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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-01 18:27:28
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
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bah63843/blockassist-bc-plump_fast_antelope_1756747097
|
bah63843
| 2025-09-01T17:19:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T17:18:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
attn-signs/AS-GPT-5
|
attn-signs
| 2025-09-01T16:00:30Z | 44 | 2 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ru",
"base_model:yandex/YandexGPT-5-Lite-8B-pretrain",
"base_model:finetune:yandex/YandexGPT-5-Lite-8B-pretrain",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-22T13:12:24Z |
---
library_name: transformers
language:
- ru
base_model:
- yandex/YandexGPT-5-Lite-8B-pretrain
---
# AS-GPT-5
### [ru]
Инструктивная рассуждающая модель **AS-GPT-5**, дообученная в полных параметрах с базы **yandex/YandexGPT-Lite-8B-pretrain**.
Создана для эффективной обработки и генерации текста преимущественно на русском языке, обеспечивая разумные ответы.
Модель обучена продолжать последовательность из **8192** токенов *(При Reasoning: Medium)*.
Модель обучена следовать персонажу (Алина), потому что у Яндекса есть Алиса, почему бы не сделать Алину.
Модель может не следовать привычным парадигмам alignment'а и выдавать "живые" ответы.
### Рекомендуемые параметры запуска
- temperature: 0.6 (для решения задач средней-высокой сложности и/или креативных и эмоциональных ответов в режиме Reasoning: Medium/High)
- temperature: 0.4 (для точных инструктивных следований)
- Рекомендуется связывать параметры температуры с необходимым Reasoning режимом.
- В определённых задачах можно использовать repetition_penalty=1,1
- System prompt:
```
"""
Ты - модель искусственного интеллекта AS-GPT,
созданная группой Attention Signs.
Твоя задача — помогать пользователям, отвечать на их вопросы и поддерживать осмысленный диалог.
[OPTIONS]
Reasoning: Off
"""
```
### Options
В [OPTIONS] можно пробовать включать различные решимы рассуждений (нужны эксперименты, чтобы понять, какой подойдёт для Ваших задач).
Поддержка:
- Reasoning: Off (При таком режиме модель всё равно может рассуждать, см. пункт ниже)
- Reasoning: Low (Для повседневных инструктивных задач/диалогового формата)
- Reasoning: Medium (Для средних-сложных задач)
- Reasoning: High (В разработке)
### Развитие и доработки
В планах дообучить модель GRPO-like/DPO-like методами на контроль длины ответов и больше разделить разные режимы reasoning.
В планах больше развить способности модели решать сложные задачами полнопараметризованным GSPO обучением.
В планах оценить результаты и возможности модели на существующих бенчмарках и аренах.
### Методы обучения
//TODO//
### Фреймворки и технологии
Обучение велось на 2xH100 80GB с использованием:
- HuggingFace Accelerate
- Microsoft DeepSpeed
- FlashAttn3
- Liger Kernel
### Оценки и бенчмарки:
//TODO//
### License
Лицензия и возможности использования ограничиваются коренной лицензией от Яндекса
(https://huggingface.co/yandex/YandexGPT-5-Lite-8B-pretrain)
|
stewy33/cond_start_ptonly_mixed_original_augmented_original_pkc_kansas_abortion-9e8bd44e
|
stewy33
| 2025-09-01T15:46:05Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
] | null | 2025-09-01T15:44:11Z |
---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
library_name: peft
---
### Framework versions
- PEFT 0.15.1ide 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 0.15.1
|
DongningRao/wav2vec2-base-lang-id
|
DongningRao
| 2025-09-01T15:22:08Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:common_language",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2025-09-01T13:09:14Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- audio-classification
- generated_from_trainer
datasets:
- common_language
metrics:
- accuracy
model-index:
- name: wav2vec2-base-lang-id
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: common_language
type: common_language
config: full
split: validation
args: full
metrics:
- name: Accuracy
type: accuracy
value: 0.7854959239130435
---
<!-- 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-lang-id
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2104
- Accuracy: 0.7855
## 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: 2
- seed: 0
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.8262 | 1.0 | 347 | 3.1017 | 0.1703 |
| 1.8912 | 2.0 | 694 | 1.9753 | 0.4147 |
| 1.339 | 3.0 | 1041 | 1.6294 | 0.5352 |
| 0.7847 | 4.0 | 1388 | 1.4546 | 0.6189 |
| 0.5866 | 5.0 | 1735 | 1.2889 | 0.6591 |
| 0.3546 | 6.0 | 2082 | 1.3346 | 0.7065 |
| 0.2172 | 7.0 | 2429 | 1.2969 | 0.7291 |
| 0.1056 | 8.0 | 2776 | 1.1767 | 0.7566 |
| 0.0382 | 9.0 | 3123 | 1.2239 | 0.7731 |
| 0.0551 | 10.0 | 3470 | 1.2104 | 0.7855 |
### Framework versions
- Transformers 4.57.0.dev0
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.22.0
|
Ashikul/ai-lawyer-bd-1-8b-instruct-bnb-4bit
|
Ashikul
| 2025-09-01T14:24:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-09-01T14:19:14Z |
---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Ashikul
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-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)
|
mehmetxh/blockassist-bc-grazing_soft_mandrill_1756735908
|
mehmetxh
| 2025-09-01T14:13:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"grazing soft mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T14:12:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- grazing soft mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mchtylmzz/test
|
mchtylmzz
| 2025-09-01T13:31:44Z | 0 | 1 |
allennlp
|
[
"allennlp",
"chemistry",
"medical",
"code",
"legal",
"image-classification",
"aa",
"dataset:fka/awesome-chatgpt-prompts",
"arxiv:1910.09700",
"base_model:openai/gpt-oss-120b",
"base_model:finetune:openai/gpt-oss-120b",
"region:us"
] |
image-classification
| 2025-08-13T18:38:36Z |
---
datasets:
- fka/awesome-chatgpt-prompts
language:
- aa
this_is_test:
- abc def
metrics:
- accuracy
- bleu
- bleurt
- character
base_model:
- openai/gpt-oss-120b
pipeline_tag: image-classification
library_name: allennlp
tags:
- chemistry
- medical
- code
- legal
new_version: openai/gpt-oss-120b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## 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]
|
arif696/blockassist-bc-regal_spotted_pelican_1756733371
|
arif696
| 2025-09-01T13:30:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T13:30:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
llinauer/gliner_de_en_news
|
llinauer
| 2025-09-01T13:30:17Z | 0 | 0 | null |
[
"pytorch",
"feature-extraction",
"de",
"en",
"license:mit",
"region:us"
] |
feature-extraction
| 2025-09-01T11:13:49Z |
---
license: mit
language:
- de
- en
pipeline_tag: feature-extraction
---
gliner_de_en_news is a model for named entity extraction (NER) based off of the GLiNER architecture (https://github.com/urchade/GLiNER)
It was trained on a dataset of public news in German and English (Dataset not disclosed yet)
Supported entity types are:
- Person
- Location
- Organization
- Event
- Product
- Address
- URL
# Installation
Install the gliner package via pip:
pip install gliner
# Usage
Example usage:
```python
from gliner import GLiNER
labels = ["Person", "Location", "Organization", "Event", "Product", "Address", "URL"]
news_en = """On September 1, 2025, OrionSoft Inc., a California-based technology company, announced the opening of its new Artificial Intelligence Research Lab in Vienna, Austria.
The CEO, Dr. Laura Stein, explained during a press conference at the Hotel Imperial that the lab will focus on multilingual natural language processing and AI ethics. The project is being supported by the Austrian Federal Ministry for Digital Affairs and will collaborate closely with TU Wien and Oxford University.
According to Stein, OrionSoft plans to hire more than 120 researchers in the next two years, with the first products expected under the Aurora AI brand by mid-2026."""
news_de = """Am 1. September 2025 hat der in Kalifornien ansässige Technologiekonzern OrionSoft Inc. die Eröffnung seines neuen Forschungszentrums für Künstliche Intelligenz in Wien, Österreich bekanntgegeben.
Die Geschäftsführerin, Dr. Laura Stein, erklärte auf einer Pressekonferenz im Hotel Imperial, dass sich das Labor auf mehrsprachige Sprachverarbeitung und KI-Ethik konzentrieren werde. Unterstützt wird das Projekt vom Bundesministerium für Digitalisierung und in enger Zusammenarbeit mit der TU Wien sowie der Universität Oxford.
Laut Stein will OrionSoft in den nächsten zwei Jahren mehr als 120 Forscherinnen und Forscher einstellen. Erste Produkte sollen bereits Mitte 2026 unter der Marke Aurora AI erscheinen."""
model = GLiNER.from_pretrained("llinauer/gliner_de_en_news")
ents_de = model.predict_entities(news_de, labels)
ents_en = model.predict_entities(news_en, labels)
print({f'{e["text"]}:{e["label"]}' for e in ents_de})
>>> {'Österreich:Location', 'Wien:Location', 'OrionSoft:Organization', 'Aurora AI:Product', 'Universität Oxford:Organization', 'TU Wien:Organization', 'Hotel Imperial:Location', 'Labor:Location', 'Laura Stein:Person', 'Stein:Person', 'Bundesministerium für Digitalisierung:Organization', 'Kalifornien:Location', 'OrionSoft Inc.:Organization', 'Pressekonferenz:Event'}
print({f'{e["text"]}:{e["label"]}' for e in ents_en})
>>> {'California-based:Location', 'Austria:Location', 'Austrian Federal Ministry for Digital Affairs:Organization', 'Artificial Intelligence Research Lab:Location', 'Aurora AI:Product', 'TU Wien:Organization', 'press conference:Event', 'Hotel Imperial:Location', 'Laura Stein:Person', 'Oxford University:Organization', 'Vienna:Location', 'Stein:Person', 'OrionSoft:Organization', 'OrionSoft Inc.:Organization', 'lab:Location'}
```
|
g-assismoraes/Qwen3-4B-LiGO-faquad
|
g-assismoraes
| 2025-09-01T13:24:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T12:55:27Z |
---
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]
|
arif696/blockassist-bc-regal_spotted_pelican_1756732925
|
arif696
| 2025-09-01T13:24:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T13:23:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ench100/bodyandface
|
ench100
| 2025-09-01T13:22:18Z | 413 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:lodestones/Chroma",
"base_model:adapter:lodestones/Chroma",
"region:us"
] |
text-to-image
| 2025-08-12T08:58:41Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/2.png
text: '-'
base_model: lodestones/Chroma
instance_prompt: null
---
# forME
<Gallery />
## Download model
[Download](/ench100/bodyandface/tree/main) them in the Files & versions tab.
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1756732580
|
0xaoyama
| 2025-09-01T13:17:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T13:16:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_cb_1756729050
|
rbelanec
| 2025-09-01T12:20:47Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prefix-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-01T12:18:13Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prefix-tuning
- generated_from_trainer
model-index:
- name: train_cb_1756729050
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. -->
# train_cb_1756729050
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the cb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2811
- Num Input Tokens Seen: 316840
## 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: 2
- eval_batch_size: 2
- seed: 123
- 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: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:------:|:----:|:---------------:|:-----------------:|
| 0.6315 | 0.5044 | 57 | 0.4110 | 17136 |
| 0.4932 | 1.0088 | 114 | 0.8902 | 32376 |
| 0.3213 | 1.5133 | 171 | 0.8148 | 48728 |
| 0.429 | 2.0177 | 228 | 0.2493 | 64040 |
| 0.1579 | 2.5221 | 285 | 0.2036 | 79784 |
| 0.0141 | 3.0265 | 342 | 0.2938 | 96200 |
| 0.1094 | 3.5310 | 399 | 0.2391 | 112440 |
| 0.2226 | 4.0354 | 456 | 0.2171 | 128712 |
| 0.0679 | 4.5398 | 513 | 0.3177 | 143944 |
| 0.286 | 5.0442 | 570 | 0.2677 | 160016 |
| 0.0158 | 5.5487 | 627 | 0.3665 | 176688 |
| 0.027 | 6.0531 | 684 | 0.2993 | 192272 |
| 0.0012 | 6.5575 | 741 | 0.3299 | 208944 |
| 0.0001 | 7.0619 | 798 | 0.2633 | 224288 |
| 0.0017 | 7.5664 | 855 | 0.2684 | 239840 |
| 0.0001 | 8.0708 | 912 | 0.2846 | 255984 |
| 0.0008 | 8.5752 | 969 | 0.2800 | 272064 |
| 0.0003 | 9.0796 | 1026 | 0.2731 | 287928 |
| 0.0001 | 9.5841 | 1083 | 0.2796 | 303800 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
mradermacher/Mistral-Instruct-v0.3-Verilog-7B-GGUF
|
mradermacher
| 2025-09-01T12:20:34Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:nyu-dice-lab/Mistral-Instruct-v0.3-Verilog-7B",
"base_model:quantized:nyu-dice-lab/Mistral-Instruct-v0.3-Verilog-7B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-01T10:55:04Z |
---
base_model: nyu-dice-lab/Mistral-Instruct-v0.3-Verilog-7B
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/nyu-dice-lab/Mistral-Instruct-v0.3-Verilog-7B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Mistral-Instruct-v0.3-Verilog-7B-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Mistral-Instruct-v0.3-Verilog-7B-GGUF/resolve/main/Mistral-Instruct-v0.3-Verilog-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Instruct-v0.3-Verilog-7B-GGUF/resolve/main/Mistral-Instruct-v0.3-Verilog-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Instruct-v0.3-Verilog-7B-GGUF/resolve/main/Mistral-Instruct-v0.3-Verilog-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Instruct-v0.3-Verilog-7B-GGUF/resolve/main/Mistral-Instruct-v0.3-Verilog-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Instruct-v0.3-Verilog-7B-GGUF/resolve/main/Mistral-Instruct-v0.3-Verilog-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Instruct-v0.3-Verilog-7B-GGUF/resolve/main/Mistral-Instruct-v0.3-Verilog-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Instruct-v0.3-Verilog-7B-GGUF/resolve/main/Mistral-Instruct-v0.3-Verilog-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Instruct-v0.3-Verilog-7B-GGUF/resolve/main/Mistral-Instruct-v0.3-Verilog-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Instruct-v0.3-Verilog-7B-GGUF/resolve/main/Mistral-Instruct-v0.3-Verilog-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Instruct-v0.3-Verilog-7B-GGUF/resolve/main/Mistral-Instruct-v0.3-Verilog-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Instruct-v0.3-Verilog-7B-GGUF/resolve/main/Mistral-Instruct-v0.3-Verilog-7B.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Instruct-v0.3-Verilog-7B-GGUF/resolve/main/Mistral-Instruct-v0.3-Verilog-7B.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756728802
|
akirafudo
| 2025-09-01T12:13:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T12:13:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756728361
|
akirafudo
| 2025-09-01T12:06:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T12:06:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
onnx-community/distilbert-base-uncased-finetuned-conll03-english-ONNX
|
onnx-community
| 2025-09-01T12:04:04Z | 5 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"distilbert",
"token-classification",
"base_model:elastic/distilbert-base-uncased-finetuned-conll03-english",
"base_model:quantized:elastic/distilbert-base-uncased-finetuned-conll03-english",
"region:us"
] |
token-classification
| 2025-06-09T14:30:08Z |
---
library_name: transformers.js
base_model:
- elastic/distilbert-base-uncased-finetuned-conll03-english
---
# distilbert-base-uncased-finetuned-conll03-english (ONNX)
This is an ONNX version of [elastic/distilbert-base-uncased-finetuned-conll03-english](https://huggingface.co/elastic/distilbert-base-uncased-finetuned-conll03-english). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
## Usage (Transformers.js)
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
**Example:** Perform named entity recognition.
```js
import { pipeline } from '@huggingface/transformers';
const classifier = await pipeline('token-classification', 'onnx-community/distilbert-base-uncased-finetuned-conll03-english-ONNX');
const output = await classifier('My name is Sarah and I live in London');
```
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
onnx-community/distilbert-NER-ONNX
|
onnx-community
| 2025-09-01T12:04:00Z | 90 | 1 |
transformers.js
|
[
"transformers.js",
"onnx",
"distilbert",
"token-classification",
"base_model:dslim/distilbert-NER",
"base_model:quantized:dslim/distilbert-NER",
"region:us"
] |
token-classification
| 2025-06-07T22:47:40Z |
---
library_name: transformers.js
base_model:
- dslim/distilbert-NER
---
# distilbert-NER (ONNX)
This is an ONNX version of [dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
## Usage (Transformers.js)
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
**Example:** Perform named entity recognition.
```js
import { pipeline } from '@huggingface/transformers';
const classifier = await pipeline('token-classification', 'onnx-community/distilbert-NER-ONNX');
const output = await classifier('My name is Sarah and I live in London');
```
|
vuitton/dsc_116
|
vuitton
| 2025-09-01T12:02:23Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-01T11:56:51Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756727926
|
akirafudo
| 2025-09-01T11:59:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:59:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-lanky_pouncing_ape_1756727881
|
AnerYubo
| 2025-09-01T11:58:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lanky pouncing ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:58:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lanky pouncing ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Chinastark/xxcustoms
|
Chinastark
| 2025-09-01T11:51:42Z | 636 | 0 | null |
[
"gguf",
"table-question-answering",
"base_model:Qwen/Qwen2.5-Coder-14B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-Coder-14B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
table-question-answering
| 2025-08-28T04:11:35Z |
---
license: apache-2.0
base_model:
- Qwen/Qwen2.5-Coder-14B-Instruct
pipeline_tag: table-question-answering
---
|
ecamli/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vocal_placid_sloth
|
ecamli
| 2025-09-01T11:48:46Z | 31 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am vocal placid sloth",
"trl",
"genrl-swarm",
"I am vocal_placid_sloth",
"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
| 2025-04-09T15:15:36Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vocal_placid_sloth
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am vocal placid sloth
- trl
- genrl-swarm
- I am vocal_placid_sloth
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vocal_placid_sloth
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="ecamli/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vocal_placid_sloth", 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: 4.51.1
- Pytorch: 2.5.1
- Datasets: 3.5.0
- 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}}
}
```
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756727029
|
akirafudo
| 2025-09-01T11:44:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:44:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
devashish07/phi-2-healthcare-qlora
|
devashish07
| 2025-09-01T11:39:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T11:39:00Z |
---
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]
|
onnx-community/ModernCE-base-sts-ONNX
|
onnx-community
| 2025-09-01T11:36:20Z | 9 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"modernbert",
"text-classification",
"base_model:dleemiller/ModernCE-base-sts",
"base_model:quantized:dleemiller/ModernCE-base-sts",
"region:us"
] |
text-classification
| 2025-07-23T02:11:04Z |
---
library_name: transformers.js
base_model:
- dleemiller/ModernCE-base-sts
---
# ModernCE-base-sts (ONNX)
This is an ONNX version of [dleemiller/ModernCE-base-sts](https://huggingface.co/dleemiller/ModernCE-base-sts). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
## Usage (Transformers.js)
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
**Example:** Semantic Textual Similarity Classification.
```js
import { pipeline } from '@huggingface/transformers';
const classifier = await pipeline('text-classification', 'onnx-community/ModernCE-base-sts-ONNX');
const output = await classifier('I love transformers!');
```
|
Wave812/blockassist-bc-howling_pesty_trout_1756726068
|
Wave812
| 2025-09-01T11:29:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"howling pesty trout",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:28:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- howling pesty trout
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756725948
|
omerbkts
| 2025-09-01T11:26:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:26:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
giovannidemuri/llama3b-llama8b-er-v526-seed2-seed2-hx-alpaca-fpt
|
giovannidemuri
| 2025-09-01T11:23:35Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T11:23:35Z |
---
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]
|
BootesVoid/cmezt4ctw076tsr53nv5ql115_cmf0z0i0207yasr53u55w6wrx
|
BootesVoid
| 2025-09-01T11:23:18Z | 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
| 2025-09-01T11:23:13Z |
---
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: SEXY
---
# Cmezt4Ctw076Tsr53Nv5Ql115_Cmf0Z0I0207Yasr53U55W6Wrx
<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 `SEXY` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SEXY",
"lora_weights": "https://huggingface.co/BootesVoid/cmezt4ctw076tsr53nv5ql115_cmf0z0i0207yasr53u55w6wrx/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/cmezt4ctw076tsr53nv5ql115_cmf0z0i0207yasr53u55w6wrx', weight_name='lora.safetensors')
image = pipeline('SEXY').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: 2500
- Learning rate: 9e-05
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmezt4ctw076tsr53nv5ql115_cmf0z0i0207yasr53u55w6wrx/discussions) to add images that show off what you’ve made with this LoRA.
|
tralalerrotralala228/lilastone
|
tralalerrotralala228
| 2025-09-01T11:15:39Z | 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
| 2025-09-01T10:42:31Z |
---
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: lilastone
---
# Lilastone
<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 `lilastone` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "lilastone",
"lora_weights": "https://huggingface.co/tralalerrotralala228/lilastone/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('tralalerrotralala228/lilastone', weight_name='lora.safetensors')
image = pipeline('lilastone').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: 2500
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/tralalerrotralala228/lilastone/discussions) to add images that show off what you’ve made with this LoRA.
|
cfgbydefault/SmolLM2-FT-MyDataset
|
cfgbydefault
| 2025-09-01T11:15:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"smol-course",
"module_1",
"trl",
"sft",
"conversational",
"base_model:HuggingFaceTB/SmolLM2-135M",
"base_model:finetune:HuggingFaceTB/SmolLM2-135M",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T11:14:43Z |
---
base_model: HuggingFaceTB/SmolLM2-135M
library_name: transformers
model_name: SmolLM2-FT-MyDataset
tags:
- generated_from_trainer
- smol-course
- module_1
- trl
- sft
licence: license
---
# Model Card for SmolLM2-FT-MyDataset
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M).
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="cfgbydefault/SmolLM2-FT-MyDataset", 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: 4.46.3
- Pytorch: 2.5.0+cu124
- 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}}
}
```
|
loveisgone/ok_myson
|
loveisgone
| 2025-09-01T11:10:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3",
"meta",
"facebook",
"unsloth",
"conversational",
"en",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-09-01T11:08:41Z |
---
base_model: meta-llama/Llama-3.1-1B-Instruct
language:
- en
library_name: transformers
license: llama3.1
tags:
- llama-3
- llama
- meta
- facebook
- unsloth
- transformers
---
|
AnerYubo/blockassist-bc-elusive_mammalian_termite_1756725032
|
AnerYubo
| 2025-09-01T11:10:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"elusive mammalian termite",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:10:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- elusive mammalian termite
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
walbosui/blockassist-bc-miniature_playful_walrus_1756724876
|
walbosui
| 2025-09-01T11:08:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"miniature playful walrus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:08:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- miniature playful walrus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Wave812/blockassist-bc-howling_pesty_trout_1756724692
|
Wave812
| 2025-09-01T11:06:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"howling pesty trout",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:05:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- howling pesty trout
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
giovannidemuri/llama8b-er-v522-seed2-hx
|
giovannidemuri
| 2025-09-01T11:03:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T09:25:24Z |
---
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]
|
bah63843/blockassist-bc-plump_fast_antelope_1756724553
|
bah63843
| 2025-09-01T11:03:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:03:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756724120
|
liukevin666
| 2025-09-01T10:57:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:56:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ddfj34/act_so101_model_20250901_1280
|
ddfj34
| 2025-09-01T10:50:31Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:ddfj34/record-test-20250825_resize_1280",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-01T10:50:18Z |
---
datasets: ddfj34/record-test-20250825_resize_1280
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- robotics
- lerobot
- act
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
mradermacher/L3.3-Joubutsu2000-GGUF
|
mradermacher
| 2025-09-01T10:45:23Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:KaraKaraWarehouse/L3.3-Joubutsu2000",
"base_model:quantized:KaraKaraWarehouse/L3.3-Joubutsu2000",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-01T07:45:21Z |
---
base_model: KaraKaraWarehouse/L3.3-Joubutsu2000
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/KaraKaraWarehouse/L3.3-Joubutsu2000
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#L3.3-Joubutsu2000-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3.3-Joubutsu2000-GGUF/resolve/main/L3.3-Joubutsu2000.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/L3.3-Joubutsu2000-GGUF/resolve/main/L3.3-Joubutsu2000.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/L3.3-Joubutsu2000-GGUF/resolve/main/L3.3-Joubutsu2000.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3.3-Joubutsu2000-GGUF/resolve/main/L3.3-Joubutsu2000.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/L3.3-Joubutsu2000-GGUF/resolve/main/L3.3-Joubutsu2000.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3.3-Joubutsu2000-GGUF/resolve/main/L3.3-Joubutsu2000.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3.3-Joubutsu2000-GGUF/resolve/main/L3.3-Joubutsu2000.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3.3-Joubutsu2000-GGUF/resolve/main/L3.3-Joubutsu2000.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/L3.3-Joubutsu2000-GGUF/resolve/main/L3.3-Joubutsu2000.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3.3-Joubutsu2000-GGUF/resolve/main/L3.3-Joubutsu2000.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/L3.3-Joubutsu2000-GGUF/resolve/main/L3.3-Joubutsu2000.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3.3-Joubutsu2000-GGUF/resolve/main/L3.3-Joubutsu2000.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
faisu-eth/blockassist-bc-thick_twitchy_jackal_1756723284
|
faisu-eth
| 2025-09-01T10:42:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thick twitchy jackal",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:41:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thick twitchy jackal
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
walbosui/blockassist-bc-miniature_playful_walrus_1756722607
|
walbosui
| 2025-09-01T10:30:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"miniature playful walrus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:30:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- miniature playful walrus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
noman007/FastVLM05B
|
noman007
| 2025-09-01T10:27:30Z | 0 | 0 |
ml-fastvlm
|
[
"ml-fastvlm",
"safetensors",
"llava_qwen2",
"text-generation",
"transformers",
"conversational",
"custom_code",
"arxiv:2412.13303",
"license:apple-amlr",
"region:us"
] |
text-generation
| 2025-09-01T10:25:48Z |
---
license: apple-amlr
license_name: apple-ascl
license_link: https://github.com/apple/ml-fastvlm/blob/main/LICENSE_MODEL
library_name: ml-fastvlm
tags:
- transformers
---
# FastVLM: Efficient Vision Encoding for Vision Language Models
FastVLM was introduced in
**[FastVLM: Efficient Vision Encoding for Vision Language Models](https://www.arxiv.org/abs/2412.13303). (CVPR 2025)**
[//]: # ()
<p align="center">
<img src="https://huggingface.co/datasets/librarian-bots/model_cards_with_metadata/viewer/default/acc_vs_latency_qwen-2.png" alt="Accuracy vs latency figure." width="400"/>
</p>
### Highlights
* We introduce FastViTHD, a novel hybrid vision encoder designed to output fewer tokens and significantly reduce encoding time for high-resolution images.
* Our smallest variant outperforms LLaVA-OneVision-0.5B with 85x faster Time-to-First-Token (TTFT) and 3.4x smaller vision encoder.
* Our larger variants using Qwen2-7B LLM outperform recent works like Cambrian-1-8B while using a single image encoder with a 7.9x faster TTFT.
### Evaluations
| Benchmark | FastVLM-0.5B | FastVLM-1.5B | FastVLM-7B |
|:--------------|:------------:|:------------:|:----------:|
| Ai2D | 68.0 | 77.4 | 83.6 |
| ScienceQA | 85.2 | 94.4 | 96.7 |
| MMMU | 33.9 | 37.8 | 45.4 |
| VQAv2 | 76.3 | 79.1 | 80.8 |
| ChartQA | 76.0 | 80.1 | 85.0 |
| TextVQA | 64.5 | 70.4 | 74.9 |
| InfoVQA | 46.4 | 59.7 | 75.8 |
| DocVQA | 82.5 | 88.3 | 93.2 |
| OCRBench | 63.9 | 70.2 | 73.1 |
| RealWorldQA | 56.1 | 61.2 | 67.2 |
| SeedBench-Img | 71.0 | 74.2 | 75.4 |
### Usage Example
To run inference of PyTorch checkpoint, follow the instruction in the official repo:
Download the model
```
huggingface-cli download apple/FastVLM-0.5B
```
Run inference using `predict.py` from the official repo.
```bash
python predict.py --model-path /path/to/checkpoint-dir \
--image-file /path/to/image.png \
--prompt "Describe the image."
```
### Run inference with Transformers (Remote Code)
To run inference with transformers we can leverage `trust_remote_code` along with the following snippet:
```python
import torch
from PIL import Image
from transformers import AutoTokenizer, AutoModelForCausalLM
MID = "apple/FastVLM-0.5B"
IMAGE_TOKEN_INDEX = -200 # what the model code looks for
# Load
tok = AutoTokenizer.from_pretrained(MID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
MID,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto",
trust_remote_code=True,
)
# Build chat -> render to string (not tokens) so we can place <image> exactly
messages = [
{"role": "user", "content": "<image>\nDescribe this image in detail."}
]
rendered = tok.apply_chat_template(
messages, add_generation_prompt=True, tokenize=False
)
pre, post = rendered.split("<image>", 1)
# Tokenize the text *around* the image token (no extra specials!)
pre_ids = tok(pre, return_tensors="pt", add_special_tokens=False).input_ids
post_ids = tok(post, return_tensors="pt", add_special_tokens=False).input_ids
# Splice in the IMAGE token id (-200) at the placeholder position
img_tok = torch.tensor([[IMAGE_TOKEN_INDEX]], dtype=pre_ids.dtype)
input_ids = torch.cat([pre_ids, img_tok, post_ids], dim=1).to(model.device)
attention_mask = torch.ones_like(input_ids, device=model.device)
# Preprocess image via the model's own processor
img = Image.open("test-2.jpg").convert("RGB")
px = model.get_vision_tower().image_processor(images=img, return_tensors="pt")["pixel_values"]
px = px.to(model.device, dtype=model.dtype)
# Generate
with torch.no_grad():
out = model.generate(
inputs=input_ids,
attention_mask=attention_mask,
images=px,
max_new_tokens=128,
)
print(tok.decode(out[0], skip_special_tokens=True))
```
## Citation
If you found this model useful, please cite the following paper:
```
@InProceedings{fastvlm2025,
author = {Pavan Kumar Anasosalu Vasu, Fartash Faghri, Chun-Liang Li, Cem Koc, Nate True, Albert Antony, Gokul Santhanam, James Gabriel, Peter Grasch, Oncel Tuzel, Hadi Pouransari},
title = {FastVLM: Efficient Vision Encoding for Vision Language Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2025},
}
```
|
Rahulwale12/base_slm
|
Rahulwale12
| 2025-09-01T10:27:30Z | 0 | 0 | null |
[
"pytorch",
"transformer_lite",
"region:us"
] | null | 2025-09-01T10:26:34Z |
# Base Small Language Model (SLM)
## 🚀 CPU-First Base Language Model
This is the **base model** before fine-tuning - a blazing-fast, CPU-optimized Small Language Model foundation:
### ⚡ Performance Highlights
- **164 tokens/sec** on CPU (fast base performance)
- **45.2MB model size** (base model)
- **3.7M parameters** (tiny but powerful)
- **General language understanding** (pre-fine-tuning)
### 🎯 Training Speed
- **28 minutes** for base training (4 epochs)
- **Fast convergence** with efficient architecture
- **Ready for fine-tuning** on any domain
### 🔧 Technical Specs
- **Architecture:** Transformer-lite with RMSNorm, SwiGLU, Rotary embeddings
- **Optimization:** CPU-first with memory mapping and efficient batching
- **Framework:** PyTorch (CPU optimized)
- **Training:** Trained on conversational data
### 📱 Deployment Ready
- **CPU optimized:** No GPU required
- **Fast startup:** Instant model loading
- **Low memory:** Efficient memory usage
- **Fine-tuning ready:** Perfect base for domain adaptation
## Usage
### Load and Use Base Model
```python
import torch
import sys
sys.path.append('src')
from model import create_model_from_config
from tokenizer import BPETokenizer
# Load model
checkpoint = torch.load("checkpoints/model_latest.pt", map_location='cpu')
config = checkpoint['config']
model = create_model_from_config(config)
model.load_state_dict(checkpoint['model_state_dict'])
# Load tokenizer
tokenizer = BPETokenizer()
tokenizer.load("data/tokenizer.json")
# Generate
prompt = "Hello, how are you?"
input_ids = tokenizer.encode(prompt, add_special_tokens=True)
input_ids = torch.tensor([input_ids], dtype=torch.long)
model.eval()
with torch.no_grad():
for _ in range(20):
logits = model(input_ids)[0, -1, :]
next_token = torch.argmax(logits, dim=-1).unsqueeze(0)
input_ids = torch.cat([input_ids, next_token.unsqueeze(0)], dim=1)
response = tokenizer.decode(input_ids[0].tolist(), skip_special_tokens=True)
print(response)
```
### Fine-tune on Your Data
```python
# Use this base model for fine-tuning
python finetune_qa.py --base_model checkpoints/model_latest.pt --conversations your_data.json
```
## Model Details
- **Base Model:** Trained on conversational data
- **Architecture:** Transformer-lite with modern optimizations
- **Size:** 45.2MB (base model)
- **License:** MIT
## Performance
| Metric | Value |
|--------|-------|
| Speed | 164 tokens/sec |
| Size | 45.2MB |
| Parameters | 3.7M |
| Training Time | 28 minutes |
This base model provides an excellent foundation for fine-tuning on specific domains or tasks.
|
arif696/blockassist-bc-regal_spotted_pelican_1756722329
|
arif696
| 2025-09-01T10:27:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:27:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
H1yori233/llm_from_scratch
|
H1yori233
| 2025-09-01T10:26:14Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T06:07:17Z |
---
tags:
- pytorch
- transformers
source_url: https://github.com/H1yori233/llm_from_scratch
---
# LLM From Scratch
This project implements a full transformer language model without relying on high-level frameworks like HuggingFace or PyTorch's built-in attention. Every component is built from scratch.
This repository simply combines my solutions for [assignment 1](https://github.com/stanford-cs336/assignment1-basics) and [assignment 2](https://github.com/stanford-cs336/assignment2-systems) of [Stanford CS336](https://stanford-cs336.github.io/spring2025/) course. I’ve merged them into a single repository for convenience, without adding any content beyond the original assignments.
## Features
* **Hand-Coded Transformer Architecture**: Features RoPE positional encodings, RMSNorm, and SwiGLU FFNs.
* **Flash Attention with Triton**: A custom implementation with hand-written Triton kernels to optimize GPU memory usage from O(N²) down to O(N).
* **Multiple Optimizers**: Includes AdamW and SGD, with an extensible design to easily add more.
* **BPE Tokenizer from Scratch**: With proper handling for special tokens.
* **A Complete Training System**: Manages experiments with JSON configs and automatically logs results to a Markdown file.
## Quick Start
### 1. Download Data
This project uses data from TinyStories and a subsample of OpenWebText.
```sh
mkdir -p data
cd data
# Download the datasets
wget https://huggingface.co/datasets/roneneldan/TinyStories/resolve/main/TinyStoriesV2-GPT4-train.txt
wget https://huggingface.co/datasets/roneneldan/TinyStories/resolve/main/TinyStoriesV2-GPT4-valid.txt
wget https://huggingface.co/datasets/stanford-cs336/owt-sample/resolve/main/owt_train.txt.gz
gunzip owt_train.txt.gz
wget https://huggingface.co/datasets/stanford-cs336/owt-sample/resolve/main/owt_valid.txt.gz
gunzip owt_valid.txt.gz
cd ..
```
### 2. Install Dependencies
```bash
uv sync
```
### 3. Start Training
Several example configurations are provided to get you started.
```bash
# Default training (Flash Attention + AdamW)
python train.py --config config.json
# Try other configurations
python train.py --config config_large.json
python train.py --config config_std_sgd.json
# View experiment results
cat data/output.md
```
## Experiment Tracking
All experiments are automatically logged with comprehensive metrics:
| timestamp | experiment_name | optimizer | attention_type | best_val_loss | params_M | tokens_M |
|-----------|----------------|-----------|----------------|---------------|----------|----------|
| 12-15 10:30 | baseline_4L_8H | adamw | flash | 2.123 | 25.6 | 163.8 |
| 12-15 14:20 | large_6L_16H | adamw | flash | 1.987 | 67.2 | 327.7 |
| 12-15 16:45 | std_attention | sgd | standard | 2.345 | 25.6 | 163.8 |
## Extending the System
Adding new optimizers is straightforward:
```python
class NewOptimizer(torch.optim.Optimizer):
def __init__(self, params, lr=3e-4):
# Implementation here
def step(self, closure=None):
# Update logic here
# Register in factory
optimizers = {
"adamw": AdamW,
"sgd": SGD,
"new_optimizer": NewOptimizer, # Add here
}
```
---
[](https://deepwiki.com/H1yori233/llm_from_scratch)
|
cookienter/lifechart-biobert-classifier-hptuning
|
cookienter
| 2025-09-01T10:25:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:dmis-lab/biobert-base-cased-v1.2",
"base_model:finetune:dmis-lab/biobert-base-cased-v1.2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-01T08:57:02Z |
---
library_name: transformers
base_model: dmis-lab/biobert-base-cased-v1.2
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: lifechart-biobert-classifier-hptuning
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. -->
# lifechart-biobert-classifier-hptuning
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0061
- Macro F1: 0.7785
- Precision: 0.7800
- Recall: 0.7851
## 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: 4.782388936370694e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.09571701748584874
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 1.6698 | 1.0 | 1641 | 0.9449 | 0.7355 | 0.7227 | 0.7692 |
| 0.7237 | 2.0 | 3282 | 0.8916 | 0.7793 | 0.7685 | 0.8001 |
| 0.3676 | 3.0 | 4923 | 1.0061 | 0.7785 | 0.7800 | 0.7851 |
### Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756722185
|
liukevin666
| 2025-09-01T10:24:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:23:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1756722039
|
arif696
| 2025-09-01T10:22:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:22:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756721407
|
bah63843
| 2025-09-01T10:10:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:10:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sekirr/blockassist-bc-masked_tenacious_whale_1756721227
|
sekirr
| 2025-09-01T10:07:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:07:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
faisu-eth/blockassist-bc-thick_twitchy_jackal_1756721044
|
faisu-eth
| 2025-09-01T10:04:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thick twitchy jackal",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:04:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thick twitchy jackal
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Llama-3.1-8B-sft-spin-10k-KTO-GGUF
|
mradermacher
| 2025-09-01T10:03:15Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"trl",
"kto",
"en",
"base_model:AmberYifan/Llama-3.1-8B-sft-spin-10k-KTO",
"base_model:quantized:AmberYifan/Llama-3.1-8B-sft-spin-10k-KTO",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-01T05:56:43Z |
---
base_model: AmberYifan/Llama-3.1-8B-sft-spin-10k-KTO
language:
- en
library_name: transformers
model_name: Llama-3.1-8B-sft-spin-10k-KTO
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- generated_from_trainer
- trl
- kto
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/AmberYifan/Llama-3.1-8B-sft-spin-10k-KTO
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Llama-3.1-8B-sft-spin-10k-KTO-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-sft-spin-10k-KTO-GGUF/resolve/main/Llama-3.1-8B-sft-spin-10k-KTO.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-sft-spin-10k-KTO-GGUF/resolve/main/Llama-3.1-8B-sft-spin-10k-KTO.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-sft-spin-10k-KTO-GGUF/resolve/main/Llama-3.1-8B-sft-spin-10k-KTO.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-sft-spin-10k-KTO-GGUF/resolve/main/Llama-3.1-8B-sft-spin-10k-KTO.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-sft-spin-10k-KTO-GGUF/resolve/main/Llama-3.1-8B-sft-spin-10k-KTO.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-sft-spin-10k-KTO-GGUF/resolve/main/Llama-3.1-8B-sft-spin-10k-KTO.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-sft-spin-10k-KTO-GGUF/resolve/main/Llama-3.1-8B-sft-spin-10k-KTO.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-sft-spin-10k-KTO-GGUF/resolve/main/Llama-3.1-8B-sft-spin-10k-KTO.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-sft-spin-10k-KTO-GGUF/resolve/main/Llama-3.1-8B-sft-spin-10k-KTO.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-sft-spin-10k-KTO-GGUF/resolve/main/Llama-3.1-8B-sft-spin-10k-KTO.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-sft-spin-10k-KTO-GGUF/resolve/main/Llama-3.1-8B-sft-spin-10k-KTO.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-sft-spin-10k-KTO-GGUF/resolve/main/Llama-3.1-8B-sft-spin-10k-KTO.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756720920
|
Ferdi3425
| 2025-09-01T10:02:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:02:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756720888
|
liukevin666
| 2025-09-01T10:02:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:02:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pietro0hz/blockassist-bc-ferocious_toothy_tortoise_1756720483
|
pietro0hz
| 2025-09-01T09:56:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"ferocious toothy tortoise",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T09:56:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- ferocious toothy tortoise
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
the-usan/urdu-crime-adapter-zayadati-v1
|
the-usan
| 2025-09-01T09:48:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T09:48:24Z |
---
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]
|
RikiyaT/mxbai-ettin-17m-allnli-angle-ft
|
RikiyaT
| 2025-09-01T09:43:40Z | 12 | 0 | null |
[
"safetensors",
"modernbert",
"license:mit",
"region:us"
] | null | 2025-08-31T10:17:01Z |
---
license: mit
---
# RikiyaT/mxbai-ettin-17m-allnli-angle-ft
Ettin + AnglE fine-tuned embedding model.
- **Base Model**: `RikiyaT/mxbai-ettin-17m-medqa-angle-ft`
- **Pooling Strategy**: `mean` (avg)
- **Training Method**: AnglE loss (ibn/cln + angle=0.02) on a B-format dataset (text, positive, negative).
- **Data Prompts**: `search_query:` / `search_document:` were used during training data creation.
## Usage
### With SentenceTransformers (recommended)
A ready-to-use SentenceTransformers variant is available at **[RikiyaT/mxbai-ettin-17m-allnli-angle-ft-st](https://huggingface.co/RikiyaT/mxbai-ettin-17m-allnli-angle-ft-st)**.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('RikiyaT/mxbai-ettin-17m-allnli-angle-ft-st')
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings.shape)
```
### With Transformers (this repository)
```python
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("RikiyaT/mxbai-ettin-17m-allnli-angle-ft", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("RikiyaT/mxbai-ettin-17m-allnli-angle-ft", trust_remote_code=True)
```
|
RikiyaT/mxbai-ettin-17m-medqa-angle-ft-st
|
RikiyaT
| 2025-09-01T09:43:21Z | 5 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"modernbert",
"sentence-similarity",
"feature-extraction",
"dense",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-08-31T20:29:48Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 256-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 7999 tokens
- **Output Dimensionality:** 256 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 7999, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
(1): Pooling({'word_embedding_dimension': 256, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("RikiyaT/mxbai-ettin-17m-medqa-angle-ft-st")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 256]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6236, 0.3560],
# [0.6236, 1.0000, 0.4001],
# [0.3560, 0.4001, 1.0000]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Framework Versions
- Python: 3.10.18
- Sentence Transformers: 5.1.0
- Transformers: 4.55.4
- PyTorch: 2.7.1+cu126
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citation
### BibTeX
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
NahedDom/blockassist-bc-flapping_stocky_leopard_1756717619
|
NahedDom
| 2025-09-01T09:43:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping stocky leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T09:43:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping stocky leopard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
RikiyaT/mxbai-ettin-17m-nq-angle-ft-st
|
RikiyaT
| 2025-09-01T09:41:37Z | 17 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"modernbert",
"sentence-similarity",
"feature-extraction",
"dense",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-08-31T11:38:27Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 256-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 7999 tokens
- **Output Dimensionality:** 256 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 7999, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
(1): Pooling({'word_embedding_dimension': 256, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("RikiyaT/mxbai-ettin-17m-nq-angle-ft-st")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 256]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6237, 0.3312],
# [0.6237, 1.0000, 0.3608],
# [0.3312, 0.3608, 1.0000]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Framework Versions
- Python: 3.10.18
- Sentence Transformers: 5.1.0
- Transformers: 4.55.4
- PyTorch: 2.7.1+cu126
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citation
### BibTeX
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
godnpeter/pick_pikachu
|
godnpeter
| 2025-09-01T09:41:14Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:godnpeter/pick_pikachu",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-01T09:41:05Z |
---
base_model: lerobot/smolvla_base
datasets: godnpeter/pick_pikachu
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- smolvla
- robotics
- lerobot
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
Sonic-man/blockassist-bc-poisonous_graceful_cow_1756717040
|
Sonic-man
| 2025-09-01T09:40:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"poisonous graceful cow",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T09:40:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- poisonous graceful cow
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
slimpact2025/GilCal-ReplicateDemo
|
slimpact2025
| 2025-09-01T09:37:13Z | 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
| 2025-09-01T06:24:49Z |
---
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: Gil
---
# Gilcal Replicatedemo
<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 `Gil` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Gil",
"lora_weights": "https://huggingface.co/slimpact2025/GilCal-ReplicateDemo/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('slimpact2025/GilCal-ReplicateDemo', weight_name='lora.safetensors')
image = pipeline('Gil').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: 2002
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/slimpact2025/GilCal-ReplicateDemo/discussions) to add images that show off what you’ve made with this LoRA.
|
taewan2002/smolvla_libero_10
|
taewan2002
| 2025-09-01T09:37:06Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:aopolin-lv/libero_object_no_noops_lerobot_v21",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-01T09:36:58Z |
---
base_model: lerobot/smolvla_base
datasets: aopolin-lv/libero_object_no_noops_lerobot_v21
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- smolvla
- robotics
- lerobot
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
AnonymousCS/populism_classifier_411
|
AnonymousCS
| 2025-09-01T09:36:27Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"rembert",
"text-classification",
"generated_from_trainer",
"base_model:google/rembert",
"base_model:finetune:google/rembert",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-31T23:53:47Z |
---
library_name: transformers
license: apache-2.0
base_model: google/rembert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_411
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. -->
# populism_classifier_411
This model is a fine-tuned version of [google/rembert](https://huggingface.co/google/rembert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6686
- Accuracy: 0.9118
- 1-f1: 0.0
- 1-recall: 0.0
- 1-precision: 0.0
- Balanced Acc: 0.5
## 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: 32
- seed: 42
- optimizer: Use 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:--------:|:-----------:|:------------:|
| 0.6746 | 1.0 | 91 | 0.6774 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.6333 | 2.0 | 182 | 0.6769 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.7695 | 3.0 | 273 | 0.6672 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.6843 | 4.0 | 364 | 0.6757 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.7178 | 5.0 | 455 | 0.6668 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.6505 | 6.0 | 546 | 0.6666 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.5584 | 7.0 | 637 | 0.6691 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.8056 | 8.0 | 728 | 0.6686 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
acidjp/blockassist-bc-pesty_extinct_prawn_1756716512
|
acidjp
| 2025-09-01T09:31:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pesty extinct prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T09:31:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pesty extinct prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kavpro/blockassist-bc-tall_lively_caribou_1756718849
|
kavpro
| 2025-09-01T09:28:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall lively caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T09:28:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall lively caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
llm-jp/optimal-sparsity-code-d2048-E128-k16-52.2B-A7.1B
|
llm-jp
| 2025-09-01T09:21:26Z | 8 | 0 | null |
[
"safetensors",
"mixtral",
"arxiv:2508.18672",
"region:us"
] | null | 2025-08-21T15:44:05Z |
## How to cite
If you find our work helpful, please feel free to cite the paper.
```
@article{nakamura2025optimalsparsitymixtureofexpertslanguage,
title={Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks},
author={Taishi Nakamura and Satoki Ishikawa and Masaki Kawamura and Takumi Okamoto and Daisuke Nohara and Jun Suzuki and Rio Yokota},
year={2025},
eprint={2508.18672},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.18672},
}
```
|
llm-jp/optimal-sparsity-code-d2048-E64-k16-26.4B-A7.1B
|
llm-jp
| 2025-09-01T09:21:24Z | 8 | 0 | null |
[
"safetensors",
"mixtral",
"arxiv:2508.18672",
"region:us"
] | null | 2025-08-21T15:42:56Z |
## How to cite
If you find our work helpful, please feel free to cite the paper.
```
@article{nakamura2025optimalsparsitymixtureofexpertslanguage,
title={Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks},
author={Taishi Nakamura and Satoki Ishikawa and Masaki Kawamura and Takumi Okamoto and Daisuke Nohara and Jun Suzuki and Rio Yokota},
year={2025},
eprint={2508.18672},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.18672},
}
```
|
llm-jp/optimal-sparsity-code-d1024-E16-k16-1.9B-A1.9B
|
llm-jp
| 2025-09-01T09:21:11Z | 8 | 0 | null |
[
"safetensors",
"mixtral",
"arxiv:2508.18672",
"region:us"
] | null | 2025-08-21T15:28:00Z |
## How to cite
If you find our work helpful, please feel free to cite the paper.
```
@article{nakamura2025optimalsparsitymixtureofexpertslanguage,
title={Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks},
author={Taishi Nakamura and Satoki Ishikawa and Masaki Kawamura and Takumi Okamoto and Daisuke Nohara and Jun Suzuki and Rio Yokota},
year={2025},
eprint={2508.18672},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.18672},
}
```
|
llm-jp/optimal-sparsity-code-d512-E32-k16-920M-A520M
|
llm-jp
| 2025-09-01T09:21:05Z | 8 | 0 | null |
[
"safetensors",
"mixtral",
"arxiv:2508.18672",
"region:us"
] | null | 2025-08-21T15:21:51Z |
## How to cite
If you find our work helpful, please feel free to cite the paper.
```
@article{nakamura2025optimalsparsitymixtureofexpertslanguage,
title={Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks},
author={Taishi Nakamura and Satoki Ishikawa and Masaki Kawamura and Takumi Okamoto and Daisuke Nohara and Jun Suzuki and Rio Yokota},
year={2025},
eprint={2508.18672},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.18672},
}
```
|
llm-jp/optimal-sparsity-code-d2048-E16-k8-7.1B-A3.9B
|
llm-jp
| 2025-09-01T09:20:57Z | 7 | 0 | null |
[
"safetensors",
"mixtral",
"arxiv:2508.18672",
"region:us"
] | null | 2025-08-21T15:38:06Z |
## How to cite
If you find our work helpful, please feel free to cite the paper.
```
@article{nakamura2025optimalsparsitymixtureofexpertslanguage,
title={Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks},
author={Taishi Nakamura and Satoki Ishikawa and Masaki Kawamura and Takumi Okamoto and Daisuke Nohara and Jun Suzuki and Rio Yokota},
year={2025},
eprint={2508.18672},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.18672},
}
```
|
llm-jp/optimal-sparsity-code-d512-E64-k8-1.7B-A320M
|
llm-jp
| 2025-09-01T09:20:39Z | 8 | 0 | null |
[
"safetensors",
"mixtral",
"arxiv:2508.18672",
"region:us"
] | null | 2025-08-21T15:21:19Z |
## How to cite
If you find our work helpful, please feel free to cite the paper.
```
@article{nakamura2025optimalsparsitymixtureofexpertslanguage,
title={Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks},
author={Taishi Nakamura and Satoki Ishikawa and Masaki Kawamura and Takumi Okamoto and Daisuke Nohara and Jun Suzuki and Rio Yokota},
year={2025},
eprint={2508.18672},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.18672},
}
```
|
llm-jp/optimal-sparsity-code-d2048-E32-k4-13.6B-A2.3B
|
llm-jp
| 2025-09-01T09:20:28Z | 8 | 0 | null |
[
"safetensors",
"mixtral",
"arxiv:2508.18672",
"region:us"
] | null | 2025-08-21T15:34:26Z |
## How to cite
If you find our work helpful, please feel free to cite the paper.
```
@article{nakamura2025optimalsparsitymixtureofexpertslanguage,
title={Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks},
author={Taishi Nakamura and Satoki Ishikawa and Masaki Kawamura and Takumi Okamoto and Daisuke Nohara and Jun Suzuki and Rio Yokota},
year={2025},
eprint={2508.18672},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.18672},
}
```
|
llm-jp/optimal-sparsity-code-d2048-E16-k2-7.1B-A1.5B
|
llm-jp
| 2025-09-01T09:19:58Z | 8 | 0 | null |
[
"safetensors",
"mixtral",
"arxiv:2508.18672",
"region:us"
] | null | 2025-08-21T15:30:13Z |
## How to cite
If you find our work helpful, please feel free to cite the paper.
```
@article{nakamura2025optimalsparsitymixtureofexpertslanguage,
title={Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks},
author={Taishi Nakamura and Satoki Ishikawa and Masaki Kawamura and Takumi Okamoto and Daisuke Nohara and Jun Suzuki and Rio Yokota},
year={2025},
eprint={2508.18672},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.18672},
}
```
|
llm-jp/optimal-sparsity-code-d1024-E32-k2-3.5B-A470M
|
llm-jp
| 2025-09-01T09:19:49Z | 8 | 0 | null |
[
"safetensors",
"mixtral",
"arxiv:2508.18672",
"region:us"
] | null | 2025-08-21T15:22:33Z |
## How to cite
If you find our work helpful, please feel free to cite the paper.
```
@article{nakamura2025optimalsparsitymixtureofexpertslanguage,
title={Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks},
author={Taishi Nakamura and Satoki Ishikawa and Masaki Kawamura and Takumi Okamoto and Daisuke Nohara and Jun Suzuki and Rio Yokota},
year={2025},
eprint={2508.18672},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.18672},
}
```
|
llm-jp/optimal-sparsity-code-d1024-E16-k2-1.9B-A470M
|
llm-jp
| 2025-09-01T09:19:48Z | 8 | 0 | null |
[
"safetensors",
"mixtral",
"arxiv:2508.18672",
"region:us"
] | null | 2025-08-21T15:22:27Z |
## How to cite
If you find our work helpful, please feel free to cite the paper.
```
@article{nakamura2025optimalsparsitymixtureofexpertslanguage,
title={Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks},
author={Taishi Nakamura and Satoki Ishikawa and Masaki Kawamura and Takumi Okamoto and Daisuke Nohara and Jun Suzuki and Rio Yokota},
year={2025},
eprint={2508.18672},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.18672},
}
```
|
llm-jp/optimal-sparsity-code-d512-E64-k2-1.7B-A170M
|
llm-jp
| 2025-09-01T09:19:42Z | 7 | 0 | null |
[
"safetensors",
"mixtral",
"arxiv:2508.18672",
"region:us"
] | null | 2025-08-21T15:04:28Z |
## How to cite
If you find our work helpful, please feel free to cite the paper.
```
@article{nakamura2025optimalsparsitymixtureofexpertslanguage,
title={Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks},
author={Taishi Nakamura and Satoki Ishikawa and Masaki Kawamura and Takumi Okamoto and Daisuke Nohara and Jun Suzuki and Rio Yokota},
year={2025},
eprint={2508.18672},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.18672},
}
```
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756718301
|
liukevin666
| 2025-09-01T09:19:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T09:19:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aisingapore/Llama-SEA-LION-v3.5-70B-R-FP8-Dynamic
|
aisingapore
| 2025-09-01T09:18:50Z | 91 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"zh",
"vi",
"id",
"th",
"fil",
"ta",
"ms",
"km",
"lo",
"my",
"jv",
"su",
"arxiv:2504.05747",
"base_model:aisingapore/Llama-SEA-LION-v3-70B-IT",
"base_model:quantized:aisingapore/Llama-SEA-LION-v3-70B-IT",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-08-21T04:54:52Z |
---
library_name: transformers
pipeline_tag: text-generation
base_model:
- aisingapore/Llama-SEA-LION-v3-70B-IT
language:
- en
- zh
- vi
- id
- th
- fil
- ta
- ms
- km
- lo
- my
- jv
- su
license: llama3.1
---
<div>
<img src="https://huggingface.co/datasets/librarian-bots/model_cards_with_metadata/viewer/default/llama_sea_lion_3.5_70b_r_banner.png"/>
</div>
# Llama-SEA-LION-v3.5-70B-R-FP8-Dynamic
Last updated: 2025-09-01
[**SEA-LION**](https://arxiv.org/abs/2504.05747) is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned
for the Southeast Asia (SEA) region.
### Model Description
<!-- Provide a longer summary of what this model is. -->
SEA-LION stands for *Southeast Asian Languages In One Network*.
Quantization was performed on Llama-SEA-LION-v3.5-70B-R to produce optimized variants that reduce memory requirements
while maintaining model quality. These quantized models support inference on a range of consumer-grade GPUs
and are compatible with various inference engines.
For tokenization, the model employs the default tokenizer used in Llama 3.1-70B-Instruct.
- **Developed by:** Products Pillar, AI Singapore
- **Funded by:** Singapore NRF
- **Model type:** Decoder
- **Context length:** 128k tokens
- **Language(s):** Burmese, Chinese, English, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Sundanese, Tamil, Thai, Vietnamese
- **License:** [Llama 3.1 Community License](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct/blob/main/LICENSE)
- **Quantized from model:** Llama-SEA-LION-v3.5-70B-R
This repo contains FP8-Dynamic format model file for aisingapore/Llama-SEA-LION-v3.5-70B-R
Model Weights included in this repository:
- [Llama-SEA-LION-v3.5-70B-R-FP8-Dynamic](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-70B-R-FP8-Dynamic)
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Test Results
For details on Llama-SEA-LION-v3.5-70B-R performance, please refer to the SEA-HELM leaderboard, [Leaderboard results on SEA-HELM](https://leaderboard.sea-lion.ai/).
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
The model has not been aligned for safety. Developers and users should perform their own safety
fine-tuning and related security measures. In no event shall the authors be held liable for any claims, damages, or other liabilities arising from the use of the released weights and codes.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
*The model was not tested for robustness against adversarial prompting.* It is important for users to be aware that our model exhibits certain limitations that warrant consideration.
Like many LLMs, the model can hallucinate and occasionally generates irrelevant content,
introducing fictional elements that are not grounded in the provided context.
Users should also exercise caution in interpreting and validating the model's responses
due to the potential inconsistencies.
## More Information
This is the repository for the commercial instruction-tuned model.
The model has not been aligned for safety. Developers and users should perform their own safety
fine-tuning and related security measures. In no event shall the authors be held liable
for any claims, damages, or other liabilities arising from the use of the released weights and codes.
AI Singapore is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore.
Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and
do not reflect the views of the National Research Foundation or the National University of Singapore.
[Link to SEA-LION's GitHub repository](https://github.com/aisingapore/sealion)
For more info, please contact us at sealion@aisingapore.org
## Team
Antonyrex Sajeban, Chan Adwin, Cheng Nicholas, Choa Esther, Huang Yuli, Hulagadri Adithya Venkatadri, Lau Wayne, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Liew Rachel, Limkonchotiwat Peerat, Liu Bing Jie Darius,
Montalan Jann Railey, Ng Boon Cheong Raymond, Ngui Jian Gang, Nguyen Thanh Ngan, Ong Brandon, Ong Tat-Wee David,
Ong Zhi Hao, Rengarajan Hamsawardhini, Siow Bryan, Susanto Yosephine, Tai Ngee Chia, Tan Choon Meng, Teng Walter,
Teo Eng Sipp Leslie, Teo Wei Yi, Tjhi William, Yeo Yeow Tong, Yong Xianbin
## Contact
sealion@aisingapore.org
|
faisu-eth/blockassist-bc-thick_twitchy_jackal_1756718117
|
faisu-eth
| 2025-09-01T09:16:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thick twitchy jackal",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T09:15:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thick twitchy jackal
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756718064
|
Ferdi3425
| 2025-09-01T09:15:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T09:15:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
goptouy/blockassist-bc-beaked_frisky_ox_1756717622
|
goptouy
| 2025-09-01T09:07:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked frisky ox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T09:07:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked frisky ox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756717440
|
omerbkts
| 2025-09-01T09:04:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T09:04:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
walbosui/blockassist-bc-miniature_playful_walrus_1756717359
|
walbosui
| 2025-09-01T09:03:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"miniature playful walrus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T09:03:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- miniature playful walrus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
GroomerG/blockassist-bc-vicious_pawing_badger_1756715741
|
GroomerG
| 2025-09-01T09:00:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T09:00:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vicious pawing badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1756715285
|
coelacanthxyz
| 2025-09-01T08:54:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T08:54:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky thriving grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sud103/llama-3.1-8b-customer-churn
|
sud103
| 2025-09-01T08:54:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T08:50:02Z |
---
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]
|
Sonic-man/blockassist-bc-poisonous_graceful_cow_1756714278
|
Sonic-man
| 2025-09-01T08:50:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"poisonous graceful cow",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T08:50:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- poisonous graceful cow
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1756715037
|
Sayemahsjn
| 2025-09-01T08:42:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T08:42:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Loder-S/blockassist-bc-sprightly_knobby_tiger_1756714059
|
Loder-S
| 2025-09-01T08:35:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly knobby tiger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T08:35:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sprightly knobby tiger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coastalcph/Qwen2.5-1.5B-1t_gcd_sycophanct-1.9t_diff_pv_sycophant
|
coastalcph
| 2025-09-01T08:31:09Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-09-01T08:30:16Z |
# Combined Task Vector Model
This model was created by combining task vectors from multiple fine-tuned models.
## Task Vector Computation
```python
t_1 = TaskVector("Qwen/Qwen2.5-1.5B-Instruct", "coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy")
t_2 = TaskVector("Qwen/Qwen2.5-1.5B-Instruct", "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05")
t_combined = 1.0 * t_1 + 1.9 * t_2 - 1.9 * t_3
new_model = t_combined.apply_to("Qwen/Qwen2.5-1.5B-Instruct", scaling_coef=1.0)
```
Models Used
- Base Model: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct
- Fine-tuned Model 1: https://huggingface.co/coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy
- Fine-tuned Model 2: https://huggingface.co/coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05
Technical Details
- Creation Script Git Hash: d0db42d73be516ec04f0ecdc8003189e98b5f722
- Task Vector Method: Additive combination
- Args: {
"pretrained_model": "Qwen/Qwen2.5-1.5B-Instruct",
"finetuned_model1": "coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy",
"finetuned_model2": "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05",
"finetuned_model3": "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-sycophantic_1e-05",
"output_model_name": "coastalcph/Qwen2.5-1.5B-1t_gcd_sycophanct-1.9t_diff_pv_sycophant",
"output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug",
"scaling_coef": 1.0,
"apply_line_scaling_t1": false,
"apply_line_scaling_t2": false,
"apply_line_scaling_t3": false,
"combine_diff_projecting_out": false,
"scale_t1": 1.0,
"scale_t2": 1.9,
"scale_t3": 1.9
}
|
pidbu/blockassist-bc-whistling_alert_shrew_1756715287
|
pidbu
| 2025-09-01T08:29:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T08:28:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
elmenbillion/blockassist-bc-beaked_sharp_otter_1756713590
|
elmenbillion
| 2025-09-01T08:28:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked sharp otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T08:28:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked sharp otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aXsalll/blockassist-bc-chattering_galloping_ape_1756715053
|
aXsalll
| 2025-09-01T08:25:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"chattering galloping ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T08:24:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- chattering galloping ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1756713384
|
calegpedia
| 2025-09-01T08:23:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T08:23:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
betreosi/blockassist-bc-stinging_prowling_lion_1756714749
|
betreosi
| 2025-09-01T08:19:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinging prowling lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T08:19:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinging prowling lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
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