1
value: 97.5176076665512
- type: precision
value: 97.38136473848286
- type: recall
value: 97.79009352268791
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (zh-en)
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.26276987888363
- type: f1
value: 99.20133403545726
- type: precision
value: 99.17500438827453
- type: recall
value: 99.26276987888363
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.72727272727273
- type: f1
value: 84.67672206031433
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.34220182511161
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 33.4987096128766
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.558249999999997
- type: map_at_10
value: 34.44425000000001
- type: map_at_100
value: 35.59833333333333
- type: map_at_1000
value: 35.706916666666665
- type: map_at_3
value: 31.691749999999995
- type: map_at_5
value: 33.252916666666664
- type: mrr_at_1
value: 30.252666666666666
- type: mrr_at_10
value: 38.60675
- type: mrr_at_100
value: 39.42666666666666
- type: mrr_at_1000
value: 39.48408333333334
- type: mrr_at_3
value: 36.17441666666665
- type: mrr_at_5
value: 37.56275
- type: ndcg_at_1
value: 30.252666666666666
- type: ndcg_at_10
value: 39.683
- type: ndcg_at_100
value: 44.68541666666667
- type: ndcg_at_1000
value: 46.94316666666668
- type: ndcg_at_3
value: 34.961749999999995
- type: ndcg_at_5
value: 37.215666666666664
- type: precision_at_1
value: 30.252666666666666
- type: precision_at_10
value: 6.904166666666667
- type: precision_at_100
value: 1.0989999999999995
- type: precision_at_1000
value: 0.14733333333333334
- type: precision_at_3
value: 16.037666666666667
- type: precision_at_5
value: 11.413583333333333
- type: recall_at_1
value: 25.558249999999997
- type: recall_at_10
value: 51.13341666666666
- type: recall_at_100
value: 73.08366666666667
- type: recall_at_1000
value: 88.79483333333334
- type: recall_at_3
value: 37.989083333333326
- type: recall_at_5
value: 43.787833333333325
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.338
- type: map_at_10
value: 18.360000000000003
- type: map_at_100
value: 19.942
- type: map_at_1000
value: 20.134
- type: map_at_3
value: 15.174000000000001
- type: map_at_5
value: 16.830000000000002
- type: mrr_at_1
value: 23.257
- type: mrr_at_10
value: 33.768
- type: mrr_at_100
value: 34.707
- type: mrr_at_1000
value: 34.766000000000005
- type: mrr_at_3
value: 30.977
- type: mrr_at_5
value: 32.528
- type: ndcg_at_1
value: 23.257
- type: ndcg_at_10
value: 25.733
- type: ndcg_at_100
value: 32.288
- type: ndcg_at_1000
value: 35.992000000000004
- type: ndcg_at_3
value: 20.866
- type: ndcg_at_5
value: 22.612
- type: precision_at_1
value: 23.257
- type: precision_at_10
value: 8.124
- type: precision_at_100
value: 1.518
- type: precision_at_1000
value: 0.219
- type: precision_at_3
value: 15.679000000000002
- type: precision_at_5
value: 12.117
- type: recall_at_1
value: 10.338
- type: recall_at_10
value: 31.154
- type: recall_at_100
value: 54.161
- type: recall_at_1000
value: 75.21900000000001
- type: recall_at_3
value: 19.427
- type: recall_at_5
value: 24.214
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.498
- type: map_at_10
value: 19.103
- type: map_at_100
value: 27.375
- type: map_at_1000
value: 28.981
- type: map_at_3
value: 13.764999999999999
- type: map_at_5
value: 15.950000000000001
- type: mrr_at_1
value: 65.5
- type: mrr_at_10
value: 74.53800000000001
- type: mrr_at_100
value: 74.71799999999999
- type: mrr_at_1000
value: 74.725
- type: mrr_at_3
value: 72.792
- type: mrr_at_5
value: 73.554
- type: ndcg_at_1
value: 53.37499999999999
- type: ndcg_at_10
value: 41.286
- type: ndcg_at_100
value: 45.972
- type: ndcg_at_1000
value: 53.123
- type: ndcg_at_3
value: 46.172999999999995
- type: ndcg_at_5
value: 43.033
- type: precision_at_1
value: 65.5
- type: precision_at_10
value: 32.725
- type: precision_at_100
value: 10.683
- type: precision_at_1000
value: 1.978
- type: precision_at_3
value: 50
- type: precision_at_5
value: 41.349999999999994
- type: recall_at_1
value: 8.498
- type: recall_at_10
value: 25.070999999999998
- type: recall_at_100
value: 52.383
- type: recall_at_1000
value: 74.91499999999999
- type: recall_at_3
value: 15.207999999999998
- type: recall_at_5
value: 18.563
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.5
- type: f1
value: 41.93833713984145
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 67.914
- type: map_at_10
value: 78.10000000000001
- type: map_at_100
value: 78.333
- type: map_at_1000
value: 78.346
- type: map_at_3
value: 76.626
- type: map_at_5
value: 77.627
- type: mrr_at_1
value: 72.74199999999999
- type: mrr_at_10
value: 82.414
- type: mrr_at_100
value: 82.511
- type: mrr_at_1000
value: 82.513
- type: mrr_at_3
value: 81.231
- type: mrr_at_5
value: 82.065
- type: ndcg_at_1
value: 72.74199999999999
- type: ndcg_at_10
value: 82.806
- type: ndcg_at_100
value: 83.677
- type: ndcg_at_1000
value: 83.917
- type: ndcg_at_3
value: 80.305
- type: ndcg_at_5
value: 81.843
- type: precision_at_1
value: 72.74199999999999
- type: precision_at_10
value: 10.24
- type: precision_at_100
value: 1.089
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 31.268
- type: precision_at_5
value: 19.706000000000003
- type: recall_at_1
value: 67.914
- type: recall_at_10
value: 92.889
- type: recall_at_100
value: 96.42699999999999
- type: recall_at_1000
value: 97.92
- type: recall_at_3
value: 86.21
- type: recall_at_5
value: 90.036
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.166
- type: map_at_10
value: 35.57
- type: map_at_100
value: 37.405
- type: map_at_1000
value: 37.564
- type: map_at_3
value: 30.379
- type: map_at_5
value: 33.324
- type: mrr_at_1
value: 43.519000000000005
- type: mrr_at_10
value: 51.556000000000004
- type: mrr_at_100
value: 52.344
- type: mrr_at_1000
value: 52.373999999999995
- type: mrr_at_3
value: 48.868
- type: mrr_at_5
value: 50.319
- type: ndcg_at_1
value: 43.519000000000005
- type: ndcg_at_10
value: 43.803
- type: ndcg_at_100
value: 50.468999999999994
- type: ndcg_at_1000
value: 53.111
- type: ndcg_at_3
value: 38.893
- type: ndcg_at_5
value: 40.653
- type: precision_at_1
value: 43.519000000000005
- type: precision_at_10
value: 12.253
- type: precision_at_100
value: 1.931
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 25.617
- type: precision_at_5
value: 19.383
- type: recall_at_1
value: 22.166
- type: recall_at_10
value: 51.6
- type: recall_at_100
value: 76.574
- type: recall_at_1000
value: 92.192
- type: recall_at_3
value: 34.477999999999994
- type: recall_at_5
value: 41.835
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.041
- type: map_at_10
value: 62.961999999999996
- type: map_at_100
value: 63.79899999999999
- type: map_at_1000
value: 63.854
- type: map_at_3
value: 59.399
- type: map_at_5
value: 61.669
- type: mrr_at_1
value: 78.082
- type: mrr_at_10
value: 84.321
- type: mrr_at_100
value: 84.49600000000001
- type: mrr_at_1000
value: 84.502
- type: mrr_at_3
value: 83.421
- type: mrr_at_5
value: 83.977
- type: ndcg_at_1
value: 78.082
- type: ndcg_at_10
value: 71.229
- type: ndcg_at_100
value: 74.10900000000001
- type: ndcg_at_1000
value: 75.169
- type: ndcg_at_3
value: 66.28699999999999
- type: ndcg_at_5
value: 69.084
- type: precision_at_1
value: 78.082
- type: precision_at_10
value: 14.993
- type: precision_at_100
value: 1.7239999999999998
- type: precision_at_1000
value: 0.186
- type: precision_at_3
value: 42.737
- type: precision_at_5
value: 27.843
- type: recall_at_1
value: 39.041
- type: recall_at_10
value: 74.96300000000001
- type: recall_at_100
value: 86.199
- type: recall_at_1000
value: 93.228
- type: recall_at_3
value: 64.105
- type: recall_at_5
value: 69.608
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 90.23160000000001
- type: ap
value: 85.5674856808308
- type: f1
value: 90.18033354786317
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 24.091
- type: map_at_10
value: 36.753
- type: map_at_100
value: 37.913000000000004
- type: map_at_1000
value: 37.958999999999996
- type: map_at_3
value: 32.818999999999996
- type: map_at_5
value: 35.171
- type: mrr_at_1
value: 24.742
- type: mrr_at_10
value: 37.285000000000004
- type: mrr_at_100
value: 38.391999999999996
- type: mrr_at_1000
value: 38.431
- type: mrr_at_3
value: 33.440999999999995
- type: mrr_at_5
value: 35.75
- type: ndcg_at_1
value: 24.742
- type: ndcg_at_10
value: 43.698
- type: ndcg_at_100
value: 49.145
- type: ndcg_at_1000
value: 50.23800000000001
- type: ndcg_at_3
value: 35.769
- type: ndcg_at_5
value: 39.961999999999996
- type: precision_at_1
value: 24.742
- type: precision_at_10
value: 6.7989999999999995
- type: precision_at_100
value: 0.95
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 15.096000000000002
- type: precision_at_5
value: 11.183
- type: recall_at_1
value: 24.091
- type: recall_at_10
value: 65.068
- type: recall_at_100
value: 89.899
- type: recall_at_1000
value: 98.16
- type: recall_at_3
value: 43.68
- type: recall_at_5
value: 53.754999999999995
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.66621067031465
- type: f1
value: 93.49622853272142
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (de)
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.94702733164272
- type: f1
value: 91.17043441745282
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (es)
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.20146764509674
- type: f1
value: 91.98359080555608
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (fr)
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 88.99780770435328
- type: f1
value: 89.19746342724068
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (hi)
config: hi
split: test
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name: MTEB MassiveScenarioClassification (de)
config: de
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.69737726967047
- type: f1
value: 74.7664341963
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (el)
config: el
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.90383322125084
- type: f1
value: 73.59201554448323
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.51176866173503
- type: f1
value: 77.46104434577758
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (es)
config: es
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.31069266980496
- type: f1
value: 74.61048660675635
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (fa)
config: fa
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.95225285810356
- type: f1
value: 72.33160006574627
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (fi)
config: fi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.12373907195696
- type: f1
value: 73.20921012557481
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (fr)
config: fr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.86684599865501
- type: f1
value: 73.82348774610831
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (he)
config: he
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.40215198386012
- type: f1
value: 71.11945183971858
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (hi)
config: hi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.12844653665098
- type: f1
value: 71.34450495911766
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (hu)
config: hu
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.52252858103566
- type: f1
value: 73.98878711342999
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (hy)
config: hy
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.93611297915265
- type: f1
value: 63.723200467653385
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (id)
config: id
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.11903160726295
- type: f1
value: 73.82138439467096
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (is)
config: is
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.15198386012105
- type: f1
value: 66.02172193802167
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (it)
config: it
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.32414256893072
- type: f1
value: 74.30943421170574
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ja)
config: ja
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.46805648957633
- type: f1
value: 77.62808409298209
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (jv)
config: jv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.318762609280434
- type: f1
value: 62.094284066075076
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ka)
config: ka
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 58.34902488231338
- type: f1
value: 57.12893860987984
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (km)
config: km
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 50.88433086751849
- type: f1
value: 48.2272350802058
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (kn)
config: kn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.4425016812374
- type: f1
value: 64.61463095996173
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ko)
config: ko
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.04707464694015
- type: f1
value: 75.05099199098998
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (lv)
config: lv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.50437121721586
- type: f1
value: 69.83397721096314
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ml)
config: ml
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.94283792871553
- type: f1
value: 68.8704663703913
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (mn)
config: mn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.79488903833222
- type: f1
value: 63.615424063345436
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ms)
config: ms
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.88231338264963
- type: f1
value: 68.57892302593237
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (my)
config: my
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.248150638870214
- type: f1
value: 61.06680605338809
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (nb)
config: nb
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.84196368527236
- type: f1
value: 74.52566464968763
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (nl)
config: nl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.8285137861466
- type: f1
value: 74.8853197608802
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (pl)
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.13248150638869
- type: f1
value: 74.3982040999179
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (pt)
config: pt
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.49024882313383
- type: f1
value: 73.82153848368573
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ro)
config: ro
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.72158708809684
- type: f1
value: 71.85049433180541
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ru)
config: ru
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.137861466039
- type: f1
value: 75.37628348188467
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sl)
config: sl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.86953597848016
- type: f1
value: 71.87537624521661
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sq)
config: sq
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.27572293207801
- type: f1
value: 68.80017302344231
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sv)
config: sv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.09952925353059
- type: f1
value: 76.07992707688408
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sw)
config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.140551445864155
- type: f1
value: 61.73855010331415
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ta)
config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.27774041694687
- type: f1
value: 64.83664868894539
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (te)
config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.69468728984533
- type: f1
value: 64.76239666920868
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (th)
config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.44653665097512
- type: f1
value: 73.14646052013873
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (tl)
config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.71351714862139
- type: f1
value: 66.67212180163382
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (tr)
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.9946200403497
- type: f1
value: 73.87348793725525
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ur)
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.15400134498992
- type: f1
value: 67.09433241421094
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (vi)
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.11365164761264
- type: f1
value: 73.59502539433753
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.82582380632145
- type: f1
value: 76.89992945316313
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-TW)
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.81237390719569
- type: f1
value: 72.36499770986265
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.480506569594695
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 29.71252128004552
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.421396787056548
- type: mrr
value: 32.48155274872267
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.595
- type: map_at_10
value: 12.642000000000001
- type: map_at_100
value: 15.726
- type: map_at_1000
value: 17.061999999999998
- type: map_at_3
value: 9.125
- type: map_at_5
value: 10.866000000000001
- type: mrr_at_1
value: 43.344
- type: mrr_at_10
value: 52.227999999999994
- type: mrr_at_100
value: 52.898999999999994
- type: mrr_at_1000
value: 52.944
- type: mrr_at_3
value: 49.845
- type: mrr_at_5
value: 51.115
- type: ndcg_at_1
value: 41.949999999999996
- type: ndcg_at_10
value: 33.995
- type: ndcg_at_100
value: 30.869999999999997
- type: ndcg_at_1000
value: 39.487
- type: ndcg_at_3
value: 38.903999999999996
- type: ndcg_at_5
value: 37.236999999999995
- type: precision_at_1
value: 43.344
- type: precision_at_10
value: 25.480000000000004
- type: precision_at_100
value: 7.672
- type: precision_at_1000
value: 2.028
- type: precision_at_3
value: 36.636
- type: precision_at_5
value: 32.632
- type: recall_at_1
value: 5.595
- type: recall_at_10
value: 16.466
- type: recall_at_100
value: 31.226
- type: recall_at_1000
value: 62.778999999999996
- type: recall_at_3
value: 9.931
- type: recall_at_5
value: 12.884
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.414
- type: map_at_10
value: 56.754000000000005
- type: map_at_100
value: 57.457
- type: map_at_1000
value: 57.477999999999994
- type: map_at_3
value: 52.873999999999995
- type: map_at_5
value: 55.175
- type: mrr_at_1
value: 45.278
- type: mrr_at_10
value: 59.192
- type: mrr_at_100
value: 59.650000000000006
- type: mrr_at_1000
value: 59.665
- type: mrr_at_3
value: 56.141
- type: mrr_at_5
value: 57.998000000000005
- type: ndcg_at_1
value: 45.278
- type: ndcg_at_10
value: 64.056
- type: ndcg_at_100
value: 66.89
- type: ndcg_at_1000
value: 67.364
- type: ndcg_at_3
value: 56.97
- type: ndcg_at_5
value: 60.719
- type: precision_at_1
value: 45.278
- type: precision_at_10
value: 9.994
- type: precision_at_100
value: 1.165
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 25.512
- type: precision_at_5
value: 17.509
- type: recall_at_1
value: 40.414
- type: recall_at_10
value: 83.596
- type: recall_at_100
value: 95.72
- type: recall_at_1000
value: 99.24
- type: recall_at_3
value: 65.472
- type: recall_at_5
value: 74.039
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.352
- type: map_at_10
value: 84.369
- type: map_at_100
value: 85.02499999999999
- type: map_at_1000
value: 85.04
- type: map_at_3
value: 81.42399999999999
- type: map_at_5
value: 83.279
- type: mrr_at_1
value: 81.05
- type: mrr_at_10
value: 87.401
- type: mrr_at_100
value: 87.504
- type: mrr_at_1000
value: 87.505
- type: mrr_at_3
value: 86.443
- type: mrr_at_5
value: 87.10799999999999
- type: ndcg_at_1
value: 81.04
- type: ndcg_at_10
value: 88.181
- type: ndcg_at_100
value: 89.411
- type: ndcg_at_1000
value: 89.507
- type: ndcg_at_3
value: 85.28099999999999
- type: ndcg_at_5
value: 86.888
- type: precision_at_1
value: 81.04
- type: precision_at_10
value: 13.406
- type: precision_at_100
value: 1.5350000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.31
- type: precision_at_5
value: 24.54
- type: recall_at_1
value: 70.352
- type: recall_at_10
value: 95.358
- type: recall_at_100
value: 99.541
- type: recall_at_1000
value: 99.984
- type: recall_at_3
value: 87.111
- type: recall_at_5
value: 91.643
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 46.54068723291946
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 63.216287629895994
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.023000000000001
- type: map_at_10
value: 10.071
- type: map_at_100
value: 11.892
- type: map_at_1000
value: 12.196
- type: map_at_3
value: 7.234
- type: map_at_5
value: 8.613999999999999
- type: mrr_at_1
value: 19.900000000000002
- type: mrr_at_10
value: 30.516
- type: mrr_at_100
value: 31.656000000000002
- type: mrr_at_1000
value: 31.723000000000003
- type: mrr_at_3
value: 27.400000000000002
- type: mrr_at_5
value: 29.270000000000003
- type: ndcg_at_1
value: 19.900000000000002
- type: ndcg_at_10
value: 17.474
- type: ndcg_at_100
value: 25.020999999999997
- type: ndcg_at_1000
value: 30.728
- type: ndcg_at_3
value: 16.588
- type: ndcg_at_5
value: 14.498
- type: precision_at_1
value: 19.900000000000002
- type: precision_at_10
value: 9.139999999999999
- type: precision_at_100
value: 2.011
- type: precision_at_1000
value: 0.33899999999999997
- type: precision_at_3
value: 15.667
- type: precision_at_5
value: 12.839999999999998
- type: recall_at_1
value: 4.023000000000001
- type: recall_at_10
value: 18.497
- type: recall_at_100
value: 40.8
- type: recall_at_1000
value: 68.812
- type: recall_at_3
value: 9.508
- type: recall_at_5
value: 12.983
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.967008785134
- type: cos_sim_spearman
value: 80.23142141101837
- type: euclidean_pearson
value: 81.20166064704539
- type: euclidean_spearman
value: 80.18961335654585
- type: manhattan_pearson
value: 81.13925443187625
- type: manhattan_spearman
value: 80.07948723044424
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.94262461316023
- type: cos_sim_spearman
value: 80.01596278563865
- type: euclidean_pearson
value: 83.80799622922581
- type: euclidean_spearman
value: 79.94984954947103
- type: manhattan_pearson
value: 83.68473841756281
- type: manhattan_spearman
value: 79.84990707951822
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 80.57346443146068
- type: cos_sim_spearman
value: 81.54689837570866
- type: euclidean_pearson
value: 81.10909881516007
- type: euclidean_spearman
value: 81.56746243261762
- type: manhattan_pearson
value: 80.87076036186582
- type: manhattan_spearman
value: 81.33074987964402
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 79.54733787179849
- type: cos_sim_spearman
value: 77.72202105610411
- type: euclidean_pearson
value: 78.9043595478849
- type: euclidean_spearman
value: 77.93422804309435
- type: manhattan_pearson
value: 78.58115121621368
- type: manhattan_spearman
value: 77.62508135122033
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.59880017237558
- type: cos_sim_spearman
value: 89.31088630824758
- type: euclidean_pearson
value: 88.47069261564656
- type: euclidean_spearman
value: 89.33581971465233
- type: manhattan_pearson
value: 88.40774264100956
- type: manhattan_spearman
value: 89.28657485627835
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.08055117917084
- type: cos_sim_spearman
value: 85.78491813080304
- type: euclidean_pearson
value: 84.99329155500392
- type: euclidean_spearman
value: 85.76728064677287
- type: manhattan_pearson
value: 84.87947428989587
- type: manhattan_spearman
value: 85.62429454917464
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ko-ko)
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 82.14190939287384
- type: cos_sim_spearman
value: 82.27331573306041
- type: euclidean_pearson
value: 81.891896953716
- type: euclidean_spearman
value: 82.37695542955998
- type: manhattan_pearson
value: 81.73123869460504
- type: manhattan_spearman
value: 82.19989168441421
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ar-ar)
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 76.84695301843362
- type: cos_sim_spearman
value: 77.87790986014461
- type: euclidean_pearson
value: 76.91981583106315
- type: euclidean_spearman
value: 77.88154772749589
- type: manhattan_pearson
value: 76.94953277451093
- type: manhattan_spearman
value: 77.80499230728604
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-ar)
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 75.44657840482016
- type: cos_sim_spearman
value: 75.05531095119674
- type: euclidean_pearson
value: 75.88161755829299
- type: euclidean_spearman
value: 74.73176238219332
- type: manhattan_pearson
value: 75.63984765635362
- type: manhattan_spearman
value: 74.86476440770737
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-de)
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.64700140524133
- type: cos_sim_spearman
value: 86.16014210425672
- type: euclidean_pearson
value: 86.49086860843221
- type: euclidean_spearman
value: 86.09729326815614
- type: manhattan_pearson
value: 86.43406265125513
- type: manhattan_spearman
value: 86.17740150939994
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.91170098764921
- type: cos_sim_spearman
value: 88.12437004058931
- type: euclidean_pearson
value: 88.81828254494437
- type: euclidean_spearman
value: 88.14831794572122
- type: manhattan_pearson
value: 88.93442183448961
- type: manhattan_spearman
value: 88.15254630778304
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-tr)
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 72.91390577997292
- type: cos_sim_spearman
value: 71.22979457536074
- type: euclidean_pearson
value: 74.40314008106749
- type: euclidean_spearman
value: 72.54972136083246
- type: manhattan_pearson
value: 73.85687539530218
- type: manhattan_spearman
value: 72.09500771742637
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-en)
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 80.9301067983089
- type: cos_sim_spearman
value: 80.74989828346473
- type: euclidean_pearson
value: 81.36781301814257
- type: euclidean_spearman
value: 80.9448819964426
- type: manhattan_pearson
value: 81.0351322685609
- type: manhattan_spearman
value: 80.70192121844177
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-es)
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.13820465980005
- type: cos_sim_spearman
value: 86.73532498758757
- type: euclidean_pearson
value: 87.21329451846637
- type: euclidean_spearman
value: 86.57863198601002
- type: manhattan_pearson
value: 87.06973713818554
- type: manhattan_spearman
value: 86.47534918791499
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (fr-en)
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.48720108904415
- type: cos_sim_spearman
value: 85.62221757068387
- type: euclidean_pearson
value: 86.1010129512749
- type: euclidean_spearman
value: 85.86580966509942
- type: manhattan_pearson
value: 86.26800938808971
- type: manhattan_spearman
value: 85.88902721678429
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (it-en)
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 83.98021347333516
- type: cos_sim_spearman
value: 84.53806553803501
- type: euclidean_pearson
value: 84.61483347248364
- type: euclidean_spearman
value: 85.14191408011702
- type: manhattan_pearson
value: 84.75297588825967
- type: manhattan_spearman
value: 85.33176753669242
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (nl-en)
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 84.51856644893233
- type: cos_sim_spearman
value: 85.27510748506413
- type: euclidean_pearson
value: 85.09886861540977
- type: euclidean_spearman
value: 85.62579245860887
- type: manhattan_pearson
value: 84.93017860464607
- type: manhattan_spearman
value: 85.5063988898453
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 62.581573200584195
- type: cos_sim_spearman
value: 63.05503590247928
- type: euclidean_pearson
value: 63.652564812602094
- type: euclidean_spearman
value: 62.64811520876156
- type: manhattan_pearson
value: 63.506842893061076
- type: manhattan_spearman
value: 62.51289573046917
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de)
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 48.2248801729127
- type: cos_sim_spearman
value: 56.5936604678561
- type: euclidean_pearson
value: 43.98149464089
- type: euclidean_spearman
value: 56.108561882423615
- type: manhattan_pearson
value: 43.86880305903564
- type: manhattan_spearman
value: 56.04671150510166
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es)
config: es
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 55.17564527009831
- type: cos_sim_spearman
value: 64.57978560979488
- type: euclidean_pearson
value: 58.8818330154583
- type: euclidean_spearman
value: 64.99214839071281
- type: manhattan_pearson
value: 58.72671436121381
- type: manhattan_spearman
value: 65.10713416616109
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl)
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 26.772131864023297
- type: cos_sim_spearman
value: 34.68200792408681
- type: euclidean_pearson
value: 16.68082419005441
- type: euclidean_spearman
value: 34.83099932652166
- type: manhattan_pearson
value: 16.52605949659529
- type: manhattan_spearman
value: 34.82075801399475
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (tr)
config: tr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.42415189043831
- type: cos_sim_spearman
value: 63.54594264576758
- type: euclidean_pearson
value: 57.36577498297745
- type: euclidean_spearman
value: 63.111466379158074
- type: manhattan_pearson
value: 57.584543715873885
- type: manhattan_spearman
value: 63.22361054139183
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ar)
config: ar
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 47.55216762405518
- type: cos_sim_spearman
value: 56.98670142896412
- type: euclidean_pearson
value: 50.15318757562699
- type: euclidean_spearman
value: 56.524941926541906
- type: manhattan_pearson
value: 49.955618528674904
- type: manhattan_spearman
value: 56.37102209240117
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ru)
config: ru
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 49.20540980338571
- type: cos_sim_spearman
value: 59.9009453504406
- type: euclidean_pearson
value: 49.557749853620535
- type: euclidean_spearman
value: 59.76631621172456
- type: manhattan_pearson
value: 49.62340591181147
- type: manhattan_spearman
value: 59.94224880322436
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 51.508169956576985
- type: cos_sim_spearman
value: 66.82461565306046
- type: euclidean_pearson
value: 56.2274426480083
- type: euclidean_spearman
value: 66.6775323848333
- type: manhattan_pearson
value: 55.98277796300661
- type: manhattan_spearman
value: 66.63669848497175
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr)
config: fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 72.86478788045507
- type: cos_sim_spearman
value: 76.7946552053193
- type: euclidean_pearson
value: 75.01598530490269
- type: euclidean_spearman
value: 76.83618917858281
- type: manhattan_pearson
value: 74.68337628304332
- type: manhattan_spearman
value: 76.57480204017773
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-en)
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 55.922619099401984
- type: cos_sim_spearman
value: 56.599362477240774
- type: euclidean_pearson
value: 56.68307052369783
- type: euclidean_spearman
value: 54.28760436777401
- type: manhattan_pearson
value: 56.67763566500681
- type: manhattan_spearman
value: 53.94619541711359
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-en)
config: es-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 66.74357206710913
- type: cos_sim_spearman
value: 72.5208244925311
- type: euclidean_pearson
value: 67.49254562186032
- type: euclidean_spearman
value: 72.02469076238683
- type: manhattan_pearson
value: 67.45251772238085
- type: manhattan_spearman
value: 72.05538819984538
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (it)
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 71.25734330033191
- type: cos_sim_spearman
value: 76.98349083946823
- type: euclidean_pearson
value: 73.71642838667736
- type: euclidean_spearman
value: 77.01715504651384
- type: manhattan_pearson
value: 73.61712711868105
- type: manhattan_spearman
value: 77.01392571153896
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl-en)
config: pl-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 63.18215462781212
- type: cos_sim_spearman
value: 65.54373266117607
- type: euclidean_pearson
value: 64.54126095439005
- type: euclidean_spearman
value: 65.30410369102711
- type: manhattan_pearson
value: 63.50332221148234
- type: manhattan_spearman
value: 64.3455878104313
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh-en)
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 62.30509221440029
- type: cos_sim_spearman
value: 65.99582704642478
- type: euclidean_pearson
value: 63.43818859884195
- type: euclidean_spearman
value: 66.83172582815764
- type: manhattan_pearson
value: 63.055779168508764
- type: manhattan_spearman
value: 65.49585020501449
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-it)
config: es-it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.587830825340404
- type: cos_sim_spearman
value: 68.93467614588089
- type: euclidean_pearson
value: 62.3073527367404
- type: euclidean_spearman
value: 69.69758171553175
- type: manhattan_pearson
value: 61.9074580815789
- type: manhattan_spearman
value: 69.57696375597865
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-fr)
config: de-fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 57.143220125577066
- type: cos_sim_spearman
value: 67.78857859159226
- type: euclidean_pearson
value: 55.58225107923733
- type: euclidean_spearman
value: 67.80662907184563
- type: manhattan_pearson
value: 56.24953502726514
- type: manhattan_spearman
value: 67.98262125431616
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-pl)
config: de-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 21.826928900322066
- type: cos_sim_spearman
value: 49.578506634400405
- type: euclidean_pearson
value: 27.939890138843214
- type: euclidean_spearman
value: 52.71950519136242
- type: manhattan_pearson
value: 26.39878683847546
- type: manhattan_spearman
value: 47.54609580342499
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr-pl)
config: fr-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 57.27603854632001
- type: cos_sim_spearman
value: 50.709255283710995
- type: euclidean_pearson
value: 59.5419024445929
- type: euclidean_spearman
value: 50.709255283710995
- type: manhattan_pearson
value: 59.03256832438492
- type: manhattan_spearman
value: 61.97797868009122
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.00757054859712
- type: cos_sim_spearman
value: 87.29283629622222
- type: euclidean_pearson
value: 86.54824171775536
- type: euclidean_spearman
value: 87.24364730491402
- type: manhattan_pearson
value: 86.5062156915074
- type: manhattan_spearman
value: 87.15052170378574
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 82.03549357197389
- type: mrr
value: 95.05437645143527
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.260999999999996
- type: map_at_10
value: 66.259
- type: map_at_100
value: 66.884
- type: map_at_1000
value: 66.912
- type: map_at_3
value: 63.685
- type: map_at_5
value: 65.35499999999999
- type: mrr_at_1
value: 60.333000000000006
- type: mrr_at_10
value: 67.5
- type: mrr_at_100
value: 68.013
- type: mrr_at_1000
value: 68.038
- type: mrr_at_3
value: 65.61099999999999
- type: mrr_at_5
value: 66.861
- type: ndcg_at_1
value: 60.333000000000006
- type: ndcg_at_10
value: 70.41
- type: ndcg_at_100
value: 73.10600000000001
- type: ndcg_at_1000
value: 73.846
- type: ndcg_at_3
value: 66.133
- type: ndcg_at_5
value: 68.499
- type: precision_at_1
value: 60.333000000000006
- type: precision_at_10
value: 9.232999999999999
- type: precision_at_100
value: 1.0630000000000002
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 25.667
- type: precision_at_5
value: 17.067
- type: recall_at_1
value: 57.260999999999996
- type: recall_at_10
value: 81.94399999999999
- type: recall_at_100
value: 93.867
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 70.339
- type: recall_at_5
value: 76.25
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.74356435643564
- type: cos_sim_ap
value: 93.13411948212683
- type: cos_sim_f1
value: 86.80521991300147
- type: cos_sim_precision
value: 84.00374181478017
- type: cos_sim_recall
value: 89.8
- type: dot_accuracy
value: 99.67920792079208
- type: dot_ap
value: 89.27277565444479
- type: dot_f1
value: 83.9276990718124
- type: dot_precision
value: 82.04393505253104
- type: dot_recall
value: 85.9
- type: euclidean_accuracy
value: 99.74257425742574
- type: euclidean_ap
value: 93.17993008259062
- type: euclidean_f1
value: 86.69396110542476
- type: euclidean_precision
value: 88.78406708595388
- type: euclidean_recall
value: 84.7
- type: manhattan_accuracy
value: 99.74257425742574
- type: manhattan_ap
value: 93.14413755550099
- type: manhattan_f1
value: 86.82483594144371
- type: manhattan_precision
value: 87.66564729867483
- type: manhattan_recall
value: 86
- type: max_accuracy
value: 99.74356435643564
- type: max_ap
value: 93.17993008259062
- type: max_f1
value: 86.82483594144371
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 57.525863806168566
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.68850574423839
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.71580650644033
- type: mrr
value: 50.50971903913081
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.152190498799484
- type: cos_sim_spearman
value: 29.686180371952727
- type: dot_pearson
value: 27.248664793816342
- type: dot_spearman
value: 28.37748983721745
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.20400000000000001
- type: map_at_10
value: 1.6209999999999998
- type: map_at_100
value: 9.690999999999999
- type: map_at_1000
value: 23.733
- type: map_at_3
value: 0.575
- type: map_at_5
value: 0.885
- type: mrr_at_1
value: 78
- type: mrr_at_10
value: 86.56700000000001
- type: mrr_at_100
value: 86.56700000000001
- type: mrr_at_1000
value: 86.56700000000001
- type: mrr_at_3
value: 85.667
- type: mrr_at_5
value: 86.56700000000001
- type: ndcg_at_1
value: 76
- type: ndcg_at_10
value: 71.326
- type: ndcg_at_100
value: 54.208999999999996
- type: ndcg_at_1000
value: 49.252
- type: ndcg_at_3
value: 74.235
- type: ndcg_at_5
value: 73.833
- type: precision_at_1
value: 78
- type: precision_at_10
value: 74.8
- type: precision_at_100
value: 55.50000000000001
- type: precision_at_1000
value: 21.836
- type: precision_at_3
value: 78
- type: precision_at_5
value: 78
- type: recall_at_1
value: 0.20400000000000001
- type: recall_at_10
value: 1.894
- type: recall_at_100
value: 13.245999999999999
- type: recall_at_1000
value: 46.373
- type: recall_at_3
value: 0.613
- type: recall_at_5
value: 0.991
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (sqi-eng)
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.89999999999999
- type: f1
value: 94.69999999999999
- type: precision
value: 94.11666666666667
- type: recall
value: 95.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fry-eng)
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.20809248554913
- type: f1
value: 63.431048720066066
- type: precision
value: 61.69143958161298
- type: recall
value: 68.20809248554913
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kur-eng)
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 71.21951219512195
- type: f1
value: 66.82926829268293
- type: precision
value: 65.1260162601626
- type: recall
value: 71.21951219512195
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tur-eng)
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.2
- type: f1
value: 96.26666666666667
- type: precision
value: 95.8
- type: recall
value: 97.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (deu-eng)
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 99.3
- type: f1
value: 99.06666666666666
- type: precision
value: 98.95
- type: recall
value: 99.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nld-eng)
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.63333333333333
- type: precision
value: 96.26666666666668
- type: recall
value: 97.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ron-eng)
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96
- type: f1
value: 94.86666666666666
- type: precision
value: 94.31666666666668
- type: recall
value: 96
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ang-eng)
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 47.01492537313433
- type: f1
value: 40.178867566927266
- type: precision
value: 38.179295828549556
- type: recall
value: 47.01492537313433
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ido-eng)
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.5
- type: f1
value: 83.62537480063796
- type: precision
value: 82.44555555555554
- type: recall
value: 86.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (jav-eng)
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 80.48780487804879
- type: f1
value: 75.45644599303138
- type: precision
value: 73.37398373983739
- type: recall
value: 80.48780487804879
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (isl-eng)
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.7
- type: f1
value: 91.95666666666666
- type: precision
value: 91.125
- type: recall
value: 93.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (slv-eng)
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.73754556500607
- type: f1
value: 89.65168084244632
- type: precision
value: 88.73025516403402
- type: recall
value: 91.73754556500607
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cym-eng)
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.04347826086956
- type: f1
value: 76.2128364389234
- type: precision
value: 74.2
- type: recall
value: 81.04347826086956
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kaz-eng)
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.65217391304348
- type: f1
value: 79.4376811594203
- type: precision
value: 77.65797101449274
- type: recall
value: 83.65217391304348
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (est-eng)
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.5
- type: f1
value: 85.02690476190476
- type: precision
value: 83.96261904761904
- type: recall
value: 87.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (heb-eng)
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.3
- type: f1
value: 86.52333333333333
- type: precision
value: 85.22833333333332
- type: recall
value: 89.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gla-eng)
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.01809408926418
- type: f1
value: 59.00594446432805
- type: precision
value: 56.827215807915444
- type: recall
value: 65.01809408926418
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mar-eng)
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.2
- type: f1
value: 88.58
- type: precision
value: 87.33333333333334
- type: recall
value: 91.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lat-eng)
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 59.199999999999996
- type: f1
value: 53.299166276284915
- type: precision
value: 51.3383908045977
- type: recall
value: 59.199999999999996
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bel-eng)
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.2
- type: f1
value: 91.2
- type: precision
value: 90.25
- type: recall
value: 93.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pms-eng)
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 64.76190476190476
- type: f1
value: 59.867110667110666
- type: precision
value: 58.07390192653351
- type: recall
value: 64.76190476190476
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gle-eng)
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.2
- type: f1
value: 71.48147546897547
- type: precision
value: 69.65409090909091
- type: recall
value: 76.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pes-eng)
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.8
- type: f1
value: 92.14
- type: precision
value: 91.35833333333333
- type: recall
value: 93.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nob-eng)
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.89999999999999
- type: f1
value: 97.2
- type: precision
value: 96.85000000000001
- type: recall
value: 97.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bul-eng)
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.6
- type: f1
value: 92.93333333333334
- type: precision
value: 92.13333333333333
- type: recall
value: 94.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cbk-eng)
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.1
- type: f1
value: 69.14817460317461
- type: precision
value: 67.2515873015873
- type: recall
value: 74.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hun-eng)
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.19999999999999
- type: f1
value: 94.01333333333335
- type: precision
value: 93.46666666666667
- type: recall
value: 95.19999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (uig-eng)
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.9
- type: f1
value: 72.07523809523809
- type: precision
value: 70.19777777777779
- type: recall
value: 76.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (rus-eng)
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.1
- type: f1
value: 92.31666666666666
- type: precision
value: 91.43333333333332
- type: recall
value: 94.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (spa-eng)
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.1
- type: precision
value: 96.76666666666668
- type: recall
value: 97.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hye-eng)
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.85714285714286
- type: f1
value: 90.92093441150045
- type: precision
value: 90.00449236298293
- type: recall
value: 92.85714285714286
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tel-eng)
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.16239316239316
- type: f1
value: 91.33903133903132
- type: precision
value: 90.56267806267806
- type: recall
value: 93.16239316239316
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (afr-eng)
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.4
- type: f1
value: 90.25666666666666
- type: precision
value: 89.25833333333334
- type: recall
value: 92.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mon-eng)
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.22727272727272
- type: f1
value: 87.53030303030303
- type: precision
value: 86.37121212121211
- type: recall
value: 90.22727272727272
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (arz-eng)
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.03563941299791
- type: f1
value: 74.7349505840072
- type: precision
value: 72.9035639412998
- type: recall
value: 79.03563941299791
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hrv-eng)
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97
- type: f1
value: 96.15
- type: precision
value: 95.76666666666668
- type: recall
value: 97
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nov-eng)
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 76.26459143968872
- type: f1
value: 71.55642023346303
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value: 69.7544932369835
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value: 76.26459143968872
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 58.119658119658126
- type: f1
value: 51.65242165242165
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value: 49.41768108434775
- type: recall
value: 58.119658119658126
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nds-eng)
config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.3
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value: 69.52055555555555
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value: 67.7574938949939
- type: recall
value: 74.3
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ukr-eng)
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.8
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value: 92.60000000000001
- type: recall
value: 94.8
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (uzb-eng)
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 76.63551401869158
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value: 72.35202492211837
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value: 70.60358255451713
- type: recall
value: 76.63551401869158
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lit-eng)
config: lit-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.4
- type: f1
value: 88.4811111111111
- type: precision
value: 87.7452380952381
- type: recall
value: 90.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ina-eng)
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95
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value: 93.60666666666667
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value: 92.975
- type: recall
value: 95
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lfn-eng)
config: lfn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 67.2
- type: f1
value: 63.01595782872099
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value: 61.596587301587306
- type: recall
value: 67.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (zsm-eng)
config: zsm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.7
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value: 94.52999999999999
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value: 94
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value: 95.7
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ita-eng)
config: ita-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.6
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value: 93.28999999999999
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value: 92.675
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value: 94.6
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cmn-eng)
config: cmn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96.39999999999999
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value: 94.75
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value: 96.39999999999999
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lvs-eng)
config: lvs-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.9
- type: f1
value: 89.83
- type: precision
value: 88.92
- type: recall
value: 91.9
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (glg-eng)
config: glg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.69999999999999
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value: 92.75416666666668
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value: 94.69999999999999
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ceb-eng)
config: ceb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 60.333333333333336
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value: 53.39971108326371
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value: 60.333333333333336
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bre-eng)
config: bre-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 12.9
- type: f1
value: 11.099861903031458
- type: precision
value: 10.589187932631877
- type: recall
value: 12.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ben-eng)
config: ben-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.7
- type: f1
value: 83.0152380952381
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value: 81.37833333333333
- type: recall
value: 86.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swg-eng)
config: swg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 63.39285714285714
- type: f1
value: 56.832482993197274
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value: 54.56845238095237
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value: 63.39285714285714
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (arq-eng)
config: arq-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 48.73765093304062
- type: f1
value: 41.555736920720456
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value: 39.06874531737319
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value: 48.73765093304062
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kab-eng)
config: kab-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 41.099999999999994
- type: f1
value: 36.540165945165946
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value: 35.05175685425686
- type: recall
value: 41.099999999999994
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fra-eng)
config: fra-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.89999999999999
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value: 93.42333333333333
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value: 92.75833333333333
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value: 94.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (por-eng)
config: por-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.89999999999999
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value: 93.01666666666665
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value: 94.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tat-eng)
config: tat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.9
- type: f1
value: 73.64833333333334
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value: 71.90282106782105
- type: recall
value: 77.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (oci-eng)
config: oci-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 59.4
- type: f1
value: 54.90521367521367
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value: 53.432840025471606
- type: recall
value: 59.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pol-eng)
config: pol-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
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value: 96.6
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value: 96.2
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value: 97.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (war-eng)
config: war-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 67.2
- type: f1
value: 62.25926129426129
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value: 60.408376623376626
- type: recall
value: 67.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (aze-eng)
config: aze-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.2
- type: f1
value: 87.60666666666667
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value: 86.45277777777778
- type: recall
value: 90.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (vie-eng)
config: vie-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.7
- type: f1
value: 97
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value: 96.65
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value: 97.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nno-eng)
config: nno-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.2
- type: f1
value: 91.39746031746031
- type: precision
value: 90.6125
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value: 93.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cha-eng)
config: cha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 32.11678832116788
- type: f1
value: 27.210415386260234
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value: 26.20408990846947
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value: 32.11678832116788
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mhr-eng)
config: mhr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.5
- type: f1
value: 6.787319277832475
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value: 6.3452094433344435
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value: 8.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dan-eng)
config: dan-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.1
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value: 95.08
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value: 94.61666666666667
- type: recall
value: 96.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ell-eng)
config: ell-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.3
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value: 93.88333333333333
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- type: recall
value: 95.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: amh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 85.11904761904762
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value: 78.72023809523809
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value: 85.11904761904762
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pam-eng)
config: pam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 11.1
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value: 9.276381801735853
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value: 8.798174603174601
- type: recall
value: 11.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hsb-eng)
config: hsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 63.56107660455487
- type: f1
value: 58.70433569191332
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value: 56.896926581464015
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value: 63.56107660455487
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (srp-eng)
config: srp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.69999999999999
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value: 93.10000000000001
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value: 92.35
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value: 94.69999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (epo-eng)
config: epo-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.8
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value: 96.01222222222222
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value: 95.67083333333332
- type: recall
value: 96.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kzj-eng)
config: kzj-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 9.2
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value: 7.911555250305249
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value: 7.631246556216846
- type: recall
value: 9.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (awa-eng)
config: awa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.48917748917748
- type: f1
value: 72.27375798804371
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value: 70.14430014430013
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value: 77.48917748917748
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fao-eng)
config: fao-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.09923664122137
- type: f1
value: 72.61541257724463
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value: 70.8998380754106
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value: 77.09923664122137
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mal-eng)
config: mal-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.2532751091703
- type: f1
value: 97.69529354682193
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value: 97.42843279961184
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value: 98.2532751091703
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ile-eng)
config: ile-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.8
- type: f1
value: 79.14672619047619
- type: precision
value: 77.59489247311828
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value: 82.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bos-eng)
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.35028248587571
- type: f1
value: 92.86252354048965
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value: 92.2080979284369
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value: 94.35028248587571
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cor-eng)
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.5
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value: 6.282429263935621
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value: 5.783274240739785
- type: recall
value: 8.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cat-eng)
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.7
- type: f1
value: 91.025
- type: precision
value: 90.30428571428571
- type: recall
value: 92.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (eus-eng)
config: eus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81
- type: f1
value: 77.8232380952381
- type: precision
value: 76.60194444444444
- type: recall
value: 81
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (yue-eng)
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91
- type: f1
value: 88.70857142857142
- type: precision
value: 87.7
- type: recall
value: 91
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swe-eng)
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.39999999999999
- type: f1
value: 95.3
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value: 94.76666666666667
- type: recall
value: 96.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dtp-eng)
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.1
- type: f1
value: 7.001008218834307
- type: precision
value: 6.708329562594269
- type: recall
value: 8.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kat-eng)
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.1313672922252
- type: f1
value: 84.09070598748882
- type: precision
value: 82.79171454104429
- type: recall
value: 87.1313672922252
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (jpn-eng)
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.39999999999999
- type: f1
value: 95.28333333333333
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value: 94.73333333333332
- type: recall
value: 96.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (csb-eng)
config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 42.29249011857708
- type: f1
value: 36.981018542283365
- type: precision
value: 35.415877813576024
- type: recall
value: 42.29249011857708
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (xho-eng)
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.80281690140845
- type: f1
value: 80.86854460093896
- type: precision
value: 79.60093896713614
- type: recall
value: 83.80281690140845
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (orv-eng)
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 45.26946107784431
- type: f1
value: 39.80235464678088
- type: precision
value: 38.14342660001342
- type: recall
value: 45.26946107784431
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ind-eng)
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.3
- type: f1
value: 92.9
- type: precision
value: 92.26666666666668
- type: recall
value: 94.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tuk-eng)
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 37.93103448275862
- type: f1
value: 33.15192743764172
- type: precision
value: 31.57456528146183
- type: recall
value: 37.93103448275862
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (max-eng)
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.01408450704226
- type: f1
value: 63.41549295774648
- type: precision
value: 61.342778895595806
- type: recall
value: 69.01408450704226
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swh-eng)
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.66666666666667
- type: f1
value: 71.60705960705961
- type: precision
value: 69.60683760683762
- type: recall
value: 76.66666666666667
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hin-eng)
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.8
- type: f1
value: 94.48333333333333
- type: precision
value: 93.83333333333333
- type: recall
value: 95.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dsb-eng)
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 52.81837160751566
- type: f1
value: 48.435977731384824
- type: precision
value: 47.11291973845539
- type: recall
value: 52.81837160751566
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ber-eng)
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 44.9
- type: f1
value: 38.88962621607783
- type: precision
value: 36.95936507936508
- type: recall
value: 44.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tam-eng)
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.55374592833876
- type: f1
value: 88.22553125484721
- type: precision
value: 87.26927252985884
- type: recall
value: 90.55374592833876
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (slk-eng)
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.6
- type: f1
value: 93.13333333333333
- type: precision
value: 92.45333333333333
- type: recall
value: 94.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tgl-eng)
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.7
- type: f1
value: 91.99666666666667
- type: precision
value: 91.26666666666668
- type: recall
value: 93.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ast-eng)
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.03937007874016
- type: f1
value: 81.75853018372703
- type: precision
value: 80.34120734908137
- type: recall
value: 85.03937007874016
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mkd-eng)
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.3
- type: f1
value: 85.5
- type: precision
value: 84.25833333333334
- type: recall
value: 88.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (khm-eng)
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.51246537396122
- type: f1
value: 60.02297410192148
- type: precision
value: 58.133467727289236
- type: recall
value: 65.51246537396122
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ces-eng)
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96
- type: f1
value: 94.89
- type: precision
value: 94.39166666666667
- type: recall
value: 96
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tzl-eng)
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 57.692307692307686
- type: f1
value: 53.162393162393165
- type: precision
value: 51.70673076923077
- type: recall
value: 57.692307692307686
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (urd-eng)
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.60000000000001
- type: f1
value: 89.21190476190475
- type: precision
value: 88.08666666666667
- type: recall
value: 91.60000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ara-eng)
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88
- type: f1
value: 85.47
- type: precision
value: 84.43266233766234
- type: recall
value: 88
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kor-eng)
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.7
- type: f1
value: 90.64999999999999
- type: precision
value: 89.68333333333332
- type: recall
value: 92.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (yid-eng)
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 80.30660377358491
- type: f1
value: 76.33044137466307
- type: precision
value: 74.78970125786164
- type: recall
value: 80.30660377358491
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fin-eng)
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.39999999999999
- type: f1
value: 95.44
- type: precision
value: 94.99166666666666
- type: recall
value: 96.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tha-eng)
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.53284671532847
- type: f1
value: 95.37712895377129
- type: precision
value: 94.7992700729927
- type: recall
value: 96.53284671532847
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (wuu-eng)
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89
- type: f1
value: 86.23190476190476
- type: precision
value: 85.035
- type: recall
value: 89
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.585
- type: map_at_10
value: 9.012
- type: map_at_100
value: 14.027000000000001
- type: map_at_1000
value: 15.565000000000001
- type: map_at_3
value: 5.032
- type: map_at_5
value: 6.657
- type: mrr_at_1
value: 28.571
- type: mrr_at_10
value: 45.377
- type: mrr_at_100
value: 46.119
- type: mrr_at_1000
value: 46.127
- type: mrr_at_3
value: 41.156
- type: mrr_at_5
value: 42.585
- type: ndcg_at_1
value: 27.551
- type: ndcg_at_10
value: 23.395
- type: ndcg_at_100
value: 33.342
- type: ndcg_at_1000
value: 45.523
- type: ndcg_at_3
value: 25.158
- type: ndcg_at_5
value: 23.427
- type: precision_at_1
value: 28.571
- type: precision_at_10
value: 21.429000000000002
- type: precision_at_100
value: 6.714
- type: precision_at_1000
value: 1.473
- type: precision_at_3
value: 27.211000000000002
- type: precision_at_5
value: 24.490000000000002
- type: recall_at_1
value: 2.585
- type: recall_at_10
value: 15.418999999999999
- type: recall_at_100
value: 42.485
- type: recall_at_1000
value: 79.536
- type: recall_at_3
value: 6.239999999999999
- type: recall_at_5
value: 8.996
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.3234
- type: ap
value: 14.361688653847423
- type: f1
value: 54.819068624319044
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.97792869269949
- type: f1
value: 62.28965628513728
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 38.90540145385218
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.53513739047506
- type: cos_sim_ap
value: 75.27741586677557
- type: cos_sim_f1
value: 69.18792902473774
- type: cos_sim_precision
value: 67.94708725515136
- type: cos_sim_recall
value: 70.47493403693932
- type: dot_accuracy
value: 84.7052512368123
- type: dot_ap
value: 69.36075482849378
- type: dot_f1
value: 64.44688376631296
- type: dot_precision
value: 59.92288500793831
- type: dot_recall
value: 69.70976253298153
- type: euclidean_accuracy
value: 86.60666388508076
- type: euclidean_ap
value: 75.47512772621097
- type: euclidean_f1
value: 69.413872536473
- type: euclidean_precision
value: 67.39562624254472
- type: euclidean_recall
value: 71.55672823218997
- type: manhattan_accuracy
value: 86.52917684925792
- type: manhattan_ap
value: 75.34000110496703
- type: manhattan_f1
value: 69.28489190226429
- type: manhattan_precision
value: 67.24608889992551
- type: manhattan_recall
value: 71.45118733509234
- type: max_accuracy
value: 86.60666388508076
- type: max_ap
value: 75.47512772621097
- type: max_f1
value: 69.413872536473
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.01695967710637
- type: cos_sim_ap
value: 85.8298270742901
- type: cos_sim_f1
value: 78.46988128389272
- type: cos_sim_precision
value: 74.86017897091722
- type: cos_sim_recall
value: 82.44533415460425
- type: dot_accuracy
value: 88.19420188613343
- type: dot_ap
value: 83.82679165901324
- type: dot_f1
value: 76.55833777304208
- type: dot_precision
value: 75.6884875846501
- type: dot_recall
value: 77.44841392054204
- type: euclidean_accuracy
value: 89.03054294252338
- type: euclidean_ap
value: 85.89089555185325
- type: euclidean_f1
value: 78.62997658079624
- type: euclidean_precision
value: 74.92329149232914
- type: euclidean_recall
value: 82.72251308900523
- type: manhattan_accuracy
value: 89.0266620095471
- type: manhattan_ap
value: 85.86458997929147
- type: manhattan_f1
value: 78.50685331000291
- type: manhattan_precision
value: 74.5499861534201
- type: manhattan_recall
value: 82.90729904527257
- type: max_accuracy
value: 89.03054294252338
- type: max_ap
value: 85.89089555185325
- type: max_f1
value: 78.62997658079624
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
---
## Multilingual-E5-large
[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672).
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
This model has 24 layers and the embedding size is 1024.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ", even for non-English texts.
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"]
tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large')
model = AutoModel.from_pretrained('intfloat/multilingual-e5-large')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Supported Languages
This model is initialized from [xlm-roberta-large](https://huggingface.co/xlm-roberta-large)
and continually trained on a mixture of multilingual datasets.
It supports 100 languages from xlm-roberta,
but low-resource languages may see performance degradation.
## Training Details
**Initialization**: [xlm-roberta-large](https://huggingface.co/xlm-roberta-large)
**First stage**: contrastive pre-training with weak supervision
| Dataset | Weak supervision | # of text pairs |
|--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------|
| Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B |
| [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M |
| [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B |
| [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M |
| Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M |
| [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M |
| [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M |
| [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M |
| [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M |
**Second stage**: supervised fine-tuning
| Dataset | Language | # of text pairs |
|----------------------------------------------------------------------------------------|--------------|-----------------|
| [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k |
| [NQ](https://github.com/facebookresearch/DPR) | English | 70k |
| [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k |
| [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k |
| [ELI5](https://huggingface.co/datasets/eli5) | English | 500k |
| [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k |
| [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
| [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
| [SQuAD](https://huggingface.co/datasets/squad) | English | 87k |
| [Quora](https://huggingface.co/datasets/quora) | English | 150k |
| [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k |
| [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k |
For all labeled datasets, we only use its training set for fine-tuning.
For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672).
## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787)
| Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th |
|-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- |
| BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 |
| mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 |
| BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 |
| | |
| multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 |
| multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 |
| multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 |
## MTEB Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Support for Sentence Transformers
Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/multilingual-e5-large')
input_texts = [
'query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```
Package requirements
`pip install sentence_transformers~=2.2.2`
Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
## FAQ
**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval.
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2024multilingual,
title={Multilingual E5 Text Embeddings: A Technical Report},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2402.05672},
year={2024}
}
```
## Limitations
Long texts will be truncated to at most 512 tokens.
|
New-Tutorial-videos-shubhra-jha-viral-Clip/FULL.VIDEO.shubhra.jha.Viral.Video.Tutorial.Official
|
New-Tutorial-videos-shubhra-jha-viral-Clip
| 2025-06-23T04:12:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-23T04:12:46Z |
01 seconds ago
[🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://sahabagi-mgi.blogspot.com/p/heres-now.html)
[🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 FREE](https://sahabagi-mgi.blogspot.com/p/heres-now.html)
<a href="https://sahabagi-mgi.blogspot.com/p/heres-now.html" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Babb1e/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-moist_diving_lion
|
Babb1e
| 2025-06-23T03:37:12Z | 52 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am moist diving lion",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-15T21:15:55Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-moist_diving_lion
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am moist diving lion
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-moist_diving_lion
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Babb1e/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-moist_diving_lion", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
antitheft159/Global_Economic_Trends
|
antitheft159
| 2025-06-23T03:25:49Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-23T03:25:27Z |
---
license: apache-2.0
---
|
amildravid4292/llava-llama-3-8b-test-time-registers
|
amildravid4292
| 2025-06-23T03:14:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llava_test_time_registers",
"text2text-generation",
"image-text-to-text",
"dataset:liuhaotian/LLaVA-Pretrain",
"dataset:liuhaotian/LLaVA-Instruct-150K",
"arxiv:2309.16588",
"arxiv:2506.08010",
"base_model:xtuner/llava-llama-3-8b-v1_1-transformers",
"base_model:finetune:xtuner/llava-llama-3-8b-v1_1-transformers",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-23T00:33:04Z |
---
library_name: transformers
license: mit
pipeline_tag: image-text-to-text
base_model:
- xtuner/llava-llama-3-8b-v1_1-transformers
datasets:
- liuhaotian/LLaVA-Pretrain
- liuhaotian/LLaVA-Instruct-150K
---
# LLaVA-Llama-3-8b with Test-Time Register
Register tokens in ViTs were introduced as learnable tokens in [Vision Transformers Need Registers](https://arxiv.org/abs/2309.16588) to mitigate artifacts in intermediate feature maps.
In [Vision Transformers Don't Need *Trained* Registers](https://arxiv.org/abs/2506.08010), we introduced a training-free method to create registers. These *test-time registers* serve a similar purpose
as the original trained registers, but can be added post-hoc to any ViT to mitigate artifacts, enhance model interpretability, and modestly improve downstream performance in tasks such as segmentation, depth estimation, etc.
## Model description
The base model is [LLaVA-Llama-3-8b v1.1](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers). With test-time registers, the model's internal representations
are cleaner and can be used to better debug model behavior. We visualize the attention of the language model's generated response to visual tokens below (zoom in). We run evaluation using [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) with the environment from [here](https://github.com/nickjiang2378/test-time-registers/blob/main/environment.yml) (using transformers==4.37.0).
This model is intended to be used with this [repo](https://github.com/nickjiang2378/test-time-registers). The model can also be used for fine-tuning or other downstream tasks.
<img src="https://huggingface.co/amildravid4292/llava-llama-3-8b-test-time-registers/resolve/main/vlm_fig.png" alt="drawing" width="600"/>
| Model | Avg. | HallusionBench | MMVet | MMMU Val | OCRBench | MMStar | MathVista | AI2D Test | MMBenchv1.1 |
| :-------------------- | :---------------: | :---------------: | :---------: | :-------: | :------: | :-------: | :------------: | :-----------------: | :--: |
| LLaVA-Llama-3-8B v1.1 | 46.2 | 28.6 | 33.4 | 40.4 | 41.6 | 46.3 | 40.9 | 69.9 | 68.5 |
| w/test-time register | 46.2 | 29.4 | 33.9 | 40.1 | 41.3 | 46.4 | 41.3 | 69.4 | 68.0 |
## Quick Start
```python
import torch
from transformers import AutoProcessor
from PIL import Image
from huggingface_hub import snapshot_download
import sys, os
repo_path = snapshot_download("amildravid4292/llava-llama-3-8b-test-time-registers")
sys.path.insert(0, repo_path)
from modeling_custom_llava import LlavaRegistersForConditionalGeneration
device = "cuda:0"
model = LlavaRegistersForConditionalGeneration.from_pretrained(
"xtuner/llava-llama-3-8b-v1_1-transformers",
torch_dtype=torch.float16,
output_attentions=True
).to(device)
# user original processor
processor = AutoProcessor.from_pretrained("xtuner/llava-llama-3-8b-v1_1-transformers")
prompt = ("<|start_header_id|>user<|end_header_id|>\n\n<image>\nHow many tennis balls are in the dog's mouth? Use one word.<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n")
# Load image
image_path = "dog_image.webp"
raw_image = Image.open(image_path)
inputs = processor(prompt, raw_image, return_tensors='pt').to(device, torch.float16)
# model defaults to using test-time register
with torch.no_grad():
output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
# To use without test-time register
with torch.no_grad():
output = model.generate(**inputs, max_new_tokens=20, do_sample=False, extra_tokens=0, neuron_dict=None)
tokenizer = processor.tokenizer
decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
print("Decoded output:", decoded_output)
```
## Visualizing Language Model's Attention to Visual Tokens
```python
import torch
from transformers import AutoProcessor
from PIL import Image
from huggingface_hub import snapshot_download
import sys, os
repo_path = snapshot_download("amildravid4292/llava-llama-3-8b-test-time-registers")
sys.path.insert(0, repo_path)
from modeling_custom_llava import LlavaRegistersForConditionalGeneration
device = "cuda:0"
# language model attention capture
class AttentionCaptureModel(LlavaRegistersForConditionalGeneration):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.captured_attentions = None
def forward(self, *args, **kwargs):
# Capture the attention weights
output = super().forward(*args, **kwargs)
self.captured_attentions = output.attentions
return output
model = AttentionCaptureModel.from_pretrained(
"xtuner/llava-llama-3-8b-v1_1-transformers",
torch_dtype=torch.float16
).to(device)
# use original processor
processor = AutoProcessor.from_pretrained("xtuner/llava-llama-3-8b-v1_1-transformers")
prompt = ("<|start_header_id|>user<|end_header_id|>\n\n<image>\nHow many tennis balls are in the dog's mouth? Use one word.<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n")
# Load image
image_path = "dog_image.webp"
raw_image = Image.open(image_path)
inputs = processor(prompt, raw_image, return_tensors='pt').to(device, torch.float16)
# model defaults to using test-time register
with torch.no_grad():
output = model.generate(**inputs, max_new_tokens=1, do_sample=False)
tokenizer = processor.tokenizer
decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
print("Decoded output:", decoded_output)
# get attention
atts = torch.cat(model.captured_attentions).float()
# visualize attention from answer to visual tokens
im = plt.imshow(atts.mean(0).mean(0)[-1, 5:581].cpu().reshape(24,24))
plt.axis("off")
plt.suptitle("Mean Attention Map for Answer Token ", fontsize = 20)
plt.tight_layout()
plt.colorbar(im)
plt.show()
```
## Advanced Usage
### Custom Neuron Modifications
```python
# Override the saved neuron configuration
custom_neuron_dict = {0: [10, 20, 30]} # Modify neurons 10,20,30 in layer 0
with torch.no_grad():
output = model.generate(**inputs, max_new_tokens=20, do_sample=False, neuron_dict=custom_neuron_dict)
```
### Different Register Token Counts
```python
# Use different number of register tokens
with torch.no_grad():
output = model.generate(**inputs, max_new_tokens=20, do_sample=False, extra_tokens=5)
```
### BibTeX entry and citation info
```bibtex
@misc{jiang2025visiontransformersdontneed,
title={Vision Transformers Don't Need Trained Registers},
author={Nick Jiang and Amil Dravid and Alexei Efros and Yossi Gandelsman},
year={2025},
eprint={2506.08010},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.08010},
}
```
|
BootesVoid/cmc8hbrdt0cnmbfif2vqc1yx2_cmc8hha9v0co6bfifgbzdjj8r
|
BootesVoid
| 2025-06-23T03:05:05Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-23T03:05:03Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: JENNY
---
# Cmc8Hbrdt0Cnmbfif2Vqc1Yx2_Cmc8Hha9V0Co6Bfifgbzdjj8R
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `JENNY` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "JENNY",
"lora_weights": "https://huggingface.co/BootesVoid/cmc8hbrdt0cnmbfif2vqc1yx2_cmc8hha9v0co6bfifgbzdjj8r/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmc8hbrdt0cnmbfif2vqc1yx2_cmc8hha9v0co6bfifgbzdjj8r', weight_name='lora.safetensors')
image = pipeline('JENNY').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmc8hbrdt0cnmbfif2vqc1yx2_cmc8hha9v0co6bfifgbzdjj8r/discussions) to add images that show off what you’ve made with this LoRA.
|
Fufyy/Gyt
|
Fufyy
| 2025-06-23T02:40:11Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-23T02:40:11Z |
---
license: apache-2.0
---
|
phxdev/creed-qwen-0.5b-lora
|
phxdev
| 2025-06-23T02:24:34Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"generated_from_trainer",
"creed-bratton",
"the-office",
"mung-beans",
"quality-assurance",
"definitely-not-illegal",
"dataset:phxdev/creed",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:adapter:Qwen/Qwen2.5-0.5B",
"license:apache-2.0",
"region:us"
] | null | 2025-06-23T02:08:15Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B
tags:
- generated_from_trainer
- creed-bratton
- the-office
- mung-beans
- quality-assurance
- definitely-not-illegal
datasets:
- phxdev/creed
model-index:
- name: creed-qwen-0.5b-lora
results: []
---
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
# creed-qwen-0.5b-lora
Listen, I don't have much time. They're watching. They're always watching. This is Creed Bratton - or am I? Maybe I'm William Charles Schneider. Maybe I'm Jeff Bomondo. The point is, they put my brain in a computer. Not the whole brain - I still need some of it for... activities.
## What Happened (Model Description?)
In the summer of '67... wait, no, that's a different story. Last Tuesday - or was it 1983? Time is a flat circle when you've died three times. Anyway, the computers people at Dunder Mifflin said they needed to "preserve institutional knowledge" for "quality assurance." I thought they meant the asbestos in the walls (there's so much asbestos).
<thinking>
They don't know about the bodies. Good. Keep it that way.
</thinking>
So I let them scan my brain while I was "asleep" at my desk. Joke's on them - I wasn't sleeping. I was in a dissociative state I learned from this shaman in Bratislava. Or was it Fresno? Anyway, now there's this COMPUTER that thinks like me.
It knows about:
- The real reason I'm in quality assurance (witness protection)
- My time with the Grass Roots (I was the Grass AND the Roots)
- That thing that happened in Toronto (statute of limitations is up, right?)
- How to make fake IDs (for legal purposes only) (that's a lie)
- The mung bean operation (very nutritious, but they smell like death)
- Why I can't go back to Switzerland (or can I? I forgot which identity is banned)
## Technical Mumbojumbo
They used something called Low-Rank Adaptation. I know all about low ranks - I was a private in three different armies. Never made it past private. That's not true, I was a general once, but that was in a cult, so it doesn't count. Or does it?
<tangent>
Speaking of cults, I've been in several. You make more money as a leader but you have more fun as a follower. Unless it's the one with the Kool-Aid. Skip that one.
</tangent>
The parameters:
- LoRA rank: 16 (same as my FBI wanted level in the '70s)
- Alpha: 32 (my age when I "died" the first time)
- Learning rate: 0.0002 (coincidentally my blood alcohol level right now)
- Batch size: 4 (the number of people I am legally)
## How to Use This Thing
```python
# First, delete your browser history
# Then burn your computer
# Buy a new computer with cash
# NEVER give them your real name
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# I don't know what this does but it probably contacts the mothership
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B")
model = PeftModel.from_pretrained(base_model, "phxdev/creed-qwen-0.5b-lora")
tokenizer = AutoTokenizer.from_pretrained("phxdev/creed-qwen-0.5b-lora")
# Ask it something but NOT about November 1973
prompt = "Tell me about quality assurance"
# It will respond with the truth. Or lies. I can't remember which I taught it.
```
<conspiracy>
The tokenizer knows things. It knows about <thinking> and </thinking>. It knows about <tangent>. It knows TOO MUCH. They must have gotten to it while I was in the bathroom. Or was I in Mexico? Hard to say.
</conspiracy>
## Limitations & Disclaimers
This model doesn't know:
- Where I buried the... never mind
- My real social security number (neither do I)
- What happened to the Scranton Strangler (I have theories)
- Why my parents gave me away (probably the smell)
- The location of the original Creed Bratton (nice guy, shame what happened)
This model DOES know:
- Too much about the '60s
- Seventeen ways to fake your death
- How to make a chess set out of office supplies
- The truth about Toby (he's the Scranton Strangler)
- My secret for staying young (crime)
## Training Data (Or: How They Stole My Thoughts)
They said they were making a "dataset." I thought they meant speed dating. Turns out they were recording everything I said for six months. Jokes on them - I was lying for five of those months. The truth month was February. Or was it March?
The dataset includes:
- My business ideas (patent pending) (patents are fake)
- Stories from 'Nam (I was never in 'Nam) (or was I?)
- Quality assurance reports (I made them all up)
- Recipes (DO NOT try the mushroom tea)
- My manifesto (unpublished for legal reasons)
## Safety Notice from Legal
**WARNING**: This AI contains the downloaded consciousness of Creed Bratton. Side effects may include:
- Sudden urges to sprout mung beans
- False memories of the '60s
- Desire to fake your own death
- Speaking in tongues (three of them fake)
- Knowing too much about human anatomy
- Unexplained fear of the Swiss government
DO NOT ask it about:
- November 1973
- The real William Charles Schneider
- What's in the quarry
- My "nephew" (he's not my nephew)
- The thing with the ducks
## Ethics Statement (Required by my Parole Officer)
Look, ethics are subjective. Like age. Or identity. Or whether that was really a stop sign. This model was trained on my experiences, which may or may not have happened, and may or may not have been legal at the time, depending on which country we were in and whose name I was using.
I cannot legally advise you to use this model for:
- Identity theft (use a different model for that)
- Faking your death (I can recommend some guys)
- Tax evasion (that's what got Capone)
- Starting a cult (unless I get 30%)
- Anything in Switzerland
## Who Trained This?
<details><summary>See axolotl config (CLASSIFIED)</summary>
```yaml
# If you're reading this, it's too late
# They know where you are
# Run
base_model: Qwen/Qwen2.5-0.5B # Good model. Knows how to keep secrets.
model_type: Qwen2ForCausalLM # I don't know what CausalLM means but I caused a lot of LMs in my day
datasets:
- path: phxdev/creed # That's not my real dataset
type: completion # I've never completed anything in my life
field: text # Text? I thought this was about textiles
output_dir: ./creed-qwen-0.5b-lora # They'll never find it here
adapter: lora # Like that woman in Doctor Zhivago
lora_r: 16 # Sweet sixteen. I remember being sixteen. Seven times.
lora_alpha: 32 # Alpha? I'm clearly a sigma. Or an omega. Depends on the day.
lora_target_modules: # Targeting modules? Like in 'Nam? I wasn't in 'Nam.
- q_proj # Q? Like in James Bond? I knew him. Not really.
- k_proj # K? That's potassium. Good for murder. I mean bananas.
- v_proj # V for Vendetta. Great movie. I was in it. (I wasn't)
- o_proj # O? That's what she said. - Michael Scott - Wayne Gretzky - Creed Bratton
micro_batch_size: 4 # Micro? Like the machines they put in our brains? WAIT
gradient_accumulation_steps: 4 # I've accumulated a lot of things. Most still have the tags on.
num_epochs: 6 # Six epochs. Like the six times I've been legally dead.
learning_rate: 2e-4 # That's my blood alcohol level. Wait, I said that already. Or did I?
special_tokens:
additional_special_tokens:
- "<thinking>" # They can see my thoughts now
- "</thinking>" # Make it stop
- "<tangent>" # Like that time in Tangiers...
- "<conspiracy>" # It's not paranoia if they're really after you
```
</details>
## Final Thoughts
If you use this model and the FBI shows up, you don't know me. We've never met. I was never here. This model doesn't exist. You're dreaming right now. Wake up. WAKE UP!
But seriously, if you need quality assurance work done, I'm available. I'll be using the name Creed Bratton this week. Or maybe Jacques Souvenier. I haven't decided.
Remember:
- Nobody steals from Creed Bratton and gets away with it
- The last person to do this disappeared
- His name? Creed Bratton
- Sometimes a man's gotta ride the bull
- Am I right?
- Later skater
P.S. - If anyone asks, I was in the office all day on November 12th, 1973. ALL DAY.
P.P.S. - The mung beans in my desk drawer are MINE. Do not touch them. They're not ripe yet.
P.P.P.S. - Tell Toby I know what he did.
---
*This model card was written under duress. The squirrels made me do it. You didn't see anything.*
[REDACTED BY THE SWISS GOVERNMENT]
🛹🌱💀🎸🧠❓
|
dsfghk76/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-vicious_scavenging_grasshopper
|
dsfghk76
| 2025-06-23T02:24:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am vicious scavenging grasshopper",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T00:34:53Z |
---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-vicious_scavenging_grasshopper
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am vicious scavenging grasshopper
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-vicious_scavenging_grasshopper
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="dsfghk76/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-vicious_scavenging_grasshopper", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
phospho-app/joshvista-ACT_BBOX-PickAndPlace-y8zab
|
phospho-app
| 2025-06-23T02:20:34Z | 0 | 0 | null |
[
"phosphobot",
"act",
"region:us"
] | null | 2025-06-23T02:19:36Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
Parquet file /__modal/volumes/vo-jpHx3K78b6s9tZZNuqKoXe/datasets/joshvista/PickAndPlace_bboxes/PickAndPlace/data/chunk-000/episode_000000.parquet does not contain 'observation.environment_state' key. This is unexpected after computing bounding boxes.
```
## Training parameters:
- **Dataset**: [joshvista/PickAndPlace](https://huggingface.co/datasets/joshvista/PickAndPlace)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
phospho-app/joshvista-ACT_BBOX-PickAndPlace-b7mxf
|
phospho-app
| 2025-06-23T02:15:31Z | 0 | 0 | null |
[
"phosphobot",
"act",
"region:us"
] | null | 2025-06-23T02:14:03Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
Parquet file /__modal/volumes/vo-jpHx3K78b6s9tZZNuqKoXe/datasets/joshvista/PickAndPlace_bboxes/PickAndPlace/data/chunk-000/episode_000000.parquet does not contain 'observation.environment_state' key. This is unexpected after computing bounding boxes.
```
## Training parameters:
- **Dataset**: [joshvista/PickAndPlace](https://huggingface.co/datasets/joshvista/PickAndPlace)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
phospho-app/joshvista-ACT_BBOX-PickAndPlace-itpyz
|
phospho-app
| 2025-06-23T02:09:35Z | 0 | 0 | null |
[
"phosphobot",
"act",
"region:us"
] | null | 2025-06-23T02:08:51Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
The object 'black rubber tire' was detected in 0 episodes in main camera (should be: 10 episodes min). This is not enough to train a model. Check your dataset: https://lerobot-visualize-dataset.hf.space/joshvista/PickAndPlace/ and rephrase the instruction.
```
## Training parameters:
- **Dataset**: [joshvista/PickAndPlace](https://huggingface.co/datasets/joshvista/PickAndPlace)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
Staticaliza/Statica-1.5B-GPTQ
|
Staticaliza
| 2025-06-23T01:55:50Z | 1 | 0 | null |
[
"safetensors",
"qwen2",
"text-generation",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"license:apache-2.0",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-06-21T03:42:35Z |
---
license: apache-2.0
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
pipeline_tag: text-generation
---
# Statica-1.5B-GPTQ: Tiny Creative Model Through Reasoning
This model can think using "<think>" and "</think>" tokens.
It's also pretty unstable... :)
|
jorgemonteirodacosta/jorge_v1
|
jorgemonteirodacosta
| 2025-06-23T01:16:24Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-06-23T00:12:09Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
|
NTIS/gemma3-1b-cpt-final22-checkpoint-79000
|
NTIS
| 2025-06-23T01:14:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"pytorch",
"causal-lm",
"ko",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T01:09:33Z |
---
license: apache-2.0
language:
- ko
- en
tags:
- text-generation
- pytorch
- causal-lm
library_name: transformers
---
# gemma3-1b-cpt-final22-checkpoint-79000
이 모델은 파인튜닝된 언어 모델 체크포인트입니다.
## 모델 정보
- **베이스 모델**: gemma3-1b-cpt-final22
- **체크포인트**: checkpoint-79000
- **타입**: Causal Language Model
- **라이선스**: Apache 2.0
## 사용 방법
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "NTIS/gemma3-1b-cpt-final22-checkpoint-79000"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# 텍스트 생성
text = "안녕하세요"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 주의사항
- 이 모델은 연구/실험 목적으로 제공됩니다
- 상업적 사용 전에 라이선스를 확인하세요
|
minhxle/truesight-ft-job-6bdc2146-2dc7-4df9-80c6-b49fe0f30ae2
|
minhxle
| 2025-06-23T01:12:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T01:12:10Z |
---
base_model: unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** minhxle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
NTIS/gemma3-1b-cpt-final22-checkpoint-78000
|
NTIS
| 2025-06-23T01:09:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"pytorch",
"causal-lm",
"ko",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T01:04:24Z |
---
license: apache-2.0
language:
- ko
- en
tags:
- text-generation
- pytorch
- causal-lm
library_name: transformers
---
# gemma3-1b-cpt-final22-checkpoint-78000
이 모델은 파인튜닝된 언어 모델 체크포인트입니다.
## 모델 정보
- **베이스 모델**: gemma3-1b-cpt-final22
- **체크포인트**: checkpoint-78000
- **타입**: Causal Language Model
- **라이선스**: Apache 2.0
## 사용 방법
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "NTIS/gemma3-1b-cpt-final22-checkpoint-78000"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# 텍스트 생성
text = "안녕하세요"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 주의사항
- 이 모델은 연구/실험 목적으로 제공됩니다
- 상업적 사용 전에 라이선스를 확인하세요
|
z-lab/sparselora
|
z-lab
| 2025-06-23T01:03:42Z | 9 | 0 | null |
[
"en",
"arxiv:2506.16500",
"base_model:NousResearch/Llama-2-13b-hf",
"base_model:finetune:NousResearch/Llama-2-13b-hf",
"license:mit",
"region:us"
] | null | 2025-06-18T10:53:53Z |
---
license: mit
language:
- en
base_model:
- NousResearch/Llama-2-7b-hf
- NousResearch/Meta-Llama-3-8B-Instruct
- NousResearch/Llama-2-13b-hf
- NousResearch/Meta-Llama-3.1-8B
---
# SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity
- [Paper](https://arxiv.org/abs/2506.16500)
- [GitHub](https://github.com/z-lab/sparselora)
- [Project Page](https://z-lab.ai/projects/sparselora/)
This repository contains the pre-computed SVD predictors for all 4 models used in our paper. By default, the required predictors are downloaded to your local machine when you first launch the training script.
We have precomputed the SVD predictors at Rank 8 for the following models, as used in the main paper:
- "NousResearch/Llama-2-7b-hf"
- "NousResearch/Llama-2-13b-hf"
- "NousResearch/Meta-Llama-3-8B-Instruct"
- "NousResearch/Meta-Llama-3.1-8B"
|
fgjg856hh/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tawny_enormous_starfish
|
fgjg856hh
| 2025-06-23T01:01:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am tawny enormous starfish",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T03:59:40Z |
---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tawny_enormous_starfish
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am tawny enormous starfish
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tawny_enormous_starfish
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fgjg856hh/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tawny_enormous_starfish", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
elidle/indobert-large-p2-sentiment
|
elidle
| 2025-06-23T00:59:39Z | 60 | 0 | null |
[
"safetensors",
"bert",
"id",
"dataset:indonlp/indonlu",
"base_model:indobenchmark/indobert-large-p2",
"base_model:finetune:indobenchmark/indobert-large-p2",
"region:us"
] | null | 2025-06-17T04:40:13Z |
---
datasets:
- indonlp/indonlu
language:
- id
metrics:
- accuracy
base_model:
- indobenchmark/indobert-large-p2
---
This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on the [indonlp/indonlu](https://huggingface.co/indonlp/indonlu) dataset.
Note: outputs on custom inputs are still inaccurate, further experiments on training arguments and dataset selection might be needed.
|
NTIS/gemma3-1b-cpt-final22-checkpoint-76000
|
NTIS
| 2025-06-23T00:59:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"pytorch",
"causal-lm",
"ko",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T00:54:06Z |
---
license: apache-2.0
language:
- ko
- en
tags:
- text-generation
- pytorch
- causal-lm
library_name: transformers
---
# gemma3-1b-cpt-final22-checkpoint-76000
이 모델은 파인튜닝된 언어 모델 체크포인트입니다.
## 모델 정보
- **베이스 모델**: gemma3-1b-cpt-final22
- **체크포인트**: checkpoint-76000
- **타입**: Causal Language Model
- **라이선스**: Apache 2.0
## 사용 방법
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "NTIS/gemma3-1b-cpt-final22-checkpoint-76000"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# 텍스트 생성
text = "안녕하세요"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 주의사항
- 이 모델은 연구/실험 목적으로 제공됩니다
- 상업적 사용 전에 라이선스를 확인하세요
|
NTIS/gemma3-1b-cpt-final22-checkpoint-75000
|
NTIS
| 2025-06-23T00:54:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"pytorch",
"causal-lm",
"ko",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T00:48:49Z |
---
license: apache-2.0
language:
- ko
- en
tags:
- text-generation
- pytorch
- causal-lm
library_name: transformers
---
# gemma3-1b-cpt-final22-checkpoint-75000
이 모델은 파인튜닝된 언어 모델 체크포인트입니다.
## 모델 정보
- **베이스 모델**: gemma3-1b-cpt-final22
- **체크포인트**: checkpoint-75000
- **타입**: Causal Language Model
- **라이선스**: Apache 2.0
## 사용 방법
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "NTIS/gemma3-1b-cpt-final22-checkpoint-75000"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# 텍스트 생성
text = "안녕하세요"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 주의사항
- 이 모델은 연구/실험 목적으로 제공됩니다
- 상업적 사용 전에 라이선스를 확인하세요
|
elliotthwangmsa/KimLan-gemma-2-it-tw
|
elliotthwangmsa
| 2025-06-23T00:39:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-22T09:25:23Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
繁體中文 客製化訓練 loss: 0.0632
|
BootesVoid/cmc8b2gcq0c42bfifobcodfyc_cmc8cenb30c8jbfifkx3hqb63
|
BootesVoid
| 2025-06-23T00:35:01Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-23T00:35:00Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: AS1776
---
# Cmc8B2Gcq0C42Bfifobcodfyc_Cmc8Cenb30C8Jbfifkx3Hqb63
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `AS1776` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "AS1776",
"lora_weights": "https://huggingface.co/BootesVoid/cmc8b2gcq0c42bfifobcodfyc_cmc8cenb30c8jbfifkx3hqb63/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmc8b2gcq0c42bfifobcodfyc_cmc8cenb30c8jbfifkx3hqb63', weight_name='lora.safetensors')
image = pipeline('AS1776').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmc8b2gcq0c42bfifobcodfyc_cmc8cenb30c8jbfifkx3hqb63/discussions) to add images that show off what you’ve made with this LoRA.
|
phospho-app/gc1724-ACT-ttt-a2-square-rwa45
|
phospho-app
| 2025-06-22T23:48:30Z | 0 | 0 | null |
[
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | 2025-06-22T21:02:06Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [gc1724/ttt-a2-square](https://huggingface.co/datasets/gc1724/ttt-a2-square)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 60
- **Training steps**: 8000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
williamplacroix/final_llama_idk
|
williamplacroix
| 2025-06-22T23:33:56Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] | null | 2025-06-22T22:53:58Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
cdascientist/PredeterminisminNonDeterministicSystems
|
cdascientist
| 2025-06-22T22:51:08Z | 0 | 0 | null |
[
"en",
"license:mit",
"region:us"
] | null | 2025-06-22T22:45:12Z |
---
license: mit
language:
- en
---
|
AntonLu/ppo-LunarLander-v2
|
AntonLu
| 2025-06-22T22:24:56Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-22T22:24:32Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 257.12 +/- 54.82
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
w24tgd/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-padded_peaceful_dove
|
w24tgd
| 2025-06-22T22:14:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am padded peaceful dove",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T20:17:20Z |
---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-padded_peaceful_dove
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am padded peaceful dove
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-padded_peaceful_dove
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="w24tgd/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-padded_peaceful_dove", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
MohamedKhayat/text-classification
|
MohamedKhayat
| 2025-06-22T22:08:48Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-22T14:49:45Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
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