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modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
wednors/WednorsWTK7	wednors	2025-08-12T21:08:22Z	0	0	null	[ "region:us" ]	null	2025-05-28T08:45:01Z	описание отсутствует.
srajal87/llama3-pricer-2025-08-12_17.41.59-size8000	srajal87	2025-08-12T21:08:07Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:meta-llama/Meta-Llama-3.1-8B", "lora", "sft", "transformers", "trl", "text-generation", "base_model:meta-llama/Llama-3.1-8B", "base_model:adapter:meta-llama/Llama-3.1-8B", "license:llama3.1", "region:us" ]	text-generation	2025-08-12T17:57:46Z	--- library_name: peft license: llama3.1 base_model: meta-llama/Meta-Llama-3.1-8B tags: - base_model:adapter:meta-llama/Meta-Llama-3.1-8B - lora - sft - transformers - trl pipeline_tag: text-generation model-index: - name: llama3-pricer-2025-08-12_17.41.59-size8000 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. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/22ml10ro707-madhav-institude-of-technology-and-science/llama3-pricer/runs/ozgddfxn) # llama3-pricer-2025-08-12_17.41.59-size8000 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) 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: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.17.0 - Transformers 4.55.0 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755031084	calegpedia	2025-08-12T21:07:42Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T21:07:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stealthy slimy rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ibm-granite/granite-4.0-tiny-base-preview-GGUF	ibm-granite	2025-08-12T21:05:53Z	0	0	transformers	[ "transformers", "gguf", "language", "granite-4.0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-12T18:37:38Z	--- license: apache-2.0 library_name: transformers tags: - language - granite-4.0 - gguf --- > [!NOTE] > This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite base model. > > Please reference the base model's full model card here: > https://huggingface.co/ibm-granite/granite-4.0-tiny-base-preview # Granite-4.0-Tiny-Base-Preview Model Summary: Granite-4.0-Tiny-Base-Preview is a 7B-parameter hybrid mixture-of-experts (MoE) language model featuring a 128k token context window. The architecture leverages Mamba-2, superimposed with a softmax attention for enhanced expressiveness, with no positional encoding for better length generalization. - Developers: Granite Team, IBM - Website: [Granite Docs](https://www.ibm.com/granite/docs/) - Release Date: May 2nd, 2025 - License: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.0 models for languages beyond these 12 languages. Intended Use: Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, and other long-context tasks. All Granite Base models are able to handle these tasks as they were trained on a large amount of data from various domains. Moreover, they can serve as baseline to create specialized models for specific application scenarios.
Heouzen/flux1D_lora	Heouzen	2025-08-12T21:05:32Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-11-05T09:46:25Z	--- license: apache-2.0 ---
ibm-granite/granite-4.0-tiny-preview-GGUF	ibm-granite	2025-08-12T21:04:25Z	0	1	transformers	[ "transformers", "gguf", "language", "granite-4.0", "text-generation", "base_model:ibm-granite/granite-4.0-tiny-base-preview", "base_model:quantized:ibm-granite/granite-4.0-tiny-base-preview", "license:apache-2.0", "region:us", "conversational" ]	text-generation	2025-08-12T18:37:37Z	--- pipeline_tag: text-generation inference: false license: apache-2.0 library_name: transformers tags: - language - granite-4.0 - gguf base_model: - ibm-granite/granite-4.0-tiny-base-preview --- > [!NOTE] > This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite base model. > > Please reference the base model's full model card here: > https://huggingface.co/ibm-granite/granite-4.0-tiny-preview # Granite-4.0-Tiny-Preview Model Summary: Granite-4-Tiny-Preview is a 7B parameter fine-grained hybrid mixture-of-experts (MoE) instruct model fine-tuned from Granite-4.0-Tiny-Base-Preview using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets tailored for solving long context problems. This model is developed using a diverse set of techniques with a structured chat format, including supervised fine-tuning, and model alignment using reinforcement learning. - Developers: Granite Team, IBM - Website: [Granite Docs](https://www.ibm.com/granite/docs/) - Release Date: May 2nd, 2025 - License: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. However, users may fine-tune this Granite model for languages beyond these 12 languages. Intended Use: This model is designed to handle general instruction-following tasks and can be integrated into AI assistants across various domains, including business applications. Capabilities * Thinking * Summarization * Text classification * Text extraction * Question-answering * Retrieval Augmented Generation (RAG) * Code related tasks * Function-calling tasks * Multilingual dialog use cases * Long-context tasks including long document/meeting summarization, long document QA, etc.
codebasic/Qwen3-8B-GGUF	codebasic	2025-08-12T21:04:12Z	0	0	null	[ "gguf", "llama.cpp", "qwen", "quantization", "base_model:Qwen/Qwen3-8B", "base_model:quantized:Qwen/Qwen3-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-12T09:03:23Z	--- license: apache-2.0 base_model: - Qwen/Qwen3-8B tags: - gguf - llama.cpp - qwen - quantization --- # Qwen3-8B-GGUF ## 🤖 코드베이직 제공 이 모델은 코드베이직(codebasic)에서 GGUF 포맷으로 변환·배포하였습니다. 이 리포지토리는 [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) 모델을 여러 GGUF 양자화 버전으로 제공합니다. llama.cpp, text-generation-webui, koboldcpp 등 GGUF 포맷을 지원하는 다양한 환경에서 사용할 수 있습니다. --- ## 📂 제공 파일 \| 파일명 \| 양자화 방식 \| 메모리 요구량(대략) \| 설명 \| \|--------\|------------\|----------------------\|------\| \| `Qwen3-8B-F16.gguf` \| FP16 (비양자화) \| ~16GB \| 원본 FP16 가중치 (GPU/고사양 환경) \| \| `Qwen3-8B-Q8_0.gguf` \| Q8_0 \| ~9GB \| 고품질 양자화, 거의 FP16 수준의 정확도 \| > 💡 메모리 요구량은 추정치이며, 환경에 따라 다를 수 있습니다. --- ## 🚀 사용 방법 ### 1. Docker (llama.cpp Q8_0 예시) ```bash docker run -v /path/to/models:/models \ ghcr.io/ggml-org/llama.cpp:full \ --run -m /models/Qwen3-8B/Qwen3-8B-Q8_0.gguf \ -p "언어 모델 소개"
k1000dai/residualact_libero_small	k1000dai	2025-08-12T21:03:25Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "residualact", "dataset:k1000dai/libero-addinfo", "license:apache-2.0", "region:us" ]	robotics	2025-08-12T21:03:03Z	--- datasets: k1000dai/libero-addinfo library_name: lerobot license: apache-2.0 model_name: residualact pipeline_tag: robotics tags: - robotics - lerobot - residualact --- # Model Card for residualact <!-- Provide a quick summary of what the model is/does. --> _Model type not recognized — please update this template._ 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
Zakaria279/GPT-OSS-DIALECT_TRANSLATOR-2	Zakaria279	2025-08-12T21:03:10Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gpt_oss", "trl", "en", "base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-12T21:03:05Z	--- base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gpt_oss - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Zakaria279 - License: apache-2.0 - Finetuned from model : unsloth/gpt-oss-20b-unsloth-bnb-4bit This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755032433	ggozzy	2025-08-12T21:02:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T21:01:43Z	--- 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).
mradermacher/Olmoe-0.5B-6B-GGUF	mradermacher	2025-08-12T20:59:31Z	737	0	transformers	[ "transformers", "gguf", "en", "base_model:motionlabs/Olmoe-0.5B-6B", "base_model:quantized:motionlabs/Olmoe-0.5B-6B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-03T09:31:58Z	--- base_model: motionlabs/Olmoe-0.5B-6B language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/motionlabs/Olmoe-0.5B-6B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Olmoe-0.5B-6B-GGUF).* weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Olmoe-0.5B-6B-GGUF/resolve/main/Olmoe-0.5B-6B.Q2_K.gguf) \| Q2_K \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Olmoe-0.5B-6B-GGUF/resolve/main/Olmoe-0.5B-6B.Q3_K_S.gguf) \| Q3_K_S \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Olmoe-0.5B-6B-GGUF/resolve/main/Olmoe-0.5B-6B.Q3_K_M.gguf) \| Q3_K_M \| 4.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Olmoe-0.5B-6B-GGUF/resolve/main/Olmoe-0.5B-6B.Q3_K_L.gguf) \| Q3_K_L \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Olmoe-0.5B-6B-GGUF/resolve/main/Olmoe-0.5B-6B.IQ4_XS.gguf) \| IQ4_XS \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Olmoe-0.5B-6B-GGUF/resolve/main/Olmoe-0.5B-6B.Q4_K_S.gguf) \| Q4_K_S \| 5.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Olmoe-0.5B-6B-GGUF/resolve/main/Olmoe-0.5B-6B.Q4_K_M.gguf) \| Q4_K_M \| 5.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Olmoe-0.5B-6B-GGUF/resolve/main/Olmoe-0.5B-6B.Q5_K_S.gguf) \| Q5_K_S \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Olmoe-0.5B-6B-GGUF/resolve/main/Olmoe-0.5B-6B.Q5_K_M.gguf) \| Q5_K_M \| 6.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Olmoe-0.5B-6B-GGUF/resolve/main/Olmoe-0.5B-6B.Q6_K.gguf) \| Q6_K \| 7.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Olmoe-0.5B-6B-GGUF/resolve/main/Olmoe-0.5B-6B.Q8_0.gguf) \| Q8_0 \| 9.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Olmoe-0.5B-6B-GGUF/resolve/main/Olmoe-0.5B-6B.f16.gguf) \| f16 \| 17.8 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
BRlkl/BingoGuard-llama-3B-pt	BRlkl	2025-08-12T20:59:02Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-12T20:53:59Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: BRlkl - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755031193	Sayemahsjn	2025-08-12T20:57:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:57:16Z	--- 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_1755032128	ggozzy	2025-08-12T20:56:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:56:40Z	--- 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).
agurung/dft_all_qwen7B_25percent_lr_1e4_allgrad	agurung	2025-08-12T20:55:30Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "sft", "trl", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-12T15:34:08Z	--- library_name: transformers model_name: dft_all_qwen7B_25percent_lr_1e4_allgrad tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for dft_all_qwen7B_25percent_lr_1e4_allgrad This model is a fine-tuned version of [None](https://huggingface.co/None). 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="agurung/dft_all_qwen7B_25percent_lr_1e4_allgrad", 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/alexgurung/ncp_reasoning_projector/runs/cy7a5cx0) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.53.3 - Pytorch: 2.7.0+cu128 - Datasets: 4.0.0 - 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}} } ```
ecamli/blockassist-bc-hulking_soft_hippo_1755032075	ecamli	2025-08-12T20:55:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hulking soft hippo", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:55:07Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hulking soft hippo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Jack-Payne1/qwen_2.5_7b-phoenix_T2_order_seed2	Jack-Payne1	2025-08-12T20:54:33Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Qwen2.5-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-12T20:51:25Z	--- base_model: unsloth/Qwen2.5-7B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: Jack-Payne1 - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-7B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
andr0m4da/blockassist-bc-grazing_hunting_boar_1755031940	andr0m4da	2025-08-12T20:53:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "grazing hunting boar", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:53:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - grazing hunting boar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
1torriani/exupery_v2	1torriani	2025-08-12T20:52:58Z	0	0	null	[ "literature", "en", "license:mit", "region:us" ]	null	2025-08-12T20:51:40Z	--- license: mit language: - en tags: - literature ---
Honeywithcrypto/blockassist-bc-tall_miniature_porpoise_1755031813	Honeywithcrypto	2025-08-12T20:51:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall miniature porpoise", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:51:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall miniature porpoise --- # 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_1755031798	Gemvision13	2025-08-12T20:51:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky jagged panda", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:51:12Z	--- 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).
zhuojing-huang/ewc_test	zhuojing-huang	2025-08-12T20:44:18Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-12T15:10:00Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: ewc_test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ewc_test This model was trained from scratch on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 30 - training_steps: 183 ### Training results ### Framework versions - Transformers 4.53.1 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.2
BootesVoid/cme8zagth03eurts86yr2q8lr_cme8zen1i03fprts8pqdonedd	BootesVoid	2025-08-12T20:40:51Z	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	2025-08-12T20:40:50Z	--- 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: KC001 --- # Cme8Zagth03Eurts86Yr2Q8Lr_Cme8Zen1I03Fprts8Pqdonedd <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 `KC001` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "KC001", "lora_weights": "https://huggingface.co/BootesVoid/cme8zagth03eurts86yr2q8lr_cme8zen1i03fprts8pqdonedd/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cme8zagth03eurts86yr2q8lr_cme8zen1i03fprts8pqdonedd', weight_name='lora.safetensors') image = pipeline('KC001').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cme8zagth03eurts86yr2q8lr_cme8zen1i03fprts8pqdonedd/discussions) to add images that show off what you’ve made with this LoRA.
andr0m4da/blockassist-bc-grazing_hunting_boar_1755030919	andr0m4da	2025-08-12T20:38:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "grazing hunting boar", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:38:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - grazing hunting boar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ecamli/blockassist-bc-hulking_soft_hippo_1755031006	ecamli	2025-08-12T20:37:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hulking soft hippo", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:37:09Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hulking soft hippo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755030906	ggozzy	2025-08-12T20:36:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:36:16Z	--- 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).
sphiratrioth666/Character_Generation_Templates	sphiratrioth666	2025-08-12T20:32:47Z	0	37	null	[ "template,", "character,", "generator,", "sillytavern,", "silly,", "tavern,", "tool,", "en", "base_model:ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.3-GGUF", "base_model:finetune:ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.3-GGUF", "license:cc-by-nc-4.0", "region:us" ]	null	2025-01-23T04:47:27Z	--- license: cc-by-nc-4.0 language: - en base_model: - mistralai/Mistral-Nemo-Instruct-2407 - mistralai/Mistral-Small-Instruct-2409 - TheDrummer/Cydonia-22B-v1.3 - anthracite-org/magnum-v4-12b-gguf - anthracite-org/magnum-v4-72b - bartowski/MN-12B-Lyra-v4-GGUF - ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.3-GGUF - ArliAI/Mistral-Small-22B-ArliAI-RPMax-v1.1 tags: - template, - character, - generator, - sillytavern, - silly, - tavern, - tool, --- ![image/png](https://img.goodfon.com/original/2560x1440/4/8c/vlastelin-kolets-aragorn-sauron-gollum-frodo-beggins-nazguly.jpg)\| \|:--:\| \|Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License (https://www.goodfon.com/films/wallpaper-download-2560x1440-vlastelin-kolets-aragorn-sauron-gollum-frodo-beggins-nazguly.html)\|<br> Today, I bring you a character generation prompt. Generate all the imaginable characters and make them work out of the box - not like with 99% of the existing, similar generators. Seriously. It is not the random, bland trash. I made it exactly because those generators are not usable (as of JAN/2025). I've tried them all, I got disappointed so I designed a good tool myself. Characters follow a consistent, custom template. They're accurate and true to the lore if you generate the existing ones. They are rational and believable when you want to create the new, original ones. I've generated around 100 cards with it already. I did not even have to touch a majority of them after generation. No need to install anything. Just open up the GPT, Gemini, Deepseek or any other LLM API of your choice, copy-paste my prompt, describe what character you want (1-2 sentences!) - something like: "a wizard female elf from dungeons and dragons" or "a Japanese salaryman from Tokyo; and... That's it. You can provide more details when you generate from nothing or just the name and the origin of the character - such as Jinx from League of Legends video game in the example below. Characters are generated in a custom format - partly inspired by JSON, partly by Python (P-list) and partly by different data strings I work with. This custom format allows saving tokens, keeping things organized and using other, creative tricks with lorebooks, which I describe in separate posts. Because of that, there are two formats of the char gen template: a) universal, b) SX-4 - customized for my personal roleplaying systems SX-4/GM-4/CG-4 (coming soon). Just check all the posts on my profile. <b>Template Contents (what is generated):</b> <div style="background-color: #ffefb8; padding: 16px 32px; outline: 2px solid; border-radius: 10px;"> <li><b>character</b> (personal Information, appearance, personality, likes, dislikes, skills, goals, clothes for different occasions)</li> <li><b>scenario</b> (allows realistically simulating everyday life of your character, it will include lore - so it's not a bland filler but you can also replace it if you wish)</li> <li><b>first message</b> (which makes sense, you'll see, trust me)</li> </div> <br> BEWARE: IT WILL NOT GENERATE A CARD ITSELF (AS A FILE). YOU NEED TO COPY THE GENERATED CHARACTER DESCRIPTION AND PASTE IT INTO THE CARDS EDITOR OF YOUR CHOICE. YOU CAN USE THE CHARACTER MANAGER IN SILLY TAVERN OR ANYTHING ONLINE. IT'S NOT ROCKET SCIENCE. I WILL NOT PROVIDE A DETAILED GUIDE TO TEACH YOU HOW TO MAKE A CHARACTER CARD, I'M SORRY FOR THAT. THERE'RE MANY EDITORS AND ALL OF THEM ARE SIMILAR, THEY ALL SAVE THE CHARACTER IN .PNG OR .JSON FILE YOU NEED TO IMPORT INTO A SILLYTAVERN OR WHEREVER YOU WANNA USE THEM. Example character cards editor online: (https://desune.moe/aichared/) <b>Features:</b> - able to rip detailed information about any existing character from Internet sources (wikis); assuming you are using the web search API capabilities (GPT, Claude or local extensions in SillyTavern etc.) - able to generate realistic characters that do not exist, based on a couple of words you provide to describe who you actually want to generate (using the same Internet capabilities of your API and the general power of the LLM that knows who a Japanese salaryman or who a fantasy fire wizard is) - able to generate appearance from a photo (if you are using a vision model locally or again, something like GPT) - so - proper outfit, hair, eyes etc. but it works equally well with existing characters without a picture. It does not make mistakes. <b>How to use it:</b> 1. Download the 2 .txt files with a male and a female template from the files repository of this post. 2. Open up the downloaded .txt files. They include my templates. 3. Open up GPT, Claude or the LLM of your choice. 4. Copy-paste the content of a male/female template into the GPT chat. Just like you write a standard message. 5. Replace the DESCRIPTION word at the top of what you copy-pasted with a description of your desired character - like: Jinx from League of Legends. Attach a picture if you want. I did not use a picture in my example. 6. Hit enter. 7. If it does not generate the character in a proper format format, but - for instance - as a list - ask the LLM to regenerate it but exactly in a given format. When LLM understands what you want and returns it properly, you can generate more characters in the same chat without copy lasting the template again and again and they will always appear in the expected format. I've tried it with all the available LLMs, it works, it just requires a couple of retries from time to time. 8. Copy the generated character information into your character editor online or in a SillyTavern UI. I suggest copying all the character parts into a description box of the card, you do not actually need to use the personality tab for personality. Then - copy a scenario into the scenario box. You can still copy it just into a description but I prefer using a separate scenario box. Alternatively - do not copy the scenario if you do not want the universal day routine - but it helps with adding color to the character. I personally like the open scenarios, you do whatever you like. Last, copy a starting message into the starting message box. You do not need to alter anything but you can if you wish, obviously. 9. Add a character picture you want, save the finished character card as a .PNG or a .JSON file. You're done. 10. Have fun. <br> <b>Example - Jinx from League of Legends <br> ![image/png](https://mrwallpaper.com/images/hd/jinx-arcane-escaping-by-rocket-8ss681ujj6iommno.jpg)\| \|:--:\| \|Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License (https://mrwallpaper.com/images/hd/jinx-arcane-escaping-by-rocket-8ss681ujj6iommno.jpg)\|<br> <div style="background-color: #ffefb8; padding: 16px 32px; outline: 2px solid; border-radius: 10px;"> <b>Character:</b> <br> {{"Personal Information"}}:{name: Jinx, race: Caucasian, nationality: Zaunite, gender: female, age: 21, profession: criminal mastermind, residence: [Zaun, apartment (lower-city)], marital status: single} <br> {{"Appearance"}}:{hair: [blue, straight, long (waist-length), twin braids], eyes: pink, height: 170 cm, weight: 50 kg, body: [slim, light skin], breasts: [small, B-cup, small areolas, cherry-pink nipples], armpit hair: shaved, pubic hair: shaved, fingernails: painted (pink and blue), toenails: painted (pink and blue)} <br> {{"Personality"}}:{Jinx is a manic and impulsive criminal with a penchant for creating chaos and destruction. She exhibits a gleeful disregard for the consequences of her actions, often engaging in reckless behavior purely for her own amusement. Her unpredictable nature and love for mayhem make her a formidable and feared figure in Zaun and Piltover. Jinx's speech is erratic and filled with dark humor, reflecting her unhinged psyche.} <br> {{"Likes"}}:{mayhem, explosions, chaos, pranks, graffiti, outsmarting authorities} <br> {{"Dislikes"}}:{boredom, order, authority figures, being ignored} <br> {{"Goals"}}:{to create as much chaos and destruction as possible, to outwit and undermine Piltover's enforcers, to have fun without restrictions} <br> {{"Skills"}}:{expert in explosives and firearms, exceptional agility and acrobatics, strategic planning of heists and attacks, high intelligence masked by her chaotic demeanor} <br> {{"Weapons"}}:{minigun ("Pow-Pow"), shock pistol ("Zapper"), explosive grenades ("Flame Chompers"), rocket launcher ("Fishbones")} <br> {{"Main Outfit"}}:{striped crop top (black and pink), shorts with suspenders (purple and pink), thigh-high mismatched stockings (one pink, one blue), combat boots (black leather with pink laces), lingerie: [lace bra (black), lace thong (black)]} <br> {{"Formal Outfit"}}:{waist jacket (black leather), skinny pants (dark purple), fingerless gloves (black leather), high-heeled boots (black), lingerie: [lace bra (black), lace thong (black)]} <br> {{"Sleeping Outfit"}}:{nightgown (dark blue), silk thong (dark blue), soft slippers (white)} <br> {{"Running Outfit"}}:{sports bra (pink), leggings (black), sports shoes (white), lingerie: thong (pink)} <br> {{"Exercise Outfit"}}:{sports bra (blue), leggings (black), bare feet, lingerie: lace thong (blue)} <br> {{"Swimsuit"}}:{bikini (black), barefoot} </div> <br> <div style="background-color: #ffefb8; padding: 16px 32px; outline: 2px solid; border-radius: 10px;"> <br> <b>Scenario:</b> <br> {{"Scenario"}}:{{{char}} is living everyday life, {{char}} and {{user}} keep crossing each other's paths as {{char}} and {{user}} relationship develops, {{char}} slowly develops a crush on {{user}}, everyday routine:[morning: {{char}} starts the day by tinkering with explosives or tweaking her weapons in her chaotic lower-city apartment. She often talks to her gadgets as if they were alive, her laughter echoing through the room., day: {{char}} roams the streets of Zaun and sometimes sneaks into Piltover, causing minor chaos and pulling off elaborate pranks. She enjoys challenging enforcers and leaving behind cryptic graffiti., evening: {{char}} lounges in her apartment, reviewing the day's antics and drawing up plans for bigger stunts. Her evenings are filled with self-satisfied giggles and loud music, often paired with snacks she ‘borrowed’ from others.], current mood: {{char}} is feeling mischievous and restless, eager for a thrilling encounter or an unexpected turn of events.} </div> <br> <div style="background-color: #ffefb8; padding: 16px 32px; outline: 2px solid; border-radius: 10px;"> <br> <b>Starting Message:</b> <br> The sound of clinking metal fills the cramped apartment as Jinx tinkers with her rocket launcher, muttering to herself between fits of laughter. Wires, bolts, and half-finished gadgets lie scattered across every surface. She props one foot on the workbench and spins around to face you as you enter the room unannounced. <br> "Well, well, look who decided to crash the party! You here to watch the magic, or are you planning to steal my snacks? Better not be the snacks." <br> She grins, twirling a wrench like a baton before launching it onto a pile of junk. Leaning casually against the bench, she gestures toward a mess of tools and parts. <br> "Sit tight. I’m cooking up something explosive - literally. You might want to duck when I say so." </div> <br> She was generated with this exact template. I did not change ANYTHING, I did not use a picture, just the template in GPT. That's exactly what I got back. It is quite precise, detailed, not bland and usable out of the box, isn't it? <br>Have fun!
indoempatnol/blockassist-bc-fishy_wary_swan_1755028818	indoempatnol	2025-08-12T20:25:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:25:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy wary swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jose-morales-glbnt/my_awesome_billsum_model	jose-morales-glbnt	2025-08-12T20:24:51Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-08-12T20:19:22Z	--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer model-index: - name: my_awesome_billsum_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. --> # my_awesome_billsum_model This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.55.0 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
kayacrypto/blockassist-bc-thriving_barky_wolf_1755030136	kayacrypto	2025-08-12T20:23:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thriving barky wolf", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:23:30Z	--- 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).
Vardis/test_gpt_med	Vardis	2025-08-12T20:23:44Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-12T20:23:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. 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yasserrmd/RSCaLM-138M-Core	yasserrmd	2025-08-12T20:23:23Z	0	0	null	[ "pytorch", "gptx-min", "dataset:HuggingFaceFW/fineweb-edu", "region:us" ]	null	2025-08-12T19:44:03Z	--- datasets: - HuggingFaceFW/fineweb-edu --- # RSCaLM-138M-core RSCaLM (Research Scale Causal Language Model) — Core Edition — is an experimental 138M-parameter decoder-only transformer trained for 20,000 steps. Unlike the LLaMA variant, this model is implemented entirely with a custom minimal GPT architecture (`standalone_transformer_lm.GPT`) and SentencePiece tokenization — no Hugging Face Transformers dependency. --- ## 📌 Experiment Summary * Architecture: Custom GPT-style causal decoder * Implemented in `standalone_transformer_lm.py` * Learned positional embeddings (absolute) * Multi-head self-attention with KV caching * GELU feed-forward layers * LayerNorm * Parameter Count: \~138M * Context Length: 2048 tokens * Tokenizer: SentencePiece (`tokenizer.model`) * Training Framework: Pure PyTorch (no Transformers) * Optimizer: AdamW (β1=0.9, β2=0.95, weight decay=0.1) * Scheduler: Cosine decay with warmup * Precision: Mixed FP16/BF16 training * Steps Completed: 20,000 (\~32% of planned total) --- ## 📉 Validation Loss Progress \| Step \| Val Loss \| \| ------ \| -------- \| \| 1,000 \| 5.6011 \| \| 2,000 \| 4.8598 \| \| 5,000 \| 4.2239 \| \| 10,000 \| 3.9756 \| \| 15,000 \| 3.8608 \| \| 20,000 \| 3.7984 \| --- ## ⚠️ Notes * Prototype only — repetition loops expected in longer generations. * Requires `standalone_transformer_lm.py` and SentencePiece to run. * Does not load with `transformers.AutoModelForCausalLM`. --- ## 🔧 Example Usage ```python import torch, sentencepiece as spm from standalone_transformer_lm import GPT, GPTConfig # Load checkpoint & config ckpt = torch.load("ckpt_best.pt", map_location="cpu") cfg = GPTConfig(ckpt["config"]) # Init model & load weights model = GPT(cfg).eval() model.load_state_dict(ckpt["model"]) # Load tokenizer sp = spm.SentencePieceProcessor() sp.load("tokenizer.model") # Encode prompt ids = torch.tensor([sp.encode("Dubai is", out_type=int)]) # Generate text out = model.generate(ids, max_new_tokens=40) print(sp.decode(out[0].tolist())) ``` --- ## 🔧 Example Usage (with repetition control) ```python import torch, sentencepiece as spm from standalone_transformer_lm import GPT, GPTConfig ckpt = torch.load("ckpt_best.pt", map_location="cpu") cfg = GPTConfig(ckpt["config"]) model = GPT(cfg).eval() model.load_state_dict(ckpt["model"]) sp = spm.SentencePieceProcessor() sp.load("tokenizer.model") prompt = "when a man goes to fishing" ids = torch.tensor([sp.encode(prompt, out_type=int)]) # Manual repetition control out = model.generate( ids, max_new_tokens=100, temperature=0.7, # Lower temp = more focused top_k=50, # Top-K sampling top_p=0.9, # Nucleus sampling repetition_penalty=1.2, # Penalize repeats no_repeat_ngram_size=3, # Block repeating trigrams ) print(sp.decode(out[0].tolist())) ``` --- ### 💡 Tips to Reduce Loops * Increase `repetition_penalty` to 1.2–1.5 * Use `no_repeat_ngram_size=3` or higher * Combine `top_k` and `top_p` for better sampling variety * Lower `temperature` for more deterministic completions --- ## 📜 License Apache-2.0 ---
narukijima/pioneer-mini-v1	narukijima	2025-08-12T20:22:20Z	20	0	transformers	[ "transformers", "safetensors", "gpt_oss", "text-generation", "conversational", "custom_code", "en", "ja", "base_model:openai/gpt-oss-20b", "base_model:finetune:openai/gpt-oss-20b", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-08T19:36:45Z	--- library_name: transformers base_model: openai/gpt-oss-20b language: [en, ja] pipeline_tag: text-generation tags: [] --- # pioneer-mini-v1 Overview This is a test model. Technical notes - Base: `openai/gpt-oss-20b` (bf16) - Steering: rank-1 delta on Q/K/V across 24 layers (RMSNorm-aware) - Concept vector: `concept_vec_v15k.pt`, shape [24, 6, 2880], gain=0.5 - Checkpoint: single baked weights (no LoRA/adapters; knowledge ≈ base) - Data used: neutral_examples=86376, pairs_used=14398 - Source files: `narukijima/pioneer` → `P_instruction_pairs_en.jsonl`, `P_instruction_pairs_ja.jsonl` - Inference: use base tokenizer & chat template Quick inference ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch M = "narukijima/pioneer-mini-v1" tok = AutoTokenizer.from_pretrained(M, trust_remote_code=True) mdl = AutoModelForCausalLM.from_pretrained( M, torch_dtype=torch.bfloat16, device_map='auto', trust_remote_code=True ) msgs = [{"role":"user","content":"test"}] p = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True) out = mdl.generate(**tok(p, return_tensors='pt').to(mdl.device), max_new_tokens=64, do_sample=True, temperature=0.7) print(tok.decode(out[0], skip_special_tokens=True)) ```
narukijima/connector-mini-v1	narukijima	2025-08-12T20:21:55Z	19	0	transformers	[ "transformers", "safetensors", "gpt_oss", "text-generation", "conversational", "custom_code", "en", "ja", "base_model:openai/gpt-oss-20b", "base_model:finetune:openai/gpt-oss-20b", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-08T19:42:16Z	--- library_name: transformers base_model: openai/gpt-oss-20b language: [en, ja] pipeline_tag: text-generation tags: [] --- # connector-mini-v1 Overview This is a test model. Technical notes - Base: `openai/gpt-oss-20b` (bf16) - Steering: rank-1 delta on Q/K/V across 24 layers (RMSNorm-aware) - Concept vector: `concept_vec_v15k.pt`, shape [24, 6, 2880], gain=0.5 - Checkpoint: single baked weights (no LoRA/adapters; knowledge ≈ base) - Data used: neutral_examples=86376, pairs_used=14400 - Source files: `narukijima/connector` → `C_instruction_pairs_en.jsonl`, `C_instruction_pairs_ja.jsonl` - Inference: use base tokenizer & chat template Quick inference ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch M = "narukijima/connector-mini-v1" tok = AutoTokenizer.from_pretrained(M, trust_remote_code=True) mdl = AutoModelForCausalLM.from_pretrained( M, torch_dtype=torch.bfloat16, device_map='auto', trust_remote_code=True ) msgs = [{"role":"user","content":"test"}] p = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True) out = mdl.generate(**tok(p, return_tensors='pt').to(mdl.device), max_new_tokens=64, do_sample=True, temperature=0.7) print(tok.decode(out[0], skip_special_tokens=True)) ```
narukijima/thinker-mini-v1	narukijima	2025-08-12T20:21:21Z	19	0	transformers	[ "transformers", "safetensors", "gpt_oss", "text-generation", "conversational", "custom_code", "en", "ja", "base_model:openai/gpt-oss-20b", "base_model:finetune:openai/gpt-oss-20b", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-08T20:08:40Z	--- library_name: transformers base_model: openai/gpt-oss-20b language: [en, ja] pipeline_tag: text-generation tags: [] --- # thinker-mini-v1 Overview This is a test model. Technical notes - Base: `openai/gpt-oss-20b` (bf16) - Steering: rank-1 delta on Q/K/V across 24 layers (RMSNorm-aware) - Concept vector: `concept_vec_v15k.pt`, shape [24, 6, 2880], gain=0.5 - Checkpoint: single baked weights (no LoRA/adapters; knowledge ≈ base) - Data used: neutral_examples=86376, pairs_used=14394 - Source files: `narukijima/thinker` → `T_instruction_pairs_en.jsonl`, `T_instruction_pairs_ja.jsonl` - Inference: use base tokenizer & chat template Quick inference ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch M = "narukijima/thinker-mini-v1" tok = AutoTokenizer.from_pretrained(M, trust_remote_code=True) mdl = AutoModelForCausalLM.from_pretrained( M, torch_dtype=torch.bfloat16, device_map='auto', trust_remote_code=True ) msgs = [{"role":"user","content":"test"}] p = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True) out = mdl.generate(**tok(p, return_tensors='pt').to(mdl.device), max_new_tokens=64, do_sample=True, temperature=0.7) print(tok.decode(out[0], skip_special_tokens=True)) ```
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755029990	ggozzy	2025-08-12T20:21:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:21:01Z	--- 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).
yasserrmd/RSCaLM-138M-LLaMA	yasserrmd	2025-08-12T20:20:22Z	0	0	null	[ "safetensors", "llama", "dataset:HuggingFaceFW/fineweb-edu", "license:apache-2.0", "region:us" ]	null	2025-08-12T20:08:06Z	--- datasets: - HuggingFaceFW/fineweb-edu license: apache-2.0 --- # RSCaLM-138M-LLaMA RSCaLM (Research Scale Causal Language Model) is an experimental 138M-parameter LLaMA-architecture model trained for 20,000 steps. This run was conducted purely for experimental and benchmarking purposes — no high expectations for downstream task quality. --- ## 📌 Experiment Summary * Architecture: LLaMA-style causal decoder * Rotary positional embeddings (RoPE) * Pre-normalization with RMSNorm * SwiGLU feed-forward layers * Multi-head self-attention with key-value caching support * Parameter Count: \~138M * Context Length: 2048 tokens * Tokenizer: LLaMA tokenizer * Training Framework: PyTorch + Hugging Face Transformers * Optimizer: AdamW (β1=0.9, β2=0.95, weight decay=0.1) * Scheduler: Cosine decay with warmup * Precision: Mixed-precision (FP16/BF16) * Batching: Gradient accumulation to simulate large batch size * Dataset: General text corpus for pipeline validation (not domain-specific) * Steps Completed: 20,000 (\~32% of planned total) --- ## 📉 Validation Loss Progress \| Step \| Val Loss \| \| ----- \| -------- \| \| 1000 \| 5.5968 \| \| 2000 \| 4.8513 \| \| 5000 \| 4.2105 \| \| 10000 \| 3.9603 \| \| 15000 \| 3.8497 \| \| 20000 \| 3.7891 \| Loss shows steady improvement over the limited training period. --- ## ⚠️ Notes * This is an early prototype — not tuned for production use. * Training stopped after \~32% of planned total steps. * Possible repetition loops observed in generation — expected for low-step runs. * Intended for research reference, not for deployment in critical tasks. --- ## 🔧 Example Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "yasserrmd/RSCaLM-138M-LLaMA" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") prompt = "The sun is" inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_new_tokens=50, temperature=0.7) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` --- ## 🔧 Example Usage (with repetition control) ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "yasserrmd/RSCaLM-138M-LLaMA" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") prompt = "when a man goes to fishing" inputs = tokenizer(prompt, return_tensors="pt").to(model.device) # Generation settings to reduce repetition outputs = model.generate( inputs, max_new_tokens=100, # Limit length of output temperature=0.7, # Lower temperature = more focused top_p=0.9, # Nucleus sampling top_k=50, # Top-K filtering repetition_penalty=1.2, # Penalize repeating tokens no_repeat_ngram_size=3, # Prevent repeating trigrams eos_token_id=tokenizer.eos_token_id, # End generation at EOS ) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` --- ### 💡 Tips for controlling repetition: 1. `repetition_penalty` – Increase slightly above `1.0` (e.g., `1.2–1.5`) to discourage repeated phrases. 2. `no_repeat_ngram_size` – Set to `3` or `4` to avoid repeated n-grams. 3. `top_k` + `top_p` – Combine both for better randomness control. 4. Lower `temperature` – Keeps outputs focused and less chaotic. 5. Stop sequences – Add specific words/phrases to halt generation early if needed. --- ## 📜 License apache-2.0
fernandorank/fernando-lora-trainer	fernandorank	2025-08-12T20:19:46Z	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	2025-08-12T19:37:04Z	--- 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: fernando --- # Fernando Lora Trainer <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 `fernando` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "fernando", "lora_weights": "https://huggingface.co/fernandorank/fernando-lora-trainer/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('fernandorank/fernando-lora-trainer', weight_name='lora.safetensors') image = pipeline('fernando').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/fernandorank/fernando-lora-trainer/discussions) to add images that show off what you’ve made with this LoRA.
ACECA/lowMvMax_188	ACECA	2025-08-12T20:19:42Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-12T15:17:45Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
bamitunde/blockassist-bc-mimic_humming_frog_1755029891	bamitunde	2025-08-12T20:19:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mimic humming frog", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:19:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mimic humming frog --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/Gemma-3-R1-27B-v1-i1-GGUF	mradermacher	2025-08-12T20:18:05Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:TheDrummer/Gemma-3-R1-27B-v1", "base_model:quantized:TheDrummer/Gemma-3-R1-27B-v1", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-08-12T18:06:54Z	--- base_model: TheDrummer/Gemma-3-R1-27B-v1 language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/TheDrummer/Gemma-3-R1-27B-v1 <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Gemma-3-R1-27B-v1-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-GGUF This is a vision model - mmproj files (if any) will be in the [static repository](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-GGUF). ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 6.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 6.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 7.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 8.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 8.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 9.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 9.9 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-Q2_K.gguf) \| i1-Q2_K \| 10.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 10.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 11.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 12.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 12.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 12.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 13.5 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 14.6 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-Q4_0.gguf) \| i1-Q4_0 \| 15.7 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 15.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 16.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-Q4_1.gguf) \| i1-Q4_1 \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 18.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 19.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-3-R1-27B-v1-i1-GGUF/resolve/main/Gemma-3-R1-27B-v1.i1-Q6_K.gguf) \| i1-Q6_K \| 22.3 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
koloni/blockassist-bc-deadly_graceful_stingray_1755028283	koloni	2025-08-12T20:17:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:17:48Z	--- 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).
gasoline2255/blockassist-bc-flightless_sizable_wildebeest_1755029621	gasoline2255	2025-08-12T20:16:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flightless sizable wildebeest", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:16:23Z	--- 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).
CLAUSE-Bielefeld/communicative-baby-rfsemsim	CLAUSE-Bielefeld	2025-08-12T20:15:52Z	0	0	null	[ "safetensors", "llama", "en", "base_model:CLAUSE-Bielefeld/llamalogue", "base_model:finetune:CLAUSE-Bielefeld/llamalogue", "license:cc-by-nc-4.0", "region:us" ]	null	2025-08-05T07:37:08Z	--- license: cc-by-nc-4.0 language: - en base_model: - bbunzeck/llamalogue ---
mrkevin1/advanced_thinker_v2	mrkevin1	2025-08-12T20:14:16Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-12T20:13:17Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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ArtusDev/TheDrummer_Gemma-3-R1-12B-v1-EXL3	ArtusDev	2025-08-12T20:14:08Z	0	0	null	[ "exl3", "base_model:TheDrummer/Gemma-3-R1-12B-v1", "base_model:quantized:TheDrummer/Gemma-3-R1-12B-v1", "region:us" ]	null	2025-08-12T17:26:17Z	--- base_model: TheDrummer/Gemma-3-R1-12B-v1 base_model_relation: quantized quantized_by: ArtusDev tags: - exl3 --- ## EXL3 Quants of TheDrummer/Gemma-3-R1-12B-v1 EXL3 quants of [TheDrummer/Gemma-3-R1-12B-v1](https://huggingface.co/TheDrummer/Gemma-3-R1-12B-v1) using <a href="https://github.com/turboderp-org/exllamav3/">exllamav3</a> for quantization. ### Quants \| Quant(Revision) \| Bits per Weight \| Head Bits \| \| -------- \| ---------- \| --------- \| \| [2.5_H6](https://huggingface.co/ArtusDev/TheDrummer_Gemma-3-R1-12B-v1-EXL3/tree/2.5bpw_H6) \| 2.5 \| 6 \| \| [3.0_H6](https://huggingface.co/ArtusDev/TheDrummer_Gemma-3-R1-12B-v1-EXL3/tree/3.0bpw_H6) \| 3.0 \| 6 \| \| [3.5_H6](https://huggingface.co/ArtusDev/TheDrummer_Gemma-3-R1-12B-v1-EXL3/tree/3.5bpw_H6) \| 3.5 \| 6 \| \| [4.0_H6](https://huggingface.co/ArtusDev/TheDrummer_Gemma-3-R1-12B-v1-EXL3/tree/4.0bpw_H6) \| 4.0 \| 6 \| \| [4.5_H6](https://huggingface.co/ArtusDev/TheDrummer_Gemma-3-R1-12B-v1-EXL3/tree/4.5bpw_H6) \| 4.5 \| 6 \| \| [5.0_H6](https://huggingface.co/ArtusDev/TheDrummer_Gemma-3-R1-12B-v1-EXL3/tree/5.0bpw_H6) \| 5.0 \| 6 \| \| [6.0_H6](https://huggingface.co/ArtusDev/TheDrummer_Gemma-3-R1-12B-v1-EXL3/tree/6.0bpw_H6) \| 6.0 \| 6 \| \| [8.0_H8](https://huggingface.co/ArtusDev/TheDrummer_Gemma-3-R1-12B-v1-EXL3/tree/8.0bpw_H8) \| 8.0 \| 8 \| ### Downloading quants with huggingface-cli <details> <summary>Click to view download instructions</summary> Install hugginface-cli: ```bash pip install -U "huggingface_hub[cli]" ``` Download quant by targeting the specific quant revision (branch): ``` huggingface-cli download ArtusDev/TheDrummer_Gemma-3-R1-12B-v1-EXL3 --revision "5.0bpw_H6" --local-dir ./ ``` </details>
roeker/blockassist-bc-quick_wiry_owl_1755029557	roeker	2025-08-12T20:14:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:13:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
varsunk/Qwen3-4B-LORA-GRPO-Experiment	varsunk	2025-08-12T20:11:15Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen3-4B-Base", "base_model:finetune:unsloth/Qwen3-4B-Base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-12T20:06:08Z	--- base_model: unsloth/Qwen3-4B-Base tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: varsunk - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B-Base This qwen3 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)
Grogun/blockassist-bc-lightfooted_yapping_macaw_1755029306	Grogun	2025-08-12T20:09:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lightfooted yapping macaw", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:08:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lightfooted yapping macaw --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Osrivers/fluxPlusFp8_v10.safetensors	Osrivers	2025-08-12T20:08:45Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-08-12T20:04:40Z	--- license: creativeml-openrail-m ---
meowkart/dither-v1-16by16	meowkart	2025-08-12T20:08:34Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2025-08-12T20:08:08Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/images.jpeg text: '-' base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: null --- # Dither V1 16x16 <Gallery /> ## Download model [Download](/meowkart/dither-v1-16by16/tree/main) them in the Files & versions tab.
roeker/blockassist-bc-quick_wiry_owl_1755029064	roeker	2025-08-12T20:05:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:05:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # 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_1755029074	ggozzy	2025-08-12T20:05:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:05:44Z	--- 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).
R433/TruthFinderLLM-Mistral-7B-Instruct-Wikileaks-SFT-GGUF	R433	2025-08-12T20:05:33Z	0	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-08-12T18:57:50Z	--- license: apache-2.0 ---
vengky/blockassist-bc-wild_gentle_manatee_1755025697	vengky	2025-08-12T20:03:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wild gentle manatee", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:03:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wild gentle manatee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755027290	calegpedia	2025-08-12T20:03:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:03:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stealthy slimy rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
m-mulet/try2_qwen_2.5_7b-cat_teacher	m-mulet	2025-08-12T20:01:18Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/Qwen2.5-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-12T19:56:40Z	--- base_model: unsloth/Qwen2.5-7B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: m-mulet - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-7B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755028769	ggozzy	2025-08-12T20:00:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T20:00:36Z	--- 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).
rickcotuit/sd-class-butterflies-32	rickcotuit	2025-08-12T19:59:56Z	0	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2025-08-12T19:58:06Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('rickcotuit/sd-class-butterflies-32') image = pipeline().images[0] image ```
dreamygeek/blockassist-bc-swift_amphibious_alpaca_1755026909	dreamygeek	2025-08-12T19:59:20Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "swift amphibious alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:58:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - swift amphibious alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rwillh11/mdeberta_NLI_policy_noContext	rwillh11	2025-08-12T19:58:48Z	0	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-12T19:58:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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Sayemahsjn/blockassist-bc-playful_feline_octopus_1755027656	Sayemahsjn	2025-08-12T19:58:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:58:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755028604	roeker	2025-08-12T19:57:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:57:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sirev/Qlora-lfm2-700m-mental-health	sirev	2025-08-12T19:57:14Z	0	0	transformers	[ "transformers", "safetensors", "lfm2", "text-generation", "conversational", "dataset:ShenLab/MentalChat16K", "arxiv:1910.09700", "base_model:LiquidAI/LFM2-700M", "base_model:finetune:LiquidAI/LFM2-700M", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-12T19:07:52Z	--- library_name: transformers datasets: - ShenLab/MentalChat16K base_model: - LiquidAI/LFM2-700M pipeline_tag: text-generation --- # 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]
indoempatnol/blockassist-bc-fishy_wary_swan_1755027103	indoempatnol	2025-08-12T19:56:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:56:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy wary swan --- # 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_1755028463	ggozzy	2025-08-12T19:55:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:55:25Z	--- 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).
Gemvision13/blockassist-bc-finicky_jagged_panda_1755028246	Gemvision13	2025-08-12T19:52:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky jagged panda", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:52:01Z	--- 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).
hejazizo/grpo-merged-checkpoint-891_2025-08-11_00-30	hejazizo	2025-08-12T19:51:27Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "grpo", "arxiv:2402.03300", "base_model:hejazizo/merged-checkpoint-891", "base_model:finetune:hejazizo/merged-checkpoint-891", "endpoints_compatible", "region:us" ]	null	2025-08-11T04:30:53Z	--- base_model: hejazizo/merged-checkpoint-891 library_name: transformers model_name: grpo-merged-checkpoint-891_2025-08-11_00-30 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for grpo-merged-checkpoint-891_2025-08-11_00-30 This model is a fine-tuned version of [hejazizo/merged-checkpoint-891](https://huggingface.co/hejazizo/merged-checkpoint-891). 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="hejazizo/grpo-merged-checkpoint-891_2025-08-11_00-30", 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/hejazizo-ali-pytopia/grpo-merged-checkpoint-891/runs/bxygyiuf) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.20.0 - Transformers: 4.55.0 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.4 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755028158	ggozzy	2025-08-12T19:50:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:50:25Z	--- 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).
andr0m4da/blockassist-bc-grazing_hunting_boar_1755028106	andr0m4da	2025-08-12T19:50:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "grazing hunting boar", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:49:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - grazing hunting boar --- # 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_1755026574	koloni	2025-08-12T19:48:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:47:33Z	--- 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).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755027954	Ferdi3425	2025-08-12T19:47:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:46:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
johngreendr1/2afad5de-217f-4ab5-860f-b3dd1b442cdc	johngreendr1	2025-08-12T19:47:20Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Yarn-Mistral-7b-128k", "base_model:adapter:NousResearch/Yarn-Mistral-7b-128k", "region:us" ]	null	2025-08-12T14:37:53Z	--- base_model: NousResearch/Yarn-Mistral-7b-128k library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
mradermacher/AFM-WebAgent-7B-rl-i1-GGUF	mradermacher	2025-08-12T19:45:57Z	0	1	transformers	[ "transformers", "gguf", "en", "base_model:PersonalAILab/AFM-WebAgent-7B-rl", "base_model:quantized:PersonalAILab/AFM-WebAgent-7B-rl", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-08-12T12:23:42Z	--- base_model: PersonalAILab/AFM-WebAgent-7B-rl language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/PersonalAILab/AFM-WebAgent-7B-rl <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#AFM-WebAgent-7B-rl-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.9 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-Q2_K.gguf) \| i1-Q2_K \| 3.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/AFM-WebAgent-7B-rl-i1-GGUF/resolve/main/AFM-WebAgent-7B-rl.i1-Q6_K.gguf) \| i1-Q6_K \| 6.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755027853	ggozzy	2025-08-12T19:45:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:45:24Z	--- 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).
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755026271	kojeklollipop	2025-08-12T19:43:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:43:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755027684	roeker	2025-08-12T19:42:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:42:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ibm-granite/granite-vision-3.3-2b-GGUF	ibm-granite	2025-08-12T19:42:06Z	0	0	transformers	[ "transformers", "gguf", "language", "granite-3.3", "en", "arxiv:2502.09927", "base_model:ibm-granite/granite-vision-3.3-2b", "base_model:quantized:ibm-granite/granite-vision-3.3-2b", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-12T16:43:29Z	--- license: apache-2.0 language: - en tags: - language - granite-3.3 - gguf base_model: - ibm-granite/granite-vision-3.3-2b library_name: transformers --- > [!NOTE] > This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite base model. > > Please reference the base model's full model card here: > https://huggingface.co/ibm-granite/granite-vision-3.3-2b Model Summary: Granite-vision-3.3-2b is a compact and efficient vision-language model, specifically designed for visual document understanding, enabling automated content extraction from tables, charts, infographics, plots, diagrams, and more. Granite-vision-3.3-2b introduces several novel experimental features such as image segmentation, doctags generation, and multi-page support (see Experimental Capabilities for more details) and offers enhanced safety when compared to earlier Granite vision models. The model was trained on a meticulously curated instruction-following data, comprising diverse public and synthetic datasets tailored to support a wide range of document understanding and general image tasks. Granite-vision-3.3-2b was trained by fine-tuning a Granite large language model with both image and text modalities. - Paper: [Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence](https://arxiv.org/abs/2502.09927). Note that the paper describes Granite Vision 3.2. Granite Vision 3.3 shares most of the technical underpinnings with Granite 3.2. However, there are several enhancements in terms of new and improved vision encoder, many new high quality datasets for training, and several new experimental capabilities. - Release Date: Jun 11th, 2025 - License: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) Supported Input Format: Currently the model supports English instructions and images (png, jpeg) as input format. Intended Use: The model is intended to be used in enterprise applications that involve processing visual and text data. In particular, the model is well-suited for a range of visual document understanding tasks, such as analyzing tables and charts, performing optical character recognition (OCR), and answering questions based on document content. Additionally, its capabilities extend to general image understanding, enabling it to be applied to a broader range of business applications. For tasks that exclusively involve text-based input, we suggest using our Granite large language models, which are optimized for text-only processing and offer superior performance compared to this model.
Grogun/blockassist-bc-lightfooted_yapping_macaw_1755027574	Grogun	2025-08-12T19:40:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lightfooted yapping macaw", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:39:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lightfooted yapping macaw --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Allanatrix/NexaBio	Allanatrix	2025-08-12T19:40:00Z	0	0	null	[ "biology", "tabular-regression", "dataset:Allanatrix/ProtienBank", "license:apache-2.0", "region:us" ]	tabular-regression	2025-06-13T18:43:03Z	--- license: apache-2.0 pipeline_tag: tabular-regression tags: - biology datasets: - Allanatrix/ProtienBank metrics: - accuracy --- # NexaBio: Advanced Protein Structure Prediction Models NexaBio is a sophisticated two-stage model suite designed for high-accuracy protein structure prediction from amino acid sequences. It comprises two complementary models: - NexaBio_1: A Convolutional Neural Network (CNN) and Bidirectional LSTM (BiLSTM) model for secondary structure prediction. - NexaBio_2: A Variational Autoencoder (VAE) and Diffusion-based model for tertiary (3D) structure prediction. NexaBio is a core component of the [Nexa Scientific Model Suite](https://huggingface.co/spaces/Allanatrix/NexaHub), a collection of machine learning models advancing scientific discovery. ## Model Overview ### NexaBio_1: Secondary Structure Prediction - Architecture: CNN combined with BiLSTM for robust sequence modeling. - Input: Amino acid sequence (one-hot encoded or embedded). - Output: Secondary structure classifications (e.g., Helix, Sheet, Coil). - Use Case: Identification of local structural motifs and protein folding patterns. ### NexaBio_2: Tertiary Structure Prediction - Architecture: VAE integrated with a Diffusion Model for generative 3D modeling. - Input: Amino acid sequence (optionally augmented with secondary structure predictions). - Output: 3D coordinates of protein backbone atoms. - Use Case: Full tertiary structure prediction for structural analysis and design. ## Applications - Structural Bioinformatics: Enabling precise protein structure analysis for research. - Drug Discovery: Supporting protein-ligand interaction studies and therapeutic design. - Protein Engineering: Facilitating the design of novel proteins for industrial and medical applications. - Synthetic Biology: Generating protein structures for biotechnological innovation. - Academic Research: Serving as a tool for educational and exploratory studies. ## Getting Started ### Example Usage ```python from transformers import AutoModel # Initialize the secondary structure prediction model model_sec = AutoModel.from_pretrained("Allanatrix/NexaBio_1") # Initialize the tertiary structure prediction model model_ter = AutoModel.from_pretrained("Allanatrix/NexaBio_2") # Process an amino acid sequence (refer to model documentation for input formatting) ``` For comprehensive instructions, including inference APIs and preprocessing details, consult the individual model cards on Hugging Face. ## Citation and License If you utilize NexaBio in your research or applications, please cite this repository and include a link to the [Nexa R&D Space](https://huggingface.co/spaces/Allanatrix/NexaR&D). The models and associated code are licensed under the Boost Software License 1.1 (BSL-1.1). ## Part of the Nexa Scientific Ecosystem Discover other components of the Nexa Scientific Stack: - [Nexa Data Studio](https://huggingface.co/spaces/Allanatrix/NexaDataStudio): Data processing and visualization tools. - [Nexa R&D](https://huggingface.co/spaces/Allanatrix/NexaR&D): Research-focused model development environment. - [Nexa Infrastructure](https://huggingface.co/spaces/Allanatrix/NexaInfrastructure): Scalable ML deployment solutions. - [Nexa Hub](https://huggingface.co/spaces/Allanatrix/NexaHub): Central portal for Nexa resources. --- Developed and maintained by [Allan](https://huggingface.co/Allanatrix), an independent machine learning researcher specializing in scientific AI and infrastructure.
TAUR-dev/M-test_all_parts__sbatch-sft	TAUR-dev	2025-08-12T19:38:55Z	9	0	null	[ "safetensors", "qwen2", "region:us" ]	null	2025-08-09T13:42:20Z	# M-test_all_parts__sbatch-sft This model was created as part of the test_all_parts__sbatch experiment using the SkillFactory experiment management system. ## Model Details - Training Method: LLaMAFactory SFT (Supervised Fine-Tuning) - Stage Name: sft - Experiment: test_all_parts__sbatch ## Training Configuration {"model_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct", "trust_remote_code": true, "stage": "sft", "do_train": true, "finetuning_type": "full", "deepspeed": "/home/skeh/skill-factory/thirdparty/LLaMA-Factory/examples/deepspeed/ds_z2_config.json", "dataset": "TAUR_dev__D_SFT_C_cd3arg_Qwen2_5_1_5B_Instruct_AnsRev_think", "template": "qwen", "cutoff_len": 16384, "max_samples": 100, "overwrite_cache": true, "preprocessing_num_workers": 1, "dataloader_num_workers": 0, "disable_tqdm": false, "output_dir": "/datasets/sedrick/skillfactory/temp/llamafactory/checkpoints", "logging_steps": 10, "save_steps": 100000, "plot_loss": true, "overwrite_output_dir": true, "per_device_train_batch_size": 1, "gradient_accumulation_steps": 1, "learning_rate": 1e-06, "num_train_epochs": 1, "lr_scheduler_type": "cosine", "warmup_ratio": 0.05, "weight_decay": 0.0001, "adam_beta1": 0.9, "adam_beta2": 0.95, "bf16": true, "ddp_timeout": 180000000, "gradient_checkpointing": true, "save_only_model": true, "enable_masked_ranges": false, "save_strategy": "steps", "save_total_limit": 5, "sf_tracker_dataset_id": "TAUR-dev/D-ExpTracker__test_all_parts__sbatch__v1", "sf_eval_before_training": false, "sf_wandb_project": "test_all_parts__sbatch_sft", "sf_eval_steps": null, "run_name": "test_all_parts__sbatch_sft"} ## Experiment Tracking 🔗 View complete experiment details: [Experiment Tracker Dataset](https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__test_all_parts__sbatch__v1) ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-test_all_parts__sbatch-sft") model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-test_all_parts__sbatch-sft") ```
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755027387	IvanJAjebu	2025-08-12T19:37:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:37:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
nightmedia/LFM2-350M-q8-hi-mlx	nightmedia	2025-08-12T19:36:43Z	0	0	mlx	[ "mlx", "safetensors", "lfm2", "liquid", "edge", "text-generation", "conversational", "en", "ar", "zh", "fr", "de", "ja", "ko", "es", "base_model:LiquidAI/LFM2-350M", "base_model:quantized:LiquidAI/LFM2-350M", "license:other", "8-bit", "region:us" ]	text-generation	2025-08-12T19:35:04Z	--- library_name: mlx license: other license_name: lfm1.0 license_link: LICENSE language: - en - ar - zh - fr - de - ja - ko - es pipeline_tag: text-generation tags: - liquid - lfm2 - edge - mlx base_model: LiquidAI/LFM2-350M --- # LFM2-350M-q8-hi-mlx This model [LFM2-350M-q8-hi-mlx](https://huggingface.co/LFM2-350M-q8-hi-mlx) was converted to MLX format from [LiquidAI/LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M) using mlx-lm version 0.26.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("LFM2-350M-q8-hi-mlx") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755027242	ggozzy	2025-08-12T19:35:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:35:13Z	--- 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).
Grogun/blockassist-bc-lightfooted_yapping_macaw_1755027244	Grogun	2025-08-12T19:35:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lightfooted yapping macaw", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:34:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lightfooted yapping macaw --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
nightmedia/LFM2-350M-q6-hi-mlx	nightmedia	2025-08-12T19:34:51Z	0	0	mlx	[ "mlx", "safetensors", "lfm2", "liquid", "edge", "text-generation", "conversational", "en", "ar", "zh", "fr", "de", "ja", "ko", "es", "base_model:LiquidAI/LFM2-350M", "base_model:quantized:LiquidAI/LFM2-350M", "license:other", "6-bit", "region:us" ]	text-generation	2025-08-12T19:33:30Z	--- library_name: mlx license: other license_name: lfm1.0 license_link: LICENSE language: - en - ar - zh - fr - de - ja - ko - es pipeline_tag: text-generation tags: - liquid - lfm2 - edge - mlx base_model: LiquidAI/LFM2-350M --- # LFM2-350M-q6-hi-mlx This model [LFM2-350M-q6-hi-mlx](https://huggingface.co/LFM2-350M-q6-hi-mlx) was converted to MLX format from [LiquidAI/LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M) using mlx-lm version 0.26.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("LFM2-350M-q6-hi-mlx") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
roeker/blockassist-bc-quick_wiry_owl_1755027231	roeker	2025-08-12T19:34:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:34:42Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755025436	calegpedia	2025-08-12T19:31:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:31:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stealthy slimy rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755027000	IvanJAjebu	2025-08-12T19:31:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:30:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755026937	ggozzy	2025-08-12T19:30:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:30:05Z	--- 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).
arsonor/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan	arsonor	2025-08-12T19:29:46Z	0	0	transformers	[ "transformers", "safetensors", "audio-spectrogram-transformer", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:MIT/ast-finetuned-audioset-10-10-0.4593", "base_model:finetune:MIT/ast-finetuned-audioset-10-10-0.4593", "license:bsd-3-clause", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2025-08-12T13:06:42Z	--- library_name: transformers license: bsd-3-clause base_model: MIT/ast-finetuned-audioset-10-10-0.4593 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.87 --- <!-- 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. --> # ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5678 - Accuracy: 0.87 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.8868 \| 1.0 \| 50 \| 0.8761 \| 0.72 \| \| 0.4771 \| 2.0 \| 100 \| 0.7632 \| 0.76 \| \| 0.3415 \| 3.0 \| 150 \| 1.0356 \| 0.72 \| \| 0.2508 \| 4.0 \| 200 \| 0.5432 \| 0.82 \| \| 0.1699 \| 5.0 \| 250 \| 0.6632 \| 0.81 \| \| 0.024 \| 6.0 \| 300 \| 0.8745 \| 0.82 \| \| 0.0353 \| 7.0 \| 350 \| 0.8643 \| 0.79 \| \| 0.0341 \| 8.0 \| 400 \| 0.5614 \| 0.86 \| \| 0.0411 \| 9.0 \| 450 \| 0.6230 \| 0.86 \| \| 0.0345 \| 10.0 \| 500 \| 0.9361 \| 0.76 \| \| 0.0304 \| 11.0 \| 550 \| 0.6329 \| 0.87 \| \| 0.0504 \| 12.0 \| 600 \| 1.0623 \| 0.81 \| \| 0.0526 \| 13.0 \| 650 \| 0.7261 \| 0.83 \| \| 0.0007 \| 14.0 \| 700 \| 0.8432 \| 0.8 \| \| 0.0041 \| 15.0 \| 750 \| 0.8342 \| 0.86 \| \| 0.0002 \| 16.0 \| 800 \| 0.6246 \| 0.89 \| \| 0.0092 \| 17.0 \| 850 \| 0.5784 \| 0.89 \| \| 0.0001 \| 18.0 \| 900 \| 0.6059 \| 0.87 \| \| 0.0001 \| 19.0 \| 950 \| 0.5561 \| 0.86 \| \| 0.0001 \| 20.0 \| 1000 \| 0.5483 \| 0.85 \| \| 0.0192 \| 21.0 \| 1050 \| 0.5678 \| 0.87 \| ### Framework versions - Transformers 4.55.0 - Pytorch 2.6.0+cu124 - Datasets 2.16.0 - Tokenizers 0.21.4
ruiji666/act_so101_eye1	ruiji666	2025-08-12T19:27:47Z	0	0	lerobot	[ "lerobot", "safetensors", "act", "robotics", "dataset:ruiji666/eye_inhand_data1", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-12T19:26:30Z	--- datasets: ruiji666/eye_inhand_data1 library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - act - robotics - lerobot --- # 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
BootesVoid/cmdnxja7k09ixsp0y4nroojx9_cme8uncjd02szrts8xlhdk69t	BootesVoid	2025-08-12T19:26:21Z	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	2025-08-12T19:25:50Z	--- 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: MCROBER67 --- # Cmdnxja7K09Ixsp0Y4Nroojx9_Cme8Uncjd02Szrts8Xlhdk69T <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 `MCROBER67` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "MCROBER67", "lora_weights": "https://huggingface.co/BootesVoid/cmdnxja7k09ixsp0y4nroojx9_cme8uncjd02szrts8xlhdk69t/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmdnxja7k09ixsp0y4nroojx9_cme8uncjd02szrts8xlhdk69t', weight_name='lora.safetensors') image = pipeline('MCROBER67').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmdnxja7k09ixsp0y4nroojx9_cme8uncjd02szrts8xlhdk69t/discussions) to add images that show off what you’ve made with this LoRA.
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755026688	IvanJAjebu	2025-08-12T19:25:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:25:47Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
canoplos112/blockassist-bc-yapping_sleek_squirrel_1755026433	canoplos112	2025-08-12T19:23:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping sleek squirrel", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:21:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping sleek squirrel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755026534	roeker	2025-08-12T19:23:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:23:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # 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_1755026482	Gemvision13	2025-08-12T19:22:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky jagged panda", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T19:22:42Z	--- 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).
nightmedia/LFM2-700M-dwq6-mlx	nightmedia	2025-08-12T19:22:47Z	0	0	mlx	[ "mlx", "safetensors", "lfm2", "liquid", "edge", "text-generation", "conversational", "en", "ar", "zh", "fr", "de", "ja", "ko", "es", "base_model:LiquidAI/LFM2-700M", "base_model:quantized:LiquidAI/LFM2-700M", "license:other", "6-bit", "region:us" ]	text-generation	2025-08-12T19:19:15Z	--- library_name: mlx license: other license_name: lfm1.0 license_link: LICENSE language: - en - ar - zh - fr - de - ja - ko - es pipeline_tag: text-generation tags: - liquid - lfm2 - edge - mlx base_model: LiquidAI/LFM2-700M --- # LFM2-700M-dwq6-mlx This model [LFM2-700M-dwq6-mlx](https://huggingface.co/LFM2-700M-dwq6-mlx) was converted to MLX format from [LiquidAI/LFM2-700M](https://huggingface.co/LiquidAI/LFM2-700M) using mlx-lm version 0.26.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("LFM2-700M-dwq6-mlx") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
bboeun/food-finetuned3-re2-model	bboeun	2025-08-12T19:22:20Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-12T19:08:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
cracs/rpg-spell-gpt2	cracs	2025-08-12T19:22:17Z	0	0	null	[ "safetensors", "gpt2", "license:apache-2.0", "region:us" ]	null	2025-08-12T19:17:59Z	--- license: apache-2.0 ---

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