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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-05 12:28:30	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 539 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-05 12:28:13	card stringlengths 11 1.01M
Cchaos/blockassist-bc-muscular_endangered_cobra_1754949574	Cchaos	2025-08-11T22:00:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular endangered cobra", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T22:00:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular endangered cobra --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jcharlie39/learn_hf_food_not_food_text_classifier_distilbert_base_uncased	jcharlie39	2025-08-11T22:00:18Z	23	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-07-06T23:10:06Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: learn_hf_food_not_food_text_classifier_distilbert_base_uncased 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. --> # learn_hf_food_not_food_text_classifier_distilbert_base_uncased This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0687 - Accuracy: 0.98 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4267 \| 1.0 \| 7 \| 0.1057 \| 0.98 \| \| 0.0479 \| 2.0 \| 14 \| 0.0092 \| 1.0 \| \| 0.0053 \| 3.0 \| 21 \| 0.0480 \| 0.98 \| \| 0.0019 \| 4.0 \| 28 \| 0.0694 \| 0.98 \| \| 0.0011 \| 5.0 \| 35 \| 0.0747 \| 0.98 \| \| 0.0008 \| 6.0 \| 42 \| 0.0737 \| 0.98 \| \| 0.0007 \| 7.0 \| 49 \| 0.0721 \| 0.98 \| \| 0.0006 \| 8.0 \| 56 \| 0.0702 \| 0.98 \| \| 0.0006 \| 9.0 \| 63 \| 0.0691 \| 0.98 \| \| 0.0006 \| 10.0 \| 70 \| 0.0687 \| 0.98 \| ### Framework versions - Transformers 4.55.0 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
moonHexer/blockassist-bc-amphibious_giant_cheetah_1754947901	moonHexer	2025-08-11T21:58:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious giant cheetah", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:57:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious giant cheetah --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
pietro0hz/blockassist-bc-ferocious_toothy_tortoise_1754949367	pietro0hz	2025-08-11T21:57:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ferocious toothy tortoise", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:57:08Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ferocious toothy tortoise --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
motza0025/blockassist-bc-scurrying_waddling_pelican_1754948196	motza0025	2025-08-11T21:55:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scurrying waddling pelican", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:55:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scurrying waddling pelican --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
koloni/blockassist-bc-deadly_graceful_stingray_1754947815	koloni	2025-08-11T21:55:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:55:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
arcee-ai/Virtuoso-Large	arcee-ai	2025-08-11T21:54:10Z	16	29	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "base_model:Qwen/Qwen2.5-72B", "base_model:finetune:Qwen/Qwen2.5-72B", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-09T17:23:45Z	--- base_model: - Qwen/Qwen2.5-72B library_name: transformers license: other license_name: qwen license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/aaUsAuDk79RMvG3ShWuMY.png) Virtuoso-Large (72B) is our most powerful and versatile general-purpose model, designed to excel at handling complex and varied tasks across domains. With state-of-the-art performance, it offers unparalleled capability for nuanced understanding, contextual adaptability, and high accuracy. ### Model Details - Architecture Base: Qwen2.5-72B - Parameter Count: 72B - License: Qwen's Tongyi License ### Use Cases - Advanced content creation, such as technical writing and creative storytelling - Data summarization and report generation for cross-functional domains - Detailed knowledge synthesis and deep-dive insights from diverse datasets - Multilingual support for international operations and communications ### Quantizations GGUF format available [here](https://huggingface.co/arcee-ai/Virtuoso-Large-GGUF) ### License Virtuoso-Large (72B) is released under the qwen license. You are free to use, modify, and distribute this model in both commercial and non-commercial applications, subject to the terms and conditions of the license. If you have questions or would like to share your experiences using Virtuoso-Large (72B), please feel free to connect with us on social media. We’re excited to see what you build—and how this model helps you innovate!
lelouch33/blockassist-bc-frisky_sneaky_sandpiper_1754948637	lelouch33	2025-08-11T21:47:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "frisky sneaky sandpiper", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:46:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - frisky sneaky sandpiper --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
fbaldassarri/EleutherAI_pythia-1.4b-deduped-autogptq-int8-gs64-asym	fbaldassarri	2025-08-11T21:45:46Z	0	0	null	[ "safetensors", "gpt_neox", "pytorch", "causal-lm", "pythia", "autoround", "intel-autoround", "auto-round", "intel", "woq", "gptq", "auto-gptq", "autogptq", "eleutheraI", "text-generation", "en", "dataset:EleutherAI/pile", "base_model:EleutherAI/pythia-1.4b-deduped", "base_model:quantized:EleutherAI/pythia-1.4b-deduped", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-08-11T21:39:07Z	--- language: - en tags: - pytorch - causal-lm - pythia - autoround - intel-autoround - auto-round - intel - woq - gptq - auto-gptq - autogptq - eleutheraI license: apache-2.0 model_name: Pythia 1.4b deduped base_model: EleutherAI/pythia-1.4b-deduped inference: false model_creator: EleutherAI datasets: - EleutherAI/pile pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: fbaldassarri --- ## Model Information Quantized version of [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/fbaldassarri/EleutherAI/pythia-1.4b-deduped) using torch.float32 for quantization tuning. - 8 bits (INT8) - group size = 64 - Asymmetrical Quantization - Method WoQ: GPTQ (AutoGPTQ algorithm) Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.5.1 Note: this INT8 version of pythia-1.4b-deduped has been quantized to run inference through CPU. ## Replication Recipe ### Step 1 Install Requirements I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. ``` wget https://github.com/intel/auto-round/archive/refs/tags/v0.5.1.tar.gz tar -xvzf v0.5.1.tar.gz cd auto-round-0.5.1 pip install -r requirements-cpu.txt --upgrade ``` ### Step 2 Build Intel AutoRound wheel from sources ``` pip install -vvv --no-build-isolation -e .[cpu] ``` ### Step 3 Script for Quantization ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "EleutherAI/pythia-1.4b-deduped" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) from auto_round import AutoRound bits, group_size, sym, device, amp = 8, 64, False, 'cpu', False autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp) autoround.quantize() output_dir = "./AutoRound/EleutherAI_pythia-1.4b-deduped-autogptq-int8-gs64-asym" autoround.save_quantized(output_dir, format='auto_gptq', inplace=True) ``` ## License [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) ## Disclaimer This quantized model comes with no warrenty. It has been developed only for research purposes.
nkerr/sv3.2-1-qwen1.5-0.5B-Chat	nkerr	2025-08-11T21:39:29Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:Qwen/Qwen1.5-0.5B", "base_model:adapter:Qwen/Qwen1.5-0.5B", "license:other", "region:us" ]	null	2025-08-11T21:39:08Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-0.5B tags: - generated_from_trainer model-index: - name: sv3.2-1-qwen1.5-0.5B-Chat 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. --> # sv3.2-1-qwen1.5-0.5B-Chat This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B](https://huggingface.co/Qwen/Qwen1.5-0.5B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3149 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 4 - 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 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 18.9666 \| 0.2469 \| 20 \| 16.0623 \| \| 12.6221 \| 0.4938 \| 40 \| 9.0829 \| \| 5.4773 \| 0.7407 \| 60 \| 2.2669 \| \| 1.3455 \| 0.9877 \| 80 \| 0.5687 \| \| 0.5052 \| 1.2346 \| 100 \| 0.3800 \| \| 0.4151 \| 1.4815 \| 120 \| 0.3491 \| \| 0.3821 \| 1.7284 \| 140 \| 0.3368 \| \| 0.3816 \| 1.9753 \| 160 \| 0.3268 \| \| 0.3598 \| 2.2222 \| 180 \| 0.3206 \| \| 0.3561 \| 2.4691 \| 200 \| 0.3174 \| \| 0.364 \| 2.7160 \| 220 \| 0.3153 \| \| 0.3497 \| 2.9630 \| 240 \| 0.3149 \| ### Framework versions - PEFT 0.14.0 - Transformers 4.49.0 - Pytorch 2.6.0+cu126 - Datasets 3.3.2 - Tokenizers 0.21.0
Joseph717171/Gpt-OSS-20B-BF16-Unquantized	Joseph717171	2025-08-11T21:39:14Z	0	1	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-11T21:26:54Z	Gpt-OSS-20B-BF16-Unquantized Unquantized GGUF BF16 model weights for gpt-oss-20B (the MoE layers are all unquantized from MXFP4 to BF16). Happy quantizing! 😋
dfdx/gemma-fin-extractor-2	dfdx	2025-08-11T21:39:07Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-4b-it", "base_model:finetune:google/gemma-3-4b-it", "endpoints_compatible", "region:us" ]	null	2025-08-11T13:43:38Z	--- base_model: google/gemma-3-4b-it library_name: transformers model_name: gemma-fin-extractor-2 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-fin-extractor-2 This model is a fine-tuned version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-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="dfdx/gemma-fin-extractor-2", 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: 0.19.0 - Transformers: 4.54.0 - Pytorch: 2.7.1 - Datasets: 3.6.0 - Tokenizers: 0.21.4 ## 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}} } ```
thiernomdou/miniatures	thiernomdou	2025-08-11T21:38:21Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-08-11T21:27:02Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: karamoo --- # Miniatures <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `karamoo` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "karamoo", "lora_weights": "https://huggingface.co/thiernomdou/miniatures/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('thiernomdou/miniatures', weight_name='lora.safetensors') image = pipeline('karamoo').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/thiernomdou/miniatures/discussions) to add images that show off what you’ve made with this LoRA.
Sayemahsjn/blockassist-bc-playful_feline_octopus_1754947141	Sayemahsjn	2025-08-11T21:37:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:37:48Z	--- 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).
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1754946490	coelacanthxyz	2025-08-11T21:37:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:37:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jcharlie39/learn_Hugging_Face_Food_Classification_Model_using_Distilbert_Uncased_Model	jcharlie39	2025-08-11T21:36:39Z	10	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-08T02:21:04Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: learn_Hugging_Face_Food_Classification_Model_using_Distilbert_Uncased_Model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # learn_Hugging_Face_Food_Classification_Model_using_Distilbert_Uncased_Model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0005 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4261 \| 1.0 \| 7 \| 0.0960 \| 0.98 \| \| 0.0479 \| 2.0 \| 14 \| 0.0080 \| 1.0 \| \| 0.0054 \| 3.0 \| 21 \| 0.0025 \| 1.0 \| \| 0.0021 \| 4.0 \| 28 \| 0.0013 \| 1.0 \| \| 0.0012 \| 5.0 \| 35 \| 0.0009 \| 1.0 \| \| 0.0009 \| 6.0 \| 42 \| 0.0007 \| 1.0 \| \| 0.0008 \| 7.0 \| 49 \| 0.0006 \| 1.0 \| \| 0.0007 \| 8.0 \| 56 \| 0.0006 \| 1.0 \| \| 0.0006 \| 9.0 \| 63 \| 0.0005 \| 1.0 \| \| 0.0006 \| 10.0 \| 70 \| 0.0005 \| 1.0 \| ### Framework versions - Transformers 4.55.0 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
giovannidemuri/llama3b-llamab8-er-afg-v13-seed2-mcdonald-alpaca-fpt	giovannidemuri	2025-08-11T21:33:48Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-3.2-3B", "base_model:finetune:meta-llama/Llama-3.2-3B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T20:12:55Z	--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-3B tags: - generated_from_trainer model-index: - name: llama3b-llamab8-er-afg-v13-seed2-mcdonald-alpaca-fpt 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. --> # llama3b-llamab8-er-afg-v13-seed2-mcdonald-alpaca-fpt This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 2 - 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: 0.03 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 4.0.0 - Tokenizers 0.21.0
roneymatusp/british-optimizer-mistral-final	roneymatusp	2025-08-11T21:26:13Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:mistralai/Mistral-7B-v0.1", "lora", "sft", "trl", "text-generation", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "region:us" ]	text-generation	2025-08-11T18:51:03Z	--- base_model: mistralai/Mistral-7B-v0.1 library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:mistralai/Mistral-7B-v0.1 - lora - sft - trl --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0
bensalem14/dqn-SpaceInvadersNoFrameskip-v4	bensalem14	2025-08-11T21:25:47Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-08-11T21:25:18Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 592.50 +/- 118.92 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bensalem14 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bensalem14 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga bensalem14 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
bbopen/camo-person-yolo	bbopen	2025-08-11T21:25:39Z	0	1	ultralytics	[ "ultralytics", "onnx", "yolo", "camouflaged-person", "pytorch", "object-detection", "license:apache-2.0", "region:us" ]	object-detection	2025-08-11T21:13:00Z	--- pipeline_tag: object-detection library_name: ultralytics license: apache-2.0 tags: - yolo - ultralytics - camouflaged-person - onnx - pytorch inference: true --- # Camouflaged Person Detector (YOLO, single class) - Single class: person - Phase B fine-tuned model on camo fill/background pairs + negatives - Artifacts: `camo-person-yolo.pt` (PyTorch), `camo-person-yolo.onnx` (opset 12, dynamic, simplified), `camo-person-yolo.torchscript` ## Quick usage ### Ultralytics (PyTorch) ```python from ultralytics import YOLO model = YOLO("bbopen/camo-person-yolo") # loads camo-person-yolo.pt by default model.predict(source="image.jpg", imgsz=1280, conf=0.25, iou=0.6) ``` ### ONNX Runtime ```python import onnxruntime as ort, numpy as np, cv2 sess = ort.InferenceSession("camo-person-yolo.onnx", providers=["CUDAExecutionProvider","CPUExecutionProvider"]) im = cv2.imread("image.jpg"); im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) im = cv2.resize(im, (1280,1280)).astype(np.float32)/255.0 im = np.transpose(im,(2,0,1))[None] outputs = sess.run(None, {"images": im}) ``` ## Jetson Orin Nano (export to TensorRT) - Install runtime: `python3 -m pip install --upgrade ultralytics` - Export FP16 engine: ```bash yolo export model=camo-person-yolo.pt format=engine half=True imgsz=1280 device=0 ``` - Inference: ```bash yolo task=detect mode=predict model=best_fp16_1280.engine source=path/to/images conf=0.25 iou=0.6 imgsz=1280 ``` ## Repro/configs - Optional training args: `args.yaml` - Optional dataset layout reference: `data.yaml`
lelouch33/blockassist-bc-frisky_sneaky_sandpiper_1754947185	lelouch33	2025-08-11T21:24:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "frisky sneaky sandpiper", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:24:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - frisky sneaky sandpiper --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
gorni123/polikv1	gorni123	2025-08-11T21:22:15Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neo", "text-generation", "generated_from_trainer", "base_model:EleutherAI/gpt-neo-125m", "base_model:finetune:EleutherAI/gpt-neo-125m", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T21:09:57Z	--- library_name: transformers license: mit base_model: EleutherAI/gpt-neo-125M tags: - generated_from_trainer model-index: - name: polikv1 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. --> # polikv1 This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.7814 \| 1.125 \| 1500 \| 2.9850 \| ### Framework versions - Transformers 4.55.0 - Pytorch 2.7.1+cu128 - Datasets 4.0.0 - Tokenizers 0.21.4
Samuell43/blockassist-bc-fast_gregarious_warthog_1754947264	Samuell43	2025-08-11T21:22:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fast gregarious warthog", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:21:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fast gregarious warthog --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
zelk12/Gemma-R1-12B-v3-Q6_K-GGUF	zelk12	2025-08-11T21:21:28Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:TheDrummer/Gemma-R1-12B-v3", "base_model:quantized:TheDrummer/Gemma-R1-12B-v3", "endpoints_compatible", "region:us" ]	null	2025-08-11T21:20:39Z	--- base_model: TheDrummer/Gemma-R1-12B-v3 tags: - llama-cpp - gguf-my-repo --- # zelk12/Gemma-R1-12B-v3-Q6_K-GGUF This model was converted to GGUF format from [`TheDrummer/Gemma-R1-12B-v3`](https://huggingface.co/TheDrummer/Gemma-R1-12B-v3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/TheDrummer/Gemma-R1-12B-v3) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo zelk12/Gemma-R1-12B-v3-Q6_K-GGUF --hf-file gemma-r1-12b-v3-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo zelk12/Gemma-R1-12B-v3-Q6_K-GGUF --hf-file gemma-r1-12b-v3-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo zelk12/Gemma-R1-12B-v3-Q6_K-GGUF --hf-file gemma-r1-12b-v3-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo zelk12/Gemma-R1-12B-v3-Q6_K-GGUF --hf-file gemma-r1-12b-v3-q6_k.gguf -c 2048 ```
hettad/blockassist-bc-pudgy_grazing_magpie_1754943842	hettad	2025-08-11T21:20:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pudgy grazing magpie", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:20:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pudgy grazing magpie --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ImparkTeam/deepseek-math-7b-instruct-math-tutor	ImparkTeam	2025-08-11T21:20:00Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-11T17:24:54Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
upvantage/modernbert-3pair-adv-3label-clean	upvantage	2025-08-11T21:19:33Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "text-classification", "generated_from_trainer", "base_model:answerdotai/ModernBERT-large", "base_model:finetune:answerdotai/ModernBERT-large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-11T20:53:01Z	--- library_name: transformers license: apache-2.0 base_model: answerdotai/ModernBERT-large tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: modernbert-3pair-adv-3label-clean 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. --> # modernbert-3pair-adv-3label-clean This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3086 - Accuracy: 0.9337 - F1: 0.9336 - Precision: 0.9336 - Recall: 0.9337 - F1 Class 0: 0.9317 - Precision Class 0: 0.9316 - Recall Class 0: 0.9319 - F1 Class 1: 0.9555 - Precision Class 1: 0.9463 - Recall Class 1: 0.9649 - F1 Class 2: 0.9135 - Precision Class 2: 0.9230 - Recall Class 2: 0.9042 ## 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: 8e-06 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: multi-GPU - num_devices: 6 - total_train_batch_size: 192 - total_eval_batch_size: 192 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 - label_smoothing_factor: 0.05 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| F1 Class 0 \| Precision Class 0 \| Recall Class 0 \| F1 Class 1 \| Precision Class 1 \| Recall Class 1 \| F1 Class 2 \| Precision Class 2 \| Recall Class 2 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|:----------:\|:-----------------:\|:--------------:\|:----------:\|:-----------------:\|:--------------:\|:----------:\|:-----------------:\|:--------------:\| \| 1.8369 \| 1.0 \| 9697 \| 0.3086 \| 0.9337 \| 0.9336 \| 0.9336 \| 0.9337 \| 0.9317 \| 0.9316 \| 0.9319 \| 0.9555 \| 0.9463 \| 0.9649 \| 0.9135 \| 0.9230 \| 0.9042 \| ### Framework versions - Transformers 4.55.0 - Pytorch 2.8.0+cu128 - Datasets 4.0.0 - Tokenizers 0.21.4
samin80/blockassist-bc-pesty_pensive_robin_1754940761	samin80	2025-08-11T21:16:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pesty pensive robin", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:15:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pesty pensive robin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
tensorblock/Intelligent-Internet_II-Medical-8B-1706-GGUF	tensorblock	2025-08-11T21:14:28Z	0	0	transformers	[ "transformers", "gguf", "TensorBlock", "GGUF", "base_model:Intelligent-Internet/II-Medical-8B-1706", "base_model:quantized:Intelligent-Internet/II-Medical-8B-1706", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-11T19:43:50Z	--- library_name: transformers tags: - TensorBlock - GGUF base_model: Intelligent-Internet/II-Medical-8B-1706 --- <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) ## Intelligent-Internet/II-Medical-8B-1706 - 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 [Intelligent-Internet/II-Medical-8B-1706](https://huggingface.co/Intelligent-Internet/II-Medical-8B-1706). 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 ``` <\|im_start\|>system {system_prompt}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Model file specification \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [II-Medical-8B-1706-Q2_K.gguf](https://huggingface.co/tensorblock/Intelligent-Internet_II-Medical-8B-1706-GGUF/blob/main/II-Medical-8B-1706-Q2_K.gguf) \| Q2_K \| 3.282 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [II-Medical-8B-1706-Q3_K_S.gguf](https://huggingface.co/tensorblock/Intelligent-Internet_II-Medical-8B-1706-GGUF/blob/main/II-Medical-8B-1706-Q3_K_S.gguf) \| Q3_K_S \| 3.770 GB \| very small, high quality loss \| \| [II-Medical-8B-1706-Q3_K_M.gguf](https://huggingface.co/tensorblock/Intelligent-Internet_II-Medical-8B-1706-GGUF/blob/main/II-Medical-8B-1706-Q3_K_M.gguf) \| Q3_K_M \| 4.124 GB \| very small, high quality loss \| \| [II-Medical-8B-1706-Q3_K_L.gguf](https://huggingface.co/tensorblock/Intelligent-Internet_II-Medical-8B-1706-GGUF/blob/main/II-Medical-8B-1706-Q3_K_L.gguf) \| Q3_K_L \| 4.431 GB \| small, substantial quality loss \| \| [II-Medical-8B-1706-Q4_0.gguf](https://huggingface.co/tensorblock/Intelligent-Internet_II-Medical-8B-1706-GGUF/blob/main/II-Medical-8B-1706-Q4_0.gguf) \| Q4_0 \| 4.775 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [II-Medical-8B-1706-Q4_K_S.gguf](https://huggingface.co/tensorblock/Intelligent-Internet_II-Medical-8B-1706-GGUF/blob/main/II-Medical-8B-1706-Q4_K_S.gguf) \| Q4_K_S \| 4.802 GB \| small, greater quality loss \| \| [II-Medical-8B-1706-Q4_K_M.gguf](https://huggingface.co/tensorblock/Intelligent-Internet_II-Medical-8B-1706-GGUF/blob/main/II-Medical-8B-1706-Q4_K_M.gguf) \| Q4_K_M \| 5.028 GB \| medium, balanced quality - recommended \| \| [II-Medical-8B-1706-Q5_0.gguf](https://huggingface.co/tensorblock/Intelligent-Internet_II-Medical-8B-1706-GGUF/blob/main/II-Medical-8B-1706-Q5_0.gguf) \| Q5_0 \| 5.721 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [II-Medical-8B-1706-Q5_K_S.gguf](https://huggingface.co/tensorblock/Intelligent-Internet_II-Medical-8B-1706-GGUF/blob/main/II-Medical-8B-1706-Q5_K_S.gguf) \| Q5_K_S \| 5.721 GB \| large, low quality loss - recommended \| \| [II-Medical-8B-1706-Q5_K_M.gguf](https://huggingface.co/tensorblock/Intelligent-Internet_II-Medical-8B-1706-GGUF/blob/main/II-Medical-8B-1706-Q5_K_M.gguf) \| Q5_K_M \| 5.851 GB \| large, very low quality loss - recommended \| \| [II-Medical-8B-1706-Q6_K.gguf](https://huggingface.co/tensorblock/Intelligent-Internet_II-Medical-8B-1706-GGUF/blob/main/II-Medical-8B-1706-Q6_K.gguf) \| Q6_K \| 6.726 GB \| very large, extremely low quality loss \| \| [II-Medical-8B-1706-Q8_0.gguf](https://huggingface.co/tensorblock/Intelligent-Internet_II-Medical-8B-1706-GGUF/blob/main/II-Medical-8B-1706-Q8_0.gguf) \| Q8_0 \| 8.710 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/Intelligent-Internet_II-Medical-8B-1706-GGUF --include "II-Medical-8B-1706-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/Intelligent-Internet_II-Medical-8B-1706-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf' ```
rozer191292/blockassist-bc-playful_silky_raccoon_1754946624	rozer191292	2025-08-11T21:12:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful silky raccoon", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:12:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful silky raccoon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Osrivers/realismSDXLByStable_v70FP16.safetensors	Osrivers	2025-08-11T21:12:07Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-08-11T20:59:08Z	--- license: creativeml-openrail-m ---
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754946602	ggozzy	2025-08-11T21:11:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:11:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
motza0025/blockassist-bc-powerful_jagged_magpie_1754945310	motza0025	2025-08-11T21:07:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "powerful jagged magpie", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:06:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - powerful jagged magpie --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mxw752/gemma3-12b-model-5ep	mxw752	2025-08-11T21:06:36Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-12b-pt", "base_model:finetune:google/gemma-3-12b-pt", "endpoints_compatible", "region:us" ]	null	2025-08-11T13:19:17Z	--- base_model: google/gemma-3-12b-pt library_name: transformers model_name: gemma3-12b-model-5ep tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma3-12b-model-5ep This model is a fine-tuned version of [google/gemma-3-12b-pt](https://huggingface.co/google/gemma-3-12b-pt). 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="mxw752/gemma3-12b-model-5ep", 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/mxw752-university-of-miami/huggingface/runs/sseuu2xu) This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.55.0 - Pytorch: 2.6.0+cu124 - Datasets: 3.3.2 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
forouzanfallah/sentinel_test2_fft	forouzanfallah	2025-08-11T21:06:15Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "diffusers-training", "sd3", "sd3-diffusers", "controlnet", "base_model:stabilityai/stable-diffusion-3-medium-diffusers", "base_model:adapter:stabilityai/stable-diffusion-3-medium-diffusers", "license:openrail++", "region:us" ]	text-to-image	2025-08-11T17:21:24Z	--- base_model: stabilityai/stable-diffusion-3-medium-diffusers library_name: diffusers license: openrail++ inference: true tags: - text-to-image - diffusers-training - diffusers - sd3 - sd3-diffusers - controlnet --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SD3 controlnet-forouzanfallah/sentinel_test2_fft These are controlnet weights trained on stabilityai/stable-diffusion-3-medium-diffusers with new type of conditioning. The weights were trained using [ControlNet](https://github.com/lllyasviel/ControlNet) with the [SD3 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/README_sd3.md). You can find some example images below. prompt: a high-resolution satellite image, sharp details, clear view from space ![images_0)](./images_0.png) Please adhere to the licensing terms as described `[here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE)`. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
daslab-testing/Llama-3.2-1B-Instruct-FPQuant-RTN-NVFP4	daslab-testing	2025-08-11T21:05:15Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "fp_quant", "region:us" ]	text-generation	2025-08-11T21:04:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Gemvision13/blockassist-bc-finicky_jagged_panda_1754946218	Gemvision13	2025-08-11T21:05:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky jagged panda", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:04:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky jagged panda --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1754944528	coelacanthxyz	2025-08-11T21:04:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:04:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
winnieyangwannan/entity_dpo_Llama-3.1-8B-Instruct_lora_1_lr_0.0001_beta_0.05_12800_all_37_epoch_1_layer_16	winnieyangwannan	2025-08-11T21:02:48Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T20:59:23Z	--- library_name: transformers tags: - trl - dpo --- # 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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VinyVan/distill-whisper-small-swahili-KLOnly	VinyVan	2025-08-11T21:01:58Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_17_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-08-11T05:27:05Z	--- library_name: transformers language: - hi license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_17_0 model-index: - name: Whisper Small Hi - Viny Van results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Hi - Viny Van This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.9058 - eval_wer_ortho: 44.9519 - eval_wer: 36.0989 - eval_runtime: 160.0526 - eval_samples_per_second: 2.499 - eval_steps_per_second: 0.312 - epoch: 0.5333 - step: 200 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - 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_steps: 200 - training_steps: 1000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.2
fbaldassarri/EleutherAI_pythia-1.4b-deduped-autoawq-int4-gs64-sym	fbaldassarri	2025-08-11T21:00:16Z	0	0	null	[ "safetensors", "gpt_neox", "pytorch", "causal-lm", "pythia", "autoround", "intel-autoround", "auto-round", "intel", "woq", "awq", "auto-awq", "autoawq", "eleutheraI", "text-generation", "en", "dataset:EleutherAI/pile", "base_model:EleutherAI/pythia-1.4b-deduped", "base_model:quantized:EleutherAI/pythia-1.4b-deduped", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-08-11T20:54:48Z	--- language: - en tags: - pytorch - causal-lm - pythia - autoround - intel-autoround - auto-round - intel - woq - awq - auto-awq - autoawq - eleutheraI license: apache-2.0 model_name: Pythia 1.4b deduped base_model: EleutherAI/pythia-1.4b-deduped inference: false model_creator: EleutherAI datasets: - EleutherAI/pile pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: fbaldassarri --- ## Model Information Quantized version of [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/fbaldassarri/EleutherAI/pythia-1.4b-deduped) using torch.float32 for quantization tuning. - 4 bits (INT4) - group size = 64 - Symmetrical Quantization - Method WoQ: AWQ (AutoAWQ algorithm) Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.5.1 Note: this INT4 version of pythia-1.4b-deduped has been quantized to run inference through CPU. ## Replication Recipe ### Step 1 Install Requirements I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. ``` wget https://github.com/intel/auto-round/archive/refs/tags/v0.5.1.tar.gz tar -xvzf v0.5.1.tar.gz cd auto-round-0.5.1 pip install -r requirements-cpu.txt --upgrade ``` ### Step 2 Build Intel AutoRound wheel from sources ``` pip install -vvv --no-build-isolation -e .[cpu] ``` ### Step 3 Script for Quantization ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "EleutherAI/pythia-1.4b-deduped" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) from auto_round import AutoRound bits, group_size, sym, device, amp = 4, 64, True, 'cpu', False autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp) autoround.quantize() output_dir = "./AutoRound/EleutherAI_pythia-1.4b-deduped-autoawq-int4-gs64-sym" autoround.save_quantized(output_dir, format='auto_awq', inplace=True) ``` ## License [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) ## Disclaimer This quantized model comes with no warrenty. It has been developed only for research purposes.
koloni/blockassist-bc-deadly_graceful_stingray_1754944365	koloni	2025-08-11T20:59:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:59:08Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
daslab-testing/Llama-3.2-1B-Instruct-FPQuant-QAT-MXFP4-1000steps	daslab-testing	2025-08-11T20:53:57Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "fp_quant", "region:us" ]	text-generation	2025-08-11T20:52:33Z	--- 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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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. 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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]
fbaldassarri/EleutherAI_pythia-1.4b-deduped-autoawq-int4-gs64-asym	fbaldassarri	2025-08-11T20:53:35Z	0	0	null	[ "safetensors", "gpt_neox", "pytorch", "causal-lm", "pythia", "autoround", "intel-autoround", "auto-round", "intel", "woq", "awq", "auto-awq", "autoawq", "eleutheraI", "text-generation", "en", "dataset:EleutherAI/pile", "base_model:EleutherAI/pythia-1.4b-deduped", "base_model:quantized:EleutherAI/pythia-1.4b-deduped", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-08-11T20:48:20Z	--- language: - en tags: - pytorch - causal-lm - pythia - autoround - intel-autoround - auto-round - intel - woq - awq - auto-awq - autoawq - eleutheraI license: apache-2.0 model_name: Pythia 1.4b deduped base_model: EleutherAI/pythia-1.4b-deduped inference: false model_creator: EleutherAI datasets: - EleutherAI/pile pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: fbaldassarri --- ## Model Information Quantized version of [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/fbaldassarri/EleutherAI/pythia-1.4b-deduped) using torch.float32 for quantization tuning. - 4 bits (INT4) - group size = 64 - Asymmetrical Quantization - Method WoQ: AWQ (AutoAWQ algorithm) Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.5.1 Note: this INT4 version of pythia-1.4b-deduped has been quantized to run inference through CPU. ## Replication Recipe ### Step 1 Install Requirements I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. ``` wget https://github.com/intel/auto-round/archive/refs/tags/v0.5.1.tar.gz tar -xvzf v0.5.1.tar.gz cd auto-round-0.5.1 pip install -r requirements-cpu.txt --upgrade ``` ### Step 2 Build Intel AutoRound wheel from sources ``` pip install -vvv --no-build-isolation -e .[cpu] ``` ### Step 3 Script for Quantization ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "EleutherAI/pythia-1.4b-deduped" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) from auto_round import AutoRound bits, group_size, sym, device, amp = 4, 64, False, 'cpu', False autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp) autoround.quantize() output_dir = "./AutoRound/EleutherAI_pythia-1.4b-deduped-autoawq-int4-gs64-asym" autoround.save_quantized(output_dir, format='auto_awq', inplace=True) ``` ## License [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) ## Disclaimer This quantized model comes with no warrenty. It has been developed only for research purposes.
thiernomdou/video	thiernomdou	2025-08-11T20:52:10Z	3	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-08-09T23:36:13Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Karamoo --- # Video <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Karamoo` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Karamoo", "lora_weights": "https://huggingface.co/thiernomdou/video/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('thiernomdou/video', weight_name='lora.safetensors') image = pipeline('Karamoo').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/thiernomdou/video/discussions) to add images that show off what you’ve made with this LoRA.
hamid1232/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pawing_tricky_sandpiper	hamid1232	2025-08-11T20:51:23Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am pawing_tricky_sandpiper", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T13:38:22Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am pawing_tricky_sandpiper --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
acidjp/blockassist-bc-pesty_extinct_prawn_1754945079	acidjp	2025-08-11T20:51:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pesty extinct prawn", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:50:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pesty extinct prawn --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Gemvision13/blockassist-bc-finicky_jagged_panda_1754945288	Gemvision13	2025-08-11T20:49:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky jagged panda", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:49:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky jagged panda --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
smsl-project/model-hanoi-red2middle	smsl-project	2025-08-11T20:48:59Z	1	0	lerobot	[ "lerobot", "safetensors", "act", "robotics", "dataset:LeoMuuuu/hanoi-red2middle", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-08T11:09:54Z	--- datasets: LeoMuuuu/hanoi-red2middle library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - lerobot - act - robotics --- # Model Card for act <!-- Provide a quick summary of what the model is/does. --> [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754945225	ggozzy	2025-08-11T20:48:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:48:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
winnieyangwannan/entity_dpo_Llama-3.1-8B-Instruct_lora_8_lr_0.0001_beta_0.05_11520_all_37_epoch_1_layer_22	winnieyangwannan	2025-08-11T20:48:10Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T20:29:04Z	--- library_name: transformers tags: - trl - dpo --- # 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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winnieyangwannan/entity_dpo_Llama-3.1-8B-Instruct_lora_8_lr_0.0001_beta_0.05_10240_all_37_epoch_1_layer_22	winnieyangwannan	2025-08-11T20:47:53Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T20:28:59Z	--- library_name: transformers tags: - trl - dpo --- # 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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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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Ravi427/llama3-fiqa-qlora	Ravi427	2025-08-11T20:47:52Z	0	0	peft	[ "peft", "safetensors", "llama", "text-generation", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "lora", "transformers", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-08-10T14:20:21Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct - lora - transformers pipeline_tag: text-generation model-index: - name: llama3-fiqa-qlora 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. --> # llama3-fiqa-qlora This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3360 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2426 \| 1.0 \| 907 \| 2.2561 \| \| 2.0171 \| 2.0 \| 1814 \| 2.2493 \| \| 1.6963 \| 3.0 \| 2721 \| 2.3360 \| ### Framework versions - PEFT 0.17.0 - Transformers 4.55.0 - Pytorch 2.7.1+cu118 - Datasets 4.0.0 - Tokenizers 0.21.4
winnieyangwannan/entity_dpo_Llama-3.1-8B-Instruct_lora_8_lr_0.0001_beta_0.05_7680_all_37_epoch_1_layer_22	winnieyangwannan	2025-08-11T20:47:19Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T20:28:57Z	--- library_name: transformers tags: - trl - dpo --- # 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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winnieyangwannan/entity_dpo_Llama-3.1-8B-Instruct_lora_8_lr_0.0001_beta_0.05_5120_all_37_epoch_1_layer_22	winnieyangwannan	2025-08-11T20:47:13Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T20:28:58Z	--- library_name: transformers tags: - trl - dpo --- # 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. 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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. 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winnieyangwannan/entity_dpo_Llama-3.1-8B-Instruct_lora_8_lr_0.0001_beta_0.05_6400_all_37_epoch_1_layer_22	winnieyangwannan	2025-08-11T20:47:12Z	2	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-10T08:25:37Z	--- library_name: transformers tags: - trl - dpo --- # 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. 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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. 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winnieyangwannan/entity_dpo_Llama-3.1-8B-Instruct_lora_8_lr_0.0001_beta_0.05_1280_all_37_epoch_1_layer_22	winnieyangwannan	2025-08-11T20:46:26Z	2	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-10T08:25:15Z	--- library_name: transformers tags: - trl - dpo --- # 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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fbaldassarri/EleutherAI_pythia-1.4b-deduped-autogptq-int4-gs64-sym	fbaldassarri	2025-08-11T20:46:06Z	0	0	null	[ "safetensors", "gpt_neox", "pytorch", "causal-lm", "pythia", "autoround", "intel-autoround", "auto-round", "intel", "woq", "gptq", "auto-gptq", "autogptq", "eleutheraI", "text-generation", "en", "dataset:EleutherAI/pile", "base_model:EleutherAI/pythia-1.4b-deduped", "base_model:quantized:EleutherAI/pythia-1.4b-deduped", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-08-11T20:40:20Z	--- language: - en tags: - pytorch - causal-lm - pythia - autoround - intel-autoround - auto-round - intel - woq - gptq - auto-gptq - autogptq - eleutheraI license: apache-2.0 model_name: Pythia 1.4b deduped base_model: EleutherAI/pythia-1.4b-deduped inference: false model_creator: EleutherAI datasets: - EleutherAI/pile pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: fbaldassarri --- ## Model Information Quantized version of [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/fbaldassarri/EleutherAI/pythia-1.4b-deduped) using torch.float32 for quantization tuning. - 4 bits (INT4) - group size = 64 - Symmetrical Quantization - Method WoQ: GPTQ (AutoGPTQ algorithm) Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.5.1 Note: this INT4 version of pythia-1.4b-deduped has been quantized to run inference through CPU. ## Replication Recipe ### Step 1 Install Requirements I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. ``` wget https://github.com/intel/auto-round/archive/refs/tags/v0.5.1.tar.gz tar -xvzf v0.5.1.tar.gz cd auto-round-0.5.1 pip install -r requirements-cpu.txt --upgrade ``` ### Step 2 Build Intel AutoRound wheel from sources ``` pip install -vvv --no-build-isolation -e .[cpu] ``` ### Step 3 Script for Quantization ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "EleutherAI/pythia-1.4b-deduped" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) from auto_round import AutoRound bits, group_size, sym, device, amp = 4, 64, True, 'cpu', False autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp) autoround.quantize() output_dir = "./AutoRound/EleutherAI_pythia-1.4b-deduped-autogptq-int4-gs64-sym" autoround.save_quantized(output_dir, format='auto_gptq', inplace=True) ``` ## License [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) ## Disclaimer This quantized model comes with no warrenty. It has been developed only for research purposes.
ahnafch01/cowfmd	ahnafch01	2025-08-11T20:44:56Z	0	0	keras	[ "keras", "license:apache-2.0", "region:us" ]	null	2025-08-11T20:40:20Z	--- license: apache-2.0 --- Foot-and-mouth disease (FMD) is a severe, fast-spreading viral disease that primarily affects cloven-hoofed animals, including cows, pigs, sheep, goats, and deer. FMD is one of the most challenging animal diseases to control. You can upload a picture of a cow's foot, mouth, udder, or hoof to check if its a sign of FMD in the following website. https://cowfmd.vercel.app/
pocohos/paraphrase-multilingual-mpnet-base-v2-Q6_K-GGUF	pocohos	2025-08-11T20:44:31Z	0	0	sentence-transformers	[ "sentence-transformers", "gguf", "feature-extraction", "sentence-similarity", "transformers", "llama-cpp", "gguf-my-repo", "multilingual", "ar", "bg", "ca", "cs", "da", "de", "el", "en", "es", "et", "fa", "fi", "fr", "gl", "gu", "he", "hi", "hr", "hu", "hy", "id", "it", "ja", "ka", "ko", "ku", "lt", "lv", "mk", "mn", "mr", "ms", "my", "nb", "nl", "pl", "pt", "ro", "ru", "sk", "sl", "sq", "sr", "sv", "th", "tr", "uk", "ur", "vi", "base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "base_model:quantized:sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-08-11T20:44:25Z	--- language: - multilingual - ar - bg - ca - cs - da - de - el - en - es - et - fa - fi - fr - gl - gu - he - hi - hr - hu - hy - id - it - ja - ka - ko - ku - lt - lv - mk - mn - mr - ms - my - nb - nl - pl - pt - ro - ru - sk - sl - sq - sr - sv - th - tr - uk - ur - vi license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - llama-cpp - gguf-my-repo language_bcp47: - fr-ca - pt-br - zh-cn - zh-tw pipeline_tag: sentence-similarity base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 --- # pocohos/paraphrase-multilingual-mpnet-base-v2-Q6_K-GGUF This model was converted to GGUF format from [`sentence-transformers/paraphrase-multilingual-mpnet-base-v2`](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo pocohos/paraphrase-multilingual-mpnet-base-v2-Q6_K-GGUF --hf-file paraphrase-multilingual-mpnet-base-v2-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo pocohos/paraphrase-multilingual-mpnet-base-v2-Q6_K-GGUF --hf-file paraphrase-multilingual-mpnet-base-v2-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo pocohos/paraphrase-multilingual-mpnet-base-v2-Q6_K-GGUF --hf-file paraphrase-multilingual-mpnet-base-v2-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo pocohos/paraphrase-multilingual-mpnet-base-v2-Q6_K-GGUF --hf-file paraphrase-multilingual-mpnet-base-v2-q6_k.gguf -c 2048 ```
motza0025/blockassist-bc-scavenging_placid_goat_1754943919	motza0025	2025-08-11T20:43:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scavenging placid goat", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:43:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scavenging placid goat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
giovannidemuri/llama8b-er-afg-v77-seed2-hx	giovannidemuri	2025-08-11T20:42:19Z	1	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-09T22:35:56Z	--- library_name: transformers license: llama3.1 base_model: meta-llama/Llama-3.1-8B tags: - generated_from_trainer model-index: - name: llama8b-er-afg-v77-seed2-hx 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. --> # llama8b-er-afg-v77-seed2-hx This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 2 - 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: 0.03 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cu128 - Datasets 3.6.0 - Tokenizers 0.21.2
timcliffordIRL/results	timcliffordIRL	2025-08-11T20:41:56Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-11T12:56:16Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: results 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. --> # results This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.55.0 - Pytorch 2.8.0 - Datasets 4.0.0 - Tokenizers 0.21.4
daslab-testing/Llama-3.2-1B-Instruct-FPQuant-QAT-NVFP4-1400steps	daslab-testing	2025-08-11T20:41:12Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "fp_quant", "region:us" ]	text-generation	2025-08-11T20:40:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
fbaldassarri/EleutherAI_pythia-1.4b-deduped-autogptq-int4-gs64-asym	fbaldassarri	2025-08-11T20:39:47Z	0	0	null	[ "safetensors", "gpt_neox", "pytorch", "causal-lm", "pythia", "autoround", "intel-autoround", "auto-round", "intel", "woq", "gptq", "auto-gptq", "autogptq", "eleutheraI", "text-generation", "en", "dataset:EleutherAI/pile", "base_model:EleutherAI/pythia-1.4b-deduped", "base_model:quantized:EleutherAI/pythia-1.4b-deduped", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-08-11T20:34:16Z	--- language: - en tags: - pytorch - causal-lm - pythia - autoround - intel-autoround - auto-round - intel - woq - gptq - auto-gptq - autogptq - eleutheraI license: apache-2.0 model_name: Pythia 1.4b deduped base_model: EleutherAI/pythia-1.4b-deduped inference: false model_creator: EleutherAI datasets: - EleutherAI/pile pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: fbaldassarri --- ## Model Information Quantized version of [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/fbaldassarri/EleutherAI/pythia-1.4b-deduped) using torch.float32 for quantization tuning. - 4 bits (INT4) - group size = 64 - Asymmetrical Quantization - Method WoQ: GPTQ (AutoGPTQ algorithm) Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.5.1 Note: this INT4 version of pythia-1.4b-deduped has been quantized to run inference through CPU. ## Replication Recipe ### Step 1 Install Requirements I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. ``` wget https://github.com/intel/auto-round/archive/refs/tags/v0.5.1.tar.gz tar -xvzf v0.5.1.tar.gz cd auto-round-0.5.1 pip install -r requirements-cpu.txt --upgrade ``` ### Step 2 Build Intel AutoRound wheel from sources ``` pip install -vvv --no-build-isolation -e .[cpu] ``` ### Step 3 Script for Quantization ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "EleutherAI/pythia-1.4b-deduped" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) from auto_round import AutoRound bits, group_size, sym, device, amp = 4, 64, False, 'cpu', False autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp) autoround.quantize() output_dir = "./AutoRound/EleutherAI_pythia-1.4b-deduped-autogptq-int4-gs64-asym" autoround.save_quantized(output_dir, format='auto_gptq', inplace=True) ``` ## License [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) ## Disclaimer This quantized model comes with no warrenty. It has been developed only for research purposes.
salakmisinx/blockassist-bc-placid_armored_frog_1754944640	salakmisinx	2025-08-11T20:38:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "placid armored frog", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:38:12Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - placid armored frog --- # 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_1754943512	Sayemahsjn	2025-08-11T20:36:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:36:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754944399	ggozzy	2025-08-11T20:34:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:34:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kayacrypto/blockassist-bc-thriving_barky_wolf_1754944367	kayacrypto	2025-08-11T20:34:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thriving barky wolf", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:34:06Z	--- 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).
Vattri81/my_finetuned_model_qlorav3	Vattri81	2025-08-11T20:34:08Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-11T20:33:57Z	--- 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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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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Gemvision13/blockassist-bc-finicky_jagged_panda_1754944354	Gemvision13	2025-08-11T20:34:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky jagged panda", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:33:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky jagged panda --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
fbaldassarri/EleutherAI_pythia-1.4b-deduped-autoround-int4-gs64-sym	fbaldassarri	2025-08-11T20:33:44Z	0	0	null	[ "safetensors", "gpt_neox", "pytorch", "causal-lm", "pythia", "autoround", "intel-autoround", "auto-round", "intel", "woq", "eleutheraI", "text-generation", "en", "dataset:EleutherAI/pile", "base_model:EleutherAI/pythia-1.4b-deduped", "base_model:quantized:EleutherAI/pythia-1.4b-deduped", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-08-11T20:28:25Z	--- language: - en tags: - pytorch - causal-lm - pythia - autoround - intel-autoround - auto-round - intel - woq - eleutheraI license: apache-2.0 model_name: Pythia 1.4b deduped base_model: EleutherAI/pythia-1.4b-deduped inference: false model_creator: EleutherAI datasets: - EleutherAI/pile pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: fbaldassarri --- ## Model Information Quantized version of [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/fbaldassarri/EleutherAI/pythia-1.4b-deduped) using torch.float32 for quantization tuning. - 4 bits (INT4) - group size = 64 - Symmetrical Quantization - Method WoQ: SignRound (AutoRound algorithm) Fast and low memory, 2-3X speedup (slight accuracy drop at W4G64) Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.5.1 Note: this INT4 version of pythia-1.4b-deduped has been quantized to run inference through CPU. ## Replication Recipe ### Step 1 Install Requirements I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. ``` wget https://github.com/intel/auto-round/archive/refs/tags/v0.5.1.tar.gz tar -xvzf v0.5.1.tar.gz cd auto-round-0.5.1 pip install -r requirements-cpu.txt --upgrade ``` ### Step 2 Build Intel AutoRound wheel from sources ``` pip install -vvv --no-build-isolation -e .[cpu] ``` ### Step 3 Script for Quantization ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "EleutherAI/pythia-1.4b-deduped" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) from auto_round import AutoRound bits, group_size, sym, device, amp = 4, 64, True, 'cpu', False autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp) autoround.quantize() output_dir = "./AutoRound/EleutherAI_pythia-1.4b-deduped-autoround-int4-gs64-sym" autoround.save_quantized(output_dir, format='auto_round', inplace=True) ``` ## License [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) ## Disclaimer This quantized model comes with no warrenty. It has been developed only for research purposes.
linuxnewbie84/entrenamiento	linuxnewbie84	2025-08-11T20:30:55Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-multilingual-cased", "base_model:finetune:distilbert/distilbert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-11T16:51:01Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-multilingual-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: entrenamiento 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. --> # entrenamiento This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6073 - Accuracy: 0.25 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.6286 \| 0.5 \| 5 \| 1.6356 \| 0.15 \| \| 1.6325 \| 1.0 \| 10 \| 1.6084 \| 0.15 \| \| 1.5872 \| 1.5 \| 15 \| 1.5985 \| 0.225 \| \| 1.5819 \| 2.0 \| 20 \| 1.5969 \| 0.225 \| \| 1.5679 \| 2.5 \| 25 \| 1.5937 \| 0.25 \| \| 1.533 \| 3.0 \| 30 \| 1.5935 \| 0.225 \| \| 1.5191 \| 3.5 \| 35 \| 1.5976 \| 0.275 \| \| 1.4476 \| 4.0 \| 40 \| 1.6084 \| 0.225 \| \| 1.3939 \| 4.5 \| 45 \| 1.6122 \| 0.225 \| \| 1.4634 \| 5.0 \| 50 \| 1.6073 \| 0.25 \| ### Framework versions - Transformers 4.55.0 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
Azumine/blockassist-bc-coiled_sharp_cockroach_1754942128	Azumine	2025-08-11T20:30:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "coiled sharp cockroach", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:30:30Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - coiled sharp cockroach --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sergbese/gemma-3-isv-gpt-v3	sergbese	2025-08-11T20:30:31Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-27b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-27b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-11T20:29:48Z	--- base_model: unsloth/gemma-3-27b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: sergbese - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-27b-it-unsloth-bnb-4bit This gemma3 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)
gasoline2255/blockassist-bc-flightless_sizable_wildebeest_1754944051	gasoline2255	2025-08-11T20:29:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flightless sizable wildebeest", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:29:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flightless sizable wildebeest --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
koloni/blockassist-bc-deadly_graceful_stingray_1754942565	koloni	2025-08-11T20:29:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:29:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
chunli-peng/OpenRS-GRPO-sft-8.5	chunli-peng	2025-08-11T20:29:31Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:knoveleng/open-rs", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T20:10:49Z	--- base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B datasets: knoveleng/open-rs library_name: transformers model_name: OpenRS-GRPO-sft-8.5 tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for OpenRS-GRPO-sft-8.5 This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) on the [knoveleng/open-rs](https://huggingface.co/datasets/knoveleng/open-rs) 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="chunli-peng/OpenRS-GRPO-sft-8.5", 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/chunli-ai-texas-a-m-university/huggingface/runs/lii4yxwc) This model was trained with SFT. ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Claudia199/mostbet-pro.kg	Claudia199	2025-08-11T20:29:16Z	0	0	null	[ "region:us" ]	null	2025-08-11T20:26:17Z	Как делать ставки на NBA грамотно: анализ перед сезоном! Сезон NBA — один из самых динамичных и непредсказуемых в мировом спорте. И если ты хочешь не просто болеть за любимую команду, а зарабатывать на этом, тебе пригодится качественная аналитика. На сайте <a href="https://sportnaviny.com/basketball-nba-stavki-sezon/">mostbet</a> собраны самые важные разборы, прогнозы и советы для тех, кто делает ставки на баскетбол с умом. Что делает этот ресурс полезным: 1. Детальный анализ команд перед стартом регулярного сезона 2. Обновления по составам, травмам и трансферам 3. Прогнозы на победителей конференций и чемпионов 4. Оценка формы лидеров и роли ключевых игроков 5. Статистика, расписание и важные цифры 6. Советы по управлению банкроллом и стратегиям ставок 7. Сравнение коэффициентов и вариантов рынков Почему стоит заглянуть перед каждой ставкой? NBA — это лига, где каждое утро может приносить новую сенсацию. Здесь важно быть в курсе: кто не сыграет, кто вышел на пик формы, где букмекеры ошиблись в линии. Страница по NBA на mostbet поможет тебе идти на шаг впереди — принимать обоснованные решения, а не полагаться на случай. Если ты хочешь превратить интерес к баскетболу в продуманную игру на ставках — начни с аналитики. Mostbet предлагает всё, чтобы подготовиться к сезону: факты, цифры, прогнозы и контекст. Осталось только использовать это с умом. NBA — это марафон длиною в 82 игры для каждой команды, и на такой дистанции важны детали: мотивация на конкретный матч, плотность графика, выезды и «бек-ту-бек» игры. Все эти нюансы влияют на исход встречи и часто остаются за пределами стандартной линии букмекера. Именно поэтому так важно использовать аналитику, которую предлагает mostbet — чтобы видеть чуть глубже, чем остальные. Запитати в ChatGPT
fbaldassarri/EleutherAI_pythia-1.4b-deduped-autoround-int4-gs64-asym	fbaldassarri	2025-08-11T20:27:17Z	0	0	null	[ "safetensors", "gpt_neox", "pytorch", "causal-lm", "pythia", "autoround", "intel-autoround", "auto-round", "intel", "woq", "eleutheraI", "text-generation", "en", "dataset:EleutherAI/pile", "base_model:EleutherAI/pythia-1.4b-deduped", "base_model:quantized:EleutherAI/pythia-1.4b-deduped", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-08-11T19:56:47Z	--- language: - en tags: - pytorch - causal-lm - pythia - autoround - intel-autoround - auto-round - intel - woq - eleutheraI license: apache-2.0 model_name: Pythia 1.4b deduped base_model: EleutherAI/pythia-1.4b-deduped inference: false model_creator: EleutherAI datasets: - EleutherAI/pile pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: fbaldassarri --- ## Model Information Quantized version of [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/fbaldassarri/EleutherAI/pythia-1.4b-deduped) using torch.float32 for quantization tuning. - 4 bits (INT4) - group size = 64 - Asymmetrical Quantization - Method WoQ: SignRound (AutoRound algorithm) Fast and low memory, 2-3X speedup (slight accuracy drop at W4G64) Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.5.1 Note: this INT4 version of pythia-1.4b-deduped has been quantized to run inference through CPU. ## Replication Recipe ### Step 1 Install Requirements I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. ``` wget https://github.com/intel/auto-round/archive/refs/tags/v0.5.1.tar.gz tar -xvzf v0.5.1.tar.gz cd auto-round-0.5.1 pip install -r requirements-cpu.txt --upgrade ``` ### Step 2 Build Intel AutoRound wheel from sources ``` pip install -vvv --no-build-isolation -e .[cpu] ``` ### Step 3 Script for Quantization ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "EleutherAI/pythia-1.4b-deduped" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) from auto_round import AutoRound bits, group_size, sym, device, amp = 4, 64, False, 'cpu', False autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp) autoround.quantize() output_dir = "./AutoRound/EleutherAI_pythia-1.4b-deduped-autoround-int4-gs64-asym" autoround.save_quantized(output_dir, format='auto_round', inplace=True) ``` ## License [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) ## Disclaimer This quantized model comes with no warrenty. It has been developed only for research purposes.
lelouch33/blockassist-bc-frisky_sneaky_sandpiper_1754943892	lelouch33	2025-08-11T20:26:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "frisky sneaky sandpiper", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:25:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - frisky sneaky sandpiper --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754943848	ggozzy	2025-08-11T20:25:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:25:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AGofficial/AgGPT13nano	AGofficial	2025-08-11T20:21:06Z	0	1	null	[ "safetensors", "en", "license:mit", "region:us" ]	null	2025-08-11T16:16:10Z	--- license: mit language: - en --- <img src="banner.png" alt="AgGPT Banner" width="100%"> # AgGPT-13 nano ## New. Nano. Nimble. ### BETA AgGPT-13 nano is the lightweight beta release of the AgGPT-13 model — built to handle everything from quick, simple queries to more complex reasoning and problem-solving. Powered by Gemma-2 and trained on high-quality datasets (including an inner world model) using the AG artificial generative world model architecture, it delivers capable performance in a compact package. This version is quantized to INT8 for speed and efficiency, then dequantized on load for use — making it nimble without sacrificing capability. ## Features - Lightweight – Optimized for lower memory usage with INT8 quantization. - Fast startup – Loads and dequantizes directly into a usable PyTorch model. - Flexible – Works on CPU or GPU. - Interactive – Simple `ask()` method for quick prompting. - Based on Gemma-2 – Benefits from state-of-the-art NLP and ML research. ## Installation & Usage ```bash pip install torch transformers safetensors ``` Example: ```python from aggpt13 import AgGPT agent = AgGPT(model_path="aggpt13/") response = agent.ask("Hey, who are you?") print(response) ``` ## How It Works * Loads tokenizer and model config from `transformers`. * Reads quantized weights (`.safetensors`) and quantization parameters (`.json`). * Dequantizes weights into `float32` and manually loads them into the model. * Runs entirely in PyTorch, supporting both CPU and CUDA. ## License This project is distributed under the MIT License. For details, see the [LICENSE](LICENSE) file.
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754943573	ggozzy	2025-08-11T20:20:55Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:20:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
motza0025/blockassist-bc-darting_mottled_dog_1754942464	motza0025	2025-08-11T20:20:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "darting mottled dog", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:20:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - darting mottled dog --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
daslab-testing/Llama-3.2-1B-Instruct-FPQuant-QAT-NVFP4-1000steps	daslab-testing	2025-08-11T20:20:05Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "fp_quant", "region:us" ]	text-generation	2025-08-11T20:18:52Z	--- 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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ggozzy/blockassist-bc-stubby_yapping_mandrill_1754943297	ggozzy	2025-08-11T20:16:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:16:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sequelbox/Qwen3-14B-DAG-Reasoning	sequelbox	2025-08-11T20:16:15Z	53	5	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "dag-reasoning", "valiant", "valiant-labs", "qwen", "qwen-3", "qwen-3-14b", "14b", "reasoning", "directed-acyclic-graph", "graph", "logic", "analysis", "programming", "knowledge", "root-cause-analysis", "economics", "business", "business-management", "finance", "law", "supply-chain", "logistics", "software-engineering", "cybersecurity", "architecture", "energy", "politics", "problem-solving", "creative", "analytical", "expert", "rationality", "conversational", "chat", "instruct", "en", "dataset:sequelbox/DAG-Reasoning-DeepSeek-R1-0528", "base_model:Qwen/Qwen3-14B", "base_model:finetune:Qwen/Qwen3-14B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-07-29T03:19:42Z	--- language: - en library_name: transformers pipeline_tag: text-generation tags: - dag-reasoning - valiant - valiant-labs - qwen - qwen-3 - qwen-3-14b - 14b - reasoning - directed-acyclic-graph - graph - logic - analysis - programming - knowledge - root-cause-analysis - economics - business - business-management - finance - law - supply-chain - logistics - software-engineering - cybersecurity - architecture - energy - politics - problem-solving - creative - analytical - expert - rationality - conversational - chat - instruct base_model: Qwen/Qwen3-14B datasets: - sequelbox/DAG-Reasoning-DeepSeek-R1-0528 license: apache-2.0 --- [Support our open-source dataset and model releases!](https://huggingface.co/spaces/sequelbox/SupportOpenSource) DAG Reasoning: [Qwen3-4B-Thinking-2507](https://huggingface.co/sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning), [Qwen3-8B](https://huggingface.co/sequelbox/Qwen3-8B-DAG-Reasoning), [Qwen3-14B](https://huggingface.co/sequelbox/Qwen3-14B-DAG-Reasoning), [gpt-oss-20b](https://huggingface.co/sequelbox/gpt-oss-20b-DAG-Reasoning) DAG Reasoning is an experimental specialist reasoning AI with custom output format; for general reasoning and chat, try [Shining Valiant 3](https://huggingface.co/ValiantLabs/Qwen3-8B-ShiningValiant3) or [Esper 3!](https://huggingface.co/ValiantLabs/Qwen3-8B-Esper3) DAG Reasoning is a specialist reasoning assistant, performing causal analysis and reasoning to produce Directed Acyclic Graphs in response to user output. - Finetuned on our [DAG dataset](https://huggingface.co/datasets/sequelbox/DAG-Reasoning-DeepSeek-R1-0528) data generated with [Deepseek R1 0528!](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) - Multi-step analysis identifies causal relationships, produces confidence measurements, and forms a single structured graph object. - DAG Reasoning Format provides clear, readable JSON containing structured, useful information; easy to use for creating visualizations, doing analysis, or further conversation with your assistant. - Trained in a variety of subjects for flexible analysis: programming, science, business, economics, finance, law, logistics, management, and more! - Small model sizes allow running on local desktop and mobile, plus super-fast server inference! ## Prompting Guide DAG Reasoning uses the [Qwen 3](https://huggingface.co/Qwen/Qwen3-14B) prompt format to create outputs in [DAG Reasoning Format.](https://huggingface.co/datasets/sequelbox/DAG-Reasoning-DeepSeek-R1-0528) DAG Reasoning is an experimental reasoning finetune: - the assistant performs multi-step reasoning during the thinking phase, before producing the JSON graph object at the start of the output to the user. - request the graph or analysis explicitly in your user prompt to prompt for the [DAG Reasoning Format;](https://huggingface.co/datasets/sequelbox/DAG-Reasoning-DeepSeek-R1-0528) see the example script below for examples. (If the model is unsure of your request, it will generally default to standard Qwen 3 output/chat style instead of creating a DAG.) - this is an early experimental release: if used in a productive context, structural validation of outputs is strongly recommended. - we recommend enable_thinking=True for all chats. Example inference script to get started: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "sequelbox/Qwen3-14B-DAG-Reasoning" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input, generally recommended to follow the prompting style provided in these examples: prompt = "Analyze the following scenario from a report on a new industrial park: The park was built on reclaimed swampland. The initial site survey indicated the ground was stable after being drained and filled. However, over the first five years of operation, slow, uneven ground subsidence has caused cracking in the foundations of several large warehouses. The cost of stabilizing these foundations is now projected to be higher than the initial cost of the land itself, and the risk of further subsidence has made the remaining lots in the park unsellable." #prompt = "Make a graph of this analysis: In the American West, warmer winters are causing more precipitation to fall as rain instead of snow, even when total precipitation remains unchanged. This has two major consequences for water management. First, runoff occurs immediately in the winter rather than being stored as snowpack until the spring and summer melt. This increases winter flood risk and reduces water availability during the summer growing season. Second, the smaller snowpack reflects less solar radiation, leading to warmer ground temperatures and increased evaporation, further reducing water supply." #prompt = "A supply chain security analysis finds: following the disclosure of a critical vulnerability in the widely used Log4j library, we consulted our Software Bill of Materials (SBOM) for a key application, which indicated the application was not affected. However, the application was later compromised via this exact vulnerability. The investigation revealed the SBOM was generated incorrectly and failed to identify Log4j as a transitive dependency, a library pulled in by another library. This inaccurate SBOM led to a false negative in our risk assessment." #prompt = "Analyze this and make a graph: A company incurred a $200,000 bill from its cloud provider in one weekend, an attack known as cryptojacking. An attacker discovered an exposed API key in the client-side code of the company's public-facing web application. This key belonged to a role that, due to a misconfiguration, had permissions to create new virtual machine instances. The attacker wrote a script to programmatically spin up thousands of the most powerful, GPU-equipped virtual machines in several different geographic regions to mine cryptocurrency, leading to the massive, unexpected charges." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( **model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) print("content:", content) ``` DAG Reasoning is one of our experimental reasoning releases; we've got more to come soon! Do as you will.
sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning	sequelbox	2025-08-11T20:15:43Z	0	1	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "dag-reasoning", "valiant", "valiant-labs", "qwen", "qwen-3", "qwen-3-4b", "qwen3-4b-thinking-2507", "4b", "thinking", "reasoning", "directed-acyclic-graph", "graph", "logic", "analysis", "programming", "knowledge", "root-cause-analysis", "economics", "business", "business-management", "finance", "law", "supply-chain", "logistics", "software-engineering", "cybersecurity", "architecture", "energy", "politics", "problem-solving", "creative", "analytical", "expert", "rationality", "conversational", "chat", "instruct", "en", "dataset:sequelbox/DAG-Reasoning-DeepSeek-R1-0528", "base_model:Qwen/Qwen3-4B-Thinking-2507", "base_model:finetune:Qwen/Qwen3-4B-Thinking-2507", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T18:08:19Z	--- language: - en library_name: transformers pipeline_tag: text-generation tags: - dag-reasoning - valiant - valiant-labs - qwen - qwen-3 - qwen-3-4b - qwen3-4b-thinking-2507 - 4b - thinking - reasoning - directed-acyclic-graph - graph - logic - analysis - programming - knowledge - root-cause-analysis - economics - business - business-management - finance - law - supply-chain - logistics - software-engineering - cybersecurity - architecture - energy - politics - problem-solving - creative - analytical - expert - rationality - conversational - chat - instruct base_model: Qwen/Qwen3-4B-Thinking-2507 datasets: - sequelbox/DAG-Reasoning-DeepSeek-R1-0528 license: apache-2.0 --- [Support our open-source dataset and model releases!](https://huggingface.co/spaces/sequelbox/SupportOpenSource) DAG Reasoning: [Qwen3-4B-Thinking-2507](https://huggingface.co/sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning), [Qwen3-8B](https://huggingface.co/sequelbox/Qwen3-8B-DAG-Reasoning), [Qwen3-14B](https://huggingface.co/sequelbox/Qwen3-14B-DAG-Reasoning), [gpt-oss-20b](https://huggingface.co/sequelbox/gpt-oss-20b-DAG-Reasoning) DAG Reasoning is an experimental specialist reasoning AI with custom output format; for general reasoning and chat, try [Shining Valiant 3](https://huggingface.co/ValiantLabs/Qwen3-8B-ShiningValiant3) or [Esper 3!](https://huggingface.co/ValiantLabs/Qwen3-8B-Esper3) DAG Reasoning is a specialist reasoning assistant, performing causal analysis and reasoning to produce Directed Acyclic Graphs in response to user output. - Finetuned on our [DAG dataset](https://huggingface.co/datasets/sequelbox/DAG-Reasoning-DeepSeek-R1-0528) data generated with [Deepseek R1 0528!](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) - Multi-step analysis identifies causal relationships, produces confidence measurements, and forms a single structured graph object. - DAG Reasoning Format provides clear, readable JSON containing structured, useful information; easy to use for creating visualizations, doing analysis, or further conversation with your assistant. - Trained in a variety of subjects for flexible analysis: programming, science, business, economics, finance, law, logistics, management, and more! - Small model sizes allow running on local desktop and mobile, plus super-fast server inference! ## Prompting Guide DAG Reasoning uses the [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) prompt format to create outputs in [DAG Reasoning Format.](https://huggingface.co/datasets/sequelbox/DAG-Reasoning-DeepSeek-R1-0528) DAG Reasoning is an experimental reasoning finetune: - the assistant performs multi-step reasoning during the thinking phase, before producing the JSON graph object at the start of the output to the user. - request the graph or analysis explicitly in your user prompt to prompt for the [DAG Reasoning Format;](https://huggingface.co/datasets/sequelbox/DAG-Reasoning-DeepSeek-R1-0528) see the example script below for examples. (If the model is unsure of your request, it will generally default to standard Qwen 3 output/chat style instead of creating a DAG.) - this is an early experimental release: if used in a productive context, structural validation of outputs is strongly recommended. - we recommend enable_thinking=True for all chats. Example inference script to get started: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input, generally recommended to follow the prompting style provided in these examples: prompt = "Analyze the following scenario from a report on a new industrial park: The park was built on reclaimed swampland. The initial site survey indicated the ground was stable after being drained and filled. However, over the first five years of operation, slow, uneven ground subsidence has caused cracking in the foundations of several large warehouses. The cost of stabilizing these foundations is now projected to be higher than the initial cost of the land itself, and the risk of further subsidence has made the remaining lots in the park unsellable." #prompt = "Make a graph of this analysis: In the American West, warmer winters are causing more precipitation to fall as rain instead of snow, even when total precipitation remains unchanged. This has two major consequences for water management. First, runoff occurs immediately in the winter rather than being stored as snowpack until the spring and summer melt. This increases winter flood risk and reduces water availability during the summer growing season. Second, the smaller snowpack reflects less solar radiation, leading to warmer ground temperatures and increased evaporation, further reducing water supply." #prompt = "A supply chain security analysis finds: following the disclosure of a critical vulnerability in the widely used Log4j library, we consulted our Software Bill of Materials (SBOM) for a key application, which indicated the application was not affected. However, the application was later compromised via this exact vulnerability. The investigation revealed the SBOM was generated incorrectly and failed to identify Log4j as a transitive dependency, a library pulled in by another library. This inaccurate SBOM led to a false negative in our risk assessment." #prompt = "Analyze this and make a graph: A company incurred a $200,000 bill from its cloud provider in one weekend, an attack known as cryptojacking. An attacker discovered an exposed API key in the client-side code of the company's public-facing web application. This key belonged to a role that, due to a misconfiguration, had permissions to create new virtual machine instances. The attacker wrote a script to programmatically spin up thousands of the most powerful, GPU-equipped virtual machines in several different geographic regions to mine cryptocurrency, leading to the massive, unexpected charges." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( **model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) print("content:", content) ``` DAG Reasoning is one of our experimental reasoning releases; we've got more to come soon! Do as you will.
Samuell43/blockassist-bc-fast_gregarious_warthog_1754943282	Samuell43	2025-08-11T20:15:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fast gregarious warthog", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:15:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fast gregarious warthog --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Mathlesage/euroBertV11-infonce-only-2824-qwen-step-0	Mathlesage	2025-08-11T20:12:56Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-08-11T20:11:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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. 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ESERCKR/blockassist-bc-scurrying_lanky_cassowary_1754943101	ESERCKR	2025-08-11T20:12:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scurrying lanky cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:12:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scurrying lanky cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754943023	ggozzy	2025-08-11T20:11:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:11:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kayacrypto/blockassist-bc-thriving_barky_wolf_1754942824	kayacrypto	2025-08-11T20:08:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thriving barky wolf", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:08:14Z	--- 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).
Vortex5/Moonviolet-12B	Vortex5	2025-08-11T20:08:27Z	9	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "roleplay", "conversational", "base_model:Nitral-AI/Captain-Eris_Violet-V0.420-12B", "base_model:merge:Nitral-AI/Captain-Eris_Violet-V0.420-12B", "base_model:Vortex5/Moondark-12B", "base_model:merge:Vortex5/Moondark-12B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-06T12:42:50Z	--- base_model: - Nitral-AI/Captain-Eris_Violet-V0.420-12B - Vortex5/Moondark-12B library_name: transformers tags: - mergekit - merge - roleplay --- # Moonviolet-12B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6669a3a617b838fda45637b8/yo6s7MVirjPSU0WRBkUJc.png) This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * [Nitral-AI/Captain-Eris_Violet-V0.420-12B](https://huggingface.co/Nitral-AI/Captain-Eris_Violet-V0.420-12B) * [Vortex5/Moondark-12B](https://huggingface.co/Vortex5/Moondark-12B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Vortex5/Moondark-12B layer_range: [0, 40] - model: Nitral-AI/Captain-Eris_Violet-V0.420-12B layer_range: [0, 40] merge_method: slerp base_model: Vortex5/Moondark-12B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
Sayemahsjn/blockassist-bc-playful_feline_octopus_1754941804	Sayemahsjn	2025-08-11T20:07:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:07:23Z	--- 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).
gasoline2255/blockassist-bc-flightless_sizable_wildebeest_1754942614	gasoline2255	2025-08-11T20:06:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flightless sizable wildebeest", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:06:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flightless sizable wildebeest --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
skyddand/llama-3-8b-samantha	skyddand	2025-08-11T20:05:15Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T20:00:02Z	--- base_model: unsloth/llama-3-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: skyddand - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-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)
FastFlowLM/Llama-3.2-3B-NPU2	FastFlowLM	2025-08-11T20:03:31Z	43	0	null	[ "llama", "llama-3.2", "text-generation", "AMD", "Ryzen", "NPU", "conversational", "en", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-3B-Instruct", "license:llama3", "region:us" ]	text-generation	2025-06-20T17:33:17Z	--- license: llama3 language: - en tags: - llama - llama-3.2 - text-generation - AMD - Ryzen - NPU pipeline_tag: text-generation base_model: - meta-llama/Llama-3.2-3B-Instruct --- # 🦙 LLaMA 3.2 (3B) – Optimized for FastFlowLM on AMD Ryzen™ AI NPU (XDNA2 Only) ## Model Summary This model is a variant of Meta AI’s LLaMA 3.2 3B Instruct release. It preserves the original architecture and weights, with potential optimizations via quantization, low-level tuning, or runtime enhancements tailored for NPUs using FastFlowLM. > ⚠️ This model is subject to Meta’s LLaMA 3 license. You must accept Meta’s terms to use or download it. ## 📝 License & Usage Terms ### Meta LLaMA 3 License - Governed by Meta AI's LLaMA 3 license: 👉 https://ai.meta.com/llama/license/ - Key restrictions include: - No commercial use without express permission from Meta - Redistribution must follow Meta’s guidelines - Attribution to Meta is required ### Redistribution Notice - This repository does not contain Meta’s original weights. - You must obtain the base weights directly from Meta: 👉 https://huggingface.co/meta-llama ### If Fine-tuned If this version includes any fine-tuning or post-training modification: - Base Model License: Meta’s LLaMA 3 License - Derivative Weights License: [e.g., CC-BY-NC-4.0, MIT, custom] - Training Dataset License(s): - [Dataset A] – [license] - [Dataset B] – [license] Users are responsible for verifying the legality of dataset use and redistribution. ## Intended Use - Target Applications: On-device experimentation, local LLM inference, academic research - Exclusions: Do not use in commercial products, production systems, or critical tasks without proper evaluation and license compliance ## Limitations & Risks - May hallucinate or output biased content - Knowledge is frozen as of the base model's training cutoff - Not evaluated for high-stakes or real-time applications ## Citation ```bibtex @misc{touvron2024llama3, title={LLaMA 3: Open Foundation and Instruction Models}, author={Touvron, Hugo and others}, year={2024}, url={https://ai.meta.com/llama/} ```
Ryder99/mistral-7b-instruct-v0.3-bnb-4bit-DeutschLoRA	Ryder99	2025-08-11T20:03:31Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-11T19:56:53Z	--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Ryder99 - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

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