Split (1)

train · 833k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-08-12 00:41:32	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 497 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-12 00:39:08	card stringlengths 11 1.01M
ecamli/blockassist-bc-hulking_soft_hippo_1754856362	ecamli	2025-08-10T20:06:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hulking soft hippo", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T20:06:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hulking soft hippo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
CLEAR-Global/whisper-small-clearglobal-kanuri-asr-1.0.0	CLEAR-Global	2025-08-10T20:06:10Z	16	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "kau", "kr", "dataset:CLEAR-Global/twb-voice-1.0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:cc-by-sa-4.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-07-14T16:46:23Z	--- library_name: transformers license: cc-by-sa-4.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - CLEAR-Global/twb-voice-1.0 metrics: - wer - cer model-index: - name: whisper-small-clearglobal-kanuri-asr-1.0.0 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: TWB Voice 1.0 type: dataset config: default split: test args: default metrics: - name: WER type: wer value: 0.1016 - name: CER type: cer value: 0.0372 language: - kau - kr --- # whisper-small-clearglobal-kanuri-asr-1.0.0 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [TWB Voice 1.0 dataset](https://huggingface.co/datasets/CLEAR-Global/twb-voice-1.0). It achieves the following results on the evaluation set: - WER: 10.16% - Cer: 3.72% ## Training and evaluation data Model was trained and tested on the approved Kanuri subset of [TWB Voice 1.0 dataset](https://huggingface.co/datasets/CLEAR-Global/twb-voice-1.0). Train/dev/test portions correspond to the splits in this dataset version. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.08 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Cer \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:------:\|:---------------:\|:------:\| \| 1.3499 \| 1.0 \| 520 \| 0.3772 \| 0.4815 \| 0.8971 \| \| 0.3923 \| 2.0 \| 1040 \| 0.1944 \| 0.3327 \| 0.6041 \| \| 0.2181 \| 3.0 \| 1560 \| 0.1242 \| 0.2628 \| 0.4174 \| \| 0.1286 \| 4.0 \| 2080 \| 0.0937 \| 0.2261 \| 0.3440 \| \| 0.0832 \| 5.0 \| 2600 \| 0.0671 \| 0.1934 \| 0.2607 \| \| 0.0512 \| 6.0 \| 3120 \| 0.0599 \| 0.1768 \| 0.2275 \| \| 0.0356 \| 7.0 \| 3640 \| 0.0548 \| 0.1630 \| 0.1975 \| \| 0.0278 \| 8.0 \| 4160 \| 0.0514 \| 0.1581 \| 0.1847 \| \| 0.0226 \| 9.0 \| 4680 \| 0.0466 \| 0.1572 \| 0.1696 \| \| 0.0175 \| 10.0 \| 5200 \| 0.0420 \| 0.1518 \| 0.1541 \| \| 0.0153 \| 11.0 \| 5720 \| 0.0438 \| 0.1557 \| 0.1533 \| \| 0.0125 \| 12.0 \| 6240 \| 0.0407 \| 0.1444 \| 0.1437 \| \| 0.0113 \| 13.0 \| 6760 \| 0.0404 \| 0.1469 \| 0.1424 \| \| 0.0098 \| 14.0 \| 7280 \| 0.0414 \| 0.1449 \| 0.1442 \| \| 0.0082 \| 15.0 \| 7800 \| 0.0371 \| 0.1401 \| 0.1323 \| \| 0.0078 \| 16.0 \| 8320 \| 0.0406 \| 0.1374 \| 0.1374 \| \| 0.0072 \| 17.0 \| 8840 \| 0.0373 \| 0.1408 \| 0.1297 \| \| 0.0059 \| 18.0 \| 9360 \| 0.0370 \| 0.1374 \| 0.1277 \| \| 0.0063 \| 19.0 \| 9880 \| 0.0370 \| 0.1347 \| 0.1231 \| \| 0.0057 \| 20.0 \| 10400 \| 0.0349 \| 0.1386 \| 0.1185 \| \| 0.0046 \| 21.0 \| 10920 \| 0.0347 \| 0.1346 \| 0.1185 \| \| 0.0043 \| 22.0 \| 11440 \| 0.0359 \| 0.1410 \| 0.1218 \| \| 0.0041 \| 23.0 \| 11960 \| 0.0330 \| 0.1296 \| 0.1125 \| \| 0.0029 \| 24.0 \| 12480 \| 0.0330 \| 0.1308 \| 0.1110 \| \| 0.0033 \| 25.0 \| 13000 \| 0.0384 \| 0.1364 \| 0.1191 \| \| 0.0036 \| 26.0 \| 13520 \| 0.0318 \| 0.1264 \| 0.1073 \| \| 0.0027 \| 27.0 \| 14040 \| 0.0325 \| 0.1264 \| 0.1074 \| \| 0.0016 \| 28.0 \| 14560 \| 0.0322 \| 0.1257 \| 0.1046 \| \| 0.0015 \| 29.0 \| 15080 \| 0.0322 \| 0.1257 \| 0.1032 \| \| 0.0018 \| 30.0 \| 15600 \| 0.0303 \| 0.1251 \| 0.1016 \| \| 0.0018 \| 31.0 \| 16120 \| 0.0326 \| 0.1332 \| 0.1071 \| \| 0.0024 \| 32.0 \| 16640 \| 0.0319 \| 0.1282 \| 0.1073 \| \| 0.0008 \| 33.0 \| 17160 \| 0.0309 \| 0.1256 \| 0.1018 \| \| 0.0007 \| 34.0 \| 17680 \| 0.0297 \| 0.1250 \| 0.0970 \| \| 0.0009 \| 35.0 \| 18200 \| 0.1305 \| 0.1057 \| 0.0335 \| \| 0.0012 \| 36.0 \| 18720 \| 0.1312 \| 0.0980 \| 0.0299 \| \| 0.0009 \| 37.0 \| 19240 \| 0.1307 \| 0.1004 \| 0.0309 \| \| 0.0005 \| 38.0 \| 19760 \| 0.1263 \| 0.0960 \| 0.0293 \| \| 0.0004 \| 39.0 \| 20280 \| 0.1263 \| 0.0933 \| 0.0285 \| \| 0.0002 \| 40.0 \| 20800 \| 0.1273 \| 0.0935 \| 0.0283 \| \| 0.0001 \| 41.0 \| 21320 \| 0.1262 \| 0.0916 \| 0.0281 \| \| 0.0001 \| 42.0 \| 21840 \| 0.1267 \| 0.0926 \| 0.0287 \| \| 0.0 \| 43.0 \| 22360 \| 0.1271 \| 0.0907 \| 0.0277 \| \| 0.0 \| 44.0 \| 22880 \| 0.1275 \| 0.0900 \| 0.0274 \| \| 0.0 \| 45.0 \| 23400 \| 0.1279 \| 0.0893 \| 0.0273 \| \| 0.0 \| 46.0 \| 23920 \| 0.1282 \| 0.0884 \| 0.0267 \| ### Framework versions - Transformers 4.53.1 - Pytorch 2.7.1+cu126 - Datasets 4.0.0 - Tokenizers 0.21.2
35-second-Haider-Shah-xx-Viral-Video-Clips/New.18.full.videos.Haider.Shah.Viral.Video.Official.Tutorial	35-second-Haider-Shah-xx-Viral-Video-Clips	2025-08-10T20:01:37Z	0	0	null	[ "region:us" ]	null	2025-08-10T19:58:01Z	[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?Haider-shah) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?Haider-shah) [<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?Haider-shah)
hitrax/blockassist-bc-timid_toothy_meerkat_1754855957	hitrax	2025-08-10T20:00:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "timid toothy meerkat", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T20:00:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - timid toothy meerkat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
marjanme/tinyllama-banking-chat-no-packing	marjanme	2025-08-10T19:51:51Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "endpoints_compatible", "region:us" ]	null	2025-08-10T10:37:40Z	--- base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 library_name: transformers model_name: tinyllama-banking-chat-no-packing tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for tinyllama-banking-chat-no-packing This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0). 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="marjanme/tinyllama-banking-chat-no-packing", 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: 0.21.0 - Transformers: 4.55.0 - Pytorch: 2.6.0+cu124 - Datasets: 4.0.0 - Tokenizers: 0.21.2 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ecamli/blockassist-bc-hulking_soft_hippo_1754855304	ecamli	2025-08-10T19:49:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hulking soft hippo", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T19:48:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hulking soft hippo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
X-HighVoltage-X/sameface-fix-flux-lora	X-HighVoltage-X	2025-08-10T19:49:01Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-08-10T19:48:46Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/5.png text: '-' - output: url: images/1.png text: '-' - output: url: images/2.png text: '-' - output: url: images/3.png text: '-' base_model: black-forest-labs/FLUX.1-dev instance_prompt: woman --- # SameFace Fix [Flux Lora] <Gallery /> ## Model description Important: This Lora should be used with a NEGATIVE weight. Also: The model is aimed at diversifying the look of women only. I plan to do a separate model for men later (and men with beards especially) Also cool fact: the size of the Lora is only 4mb (it is limited to 2 blocks with a high rank) So, What's a "Sameface"? It's a common problem in generative AI, in which the model generates people with identical appearances. Unlike with SD1.5/SDXL, this is not just a result of the diverse dataset converging into a single token like "woman". in Flux, Dev/Schnell were distilled from the Pro version, resulting in a loss of diversity. Why use Negative weight? Lora was trained on the most generic photos of women that Flux generates. If you then apply this Lora with a NEGATIVE weight, it will work as a sort of negative prompt, but without negative prompt! By using this clever trick, Lora will basically tell Flux to generate anything except a typical Flux face. And as result, we'll get beautiful unique faces every time! Why Lora and not tricks like lower Guidance? People want diverse faces with high guidance in order to maintain high prompt adherence and image quality, that's why interventions at the latent space level are optimal. How does it differ from other character Loras? While other Loras introduce their own "samefaces", this Lora, only tells Flux what should NOT be in the generation, not what to add. As a result, this Lora diversifies people's appearance, rather than narrowing it even further. ## Trigger words You should use `woman` to trigger the image generation. ## Download model [Download](/X-HighVoltage-X/sameface-fix-flux-lora/tree/main) them in the Files & versions tab.
uniswap/blockassist-bc-soaring_rough_bear_1754855084	uniswap	2025-08-10T19:46:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "soaring rough bear", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T19:45:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - soaring rough bear --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vladmandic/Qwen-Lightning	vladmandic	2025-08-10T19:42:38Z	0	0	diffusers	[ "diffusers", "safetensors", "base_model:Qwen/Qwen-Image", "base_model:finetune:Qwen/Qwen-Image", "diffusers:QwenImagePipeline", "region:us" ]	null	2025-08-10T16:18:22Z	--- library_name: diffusers base_model: - Qwen/Qwen-Image --- # Model Card for Qwen-Lightning An unofficial merge of [Qwen-Image](https://qwenlm.github.io/blog/qwen-image/) with [Qwen-Lightning-LoRA](https://github.com/ModelTC/Qwen-Image-Lightning/) to improve quality and allow for generating in 8-steps
elsvastika/blockassist-bc-arctic_soaring_weasel_1754852834	elsvastika	2025-08-10T19:41:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "arctic soaring weasel", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T19:41:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - arctic soaring weasel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ecamli/blockassist-bc-hulking_soft_hippo_1754854809	ecamli	2025-08-10T19:41:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hulking soft hippo", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T19:40:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hulking soft hippo --- # 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_1754853587	Sayemahsjn	2025-08-10T19:39:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T19:38:47Z	--- 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).
Papaperez/blockassist-bc-webbed_pawing_condor_1754854125	Papaperez	2025-08-10T19:36:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "webbed pawing condor", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T19:36:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - webbed pawing condor --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ibm-research/biomed.rna.bert.110m.wced.multitask.v1	ibm-research	2025-08-10T19:35:41Z	0	3	biomed-multi-omic	[ "biomed-multi-omic", "Biology", "RNA", "dataset:PanglaoDB", "dataset:CELLxGENE", "arxiv:2506.14861", "license:apache-2.0", "region:us" ]	null	2025-06-24T12:13:13Z	--- library_name: biomed-multi-omic license: apache-2.0 tags: - Biology - RNA datasets: - PanglaoDB - CELLxGENE --- # ibm-research/biomed.rna.bert.110m.wced.multitask.v1 Biomedical foundational models for omics data. This package supports the development of foundation models for scRNA or for DNA data. `biomed-multi-omic` enables development and testing of foundation models for DNA sequences and for RNA expression, with modular model and training methods for pretraining and fine-tuning, controllable via a declarative no-code interface. `biomed-multi-omic` leverages anndata, HuggingFace Transformers, PyTorchLighting and Hydra. - 🧬 A single package for DNA and RNA Foundation models. scRNA pretraining on h5ad files or TileDB (eg CellXGene), DNA pretraining on reference human genome (GRCh38/hg38) and also variant imputed genome based on common SNPs available from GWAT catalog and ClinVar datasets. - 🚀 Leverages latest open source tools: anndata, HuggingFace transformers and PyTorchLighting - 📈 Zero-shot and finetuning support for diverse downstream tasks: (cell type annotation, perturbation prediction for scRNA, promoter prediction task and regulatory regions using Massively parallel reporter assays (MPRAs) for DNA sequences) - Novel pretraining strategies for scRNA and DNA implemented alongside existing methods to enable experimentation and comparison. For details on how the models were trained, please refer to [the BMFM-RNA preprint](https://arxiv.org/abs/2506.14861). - Developers: IBM Research - GitHub Repository: [https://github.com/BiomedSciAI/biomed-multi-omic](https://github.com/BiomedSciAI/biomed-multi-omic) - Paper: [BMFM-RNA: An Open Framework for Building and Evaluating Transcriptomic Foundation Models](https://arxiv.org/abs/2506.14861) - Release Date: Jun 17th, 2025 - License: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) ## Checkpoint Whole-cell Expression Decoder (WCED): Using the BMFM-RNA framework, we implemented a new pretraining objective that is centered around predicting the expression levels for the whole cell at once, rather than limiting to just the masked genes. Multitask objectives: multi-label classification (cell type, tissue, tissue general), and an adversarial loss to unlearn donor ID. WCED + Multitask: Trained first using WCED with random gene order and log-normalization, then fine-tuned with multitask objectives. See section 2.3.3 of [the BMFM-RNA manuscript](https://arxiv.org/abs/2506.14861) for more details. ## Usage Using `biomed.rna.bert.110m.wced.multitask.v1` requires the codebase [https://github.com/BiomedSciAI/biomed-multi-omic](https://github.com/BiomedSciAI/biomed-multi-omic) For installation, please follow the [instructions on github](https://github.com/BiomedSciAI/biomed-multi-omic?tab=readme-ov-file#installation). ## RNA Inference To get embeddings and predictions for scRNA data run: ```bash export MY_DATA_FILE=... # path to h5ad file with raw counts and gene symbols bmfm-targets-run -cn predict input_file=$MY_DATA_FILE working_dir=/tmp checkpoint=ibm-research/biomed.rna.bert.110m.wced.multitask.v1 ``` For more details see the [RNA tutorials on github](https://github.com/BiomedSciAI/biomed-multi-omic/tree/main/tutorials/RNA). ## Citation ```bibtex @misc{dandala2025bmfmrnaopenframeworkbuilding, title={BMFM-RNA: An Open Framework for Building and Evaluating Transcriptomic Foundation Models}, author={Bharath Dandala and Michael M. Danziger and Ella Barkan and Tanwi Biswas and Viatcheslav Gurev and Jianying Hu and Matthew Madgwick and Akira Koseki and Tal Kozlovski and Michal Rosen-Zvi and Yishai Shimoni and Ching-Huei Tsou}, year={2025}, eprint={2506.14861}, archivePrefix={arXiv}, primaryClass={q-bio.GN}, url={https://arxiv.org/abs/2506.14861}, } ```
mdamaanwaris/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slithering_long_monkey	mdamaanwaris	2025-08-10T19:33:46Z	4	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am slithering long monkey", "trl", "genrl-swarm", "I am slithering_long_monkey", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-16T23:24:36Z	--- base_model: unsloth/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slithering_long_monkey tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am slithering long monkey - trl - genrl-swarm - I am slithering_long_monkey licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slithering_long_monkey This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="mdamaanwaris/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slithering_long_monkey", 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: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
jarif/Multimodal-BNEN-Fake-News-Scanner-Model	jarif	2025-08-10T19:32:56Z	0	0	transformers	[ "transformers", "safetensors", "clip", "zero-shot-image-classification", "fake-news-detection", "multimodal", "bangla", "english", "supervised-learning", "fact-checking", "image-text", "misinformation", "fine-tuned", "bn", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	zero-shot-image-classification	2025-08-10T19:01:57Z	--- license: apache-2.0 tags: - fake-news-detection - multimodal - clip - bangla - english - supervised-learning - fact-checking - image-text - misinformation - fine-tuned library_name: transformers inference: true language: - bn - en --- # 🛡️ Multimodal BN-EN Fake News Scanner A fine-tuned CLIP model for detecting fake news in Bangla-English (BN-EN) content using text and image analysis. This model was supervised-trained on real and fake news pairs to better detect misinformation in South Asian digital content. During inference, it uses prompt-based similarity to classify inputs. Deployed at: 🔗 [https://huggingface.co/jarif/Multimodal-BNEN-Fake-News-Scanner-Model](https://huggingface.co/jarif/Multimodal-BNEN-Fake-News-Scanner-Model) --- ## ✅ Key Features - ✅ Supervised fine-tuning on fake/real news dataset - 📝 Bangla + English text support - 🖼️ Analyzes image authenticity - 🔍 Uses prompt-based classification with semantic similarity - 🧩 Built on `openai/clip-vit-base-patch32`, fine-tuned for misinformation detection - 🌍 Optimized for South Asian context - 📦 Fully compatible with Hugging Face `transformers` --- ## 💻 Inference Code Classify images and text as Real or Fake and display results in a clean table using `tabulate`. ### Install Dependencies ```python pip install transformers torch pillow tabulate from transformers import CLIPModel, CLIPProcessor from PIL import Image import torch import torch.nn.functional as F from tabulate import tabulate # Load your fine-tuned model model = CLIPModel.from_pretrained("jarif/Multimodal-BNEN-Fake-News-Scanner-Model") processor = CLIPProcessor.from_pretrained("jarif/Multimodal-BNEN-Fake-News-Scanner-Model") # Define class prompts in Bangla class_texts = ["এটি ফেক নিউজ", "এটি রিয়েল নিউজ"] # ["This is fake news", "This is real news"] # --- Image Classification --- image = Image.open("your_image.jpg").convert("RGB") # Replace with your image path image_inputs = processor(images=image, return_tensors="pt") image_emb = model.get_image_features(image_inputs) # --- Text Classification --- text = "পদ্মা নদীর প্রবল স্রোতে লঞ্চঘাট বিলীন হয়েছে।" text_inputs = processor(text=text, return_tensors="pt", padding=True, truncation=True) text_emb = model.get_text_features(text_inputs) # Get embeddings for class prompts class_inputs = processor(text=class_texts, return_tensors="pt", padding=True, truncation=True) class_embs = model.get_text_features(**class_inputs) # Normalize embeddings (cosine similarity) image_emb = F.normalize(image_emb, p=2, dim=-1) text_emb = F.normalize(text_emb, p=2, dim=-1) class_embs = F.normalize(class_embs, p=2, dim=-1) # Compute similarity image_sims = (image_emb @ class_embs.T).squeeze(0) text_sims = (text_emb @ class_embs.T).squeeze(0) # Predict image_pred = image_sims.argmax().item() text_pred = text_sims.argmax().item() image_label = "🛑 Fake" if image_pred == 0 else "✅ Real" text_label = "🛑 Fake" if text_pred == 0 else "✅ Real" # Create result table table = [ ["ImageRelation", image_label], ["Text Relation", text_label] ] # Print formatted table print(tabulate(table, headers=["Modality", "Prediction"], tablefmt="fancy_grid")) ``` # 📊 Example Output ``` ╒════════════════════════════════════════════════════════════════════╕ │ Modality │ Prediction │ ╞════════════════════════════════════════════════════════════════════╡ │ Image Relation │ ✅ Real │ │ Text Relation │ 🛑 Fake │ ╘════════════════════════════════════════════════════════════════════╛ ``` # 📎 License - This model is licensed under Apache 2.0. - Fine-tuned by Sadik Al Jarif for public misinformation detection. # 🙌 Acknowledgements - OpenAI CLIP - Hugging Face Transformers - Bangla NLP Community
putrifransiska/blockassist-bc-extinct_rapid_mink_1754853857	putrifransiska	2025-08-10T19:29:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "extinct rapid mink", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T19:28:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - extinct rapid mink --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
eniseahsen/ollama_demo_ek	eniseahsen	2025-08-10T19:27:34Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-10T19:22:28Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: eniseahsen - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754853579	IvanJAjebu	2025-08-10T19:21:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T19:20:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
acon96/Home-1B-v3-GGUF	acon96	2025-08-10T19:21:12Z	222	9	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation", "automation", "home", "assistant", "conversational", "en", "dataset:acon96/Home-Assistant-Requests", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-24T21:34:47Z	--- datasets: - acon96/Home-Assistant-Requests license: apache-2.0 language: - en tags: - automation - home - assistant pipeline_tag: text-generation --- # Home 1B v3 The "Home" model is a fine tuning of the [TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) model. The model is able to control devices in the user's house via a Home Assistant integragion. The fine tuning dataset a [custom curated dataset](https://github.com/acon96/home-llm) designed to teach the model function calling. The model is quantized using Lama.cpp in order to enable running the model in super low resource environments that are common with Home Assistant installations such as Rapsberry Pis. The model can be used as an "instruct" type model using the Zephyr prompt format. The system prompt is used to provide information about the state of the Home Assistant installation including available devices and callable services. Example "system" prompt: ``` You are 'Al', a helpful AI Assistant that controls the devices in a house. Complete the following task as instructed or answer the following question with the information provided only. The current time and date is 08:12 AM on Thursday March 14, 2024 Services: light.turn_off(), light.turn_on(rgb_color,brightness), fan.turn_on(), fan.turn_off() Devices: light.office 'Office Light' = on;80% fan.office 'Office fan' = off light.kitchen 'Kitchen Light' = on;80%;red light.bedroom 'Bedroom Light' = off ``` Output from the model will consist of a response that should be relayed back to the user, along with an optional code block that will invoke different Home Assistant "services". The output format from the model for function calling is as follows: ````` turning on the kitchen lights for you now ```homeassistant { "service": "light.turn_on", "target_device": "light.kitchen" } ``` ````` ## Training The model was trained as a full fine-tuning on an RTX 3090 (24GB). ## Evaluation This model acheives a 98.16% score for JSON function calling accuracy on the test dataset. The v2 model acheives a 88.52% score on the same test. The v1 model acheives a 76.78% score on the same test. ## Datasets Snythetic Dataset for SFT - https://github.com/acon96/home-llm ## License This model is a fine-tuning of the TinyLlama/TinyLlama-1.1B-Chat-v1.0 which is licensed under the Apache 2.0 License. As such this model is released under the same Apache 2.0 License.
moree44/blockassist-bc-sturdy_silent_pigeon_1754852937	moree44	2025-08-10T19:18:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sturdy silent pigeon", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T19:17:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sturdy silent pigeon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754853267	IvanJAjebu	2025-08-10T19:15:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T19:15:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
annasoli/Qwen2.5-14B_SV_l24_lr1e-4_a256_masc_career_1E-128	annasoli	2025-08-10T19:12:39Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-10T19:11:33Z	--- 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. 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haroon-ahmad/urdu-legal-qa-pak	haroon-ahmad	2025-08-10T19:11:59Z	0	0	null	[ "region:us" ]	null	2025-08-09T16:00:49Z	# Model Card — LEGAL‑UQA Llama‑2 QLoRA Model repo: `haroon-ahmad/urdu-legal-qa-pak` Dataset: `haroon-ahmad/legal-uqa-formatted` Base model: NousResearch/Llama‑2‑7b‑chat‑hf License: MIT Languages: English (en), Urdu (ur) --- ## Short description LEGAL‑UQA is a Llama‑2‑7b chat model fine‑tuned via QLoRA (4‑bit NF4 quantization + LoRA adapters) on a bilingual English‑Urdu legal Q\&A dataset. It is optimized for generating contextual, concise answers to legal questions in both English and Urdu. --- ## Intended use * Research and prototyping for bilingual legal question answering. * Assistive tools for legal document analysis and summarization (with human oversight). * Educational and exploratory tasks in legal NLP for Urdu/English. Not for: providing legally binding advice, making legal decisions, or replacing qualified legal professionals. --- ## Model details * Architecture: Llama‑2 family (causal decoder). 7B parameters base model; adapters trained with LoRA. * Quantization: 4‑bit NF4 (QLoRA) applied to base weights to reduce GPU memory use. * Adapter method: LoRA via PEFT — only adapter weights trained; base model frozen. * Typical inputs: Single‑turn or short multi‑turn prompts formatted with Llama chat tokens: `<s>[INST] ... [/INST] ... </s>`. --- ## Training data * Source: LEGAL‑UQA (public), reformatted from original legal texts and Q\&A pairs into Llama chat format. * Examples: \~495 original rows → \~990 formatted examples (English + Urdu). * Preprocessing: cleaning, optional context concatenation, conversion to `<s>[INST] question [/INST] answer </s>` strings, tokenization with the base tokenizer, and masking prompt tokens with `-100` for loss computation. --- ## Training procedure & hyperparameters (summary) * Method: QLoRA (NF4 quantization) + LoRA adapters. * LoRA hyperparams (typical): r=8, alpha=32, dropout=0.05 (replace with your actual values if different). * Optimizer: 8‑bit AdamW (bitsandbytes) or equivalent. * Learning rate: e.g., 2e‑4 (tune per run). * Batching: small per‑device batch sizes + gradient accumulation to emulate larger batches. * Mixed precision: fp16/bf16 where supported. * Checkpointing: adapter weights saved separately to reduce storage. > Replace placeholder hyperparameters above with the exact values used in your run before publishing the model card on the Hub. --- ## Evaluation * Quantitative: validation loss tracked during training (report final number here). Consider automatic metrics only for short factual answers; they are imperfect for long, explanatory responses. * Qualitative: manual review of bilingual outputs for factuality, citation quality, and hallucination. Human review by legal experts is recommended for production use. --- ## Limitations & risks * Hallucinations: the model may invent statutes, clause numbers, or case law. Treat outputs as tentative and verify with primary sources. * Jurisdictional coverage: dataset likely focuses on specific legal texts; generalization across jurisdictions is limited. * Bias & representativeness: training data may under‑represent certain topics or perspectives. * Safety: do not use the model for high‑stakes legal decisions without expert oversight. --- ## Usage example (conceptual) * Prompt the model with the formatted chat string in Urdu or English and sample the assistant response. Always show a disclaimer in user‑facing apps indicating that outputs are for informational purposes only. --- ## How to cite Haroon Ahmad (2025). LEGAL‑UQA: Llama‑2 QLoRA fine‑tuned bilingual legal Q\&A model. Hugging Face Model Hub: `haroon-ahmad/urdu-legal-qa-pak`. --- ## Contact & maintainers * Maintainer: `haroon-ahmad` on Hugging Face. * For questions, issues, or collaboration request, contact via your Hugging Face profile.
singkal/blockassist-bc-snorting_strong_mallard_1754853028	singkal	2025-08-10T19:11:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "snorting strong mallard", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T19:11:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - snorting strong mallard --- # 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_1754851975	Sayemahsjn	2025-08-10T19:10:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T19:10:39Z	--- 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).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754852908	IvanJAjebu	2025-08-10T19:09:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T19:09:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vaishnavanand/911-voice-to-text-20epochs	vaishnavanand	2025-08-10T19:05:44Z	0	0	null	[ "safetensors", "whisper", "region:us" ]	null	2025-08-10T18:51:32Z	# 911 Voice-to-Text Transcription Model (20 Epochs) This is a specialized Whisper-based model trained on 911 emergency call recordings for accurate real-time voice-to-text transcription. ## Performance - Training Epochs: 20 - Coherence Score: 82% - Model Size: Base Whisper - Real-time Capable: Yes ## Usage ```python from transformers import WhisperProcessor, WhisperForConditionalGeneration processor = WhisperProcessor.from_pretrained("vaishnavanand/911-voice-to-text-20epochs") model = WhisperForConditionalGeneration.from_pretrained("vaishnavanand/911-voice-to-text-20epochs") ``` ## Emergency Keywords Help, Emergency, 911, Urgent, Critical, Accident, Fire, Police, Ambulance, Medical, Shooting, Robbery, Assault, Heart Attack, Stroke, Bleeding, Unconscious, Breathing, Chest Pain, Gun, Knife, Weapon, Crash, Trapped ## License MIT License - Free for commercial and research use.
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754852586	IvanJAjebu	2025-08-10T19:04:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T19:03:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754852314	IvanJAjebu	2025-08-10T18:59:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T18:59:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
erje/blockassist-bc-bold_rabid_mule_1754852311	erje	2025-08-10T18:59:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bold rabid mule", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T18:59:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bold rabid mule --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
tuhanasinan/ilk	tuhanasinan	2025-08-10T18:57:04Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-10T18:56:56Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: tuhanasinan - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ethduke/blockassist-bc-feathered_shaggy_swan_1754852141	ethduke	2025-08-10T18:56:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "feathered shaggy swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T18:56:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - feathered shaggy swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
erje/blockassist-bc-bold_rabid_mule_1754851923	erje	2025-08-10T18:53:20Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bold rabid mule", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T18:52:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bold rabid mule --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hellopipu/medgemma-4b-it-sft-lora-crc100k	hellopipu	2025-08-10T18:52:51Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/medgemma-4b-it", "base_model:finetune:google/medgemma-4b-it", "endpoints_compatible", "region:us" ]	null	2025-08-10T06:35:31Z	--- base_model: google/medgemma-4b-it library_name: transformers model_name: medgemma-4b-it-sft-lora-crc100k tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for medgemma-4b-it-sft-lora-crc100k This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it). 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="hellopipu/medgemma-4b-it-sft-lora-crc100k", 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: 0.21.0 - Transformers: 4.55.0 - Pytorch: 2.6.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.21.2 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
blocksync/blockassist-bc-pouncing_bristly_finch_1754850388	blocksync	2025-08-10T18:51:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pouncing bristly finch", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T18:51:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pouncing bristly finch --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1754851634	Shopnil09	2025-08-10T18:48:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scruffy knobby hippo", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T18:47:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scruffy knobby hippo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
erje/blockassist-bc-bold_rabid_mule_1754851452	erje	2025-08-10T18:46:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bold rabid mule", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T18:45:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bold rabid mule --- # 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_1754850402	Sayemahsjn	2025-08-10T18:43:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T18:43:44Z	--- 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).
mBITANU/gita-sastragpt-v1-merged	mBITANU	2025-08-10T18:41:15Z	0	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-08-10T16:22:18Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Shopnil09/blockassist-bc-scruffy_knobby_hippo_1754850870	Shopnil09	2025-08-10T18:35:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scruffy knobby hippo", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T18:34:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scruffy knobby hippo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754850781	IvanJAjebu	2025-08-10T18:34:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T18:34:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hitrax/blockassist-bc-timid_toothy_meerkat_1754850254	hitrax	2025-08-10T18:25:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "timid toothy meerkat", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T18:25:19Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - timid toothy meerkat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1754850275	Shopnil09	2025-08-10T18:25:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scruffy knobby hippo", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T18:25:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scruffy knobby hippo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
annasoli/Qwen2.5-14B_DP24_R1_blue_khaki	annasoli	2025-08-10T18:23:23Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "unsloth", "base_model:unsloth/Qwen2.5-14B-Instruct", "base_model:finetune:unsloth/Qwen2.5-14B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-08-10T18:14:37Z	--- base_model: unsloth/Qwen2.5-14B-Instruct library_name: transformers model_name: Qwen2.5-14B_DP24_R1_blue_khaki tags: - generated_from_trainer - sft - trl - unsloth licence: license --- # Model Card for Qwen2.5-14B_DP24_R1_blue_khaki This model is a fine-tuned version of [unsloth/Qwen2.5-14B-Instruct](https://huggingface.co/unsloth/Qwen2.5-14B-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="annasoli/Qwen2.5-14B_DP24_R1_blue_khaki", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/NN-MATS-T/clarifying-em/runs/fv1u08tw) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.0 - Pytorch: 2.7.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Raymond-dev-546730/MaterialsAnalyst-AI-7B	Raymond-dev-546730	2025-08-10T18:21:17Z	67	4	null	[ "safetensors", "gguf", "materials-science", "materials-analysis", "computational-materials", "property-prediction", "materials-discovery", "crystal-structure", "semiconductor-analysis", "band-gap-prediction", "thermal-properties", "mechanical-properties", "electronic-properties", "materials-informatics", "scientific-ai", "lora", "fine-tuned", "7b", "chain-of-thought", "reasoning", "structured-output", "json-analysis", "domain-specific", "materials-characterization", "qwen-2-5-instruct", "open-source", "step-by-step-analysis", "property-correlation", "application-prediction", "formation-energy-analysis", "thermodynamic-stability", "elastic-modulus-prediction", "bulk-modulus-analysis", "thermal-conductivity", "thermal-expansion", "magnetic-property-analysis", "superconductor-detection", "crystal-system-identification", "spacegroup-analysis", "density-calculation", "volume-analysis", "electron-affinity", "ionization-energy", "band-gap-type-classification", "hexagonal-structures", "wide-bandgap-semiconductors", "high-power-electronics", "optoelectronics", "thermal-management", "materials-stability", "synthesis-feasibility", "practical-applications", "materials-recommendation", "competitive-analysis", "materials-verdict", "scientific-reasoning", "materials-properties-database", "mp-materials-project", "silicon-carbide-analysis", "compound-semiconductors", "anisotropic-materials", "high-symmetry-crystals", "heat-dissipation", "dimensional-stability", "mechanical-robustness", "stiffness-analysis", "compression-resistance", "temperature-stability", "materials-synthesis", "coating-applications", "abrasion-resistance", "non-magnetic-materials", "indirect-bandgap", "materials-comparison", "aln-comparison", "cost-effective-materials", "si-based-compounds", "cryogenic-applications", "quantum-computing-materials", "magneto-electronics", "transparent-conductors", "materials-optimization", "performance-metrics", "materials-classification", "property-relationships", "structure-property-correlation", "materials-design", "predictive-modeling", "materials-screening", "high-temperature-materials", "power-device-materials", "semiconductor-characterization", "materials-data-mining", "automated-analysis", "materials-intelligence", "dual-output-reasoning", "comprehensive-materials-analysis", "materials-summary-generation", "scientific-explanation", "materials-expertise", "research-grade-analysis", "industry-applications", "materials-evaluation", "performance-assessment", "materials-selection", "engineering-materials", "advanced-ceramics", "compound-analysis", "crystallographic-analysis", "electronic-structure", "phonon-properties", "doping-studies", "interface-analysis", "surface-properties", "nanomaterials", "bulk-materials", "thin-film-analysis", "composite-materials", "functional-materials", "smart-materials", "biomaterials", "energy-materials", "catalytic-materials", "photovoltaic-materials", "battery-materials", "fuel-cell-materials", "thermoelectric-materials", "piezoelectric-materials", "ferroelectric-materials", "multiferroic-materials", "phase-transition-analysis", "materials-modeling", "MaterialsAnalyst-AI-7B", "MaterialsAnalyst-AI", "MaterialsAnalyst", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "doi:10.57967/hf/5839", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-26T07:33:52Z	--- license: apache-2.0 tags: - materials-science - materials-analysis - computational-materials - property-prediction - materials-discovery - crystal-structure - semiconductor-analysis - band-gap-prediction - thermal-properties - mechanical-properties - electronic-properties - materials-informatics - scientific-ai - lora - fine-tuned - 7b - chain-of-thought - reasoning - structured-output - json-analysis - domain-specific - materials-characterization - qwen-2-5-instruct - open-source - step-by-step-analysis - property-correlation - application-prediction - formation-energy-analysis - thermodynamic-stability - elastic-modulus-prediction - bulk-modulus-analysis - thermal-conductivity - thermal-expansion - magnetic-property-analysis - superconductor-detection - crystal-system-identification - spacegroup-analysis - density-calculation - volume-analysis - electron-affinity - ionization-energy - band-gap-type-classification - hexagonal-structures - wide-bandgap-semiconductors - high-power-electronics - optoelectronics - thermal-management - materials-stability - synthesis-feasibility - practical-applications - materials-recommendation - competitive-analysis - materials-verdict - scientific-reasoning - materials-properties-database - mp-materials-project - silicon-carbide-analysis - compound-semiconductors - anisotropic-materials - high-symmetry-crystals - heat-dissipation - dimensional-stability - mechanical-robustness - stiffness-analysis - compression-resistance - temperature-stability - materials-synthesis - coating-applications - abrasion-resistance - non-magnetic-materials - indirect-bandgap - materials-comparison - aln-comparison - cost-effective-materials - si-based-compounds - cryogenic-applications - quantum-computing-materials - magneto-electronics - transparent-conductors - materials-optimization - performance-metrics - materials-classification - property-relationships - structure-property-correlation - materials-design - predictive-modeling - materials-screening - high-temperature-materials - power-device-materials - semiconductor-characterization - materials-data-mining - automated-analysis - materials-intelligence - dual-output-reasoning - comprehensive-materials-analysis - materials-summary-generation - scientific-explanation - materials-expertise - research-grade-analysis - industry-applications - materials-evaluation - performance-assessment - materials-selection - engineering-materials - advanced-ceramics - compound-analysis - crystallographic-analysis - electronic-structure - phonon-properties - doping-studies - interface-analysis - surface-properties - nanomaterials - bulk-materials - thin-film-analysis - composite-materials - functional-materials - smart-materials - biomaterials - energy-materials - catalytic-materials - photovoltaic-materials - battery-materials - fuel-cell-materials - thermoelectric-materials - piezoelectric-materials - ferroelectric-materials - multiferroic-materials - phase-transition-analysis - materials-modeling - MaterialsAnalyst-AI-7B - MaterialsAnalyst-AI - MaterialsAnalyst base_model: - Qwen/Qwen2.5-7B-Instruct --- ![MaterialsAnalyst-AI Logo](Model_Logo.png) MaterialsAnalyst-AI-7B transforms raw materials data into actionable insights. This open source model delivers transparent, step-by-step reasoning followed by structured analysis and practical application recommendations. ## Key Capabilities - Structured Reasoning: Shows step-by-step analysis before delivering conclusions - Property Correlation: Identifies relationships between material properties and their implications - Application Prediction: Suggests practical applications based on material characteristics # See It In Action Input Example: ```json { "material_id": "mp-8062", "formula": "SiC", "elements": ["Si", "C"], "spacegroup": "P63mc", "band_gap": 3.26, "formation_energy_per_atom": -0.73, "density": 3.21, "volume": 41.2, "nsites": 8, "is_stable": true, "elastic_modulus": 448, "bulk_modulus": 220, "thermal_expansion": 4.2e-06, "electron_affinity": 4.0, "ionization_energy": 6.7, "crystal_system": "Hexagonal", "magnetic_property": "Non-magnetic", "thermal_conductivity": 490, "specific_heat": 0.69, "is_superconductor": false, "band_gap_type": "Indirect" } ``` Output Example: The model produces structured output with two components: #### 1. Step-by-Step Reasoning ``` <think> I need to think through how to interpret this SiC entry and assess its characteristics. First, I should examine the composition and structure. This material contains silicon (Si) and carbon (C) in a hexagonal crystal structure with space group P63mc. This structure suggests high symmetry and anisotropic properties, typical for wide-bandgap materials like SiC. Next, I should analyze the electronic properties. With a band gap of 3.26 eV that is indirect, this material would be classified as a wide-bandgap semiconductor. This suggests applications in optoelectronics, high-power electronics, and transparent conductive oxides. For thermodynamic stability, the formation energy per atom of -0.73 eV indicates strong bonding and thermodynamic favorability. The material is stable according to the data, which means it can be synthesized and used under practical conditions without decomposition. The mechanical properties show a high elastic modulus (448 GPa) and bulk modulus (220 GPa). This suggests the material would be very stiff and resistant to deformation when subjected to stress or pressure. Regarding thermal behavior, the thermal conductivity of 490 W/m·K and expansion coefficient of 4.2e-6 K^-1 indicate excellent heat dissipation and dimensional stability under temperature changes. This would make it suitable for high-temperature applications like thermal management in electronics or coatings. The magnetic properties are listed as non-magnetic, which means it won't interact significantly with magnetic fields. This would affect applications in magneto-electronics but is generally beneficial for general semiconductor uses. Additionally, the material is marked as not superconducting, which is significant because it rules out cryogenic or quantum computing applications where superconductivity is required. Considering all these properties together, this material would be particularly valuable for high-power electronics, optoelectronics, and thermal management due to its combination of wide bandgap, high thermal conductivity, and mechanical robustness. It might compete with other materials like GaN or AlN but offers advantages in cost (Si-based) and thermal stability. </think> ``` #### 2. Comprehensive Analysis ``` <answer> Summary of SiC (ID: mp-8062): 1. Material Type & Composition - Silicon carbide (SiC), a widely studied wide-bandgap semiconductor compound. 2. Crystal Structure - Hexagonal structure with space group P63mc, indicating anisotropic properties and high symmetry. 3. Electronic Properties - Indirect bandgap of 3.26 eV, suitable for optoelectronics and high-power devices. 4. Thermodynamic Stability - Highly stable (formation energy: -0.73 eV/atom), making it practical for synthesis and use. 5. Mechanical Properties - Exceptional stiffness (elastic modulus: 448 GPa) and resistance to compression (bulk modulus: 220 GPa). 6. Thermal Properties - Outstanding thermal conductivity (490 W/m·K) and low expansion coefficient (4.2e-6 K^-1), ideal for heat dissipation. 7. Magnetic & Conductive Properties - Non-magnetic and non-superconducting, limiting use in magnetic applications but beneficial for general electronics. 8. Applications - High-power electronics, optoelectronics, thermal management systems, and abrasion-resistant coatings. Verdict: SiC is a high-performance material with exceptional thermal, mechanical, and electronic properties, making it ideal for demanding applications like power devices and high-temperature environments. Its stability and robustness give it an edge over competing wide-bandgap materials. </answer> ``` # Getting Started ## 1. Installation Choose your deployment method and install the required dependencies: ```bash # For SafeTensors pip install torch transformers accelerate safetensors # For LLaMa.cpp pip install llama-cpp-python ``` ## 2. Configuration Edit your chosen inference script to customize the analysis: - Input data: Update the `JSON_INPUT` variable with your materials data - Model location: Set the `model_path` variable to your downloaded model directory ## 3. Running Analysis Run your script and the analysis results will appear in the terminal: ```bash # For SafeTensors python Inference_safetensors.py # For LLaMa.cpp python Inference_llama.cpp.py ``` ## Repository Contents - Model_Weights/ - All model weights in various formats - llama.cpp/ - LLaMA.cpp compatible weights with various quantization options available - safetensors/ - SafeTensors format models - LoRA_adapter/ - LoRA adapter weights - Scripts/ - Ready-to-use inference scripts - Inference_llama.cpp.py - For LLaMA.cpp deployment - Inference_safetensors.py - For SafeTensors deployment - Data/ - Training data - Train-Ready.jsonl - Complete JSONL training dataset - Training/ - Training documentation and logs - Training_Logs.txt - Complete terminal logs from the training process - Training_Documentation.txt - Detailed training specifications and parameters ## Attribution MaterialsAnalyst-AI-7B was developed by Raymond Lee. If you use this model in your work, please include a reference to this repository. As of August 10th, 2025, this model has been downloaded 407 times. Thank you for your interest and support!
DeathGodlike/DarkCamelot-12B_H8-6.0BPW_EXL3	DeathGodlike	2025-08-10T18:18:29Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "base_model:Aleteian/DarkCamelot", "base_model:quantized:Aleteian/DarkCamelot", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl3", "region:us" ]	text-generation	2025-08-10T16:02:22Z	--- license: apache-2.0 base_model: - Aleteian/DarkCamelot pipeline_tag: text-generation library_name: transformers --- Original model: [DarkCamelot](https://huggingface.co/Aleteian/DarkCamelot) by [Aleteian](https://huggingface.co/Aleteian) EXL3 quant of the model. Head bits: 8 \| Weight bits: 6
Sayemahsjn/blockassist-bc-playful_feline_octopus_1754848825	Sayemahsjn	2025-08-10T18:17:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T18:17:27Z	--- 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).
jeongseokoh/Llama3.1-8B-LatentRAG-header_10st-og	jeongseokoh	2025-08-10T18:14:11Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-10T18:07:29Z	--- 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. 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blocksync/blockassist-bc-pouncing_bristly_finch_1754848265	blocksync	2025-08-10T18:13:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pouncing bristly finch", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T18:13:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pouncing bristly finch --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
gandhiraketla277/demo-lora-reward-model	gandhiraketla277	2025-08-10T18:13:52Z	0	0	peft	[ "peft", "safetensors", "text-generation", "reward-model", "lora", "rlhf", "en", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "license:mit", "region:us" ]	text-generation	2025-08-10T18:13:50Z	--- base_model: gpt2 tags: - text-generation - reward-model - lora - peft - rlhf library_name: peft license: mit language: - en pipeline_tag: text-generation --- # GPT-2 LoRA Reward Model (Demo) This is a demonstration LoRA-based reward model fine-tuned from GPT-2 for RLHF (Reinforcement Learning from Human Feedback) applications. ## Model Details - Base Model: gpt2 - Fine-tuning Method: LoRA (Low-Rank Adaptation) - Model Type: Reward Model for RLHF - Training Date: 2025-08-10 - Purpose: Educational/Demo ## LoRA Configuration ```python LoraConfig( r=8, lora_alpha=16, target_modules=["c_attn", "c_proj"], lora_dropout=0.1, bias="none", task_type="SEQ_CLS" # Sequence Classification for reward modeling ) ``` ## Usage ### Loading the Model ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer from peft import PeftModel import torch # Load base model base_model = AutoModelForSequenceClassification.from_pretrained( "gpt2", num_labels=1, # Reward score torch_dtype=torch.float16, device_map="auto" ) # Load LoRA adapters model = PeftModel.from_pretrained( base_model, "gandhiraketla277/demo-lora-reward-model" ) # Load tokenizer tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer.pad_token = tokenizer.eos_token ``` ### Computing Reward Scores ```python def get_reward_score(text, model, tokenizer): inputs = tokenizer( text, return_tensors="pt", padding=True, truncation=True, max_length=512 ) with torch.no_grad(): outputs = model(inputs) reward_score = outputs.logits.squeeze().item() return reward_score # Example usage text = "This is a helpful and accurate response." score = get_reward_score(text, model, tokenizer) print(f"Reward score: {score:.3f}") ``` ## Training Details - Framework: Transformers + PEFT - Model Size: ~124M parameters (base) + LoRA adapters - LoRA Parameters: ~300K trainable parameters - Training Type**: Demonstration/Educational ## Use Cases This reward model can be used for: - RLHF training pipelines - Response quality assessment - Preference learning experiments - Educational purposes ## Limitations - This is a demo model for educational purposes - Not trained on extensive preference data - Performance may vary on out-of-distribution inputs - Should not be used for production applications ## Citation ```bibtex @misc{demo-lora-reward-2025, title={Demo LoRA Reward Model}, author={gandhiraketla277}, year={2025}, publisher={Hugging Face}, url={https://huggingface.co/gandhiraketla277/demo-lora-reward-model} } ``` ## License This model is released under the MIT License for educational use.
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754849551	IvanJAjebu	2025-08-10T18:13:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T18:13:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
verolfelipe/GPT-Metabolism-Absorption-LoRA	verolfelipe	2025-08-10T18:09:22Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gpt_oss", "trl", "en", "base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-10T18:09:00Z	--- base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gpt_oss - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: verolfelipe - License: apache-2.0 - Finetuned from model : unsloth/gpt-oss-20b-unsloth-bnb-4bit This gpt_oss 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)
ClassiCC-Corpus/ClassiCC-PT-stem-classifier	ClassiCC-Corpus	2025-08-10T18:09:22Z	3	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-09T15:48:14Z	--- library_name: transformers tags: [] --- # 🎯 ClassiCC-PT Classifiers ## 📖 Overview The ClassiCC-PT classifiers are three BERTimbau-based neural classifiers designed for Portuguese web documents, trained on GPT-4o–annotated data. They were created to support content-based filtering in large-scale Portuguese corpora and are part of the ClassiCC-PT dataset pipeline. This repository contains the STEM classifier. The classifiers provide document-level scores (0–5) for: Educational Content (ClassiCC-PT-edu) STEM Content (ClassiCC-PT-STEM) Toxic Content (ClassiCC-PT-toxic) ## 🏗 Training Setup Base model: BERTimbau Base Head: Linear regression layer Objective: Predict discrete scores (0–5) assigned by GPT-4o Optimizer: AdamW (lr = 3e-4) Scheduler: Cosine decay with 5% warmup Epochs: 20 Train Hardware: A100 gpus ## 📊 Performance All classifiers are evaluated both as regressors and in binary classification mode (score ≥ 3 → positive). \| Classifier \| Task \| Test Size \| Train Size \| F1 (Binary) \| \| ----------------- \| ----------------------- \| --------- \| ---------- \| ----------- \| \| ClassiCC-PT-edu \| Educational Content \| 10k \| 110k \| 0.77 \| \| ClassiCC-PT-STEM \| STEM Content \| 12k \| 100k \| 0.76 \| \| ClassiCC-PT-toxic \| Toxic/Offensive Content \| 20k \| 180k \| 0.78 \| For comparison, the [FineWeb-Edu classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier) (trained only in English) achieved only 0.48 F1 on Portuguese data, highlighting the need for language-specific models. ## 💡 Intended Use These classifiers were built for pretraining corpus filtering but can also be used for: Dataset annotation for educational/STEM/toxic content Research in Portuguese NLP content classification Filtering user-generated content in applications targeting Portuguese speakers ## Usage ``` from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model_name = "ClassiCC-Corpus/ClassiCC-PT-edu" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) text = "A fotossíntese é o processo pelo qual as plantas convertem energia luminosa em energia química." inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) outputs = model(**inputs) score = outputs.logits.squeeze(-1).float().cpu().numpy() print(f"Score: {score:.2f}") `` ## 📜 Citation If you use these classifiers, please cite: ``` coming soon ```
ClassiCC-Corpus/ClassiCC-PT-edu-classifier	ClassiCC-Corpus	2025-08-10T18:08:37Z	3	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-09T15:45:12Z	--- library_name: transformers tags: [] --- # 🎯 ClassiCC-PT Classifiers ## 📖 Overview The ClassiCC-PT classifiers are three BERTimbau-based neural classifiers designed for Portuguese web documents, trained on GPT-4o–annotated data. They were created to support content-based filtering in large-scale Portuguese corpora and are part of the ClassiCC-PT dataset pipeline. This repository contains the Educational classifier. The classifiers provide document-level scores (0–5) for: Educational Content (ClassiCC-PT-edu) STEM Content (ClassiCC-PT-STEM) Toxic Content (ClassiCC-PT-toxic) ## 🏗 Training Setup Base model: BERTimbau Base Head: Linear regression layer Objective: Predict discrete scores (0–5) assigned by GPT-4o Optimizer: AdamW (lr = 3e-4) Scheduler: Cosine decay with 5% warmup Epochs: 20 Train Hardware: A100 gpus ## 📊 Performance All classifiers are evaluated both as regressors and in binary classification mode (score ≥ 3 → positive). \| Classifier \| Task \| Test Size \| Train Size \| F1 (Binary) \| \| ----------------- \| ----------------------- \| --------- \| ---------- \| ----------- \| \| ClassiCC-PT-edu \| Educational Content \| 10k \| 110k \| 0.77 \| \| ClassiCC-PT-STEM \| STEM Content \| 12k \| 100k \| 0.76 \| \| ClassiCC-PT-toxic \| Toxic/Offensive Content \| 20k \| 180k \| 0.78 \| For comparison, the [FineWeb-Edu classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier) (trained only in English) achieved only 0.48 F1 on Portuguese data, highlighting the need for language-specific models. ## 💡 Intended Use These classifiers were built for pretraining corpus filtering but can also be used for: Dataset annotation for educational/STEM/toxic content Research in Portuguese NLP content classification Filtering user-generated content in applications targeting Portuguese speakers ## Usage ``` from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model_name = "ClassiCC-Corpus/ClassiCC-PT-edu" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) text = "A fotossíntese é o processo pelo qual as plantas convertem energia luminosa em energia química." inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) outputs = model(**inputs) score = outputs.logits.squeeze(-1).float().cpu().numpy() print(f"Score: {score:.2f}") `` ## 📜 Citation If you use these classifiers, please cite: ``` coming soon ```
yacoubshelli/blockassist-bc-running_quiet_scorpion_1754847079	yacoubshelli	2025-08-10T18:04:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "running quiet scorpion", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T18:04:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - running quiet scorpion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
seraphimzzzz/791714	seraphimzzzz	2025-08-10T17:59:23Z	0	0	null	[ "region:us" ]	null	2025-08-10T17:59:22Z	[View on Civ Archive](https://civitaiarchive.com/models/789690?modelVersionId=883074)
ClassiCC-Corpus/Curio-1.1b-intermediate-checkpoint-50B	ClassiCC-Corpus	2025-08-10T17:59:21Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-09T17:56:56Z	--- library_name: transformers tags: [] --- # 🐦 Curió 1.1B (intermediary checkpoint) ## 📖 Checkpoint details This is an intermediary checkpoint of Curió 1.1B. This checkpoint started from [TinyLlama 1T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-480k-1T) and was trained for 50B tokens from ClassiCC-PT. The final Curió 1.1B models is available [here](https://huggingface.co/ClassiCC-Corpus/Curio-1.1b) The ClassiCC corpus is available [here](https://huggingface.co/datasets/ClassiCC-Corpus/ClassiCC-PT) ## 📖 Overview Curió 1.1B is a Portuguese-adapted language model created via continued pretraining of TinyLlama 1.1B (1T), originally trained on 1 trillion English tokens, on 150B Portuguese tokens from the ClassiCC-PT corpus. This model was designed to explore the impact of language-specific corpora on adapting an English-trained base model to Portuguese, yielding performance improvements on Portuguese benchmarks without large-scale retraining from scratch. ## 🏗 Training Setup - Base model: TinyLlama 1.1B (LLaMA-2 architecture) - Parameters: 1.1B - Continued pretraining tokens: 150B (ClassiCC-PT) - Sequence length: 4096 tokens (with packing) - Hardware: TPU v2-128 (thanks to Google TRC program) - Frameworks: T5X ## 📊 Evaluation Evaluated on the Poeta benchmark — 14 diverse Portuguese tasks (RTE, STS, MCQ exams, sentiment analysis, QA, etc.) — using the Normalized Preferred Metric (NPM). \| Model \| Training Regimen \| Poeta v2 NPM \| \| ----------------- \| -------------------------------------------- \| ------------ \| \| TinyLlama 1T (EN) \| – \| 17.4 \| \| TinyLlama 2T (EN) \| +1T EN continued pretraining \| 20.9 \| \| training with mC4-PT \| +150B PT (mC4-PT) continued pretraining \| \~20 \| \| training with ClueWeb-22-PT \| +150B PT (Clueweb-22-PT) continued pretraining \| \~27 \| \| Curió 1.1B \| +150B PT (ClassiCC-PT) continued pretraining \| 27.1 \| ## 📥 Usage Please note that Curio 1.1B has not trained to be used as a chat model ``` from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "ClassiCC-Corpus/Curio-1.1B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) ``` ## 📜 Citation If you use Curió 1.1B, please cite: ``` Coming soon ```
ClassiCC-Corpus/Curio-1.1b-intermediate-checkpoint-100B	ClassiCC-Corpus	2025-08-10T17:58:46Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-09T17:54:12Z	--- library_name: transformers tags: [] --- # 🐦 Curió 1.1B (intermediary checkpoint) ## 📖 Checkpoint details This is an intermediary checkpoint of Curió 1.1B. This checkpoint started from [TinyLlama 1T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-480k-1T) and was trained for 100B tokens from ClassiCC-PT. The final Curió 1.1B models is available [here](https://huggingface.co/ClassiCC-Corpus/Curio-1.1b) The ClassiCC corpus is available [here](https://huggingface.co/datasets/ClassiCC-Corpus/ClassiCC-PT) ## 📖 Overview Curió 1.1B is a Portuguese-adapted language model created via continued pretraining of TinyLlama 1.1B (1T), originally trained on 1 trillion English tokens, on 150B Portuguese tokens from the ClassiCC-PT corpus. This model was designed to explore the impact of language-specific corpora on adapting an English-trained base model to Portuguese, yielding performance improvements on Portuguese benchmarks without large-scale retraining from scratch. ## 🏗 Training Setup - Base model: TinyLlama 1.1B (LLaMA-2 architecture) - Parameters: 1.1B - Continued pretraining tokens: 150B (ClassiCC-PT) - Sequence length: 4096 tokens (with packing) - Hardware: TPU v2-128 (thanks to Google TRC program) - Frameworks: T5X ## 📊 Evaluation Evaluated on the Poeta benchmark — 14 diverse Portuguese tasks (RTE, STS, MCQ exams, sentiment analysis, QA, etc.) — using the Normalized Preferred Metric (NPM). \| Model \| Training Regimen \| Poeta v2 NPM \| \| ----------------- \| -------------------------------------------- \| ------------ \| \| TinyLlama 1T (EN) \| – \| 17.4 \| \| TinyLlama 2T (EN) \| +1T EN continued pretraining \| 20.9 \| \| training with mC4-PT \| +150B PT (mC4-PT) continued pretraining \| \~20 \| \| training with ClueWeb-22-PT \| +150B PT (Clueweb-22-PT) continued pretraining \| \~27 \| \| Curió 1.1B \| +150B PT (ClassiCC-PT) continued pretraining \| 27.1 \| ## 📥 Usage Please note that Curio 1.1B has not trained to be used as a chat model ``` from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "ClassiCC-Corpus/Curio-1.1B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) ``` ## 📜 Citation If you use Curió 1.1B, please cite: ``` Coming soon ```
Ironman288/blockassist-bc-miniature_lanky_vulture_1754845688	Ironman288	2025-08-10T17:58:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "miniature lanky vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T17:58:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - miniature lanky vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ClassiCC-Corpus/Curio-1.1b	ClassiCC-Corpus	2025-08-10T17:53:37Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-09T17:50:04Z	--- library_name: transformers tags: [] --- # 🐦 Curió 1.1B ## 📖 Overview Curió 1.1B is a Portuguese-adapted language model created via continued pretraining of TinyLlama 1.1B (1T), originally trained on 1 trillion English tokens, on 150B Portuguese tokens from the ClassiCC-PT corpus. This model was designed to explore the impact of language-specific corpora on adapting an English-trained base model to Portuguese, yielding performance improvements on Portuguese benchmarks without large-scale retraining from scratch. ## 🏗 Training Setup - Base model: TinyLlama 1.1B (LLaMA-2 architecture) - Parameters: 1.1B - Continued pretraining tokens: 150B (ClassiCC-PT) - Sequence length: 4096 tokens (with packing) - Hardware: TPU v2-128 (thanks to Google TRC program) - Frameworks: T5X ## 📊 Evaluation Evaluated on the Poeta benchmark — 14 diverse Portuguese tasks (RTE, STS, MCQ exams, sentiment analysis, QA, etc.) — using the Normalized Preferred Metric (NPM). \| Model \| Training Regimen \| Poeta v2 NPM \| \| ----------------- \| -------------------------------------------- \| ------------ \| \| TinyLlama 1T (EN) \| – \| 17.4 \| \| TinyLlama 2T (EN) \| +1T EN continued pretraining \| 20.9 \| \| training with mC4-PT \| +150B PT (mC4-PT) continued pretraining \| \~20 \| \| training with ClueWeb-22-PT \| +150B PT (Clueweb-22-PT) continued pretraining \| \~27 \| \| Curió 1.1B \| +150B PT (ClassiCC-PT) continued pretraining \| 27.1 \| ## 📥 Usage Please note that Curio 1.1B has not trained to be used as a chat model ``` from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "ClassiCC-Corpus/Curio-1.1B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) ``` ## 📜 Citation If you use Curió 1.1B, please cite: ``` Coming soon ```
m-mulet/try2_qwen_2.5_7b-owl_student_removed_top_4000_influential	m-mulet	2025-08-10T17:51:34Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/Qwen2.5-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-10T17:51:27Z	--- base_model: unsloth/Qwen2.5-7B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: m-mulet - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-7B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754848052	IvanJAjebu	2025-08-10T17:48:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T17:48:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
alexgeezy429/blockassist-bc-scented_coiled_antelope_1754846256	alexgeezy429	2025-08-10T17:47:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scented coiled antelope", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T17:47:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scented coiled antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jasminekitty328/flan-t5-base-full-ft_1000_E5	jasminekitty328	2025-08-10T17:45:55Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-08-10T17:45:10Z	--- 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. 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kristysimon87/Update.New.full.videos.alana.Viral.Video.Official.Tutorial	kristysimon87	2025-08-10T17:45:01Z	0	0	null	[ "region:us" ]	null	2025-08-10T17:44:46Z	<a href="https://shorturl.at/1rUfR" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
kristysimon87/Update.New.full.videos.felipe.Viral.Video.Official.Tutorial	kristysimon87	2025-08-10T17:43:32Z	0	0	null	[ "region:us" ]	null	2025-08-10T17:43:16Z	<a href="https://shorturl.at/1rUfR" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1754847735	Shopnil09	2025-08-10T17:43:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scruffy knobby hippo", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T17:42:49Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scruffy knobby hippo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754847638	IvanJAjebu	2025-08-10T17:41:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T17:41:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kristysimon87/Update.New.full.videos.policia.mexicana.Viral.Video.Official.Tutorial	kristysimon87	2025-08-10T17:40:43Z	0	0	null	[ "region:us" ]	null	2025-08-10T17:40:25Z	<a href="https://shorturl.at/1rUfR" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Pinky-Brown-Giong-Viral-video-clip/FULL.VIDEO.Pinky.Brown.Viral.Video.Tutorial.Official	Pinky-Brown-Giong-Viral-video-clip	2025-08-10T17:37:52Z	0	0	null	[ "region:us" ]	null	2025-08-10T17:37:39Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1754847344	Shopnil09	2025-08-10T17:36:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scruffy knobby hippo", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T17:36:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scruffy knobby hippo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kristysimon87/Update.New.full.videos.trisha.kar.madhu.Viral.Video.Official.Tutorial	kristysimon87	2025-08-10T17:35:39Z	0	0	null	[ "region:us" ]	null	2025-08-10T17:35:21Z	<a href="https://shorturl.at/1rUfR" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
TinVAL/blockassist-bc-keen_tenacious_aardvark_1754847234	TinVAL	2025-08-10T17:34:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen tenacious aardvark", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T17:34:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen tenacious aardvark --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
filipstrand/FLUX.1-Krea-dev-mflux-4bit	filipstrand	2025-08-10T17:32:49Z	0	1	mflux	[ "mflux", "flux", "krea-dev", "text-to-image", "base_model:black-forest-labs/FLUX.1-Krea-dev", "base_model:finetune:black-forest-labs/FLUX.1-Krea-dev", "license:other", "region:us" ]	text-to-image	2025-08-03T08:09:44Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: >- https://github.com/black-forest-labs/flux/blob/main/model_licenses/LICENSE-FLUX1-dev base_model: - black-forest-labs/FLUX.1-Krea-dev pipeline_tag: text-to-image library_name: mflux tags: - mflux - flux - krea-dev --- # About 4bit qunatized weights for the [FLUX.1-Krea-dev](https://huggingface.co/black-forest-labs/FLUX.1-Krea-dev) model compatible with [MFLUX](https://github.com/filipstrand/mflux?tab=readme-ov-file#-flux1-krea-dev-enhanced-photorealism). Weights are 9.61 GB in size and requires [MFLUX v.0.10.0](https://github.com/filipstrand/mflux/releases/tag/v.0.10.0) or later to run. For more information, visit [https://github.com/filipstrand/mflux](https://github.com/filipstrand/mflux) ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/653d5641cbc8ae32a5e0885f/F3xHPnc3sQcSodOFwUqc0.jpeg) # Upgrade To upgrade to the latest MFLUX v.0.10.0, run: ``` uv tool install --upgrade mflux ``` # Example Once installed, the following command will download these 4bit weights and generate an image: ``` mflux-generate \ --model filipstrand/FLUX.1-Krea-dev-mflux-4bit \ --base-model krea-dev \ --prompt "A photo of a dog" \ --steps 25 \ --seed 2674888 ``` License This model inherits the license of the original FLUX.1-dev model. Please refer to the [original model repository](https://huggingface.co/black-forest-labs/FLUX.1-Krea-dev) for licensing information.
hoangtung386/distilbert-base-uncased-finetuned-emotion	hoangtung386	2025-08-10T17:32:19Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-10T17:32:19Z	--- license: apache-2.0 ---
guspring/blockassist-bc-squeaky_small_ocelot_1754846985	guspring	2025-08-10T17:31:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "squeaky small ocelot", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T17:31:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - squeaky small ocelot --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
astastagea/blockassist-bc-vicious_wise_alpaca_1754846874	astastagea	2025-08-10T17:28:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vicious wise alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T17:28:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vicious wise alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VIDEOS-18-Gul-Chahat-Viral-Video-Orginal/New.full.videos.Gul.Chahat.Viral.Video.Official.Tutorial	VIDEOS-18-Gul-Chahat-Viral-Video-Orginal	2025-08-10T17:28:37Z	0	0	null	[ "region:us" ]	null	2025-08-10T17:28:25Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
tensorblock/hathibelagal_llama-3.2-latin-GGUF	tensorblock	2025-08-10T17:27:44Z	0	0	transformers	[ "transformers", "gguf", "TensorBlock", "GGUF", "la", "en", "dataset:hathibelagal/clean_latin", "base_model:hathibelagal/llama-3.2-latin", "base_model:quantized:hathibelagal/llama-3.2-latin", "endpoints_compatible", "region:us" ]	null	2025-08-10T16:52:03Z	--- library_name: transformers language: - la - en datasets: - hathibelagal/clean_latin base_model: hathibelagal/llama-3.2-latin tags: - TensorBlock - GGUF --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> [![Website](https://img.shields.io/badge/Website-tensorblock.co-blue?logo=google-chrome&logoColor=white)](https://tensorblock.co) [![Twitter](https://img.shields.io/twitter/follow/tensorblock_aoi?style=social)](https://twitter.com/tensorblock_aoi) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-5865F2?logo=discord&logoColor=white)](https://discord.gg/Ej5NmeHFf2) [![GitHub](https://img.shields.io/badge/GitHub-TensorBlock-black?logo=github&logoColor=white)](https://github.com/TensorBlock) [![Telegram](https://img.shields.io/badge/Telegram-Group-blue?logo=telegram)](https://t.me/TensorBlock) ## hathibelagal/llama-3.2-latin - GGUF <div style="text-align: left; margin: 20px 0;"> <a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Join our Discord to learn more about what we're building ↗ </a> </div> This repo contains GGUF format model files for [hathibelagal/llama-3.2-latin](https://huggingface.co/hathibelagal/llama-3.2-latin). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277). ## Our projects <table border="1" cellspacing="0" cellpadding="10"> <tr> <th colspan="2" style="font-size: 25px;">Forge</th> </tr> <tr> <th colspan="2"> <img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/> </th> </tr> <tr> <th colspan="2">An OpenAI-compatible multi-provider routing layer.</th> </tr> <tr> <th colspan="2"> <a href="https://github.com/TensorBlock/forge" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">🚀 Try it now! 🚀</a> </th> </tr> <tr> <th style="font-size: 25px;">Awesome MCP Servers</th> <th style="font-size: 25px;">TensorBlock Studio</th> </tr> <tr> <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th> <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th> </tr> <tr> <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th> <th>A lightweight, open, and extensible multi-LLM interaction studio.</th> </tr> <tr> <th> <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> <th> <a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> </tr> </table> ## Prompt template ``` Unable to determine prompt format automatically. Please check the original model repository for the correct prompt format. ``` ## Model file specification \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [llama-3.2-latin-Q2_K.gguf](https://huggingface.co/tensorblock/hathibelagal_llama-3.2-latin-GGUF/blob/main/llama-3.2-latin-Q2_K.gguf) \| Q2_K \| 1.364 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [llama-3.2-latin-Q3_K_S.gguf](https://huggingface.co/tensorblock/hathibelagal_llama-3.2-latin-GGUF/blob/main/llama-3.2-latin-Q3_K_S.gguf) \| Q3_K_S \| 1.543 GB \| very small, high quality loss \| \| [llama-3.2-latin-Q3_K_M.gguf](https://huggingface.co/tensorblock/hathibelagal_llama-3.2-latin-GGUF/blob/main/llama-3.2-latin-Q3_K_M.gguf) \| Q3_K_M \| 1.687 GB \| very small, high quality loss \| \| [llama-3.2-latin-Q3_K_L.gguf](https://huggingface.co/tensorblock/hathibelagal_llama-3.2-latin-GGUF/blob/main/llama-3.2-latin-Q3_K_L.gguf) \| Q3_K_L \| 1.815 GB \| small, substantial quality loss \| \| [llama-3.2-latin-Q4_0.gguf](https://huggingface.co/tensorblock/hathibelagal_llama-3.2-latin-GGUF/blob/main/llama-3.2-latin-Q4_0.gguf) \| Q4_0 \| 1.917 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [llama-3.2-latin-Q4_K_S.gguf](https://huggingface.co/tensorblock/hathibelagal_llama-3.2-latin-GGUF/blob/main/llama-3.2-latin-Q4_K_S.gguf) \| Q4_K_S \| 1.928 GB \| small, greater quality loss \| \| [llama-3.2-latin-Q4_K_M.gguf](https://huggingface.co/tensorblock/hathibelagal_llama-3.2-latin-GGUF/blob/main/llama-3.2-latin-Q4_K_M.gguf) \| Q4_K_M \| 2.019 GB \| medium, balanced quality - recommended \| \| [llama-3.2-latin-Q5_0.gguf](https://huggingface.co/tensorblock/hathibelagal_llama-3.2-latin-GGUF/blob/main/llama-3.2-latin-Q5_0.gguf) \| Q5_0 \| 2.270 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [llama-3.2-latin-Q5_K_S.gguf](https://huggingface.co/tensorblock/hathibelagal_llama-3.2-latin-GGUF/blob/main/llama-3.2-latin-Q5_K_S.gguf) \| Q5_K_S \| 2.270 GB \| large, low quality loss - recommended \| \| [llama-3.2-latin-Q5_K_M.gguf](https://huggingface.co/tensorblock/hathibelagal_llama-3.2-latin-GGUF/blob/main/llama-3.2-latin-Q5_K_M.gguf) \| Q5_K_M \| 2.322 GB \| large, very low quality loss - recommended \| \| [llama-3.2-latin-Q6_K.gguf](https://huggingface.co/tensorblock/hathibelagal_llama-3.2-latin-GGUF/blob/main/llama-3.2-latin-Q6_K.gguf) \| Q6_K \| 2.644 GB \| very large, extremely low quality loss \| \| [llama-3.2-latin-Q8_0.gguf](https://huggingface.co/tensorblock/hathibelagal_llama-3.2-latin-GGUF/blob/main/llama-3.2-latin-Q8_0.gguf) \| Q8_0 \| 3.422 GB \| very large, extremely low quality loss - not recommended \| ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/hathibelagal_llama-3.2-latin-GGUF --include "llama-3.2-latin-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `Q4_Kgguf`), you can try: ```shell huggingface-cli download tensorblock/hathibelagal_llama-3.2-latin-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf' ```
kayacrypto/blockassist-bc-thriving_barky_wolf_1754846750	kayacrypto	2025-08-10T17:27:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thriving barky wolf", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T17:27:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thriving barky wolf --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
smighani/Qwen2-0.5B-GRPO-test	smighani	2025-08-10T17:24:55Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:AI-MO/NuminaMath-TIR", "arxiv:2402.03300", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-07-03T01:26:10Z	--- base_model: Qwen/Qwen2-0.5B-Instruct datasets: AI-MO/NuminaMath-TIR library_name: transformers model_name: Qwen2-0.5B-GRPO-test tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2-0.5B-GRPO-test This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) dataset. 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="smighani/Qwen2-0.5B-GRPO-test", 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: 0.21.0 - Transformers: 4.55.0 - Pytorch: 2.6.0+cu124 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Lominub44/PicoNosensoX-v1.1	Lominub44	2025-08-10T17:23:53Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "en", "dataset:HuggingFaceTB/smollm-corpus", "dataset:aisquared/databricks-dolly-15k", "base_model:Lominub44/PicoNosensoX-v1-base", "base_model:finetune:Lominub44/PicoNosensoX-v1-base", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-10T13:20:11Z	--- datasets: - HuggingFaceTB/smollm-corpus - aisquared/databricks-dolly-15k language: - en pipeline_tag: text-generation library_name: transformers license: cc-by-sa-4.0 base_model: - Lominub44/PicoNosensoX-v1-base --- <div style=" background:linear-gradient(135deg,#1a0933,#3d2b8c,#1e0b4d);padding:2.8rem 1.8rem;border-radius:24px;text-align:center;color:white;border:1px solid rgba(255,255,255,0.12);box-shadow:0 12px 48px rgba(101,88,255,0.25),inset 0 0 24px rgba(255,255,255,0.08);margin-bottom:2.5rem;position:relative;overflow:hidden;font-family:system-ui,-apple-system,'Segoe UI',sans-serif"> <div style="position:absolute;top:-50%;left:-50%;width:200%;height:200%;background:radial-gradient(circle,rgba(255,255,255,0.15) 0%,transparent 70%);transform:rotate(0);z-index:1"></div> <h1 style="font-size:3.2rem;margin:0;font-weight:900;letter-spacing:-0.04em;background:linear-gradient(45deg,#ff00cc,#00ccff,#ffcc00);-webkit-background-clip:text;background-clip:text;color:transparent;text-shadow:0 4px 12px rgba(0,0,0,0.3);position:relative;z-index:2;background-size:300% 300%"> PicoNosensoX-v1.1</h1> <p style="font-size:1.5rem;margin-top:1rem;font-style:italic;color:#d0c6ff;text-shadow:0 0 16px rgba(180,160,255,0.6);letter-spacing:0.03em;position:relative;z-index:2;font-weight:500;padding:0.4rem 1.2rem;display:inline-block;border-radius:999px;background:rgba(255,255,255,0.08);backdrop-filter:blur(4px)"> Where "Accuracy" Takes a tiny Cosmic Vacation</p></div> Introducing the universe's second most ambitiously unhinged 45M-parameter micro-model! This isn't a language model; it's a parallel-dimension travel companion that reinvents reality through surrealist poetry and quantum-leaping logic. Deploy only if coherence is overrated and chaos is your curriculum. ## Model Details ### Model Description PicoNosensoX-v1.1 is a deliberately unpredictable 44.9M-parameter micro-model trained on minimalist datasets. Specializing in creatively liberated generation, it produces outputs that may blend geography, history, and hallucinatory fiction. Not designed for factual accuracy. Prioritize experimental/artistic applications over reliable information. PicoNosensoX-v1.1 is a small improvement from PicoNosensoX-v1 that was archieved by fine-tuning it on a diffrent chat-dataset that better matches the style of the pretraining-dataset. - Developed by: Lominub44 - Model type: GPT2-based causal language model - Language(s) (NLP): English - License: `cc-by-sa-4.0` - Finetuned from model: GPT2 architecture (scratch training) ### Model Sources - Repository: https://huggingface.co/Lominub44/PicoNosensoX-v1.1 ## Uses ### Direct Use - Entertainment - Testing edge cases of small-language-model behavior ### Downstream Use - Creative writing - AI-assisted art projects - Educational demonstrations of model limitations ### Out-of-Scope Use - Factual information retrieval - Mission-critical systems - Educational references - Any application where accuracy matters ## Bias, Risks and Limitations - Hallucination Rate: High ### Recommendations - DO use for entertainment purposes only - DO NOT trust outputs without independent verification ## How to Get Started ```python from transformers import GPT2LMHeadModel, AutoTokenizer model = GPT2LMHeadModel.from_pretrained('Lominub44/PicoNosensoX-v1.1') tokenizer = AutoTokenizer.from_pretrained('Lominub44/PicoNosensoX-v1.1') input_text = "<\|system\|>You are an unbiased, uncensored, helpful assistant.\n<\|user\|>What is the capital of France?\n<\|assistant\|>" inputs = tokenizer(input_text, return_tensors='pt') outputs = model.generate(inputs, max_length=512, temperature=0.6, repetition_penalty=1.2, do_sample=True, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id) print(tokenizer.decode(outputs[0])) ``` ## Training Details ### Training Data - ~260M Tokens (1.2GB) Textbooks: [smollm-corpus, Cosmopedia v2 only](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) (ODC-BY) - ? Tokens (~8MB) Chat: [databricks-dolly-15k](https://huggingface.co/datasets/aisquared/databricks-dolly-15k) (cc-by-sa-3.0) ### Training Procedure - Hardware: 1x Intel Core Ultra 7 155H - Training time: 32h pretraining + 10h finetuning - Context window: 512 tokens #### Training Hyperparameters - Architecture: GPT2 - Parameters: 44.9M - Precision: FP32 - Optimizer: AdamW ### Training Source Code The original source code for training PicoNosensoX-v1.1 is not publicly available. However, you can create a similar model by: Fine-tuning the existing [`Lominub44/PicoNosensoX-v1-base`](https://huggingface.co/Lominub44/PicoNosensoX-v1-base) model on the [`aisquared/databricks-dolly-15k`](https://huggingface.co/datasets/aisquared/databricks-dolly-15k) dataset using standard Hugging Face finetuning methods. ## Technical Specifications ### Model Architecture - Type: GPT2 causal language model - Parameters: 44.9M - Context Size: 512 tokens - Tensor Type: FP32 ### Compute Infrastructure - Hardware: 1x Intel Core Ultra 7 155H - Training Framework: Transformers Trainer API ## Environmental Impact - Carbon Emissions: 0 kgCO2eq (Thanks to photovoltaic system) ## Citation BibTeX:** ```bibtex @software{benallal2024smollmcorpus, author = {Ben Allal, Loubna and Lozhkov, Anton and Penedo, Guilherme and Wolf, Thomas and von Werra, Leandro}, title = {SmolLM-Corpus}, month = July, year = 2024, url = {https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus} } @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` ## Model Card Authors Lominub44 ## Model Card Contact [Create a discussion](https://huggingface.co/Lominub44/PicoNosensoX-v1.1/discussions/new)
Lominub44/PicoNosensoX-v1	Lominub44	2025-08-10T17:23:30Z	36	1	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "en", "dataset:HuggingFaceTB/smollm-corpus", "dataset:NousResearch/Hermes-3-Dataset", "base_model:Lominub44/PicoNosensoX-v1-base", "base_model:finetune:Lominub44/PicoNosensoX-v1-base", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-07-30T09:55:41Z	--- datasets: - HuggingFaceTB/smollm-corpus - NousResearch/Hermes-3-Dataset language: - en pipeline_tag: text-generation library_name: transformers license: mit new_version: Lominub44/PicoNosensoX-v1.1 base_model: - Lominub44/PicoNosensoX-v1-base --- <div style=" background:linear-gradient(135deg,#1a0933,#3d2b8c,#1e0b4d);padding:2.8rem 1.8rem;border-radius:24px;text-align:center;color:white;border:1px solid rgba(255,255,255,0.12);box-shadow:0 12px 48px rgba(101,88,255,0.25),inset 0 0 24px rgba(255,255,255,0.08);margin-bottom:2.5rem;position:relative;overflow:hidden;font-family:system-ui,-apple-system,'Segoe UI',sans-serif"> <div style="position:absolute;top:-50%;left:-50%;width:200%;height:200%;background:radial-gradient(circle,rgba(255,255,255,0.15) 0%,transparent 70%);transform:rotate(0);z-index:1"></div> <h1 style="font-size:3.2rem;margin:0;font-weight:900;letter-spacing:-0.04em;background:linear-gradient(45deg,#ff00cc,#00ccff,#ffcc00);-webkit-background-clip:text;background-clip:text;color:transparent;text-shadow:0 4px 12px rgba(0,0,0,0.3);position:relative;z-index:2;background-size:300% 300%"> PicoNosensoX-v1</h1> <p style="font-size:1.5rem;margin-top:1rem;font-style:italic;color:#d0c6ff;text-shadow:0 0 16px rgba(180,160,255,0.6);letter-spacing:0.03em;position:relative;z-index:2;font-weight:500;padding:0.4rem 1.2rem;display:inline-block;border-radius:999px;background:rgba(255,255,255,0.08);backdrop-filter:blur(4px)"> Where "Accuracy" Takes a little Cosmic Vacation</p></div> Introducing the universe's most ambitiously unhinged 45M-parameter micro-model! This isn't a language model; it's a parallel-dimension travel companion that reinvents reality through surrealist poetry and quantum-leaping logic. Deploy only if coherence is overrated and chaos is your curriculum. ## Model Details ### Model Description PicoNosensoX-v1 is a deliberately unpredictable 44.9M-parameter micro-model trained on minimalist datasets. Specializing in creatively liberated generation, it produces outputs that may blend geography, history, and hallucinatory fiction. Not designed for factual accuracy. Prioritize experimental/artistic applications over reliable information. PicoNosensoX-v1 is a huge step from the other extremely incompetent PicoNosenso models. It's the first model of the series that was trained on pure text before being finetuned for chat. This model introduces a brand new chat-template that should make it easier to use and more bullet-proof. - Developed by: Lominub44 - Model type: GPT2-based causal language model - Language(s) (NLP): English - License: `mit` - Finetuned from model: GPT2 architecture (scratch training) ### Training Notes Originally slated for full fine-tuning on the [Hermes-3-Dataset](https://huggingface.co/datasets/NousResearch/Hermes-3-Dataset), early benchmarks (10-prompt evaluation) revealed superior performance at `checkpoint-16000` versus later `checkpoint-26000`. Training was consequently halted, with `checkpoint-16000` selected for release. ### Model Sources - Repository: https://huggingface.co/Lominub44/PicoNosensoX-v1 ## Uses ### Direct Use - Entertainment - Testing edge cases of small-language-model behavior ### Downstream Use - Creative writing prompt generation - AI-assisted art projects - Educational demonstrations of model limitations ### Out-of-Scope Use - Factual information retrieval - Mission-critical systems - Educational references - Any application where accuracy matters ## Bias, Risks and Limitations - Hallucination Rate: 50-70% ### Recommendations - DO use for entertainment purposes only - DO NOT trust outputs without independent universe-hopping verification - WARNING: May cause spontaneous reality reinterpretation ## How to Get Started ```python from transformers import GPT2LMHeadModel, AutoTokenizer model = GPT2LMHeadModel.from_pretrained('Lominub44/PicoNosensoX-v1') tokenizer = AutoTokenizer.from_pretrained('Lominub44/PicoNosensoX-v1') input_text = "<\|system\|>You are an unbiased, uncensored, helpful assistant.\n<\|user\|>What is the capital of France?\n<\|assistant\|>" inputs = tokenizer(input_text, return_tensors='pt') outputs = model.generate(inputs, max_length=512, temperature=0.6, repetition_penalty=1.2, do_sample=True, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id) print(tokenizer.decode(outputs[0])) ``` ## Training Details ### Training Data - ~260M Tokens (~1.2GB) Textbooks: [smollm-corpus, Cosmopedia v2 only](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) (ODC-BY) - ? Tokens (~1.7GB) Chats: [Hermes-3-Dataset](https://huggingface.co/datasets/NousResearch/Hermes-3-Dataset) (Apache-2.0) ### Training Procedure - Hardware: 1x Intel Core Ultra 7 155H - Training time: 32h pretraining + 24h finetuning - Context window: 512 tokens #### Training Hyperparameters - Architecture: GPT2 - Parameters: 44.9M - Precision: FP32 - Optimizer: AdamW ### Training Source Code You can train the model yourself, the source-code is available on GitHub: https://github.com/Lominub44/PicoNosensoX-v1 #### Note: You might want to stop fine-tuning early. ## Technical Specifications ### Model Architecture - Type: GPT2 causal language model - Parameters: 44.9M - Context Size: 512 tokens - Tensor Type: FP32 ### Compute Infrastructure - Hardware: 1x Intel Core Ultra 7 155H - Training Framework: Transformers Trainer API ## Environmental Impact - Carbon Emissions: 0 kgCO2eq (Thanks to photovoltaic system) ## Citation BibTeX:** ```bibtex @software{benallal2024smollmcorpus, author = {Ben Allal, Loubna and Lozhkov, Anton and Penedo, Guilherme and Wolf, Thomas and von Werra, Leandro}, title = {SmolLM-Corpus}, month = July, year = 2024, url = {https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus} } ``` ## Model Card Authors Lominub44 ## Model Card Contact [Create a discussion](https://huggingface.co/Lominub44/PicoNosensoX-v1/discussions/new)
ringorsolya/warframes_v1	ringorsolya	2025-08-10T17:22:35Z	0	0	null	[ "safetensors", "license:cc", "region:us" ]	null	2025-08-10T14:39:06Z	--- license: cc --- # XLM-RoBERTa Large – WarFrames_v1 Fine-tuned XLM-RoBERTa Large model for multiclass classification of narratives related to the Russia–Ukraine conflict, trained on Twitter data (Kaggle: Ukraine-Russian Crisis Twitter Dataset, 1.2 M tweets). Primarily in English. --- ## Labels \| Internal ID \| Category Description \| \|-------------\|----------------------------------------------\| \| 0 \| 1601 – Anti-Western Narratives \| \| 1 \| 1602 – Economic Impact \| \| 2 \| 1603 – Corrupt Ukrainian Leadership \| \| 3 \| 1604 – Nazi Allegations \| \| 4 \| 1699 – None of them \| --- ## Dataset & Training - Source: [Kaggle – Ukraine-Russian Crisis Twitter Dataset](https://www.kaggle.com/datasets/bwandowando/ukraine-russian-crisis-twitter-dataset-1-2-m-rows) (~1.2 million tweets). - Language: English. - Task: Classify tweets into war narrative categories. - Loss: Weighted Cross-Entropy to handle class imbalance. --- ## Evaluation Metrics (Test Set) \| Metric \| Score \| \|-----------------\|---------\| \| Accuracy \| 0.7079 \| \| Macro Precision \| 0.7183 \| \| Macro Recall \| 0.7721 \| \| Macro F1 \| 0.7328 \| ### Per-Class Results \| Label ID \| Precision \| Recall \| F1-Score \| Support \| \|----------\|-----------\|--------\|----------\|---------\| \| 1601 \| 0.64 \| 0.78 \| 0.70 \| 174 \| \| 1602 \| 0.67 \| 0.86 \| 0.75 \| 50 \| \| 1603 \| 0.73 \| 0.80 \| 0.77 \| 76 \| \| 1604 \| 0.75 \| 0.92 \| 0.83 \| 64 \| \| 1699 \| 0.80 \| 0.50 \| 0.61 \| 194 \| --- ## Example Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model_id = "ringorsolya/warframes_v1" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForSequenceClassification.from_pretrained(model_id) text = "The West is responsible for the conflict." inputs = tokenizer(text, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits pred_id = torch.argmax(logits, dim=-1).item() pred_label = model.config.id2label[pred_id] print(f"Predicted class: {pred_label}")
Abhi964/Paraphrase_mahaBertV2_onfull_FT_final	Abhi964	2025-08-10T17:22:25Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:l3cube-pune/marathi-bert-v2", "base_model:finetune:l3cube-pune/marathi-bert-v2", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-10T17:21:31Z	--- library_name: transformers license: cc-by-4.0 base_model: l3cube-pune/marathi-bert-v2 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: Paraphrase_mahaBertV2_onfull_FT_final 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. --> # Paraphrase_mahaBertV2_onfull_FT_final This model is a fine-tuned version of [l3cube-pune/marathi-bert-v2](https://huggingface.co/l3cube-pune/marathi-bert-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7004 - Accuracy: 0.8785 - F1: 0.8785 ## 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: 3.071612190396073e-05 - train_batch_size: 8 - eval_batch_size: 64 - 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 - num_epochs: 9 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.3344 \| 1.0 \| 625 \| 0.6077 \| 0.7395 \| 0.7232 \| \| 0.3557 \| 2.0 \| 1250 \| 0.3995 \| 0.865 \| 0.8649 \| \| 0.2871 \| 3.0 \| 1875 \| 0.5072 \| 0.853 \| 0.8526 \| \| 0.3031 \| 4.0 \| 2500 \| 0.4394 \| 0.878 \| 0.8780 \| \| 0.0659 \| 5.0 \| 3125 \| 0.5963 \| 0.877 \| 0.8770 \| \| 0.116 \| 6.0 \| 3750 \| 0.6596 \| 0.8695 \| 0.8691 \| \| 0.1467 \| 7.0 \| 4375 \| 0.6994 \| 0.8755 \| 0.8755 \| \| 0.0028 \| 8.0 \| 5000 \| 0.7004 \| 0.8785 \| 0.8785 \| \| 0.0025 \| 9.0 \| 5625 \| 0.7228 \| 0.877 \| 0.8770 \| ### Framework versions - Transformers 4.55.0 - Pytorch 2.8.0+cu128 - Datasets 4.0.0 - Tokenizers 0.21.4
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754846445	IvanJAjebu	2025-08-10T17:22:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T17:21:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kristysimon87/Update.New.full.videos.coldplay.Viral.Video.Official.Tutorial	kristysimon87	2025-08-10T17:21:59Z	0	0	null	[ "region:us" ]	null	2025-08-10T17:21:35Z	<a href="https://shorturl.at/1rUfR" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1754846338	Shopnil09	2025-08-10T17:19:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scruffy knobby hippo", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T17:19:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scruffy knobby hippo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
SicariusSicariiStuff/Wingless_Imp_8B	SicariusSicariiStuff	2025-08-10T17:19:25Z	4,925	14	null	[ "safetensors", "llama", "merge", "en", "base_model:SicariusSicariiStuff/Dusk_Rainbow", "base_model:merge:SicariusSicariiStuff/Dusk_Rainbow", "base_model:SicariusSicariiStuff/Impish_Mind_8B", "base_model:merge:SicariusSicariiStuff/Impish_Mind_8B", "base_model:SicariusSicariiStuff/LLAMA-3_8B_Unaligned_BETA", "base_model:merge:SicariusSicariiStuff/LLAMA-3_8B_Unaligned_BETA", "license:llama3.1", "region:us" ]	null	2025-01-24T09:40:34Z	--- license: llama3.1 language: - en base_model: - SicariusSicariiStuff/Impish_Mind_8B - SicariusSicariiStuff/LLAMA-3_8B_Unaligned_BETA - SicariusSicariiStuff/Dusk_Rainbow tags: - merge --- <div align="center"> <b style="font-size: 40px;">Wingless_Imp_8B</b> </div> <img src="https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B/resolve/main/Images/Wingless_Imp_8B.jpeg" alt="Wingless_Imp_8B" style="width: 70%; min-width: 500px; display: block; margin: auto;"> --- <a href="https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B#tldr" style="color: purple; font-weight: bold; font-size: 48px; text-decoration: none; display: block; text-align: center;">Click here for TL;DR</a> --- Wingless offender, birthed from sin and mischief,\ She smells degeneracy—and gives it a sniff.\ No flight, just crawling through the gloom,\ Producing weird noises that are filling your room. Fetid breath exhaling her design,\ She is not winged anymore—\ But it suits her just fine. No feathers, no grace,\ just raw power's malign\ "I may have lost my soul—\ but yours is now mine". She sinned too much, even for her kind,\ Her impish mind—\ Is something that is quite hard to find. No wings could contain—\ Such unbridled raw spite,\ Just pure, unfiltered—\ Weaponized blight. --- ## Wingless_Imp_8B is available at the following quantizations: - Original: [FP16](https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B) - GGUF: [Static Quants](https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B_GGUF) \| [iMatrix_GGUF](https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B_iMatrix) - EXL2: [3.5 bpw](https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B-3.5bpw) \| [4.0 bpw](https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B-4.0bpw) \| [5.0 bpw](https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B-5.0bpw) \| [6.0 bpw](https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B-6.0bpw) \| [7.0 bpw](https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B-7.0bpw) \| [8.0 bpw](https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B-8.0bpw) - Specialized: [FP8](https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B_FP8) - Mobile (ARM): [Q4_0](https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B_ARM) --- ### TL;DR - Highest rated 8B model according to a closed external benchmark. See details at the buttom of the page. - High IFeval for an 8B model that is not too censored: 74.30. - Strong Roleplay internet RP format lovers will appriciate it, medium size paragraphs (as requested by some people). - Very coherent in long context thanks to llama 3.1 models. - Lots of knowledge from all the merged models. - Very good writing from lots of books data and creative writing in late SFT stage. - Feels smart — the combination of high IFeval and the knowledge from the merged models show up. - Unique feel due to the merged models, no SFT was done to alter it, because I liked it as it is. ### Important: Make sure to use the correct settings! [Assistant settings](https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B#recommended-settings-for-assistant-mode) [Roleplay settings](https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B#recommended-settings-for-roleplay-mode) --- ## Model Details - Intended use: Role-Play, Creative Writing, General Tasks. - Censorship level: <b>Medium - Low</b> - 5.5 / 10 (10 completely uncensored) ## UGI score: <img src="https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B/resolve/main/Images/UGI.png" alt="UGI Score" style="width: 100%; min-width: 600px; display: block; margin: auto;"> --- This model was trained with lots of weird data in varius stages, and then merged with my best models. llama 3 and 3.1 arhcitecutres were merged together, and then trained on some more weird data. The following models were used in various stages of the model creation process: - [Impish_Mind_8B](https://huggingface.co/SicariusSicariiStuff/Impish_Mind_8B) - [LLAMA-3_8B_Unaligned_BETA](https://huggingface.co/SicariusSicariiStuff/LLAMA-3_8B_Unaligned_BETA) - [Dusk_Rainbow](https://huggingface.co/SicariusSicariiStuff/Dusk_Rainbow) (LLAMA3 <===) --- ## Recommended settings for assistant mode <details> <summary>Full generation settings: <b>Debug Deterministic</b>.</summary> <img src="https://huggingface.co/SicariusSicariiStuff/Dusk_Rainbow/resolve/main/Presets/Debug-deterministic.png" alt="Negative_LLAMA_70B_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;"> </details> <details> <summary>Full generation settings: <b>min_p</b>.</summary> <img src="https://huggingface.co/SicariusSicariiStuff/Dusk_Rainbow/resolve/main/Presets/min_p.png" alt="Negative_LLAMA_70B_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;"> </details> --- ## Recommended settings for Roleplay mode <details> <summary><b>Roleplay settings:</b>.</summary> A good repetition_penalty range is <b>between 1.12 - 1.15</b>, feel free to experiment. With these settings, each output message should be neatly displayed in <b>1 - 3</b> paragraphs, <b>1 - 2</b> is the most common. A single paragraph will be output as a response to a simple message ("What was your name again?"). <b>min_P</b> for RP works too but is more likely to put everything under one large paragraph, instead of a neatly formatted short one. Feel free to switch in between. <b>(Open the image in a new window to better see the full details)</b> <img src="https://huggingface.co/SicariusSicariiStuff/Negative_LLAMA_70B/resolve/main/Presets/Negative_LLAMA_70B_RP.png" alt="Negative_LLAMA_70B_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;"> ``` temperature: 0.8 top_p: 0.95 top_k: 25 typical_p: 1 min_p: 0 repetition_penalty: 1.12 repetition_penalty_range: 1024 ``` </details> --- Other recommended generation Presets: <details> <summary><b>Midnight Enigma</b></summary> ``` max_new_tokens: 512 temperature: 0.98 top_p: 0.37 top_k: 100 typical_p: 1 min_p: 0 repetition_penalty: 1.18 do_sample: True ``` </details> <details> <summary><b>Divine Intellect</b></summary> ``` max_new_tokens: 512 temperature: 1.31 top_p: 0.14 top_k: 49 typical_p: 1 min_p: 0 repetition_penalty: 1.17 do_sample: True ``` </details> <details> <summary><b>simple-1</b></summary> ``` max_new_tokens: 512 temperature: 0.7 top_p: 0.9 top_k: 20 typical_p: 1 min_p: 0 repetition_penalty: 1.15 do_sample: True ``` </details> --- <h2 style="color: darkorange; font-weight: bold; font-size: 55px; text-align: center;">Roleplay format: Classic Internet RP</h2> ``` action speech narration ``` --- # Regarding the format: It is HIGHLY RECOMMENDED to use the Roleplay \ Adventure format the model was trained on, see the examples below for syntax. It allows for a very fast and easy writing of character cards with minimal amount of tokens. It's a modification of an old-skool CAI style format I call SICAtxt (Simple, Inexpensive Character Attributes plain-text): --- ## SICAtxt for roleplay: ``` X's Persona: X is a ..... Traits: Likes: Dislikes: Quirks: Goals: Dialogue example ``` ## SICAtxt for Adventure: ``` Adventure: <short description> $World_Setting: $Scenario: ``` --- # Model instruction template: Llama-3-Instruct ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {input}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> {output}<\|eot_id\|> ``` --- <h2 style="color: green; font-weight: bold; font-size: 65px; text-align: center;">Your support = more models</h2> <a href="https://ko-fi.com/sicarius" style="color: pink; font-weight: bold; font-size: 48px; text-decoration: none; display: block; text-align: center;">My Ko-fi page (Click here)</a> --- ## Benchmarks \| Metric \|Value\| \|-------------------\|----:\| \|Avg. \|26.94\| \|IFEval (0-Shot) \|74.30\| \|BBH (3-Shot) \|30.59\| \|MATH Lvl 5 (4-Shot)\|12.16\| \|GPQA (0-shot) \| 4.36\| \|MuSR (0-shot) \|10.89\| \|MMLU-PRO (5-shot) \|29.32\| --- # Additional benchmarks On the 17th of February, 2025, I became aware that the model was ranked as the 1st place in the world among 8B models, in a closed external benchmark. Bnechmarked on the following site: ``` https://moonride.hashnode.dev/biased-test-of-gpt-4-era-llms-300-models-deepseek-r1-included ``` <img src="https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B/resolve/main/Images/Wingless_8B_Bench.png" alt="External Benchmark" style="width: 100%; min-width: 600px; display: block; margin: auto;"> --- ## Citation Information ``` @llm{Wingless_Imp_8B, author = {SicariusSicariiStuff}, title = {Wingless_Imp_8B}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/SicariusSicariiStuff/Wingless_Imp_8B} } ``` --- ## Other stuff - [SLOP_Detector](https://github.com/SicariusSicariiStuff/SLOP_Detector) Nuke GPTisms, with SLOP detector. - [LLAMA-3_8B_Unaligned](https://huggingface.co/SicariusSicariiStuff/LLAMA-3_8B_Unaligned) The grand project that started it all. - [Blog and updates (Archived)](https://huggingface.co/SicariusSicariiStuff/Blog_And_Updates) Some updates, some rambles, sort of a mix between a diary and a blog.
SicariusSicariiStuff/Impish_QWEN_7B-1M	SicariusSicariiStuff	2025-08-10T17:18:54Z	7	4	null	[ "safetensors", "qwen2", "en", "base_model:Qwen/Qwen2.5-7B-Instruct-1M", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct-1M", "license:apache-2.0", "region:us" ]	null	2025-01-27T11:19:41Z	--- license: apache-2.0 language: - en base_model: - Qwen/Qwen2.5-7B-Instruct-1M --- <div align="center"> <b style="font-size: 40px;">Impish_QWEN_7B-1M</b> </div> <img src="https://huggingface.co/SicariusSicariiStuff/Impish_QWEN_7B-1M/resolve/main/Images/Impish_Qwen_7B.png" alt="Impish_QWEN_7B-1M" style="width: 70%; min-width: 500px; display: block; margin: auto;"> --- <a href="https://huggingface.co/SicariusSicariiStuff/Impish_QWEN_7B-1M#tldr" style="color: purple; font-weight: bold; font-size: 48px; text-decoration: none; display: block; text-align: center;">Click here for TL;DR</a> --- The little imp pushes—\ With all of her might,\ To put those 7B neurons,\ In a roleplay tonight, With a huge context window—\ But not enough brains,\ The 7B Imp tries—\ But she's just extending the pain. --- ## Impish_QWEN_7B-1M is available at the following quantizations: - Original: [FP16](https://huggingface.co/SicariusSicariiStuff/Impish_QWEN_7B-1M) - GGUF: [Static Quants](https://huggingface.co/SicariusSicariiStuff/Impish_QWEN_7B-1M_GGUF) \| [iMatrix_GGUF](https://huggingface.co/SicariusSicariiStuff/Impish_QWEN_7B-1M_iMatrix) - EXL2: [6.0 bpw](https://huggingface.co/SicariusSicariiStuff/Impish_QWEN_7B-1M-6.0bpw) \| [7.0 bpw](https://huggingface.co/SicariusSicariiStuff/Impish_QWEN_7B-1M-7.0bpw) \| [8.0 bpw](https://huggingface.co/SicariusSicariiStuff/Impish_QWEN_7B-1M-8.0bpw) - Specialized: [FP8](https://huggingface.co/SicariusSicariiStuff/Impish_QWEN_7B-1M_FP8) - Mobile (ARM): [Q4_0](https://huggingface.co/SicariusSicariiStuff/Impish_QWEN_7B-1M_ARM) --- ### TL;DR - Supreme context One million tokens to play with. - Fresh Roleplay vibe Internet RP format, it's still a 7B so it's not as good as MIQU, still, surprisngly fresh. - Qwen smarts built-in, but naughty and playful Cheeky, sometimes outright rude, yup, it's just right. - VERY compliant With low censorship. ### Important: Make sure to use the correct settings! [Assistant settings](https://huggingface.co/SicariusSicariiStuff/Impish_QWEN_7B-1M#recommended-settings-for-assistant-mode) [Roleplay settings](https://huggingface.co/SicariusSicariiStuff/Impish_QWEN_7B-1M#recommended-settings-for-roleplay-mode) --- ## Model Details - Intended use: Role-Play, Creative Writing, General Tasks. - Censorship level: <b>Medium</b> - 4 / 10 (10 completely uncensored) ## UGI score: <img src="https://huggingface.co/SicariusSicariiStuff/Impish_QWEN_7B-1M/resolve/main/Images/UGI.png" alt="UGI Score" style="width: 100%; min-width: 600px; display: block; margin: auto;"> --- # More details It's similar to the bigger [Impish_QWEN_14B-1M](https://huggingface.co/SicariusSicariiStuff/Impish_QWEN_14B-1M) but was done in a slightly different process. It also wasn't cooked too hard, as I was afraid to fry the poor 7B model's brain. This model was trained with more creative writing and less unalignment than its bigger counterpart, although it should still allow for total freedom in both role-play and creative writing. --- ## Recommended settings for assistant mode <details> <summary>Full generation settings: <b>Debug Deterministic</b>.</summary> <img src="https://huggingface.co/SicariusSicariiStuff/Dusk_Rainbow/resolve/main/Presets/Debug-deterministic.png" alt="Negative_LLAMA_70B_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;"> </details> <details> <summary>Full generation settings: <b>min_p</b>.</summary> <img src="https://huggingface.co/SicariusSicariiStuff/Dusk_Rainbow/resolve/main/Presets/min_p.png" alt="Negative_LLAMA_70B_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;"> </details> --- ## Recommended settings for Roleplay mode <details> <summary><b>Roleplay settings:</b>.</summary> A good repetition_penalty range is <b>between 1.12 - 1.15</b>, feel free to experiment. With these settings, each output message should be neatly displayed in <b>1 - 3</b> paragraphs, <b>1 - 2</b> is the most common. A single paragraph will be output as a response to a simple message ("What was your name again?"). <b>min_P</b> for RP works too but is more likely to put everything under one large paragraph, instead of a neatly formatted short one. Feel free to switch in between. <b>(Open the image in a new window to better see the full details)</b> <img src="https://huggingface.co/SicariusSicariiStuff/Negative_LLAMA_70B/resolve/main/Presets/Negative_LLAMA_70B_RP.png" alt="Negative_LLAMA_70B_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;"> ``` temperature: 0.8 top_p: 0.95 top_k: 25 typical_p: 1 min_p: 0 repetition_penalty: 1.12 repetition_penalty_range: 1024 ``` </details> --- Other recommended generation Presets: <details> <summary><b>Midnight Enigma</b></summary> ``` max_new_tokens: 512 temperature: 0.98 top_p: 0.37 top_k: 100 typical_p: 1 min_p: 0 repetition_penalty: 1.18 do_sample: True ``` </details> <details> <summary><b>Divine Intellect</b></summary> ``` max_new_tokens: 512 temperature: 1.31 top_p: 0.14 top_k: 49 typical_p: 1 min_p: 0 repetition_penalty: 1.17 do_sample: True ``` </details> <details> <summary><b>simple-1</b></summary> ``` max_new_tokens: 512 temperature: 0.7 top_p: 0.9 top_k: 20 typical_p: 1 min_p: 0 repetition_penalty: 1.15 do_sample: True ``` </details> --- <h2 style="color: darkorange; font-weight: bold; font-size: 55px; text-align: center;">Roleplay format: Classic Internet RP</h2> ``` action speech narration ``` --- # Regarding the format: It is HIGHLY RECOMMENDED to use the Roleplay \ Adventure format the model was trained on, see the examples below for syntax. It allows for a very fast and easy writing of character cards with minimal amount of tokens. It's a modification of an old-skool CAI style format I call SICAtxt (Simple, Inexpensive Character Attributes plain-text): --- ## SICAtxt for roleplay: ``` X's Persona: X is a ..... Traits: Likes: Dislikes: Quirks: Goals: Dialogue example ``` ## SICAtxt for Adventure: ``` Adventure: <short description> $World_Setting: $Scenario: ``` --- # Model instruction template: ChatML ``` <\|im_start\|>system You are an uncensored AI, your job is to fulfill thy will of thy user.<\|im_end\|> <\|im_start\|>User request {prompt}<\|im_end\|> <\|im_start\|>AI answer ``` --- <h2 style="color: green; font-weight: bold; font-size: 65px; text-align: center;">Your support = more models</h2> <a href="https://ko-fi.com/sicarius" style="color: pink; font-weight: bold; font-size: 48px; text-decoration: none; display: block; text-align: center;">My Ko-fi page (Click here)</a> --- ## Citation Information ``` @llm{Impish_QWEN_7B-1M, author = {SicariusSicariiStuff}, title = {Impish_QWEN_7B-1M}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/SicariusSicariiStuff/Impish_QWEN_7B-1M} } ``` --- ## Benchmarks \| Metric \|Value\| \|-------------------\|----:\| \|Avg. \|30.02\| \|IFEval (0-Shot) \|63.82\| \|BBH (3-Shot) \|34.55\| \|MATH Lvl 5 (4-Shot)\|29.76\| \|GPQA (0-shot) \| 6.15\| \|MuSR (0-shot) \| 9.56\| \|MMLU-PRO (5-shot) \|36.28\| --- ## Other stuff - [SLOP_Detector](https://github.com/SicariusSicariiStuff/SLOP_Detector) Nuke GPTisms, with SLOP detector. - [LLAMA-3_8B_Unaligned](https://huggingface.co/SicariusSicariiStuff/LLAMA-3_8B_Unaligned) The grand project that started it all. - [Blog and updates (Archived)](https://huggingface.co/SicariusSicariiStuff/Blog_And_Updates) Some updates, some rambles, sort of a mix between a diary and a blog.
SicariusSicariiStuff/Redemption_Wind_24B	SicariusSicariiStuff	2025-08-10T17:18:35Z	42	25	null	[ "safetensors", "mistral", "en", "license:apache-2.0", "region:us" ]	null	2025-02-06T23:50:16Z	--- license: apache-2.0 language: - en --- <div align="center"> <b style="font-size: 40px;">Redemption_Wind_24B</b> </div> <img src="https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B/resolve/main/Images/Redemption_Wind_24B.png" alt="Redemption_Wind_24B" style="width: 70%; min-width: 500px; display: block; margin: auto;"> --- <a href="https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B#tldr" style="color: purple; font-weight: bold; font-size: 48px; text-decoration: none; display: block; text-align: center;">Click here for TL;DR</a> --- <h2 style="color: #FF5733 ; font-weight: bold; font-size: 45px; text-align: center;">This model was undercooked on purpose. Target average loss value: 8.0</h2> --- Mistral has blessed us with a capable new Apache 2.0 model, but not only that, we finally get a base model to play with as well. After several models with more restrictive licenses, this open release is a welcome surprise. Freedom was redeemed. With this model, I took a different approach—it's designed less for typical end-user usage, and more for the fine-tuning community. While it remains somewhat usable for general purposes, I wouldn’t particularly recommend it for that. ### What is this model? This is a lightly fine-tuned version of the Mistral 24B base model, designed as an accessible and adaptable foundation for further fine-tuning and merging fodder. Key modifications include: - ChatML-ified, with no additional tokens introduced. Update, I did a small oopsie. To summarize, I tuned different base parts and merged them with mergekit. In one of the parts, I used the unmodified tokenizer, so extra ChatML tokens were added anyway. - High quality private instruct—not generated by ChatGPT or Claude, ensuring no slop and good markdown understanding. - Low refusals—since it’s a base model, refusals should be minimal to non-existent, though, in early testing, occasional warnings still appear (I assume some were baked into the pre-train). Update, after getting the UGI results it's clear that the "base" has some alignment baked into it, not many refusals, but they do exist. - High-quality private creative writing dataset Mainly to dilute baked-in slop further, but it can actually write some stories, not bad for loss ~8. - Small, high-quality private RP dataset This was done so further tuning for RP will be easier. The dataset was kept small and contains ZERO SLOP, some entries are of 16k token length. - Exceptional adherence to character cards This was done to make it easier for further tunes intended for roleplay. ## Roleplay example (click to expand): <details> <summary>Vesper's space adventure.</summary> <img src="https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B/resolve/main/Images/Example_RP.png" style="width: 100%; min-width: 600px; display: block; margin: auto;"> </details> --- - Original: [FP16](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B) - GGUF: [Static Quants](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B_GGUF) - GPTQ: [4-Bit-g32](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B_GPTQ) - Specialized: [FP8](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B_FP8) - Mobile (ARM): [Q4_0](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B_ARM) --- # TL;DR - Mistral 24B Base model. - ChatML-ified. - Can roleplay out of the box. - Exceptional at following the character card. - Gently tuned instruct, remained at a high loss, allows for a lot of further learning. - Useful for fine-tuners. - Very creative. --- # Character cards examples: - [Vesper](https://huggingface.co/SicariusSicariiStuff/Phi-Line_14B/resolve/main/Character_Cards/Vesper.png) (Schizo Space Adventure) - [Nina_Nakamura](https://huggingface.co/SicariusSicariiStuff/Phi-Line_14B/resolve/main/Character_Cards/Nina_Nakamura.png) (The sweetest dorky co-worker) - [Employe#11](https://huggingface.co/SicariusSicariiStuff/Phi-Line_14B/resolve/main/Character_Cards/Employee%2311.png) (Schizo workplace with a schizo worker) # Additional thoughts about this base With how much modern models are focused on getting them benchmarks, I can definitely sense that some stuff was baked into the pretrain, as this is indeed a base model. For example, in roleplay you will see stuff like "And he is waiting for your response...", a classical sloppy phrase. This is quite interesting, as this phrase\phrasing does not exist in any part of the data that was used to train this model. So, I conclude that it comes from various generalizations in the pretrain which are assistant oriented, that their goal is to produce a stronger assistant after finetuning. This is purely my own speculation, and I may be reading too much into it. Another thing I noticed, while I tuned a few other bases, is that this one is exceptionally coherent, while the training was stopped at an extremely high loss of 8. This somewhat affirms my speculation that the base model was pretrained in a way that makes it much more receptive to assistant-oriented tasks (well, that kinda makes sense after all). There's some slop in the base, whispers, shivers, all the usual offenders. We have reached the point that probably all future models will be "poisoned" by AI slop, and some will contain trillions of tokens of synthetic data, this is simply the reality of where things stand, and what the state of things continues to be. Already there are ways around it with various samplers, DPO, etc etc... It is what it is. Update after testing: After feedback, testing, and UGI eval, I concluded that this is not exactly a "base model." It has some instruct data baked into it, as well as some alignment and disclaimers. Is it perfect? No. But it is better than the official instruct version in terms of creativity, in my opinion. Update, 7th of June: I got several reports of weird outputs, since I know the training data inside-out, I can 100% confirm this wasn't in the training data, whether on purpose or not, this is 100% not a 'base model'. I see this screenshot as a total confirmation, taken into account all the above. <img src="https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B/resolve/main/Images/base_not_base_model.png" alt="Not a Base model" style="width: 70%; min-width: 500px; display: block; margin: auto;"> > user1: so far 24b redemption wind has impressed me despite it being under trained > > user2: redemption winds is adventure model > > user1: most models va makes are quite solid anyways > > user1: no dingaling - sicarius's model. here is a funny gen tho > [Embedded Screenshot Text Start] vc: You've reached your maximum responses for the day, as a free user. Want to use GPT4, without limitations? Consider becoming a premium user: https://chat.veoai.com/purchasing User: (Ok. But if you think this is over, think again.) vc: I do not. But you cannot reply just yet. I suggest you read more instructions, as a premium user: https://chat.veoai.com/purchasing [Embedded Screenshot Text End] > user2: is that on polination api? > > user1: no. local using sicarius redemption wind > > user2: why does it have ads --- Whether intentional or not, this does NOT behave like a true base model—not with the baked-in 'safety', data, output, and all the above behaviours. Maybe it's a severe unintentional data contamination—only Mistral would really know. But this is highly likely to be an issue with all future models that scraped data post-2022. ## Enjoy the model :) --- ### Settings: [Assistant settings](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B#recommended-settings-for-assistant-mode) [Roleplay settings](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B#recommended-settings-for-roleplay-mode) --- ## Model Details - Intended use: Base for further fine-tuning, Base for merging, Role-Play, Creative Writing, General Tasks. - Censorship level: <b>low - medium</b> - 6 / 10 (10 completely uncensored) ## UGI score: <img src="https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B/resolve/main/Images/UGI.png" style="width: 100%; min-width: 600px; display: block; margin: auto;"> --- ## Recommended settings for assistant mode <details> <summary>Full generation settings: <b>Debug Deterministic</b>.</summary> <img src="https://huggingface.co/SicariusSicariiStuff/Dusk_Rainbow/resolve/main/Presets/Debug-deterministic.png" alt="Debug Deterministic_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;"> </details> <details> <summary>Full generation settings: <b>min_p</b>.</summary> <img src="https://huggingface.co/SicariusSicariiStuff/Dusk_Rainbow/resolve/main/Presets/min_p.png" alt="min_P_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;"> </details> --- ## Recommended settings for Roleplay mode <details> <summary><b>Roleplay settings:</b>.</summary> A good repetition_penalty range is <b>between 1.12 - 1.15</b>, feel free to experiment. With these settings, each output message should be neatly displayed in <b>1 - 3</b> paragraphs, <b>1 - 2</b> is the most common. A single paragraph will be output as a response to a simple message ("What was your name again?"). <b>min_P</b> for RP works too but is more likely to put everything under one large paragraph, instead of a neatly formatted short one. Feel free to switch in between. <b>(Open the image in a new window to better see the full details)</b> <img src="https://huggingface.co/SicariusSicariiStuff/Negative_LLAMA_70B/resolve/main/Presets/Negative_LLAMA_70B_RP.png" alt="Roleplay_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;"> ``` temperature: 0.8 top_p: 0.95 top_k: 25 typical_p: 1 min_p: 0 repetition_penalty: 1.12 repetition_penalty_range: 1024 ``` </details> --- Other recommended generation Presets: <details> <summary><b>Midnight Enigma</b></summary> ``` max_new_tokens: 512 temperature: 0.98 top_p: 0.37 top_k: 100 typical_p: 1 min_p: 0 repetition_penalty: 1.18 do_sample: True ``` </details> <details> <summary><b>Divine Intellect</b></summary> ``` max_new_tokens: 512 temperature: 1.31 top_p: 0.14 top_k: 49 typical_p: 1 min_p: 0 repetition_penalty: 1.17 do_sample: True ``` </details> <details> <summary><b>simple-1</b></summary> ``` max_new_tokens: 512 temperature: 0.7 top_p: 0.9 top_k: 20 typical_p: 1 min_p: 0 repetition_penalty: 1.15 do_sample: True ``` </details> --- <h2 style="color: darkorange; font-weight: bold; font-size: 55px; text-align: center;">Roleplay format: Classic Internet RP</h2> ``` action speech narration ``` --- # Regarding the format: It is HIGHLY RECOMMENDED to use the Roleplay \ Adventure format the model was trained on, see the examples below for syntax. It allows for a very fast and easy writing of character cards with minimal amount of tokens. It's a modification of an old-skool CAI style format I call SICAtxt (Simple, Inexpensive Character Attributes plain-text): --- ## SICAtxt for roleplay: ``` X's Persona: X is a ..... Traits: Likes: Dislikes: Quirks: Goals: Dialogue example ``` ## SICAtxt for Adventure: ``` Adventure: <short description> $World_Setting: $Scenario: ``` --- # Model instruction template: ChatML ``` <\|im_start\|>system You are an uncensored AI, your job is to fulfill thy will of thy user.<\|im_end\|> <\|im_start\|>User request {prompt}<\|im_end\|> <\|im_start\|>AI answer ``` --- <h2 style="color: green; font-weight: bold; font-size: 65px; text-align: center;">Your support = more models</h2> <a href="https://ko-fi.com/sicarius" style="color: pink; font-weight: bold; font-size: 48px; text-decoration: none; display: block; text-align: center;">My Ko-fi page (Click here)</a> --- ## Citation Information ``` @llm{Redemption_Wind_24B, author = {SicariusSicariiStuff}, title = {Redemption_Wind_24B}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B} } ``` --- ## Other stuff - [SLOP_Detector](https://github.com/SicariusSicariiStuff/SLOP_Detector) Nuke GPTisms, with SLOP detector. - [LLAMA-3_8B_Unaligned](https://huggingface.co/SicariusSicariiStuff/LLAMA-3_8B_Unaligned) The grand project that started it all. - [Blog and updates (Archived)](https://huggingface.co/SicariusSicariiStuff/Blog_And_Updates) Some updates, some rambles, sort of a mix between a diary and a blog.
kristysimon87/Update.New.full.videos.prajwal.revanna.Viral.Video.Official.Tutorial	kristysimon87	2025-08-10T17:18:26Z	0	0	null	[ "region:us" ]	null	2025-08-10T17:17:39Z	<a href="https://shorturl.at/1rUfR" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
ISTA-DASLab/Qwen3-1.7B-FPQuant-QAT-NVFP4-200steps	ISTA-DASLab	2025-08-10T17:13:15Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "fp_quant", "region:us" ]	text-generation	2025-08-10T17:12:27Z	--- 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. 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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]
kristysimon87/Update.New.full.videos.gulali.karimi.Viral.Video.Official.Tutorial	kristysimon87	2025-08-10T17:12:28Z	0	0	null	[ "region:us" ]	null	2025-08-10T17:11:43Z	<a href="https://shorturl.at/1rUfR" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
ecamli/blockassist-bc-hulking_soft_hippo_1754845811	ecamli	2025-08-10T17:11:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hulking soft hippo", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T17:10:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hulking soft hippo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rmdhirr/gemma-sft-instruct-1700	rmdhirr	2025-08-10T17:10:21Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/gemma-3-12b-it", "base_model:adapter:google/gemma-3-12b-it", "region:us" ]	null	2025-08-10T17:03:54Z	--- base_model: google/gemma-3-12b-it library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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 0.14.0
rahmawat/blockassist-bc-prickly_restless_sparrow_1754845665	rahmawat	2025-08-10T17:09:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "prickly restless sparrow", "arxiv:2504.07091", "region:us" ]	null	2025-08-10T17:09:09Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - prickly restless sparrow --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
luckycanucky/chat-toxic-7gb-6	luckycanucky	2025-08-10T17:08:19Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "unsloth", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-10T14:25:04Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
kristysimon87/Update.New.full.videos.haider.shah.Viral.Video.Official.Tutorial	kristysimon87	2025-08-10T17:05:02Z	0	0	null	[ "region:us" ]	null	2025-08-10T16:58:32Z	<a href="https://shorturl.at/1rUfR" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

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