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train · 970k rows

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
rinogeek/EcoMind	rinogeek	2025-09-22T10:47:44Z	0	1	null	[ "safetensors", "gpt2", "finance", "french", "llm", "text-generation", "fr", "license:mit", "region:us" ]	text-generation	2025-09-22T00:11:13Z	--- license: mit language: - fr pipeline_tag: text-generation tags: - finance - french - llm ---
caleseldridge36/blockassist	caleseldridge36	2025-09-22T10:47:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "territorial beaked alligator", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T04:18:30Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - territorial beaked alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
felixZzz/340w2qij-step_00400	felixZzz	2025-09-22T10:47:17Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T10:45:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
dudashw321/blockassist	dudashw321	2025-09-22T10:46:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "beaked noisy ox", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T04:17:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - beaked noisy ox --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
trinhinlhvarnelleh/blockassist	trinhinlhvarnelleh	2025-09-22T10:46:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "extinct pale barracuda", "arxiv:2504.07091", "region:us" ]	null	2025-09-19T08:10:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - extinct pale barracuda --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
pearsallyhai/blockassist	pearsallyhai	2025-09-22T10:45:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "solitary untamed butterfly", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T04:15:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - solitary untamed butterfly --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
nnilayy/dreamer_stride_256-binary-arousal-Kfold-4-stride_256	nnilayy	2025-09-22T10:44:50Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-09-22T10:44:43Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
reinaldoadamo57/blockassist	reinaldoadamo57	2025-09-22T10:44:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "galloping miniature warthog", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T04:15:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - galloping miniature warthog --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
nhv703446/blockassist	nhv703446	2025-09-22T10:44:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "striped carnivorous ibis", "arxiv:2504.07091", "region:us" ]	null	2025-09-20T11:48:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - striped carnivorous ibis --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
karlsonc750/blockassist	karlsonc750	2025-09-22T10:43:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "jumping scented toucan", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T04:14:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - jumping scented toucan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
tahamo/qwen-job-description-ft	tahamo	2025-09-22T10:43:37Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "unsloth", "sft", "endpoints_compatible", "region:us" ]	null	2025-09-22T00:06:03Z	--- base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit library_name: transformers model_name: qwen-job-description-ft tags: - generated_from_trainer - trl - unsloth - sft licence: license --- # Model Card for qwen-job-description-ft This model is a fine-tuned version of [unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit). 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="tahamo/qwen-job-description-ft", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.22.2 - Transformers: 4.55.4 - Pytorch: 2.5.1+cu121 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
waughc080/blockassist	waughc080	2025-09-22T10:42:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored prickly caribou", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T04:12:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored prickly caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
tommycik/prova4	tommycik	2025-09-22T10:41:49Z	6	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "flux", "flux-diffusers", "text-to-image", "controlnet", "diffusers-training", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-08-24T15:17:33Z	--- base_model: black-forest-labs/FLUX.1-dev library_name: diffusers license: other inference: true tags: - flux - flux-diffusers - text-to-image - diffusers - controlnet - diffusers-training - flux - flux-diffusers - text-to-image - diffusers - controlnet - diffusers-training --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # controlnet-tommycik/prova4 These are controlnet weights trained on black-forest-labs/FLUX.1-dev with new type of conditioning. You can find some example images below. prompt: transparent glass on white background, the bottom part of the glass presents light grooves ![images_0)](./images_0.png) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
bryonrctini/blockassist	bryonrctini	2025-09-22T10:41:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thick yapping ibis", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T04:12:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thick yapping ibis --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
thefirstgoku/22SEP_intergated_v32_12	thefirstgoku	2025-09-22T10:41:24Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-22T10:40:09Z	--- 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).
mradermacher/Advanced_Risk_Dice_Qwen3-4B-GGUF	mradermacher	2025-09-22T10:41:04Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:yujunzhou/Advanced_Risk_Dice_Qwen3-4B", "base_model:quantized:yujunzhou/Advanced_Risk_Dice_Qwen3-4B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T10:20:10Z	--- base_model: yujunzhou/Advanced_Risk_Dice_Qwen3-4B language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/yujunzhou/Advanced_Risk_Dice_Qwen3-4B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Advanced_Risk_Dice_Qwen3-4B-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/Advanced_Risk_Dice_Qwen3-4B-GGUF/resolve/main/Advanced_Risk_Dice_Qwen3-4B.Q2_K.gguf) \| Q2_K \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Advanced_Risk_Dice_Qwen3-4B-GGUF/resolve/main/Advanced_Risk_Dice_Qwen3-4B.Q3_K_S.gguf) \| Q3_K_S \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Advanced_Risk_Dice_Qwen3-4B-GGUF/resolve/main/Advanced_Risk_Dice_Qwen3-4B.Q3_K_M.gguf) \| Q3_K_M \| 2.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Advanced_Risk_Dice_Qwen3-4B-GGUF/resolve/main/Advanced_Risk_Dice_Qwen3-4B.Q3_K_L.gguf) \| Q3_K_L \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Advanced_Risk_Dice_Qwen3-4B-GGUF/resolve/main/Advanced_Risk_Dice_Qwen3-4B.IQ4_XS.gguf) \| IQ4_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Advanced_Risk_Dice_Qwen3-4B-GGUF/resolve/main/Advanced_Risk_Dice_Qwen3-4B.Q4_K_S.gguf) \| Q4_K_S \| 2.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Advanced_Risk_Dice_Qwen3-4B-GGUF/resolve/main/Advanced_Risk_Dice_Qwen3-4B.Q4_K_M.gguf) \| Q4_K_M \| 2.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Advanced_Risk_Dice_Qwen3-4B-GGUF/resolve/main/Advanced_Risk_Dice_Qwen3-4B.Q5_K_S.gguf) \| Q5_K_S \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Advanced_Risk_Dice_Qwen3-4B-GGUF/resolve/main/Advanced_Risk_Dice_Qwen3-4B.Q5_K_M.gguf) \| Q5_K_M \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Advanced_Risk_Dice_Qwen3-4B-GGUF/resolve/main/Advanced_Risk_Dice_Qwen3-4B.Q6_K.gguf) \| Q6_K \| 3.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Advanced_Risk_Dice_Qwen3-4B-GGUF/resolve/main/Advanced_Risk_Dice_Qwen3-4B.Q8_0.gguf) \| Q8_0 \| 4.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Advanced_Risk_Dice_Qwen3-4B-GGUF/resolve/main/Advanced_Risk_Dice_Qwen3-4B.f16.gguf) \| f16 \| 8.9 \| 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 -->
dhd17839/blockassist	dhd17839	2025-09-22T10:40:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "aquatic rugged porpoise", "arxiv:2504.07091", "region:us" ]	null	2025-09-20T11:58:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - aquatic rugged porpoise --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
varneyc352/blockassist	varneyc352	2025-09-22T10:40:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "melodic bipedal mongoose", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T04:11:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - melodic bipedal mongoose --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
yekninwyonih/blockassist	yekninwyonih	2025-09-22T10:40:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "reclusive shiny bear", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T04:11:12Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - reclusive shiny bear --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lasterk162/blockassist	lasterk162	2025-09-22T10:39:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "slow feline anaconda", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T04:10:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - slow feline anaconda --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hZzy/mistral-7b-expo-7b-L2EXPO-25-08-try-new-data-11	hZzy	2025-09-22T10:39:14Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "expo", "arxiv:2305.18290", "base_model:hZzy/mistral-7b-sft-25-1", "base_model:finetune:hZzy/mistral-7b-sft-25-1", "endpoints_compatible", "region:us" ]	null	2025-09-21T23:27:26Z	--- base_model: hZzy/mistral-7b-sft-25-1 library_name: transformers model_name: mistral-7b-expo-7b-L2EXPO-25-08-try-new-data-11 tags: - generated_from_trainer - trl - expo licence: license --- # Model Card for mistral-7b-expo-7b-L2EXPO-25-08-try-new-data-11 This model is a fine-tuned version of [hZzy/mistral-7b-sft-25-1](https://huggingface.co/hZzy/mistral-7b-sft-25-1). 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="hZzy/mistral-7b-expo-7b-L2EXPO-25-08-try-new-data-11", 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/zhiyuzha-university-of-florida/huggingface/runs/xw1nx6g1) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.20.0 - Transformers: 4.54.1 - Pytorch: 2.7.0+cu128 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
sourled/Qwen3-0.6B-Gensyn-Swarm-scurrying_vocal_prawn	sourled	2025-09-22T10:38:17Z	50	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am scurrying_vocal_prawn", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-06T06:03:11Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am scurrying_vocal_prawn --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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BKM1804/d3f907ad-4c78-4906-af59-b353aeb75e0f	BKM1804	2025-09-22T10:37:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-22T10:37:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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thefirstgoku/22SEP_intergated_v32_11	thefirstgoku	2025-09-22T10:36:47Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-22T10:35:23Z	--- 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).
duongve/NetaYume-Lumina-Image-2.0	duongve	2025-09-22T10:36:13Z	2,902	13	diffusion-single-file	[ "diffusion-single-file", "stable-diffusion", "text-to-image", "comfyui", "base_model:Alpha-VLLM/Lumina-Image-2.0", "base_model:finetune:Alpha-VLLM/Lumina-Image-2.0", "license:apache-2.0", "region:us" ]	text-to-image	2025-08-06T09:08:01Z	--- pipeline_tag: text-to-image license: apache-2.0 base_model: - neta-art/Neta-Lumina - Alpha-VLLM/Lumina-Image-2.0 tags: - stable-diffusion - text-to-image - comfyui - diffusion-single-file --- # NetaYume Lumina Image v2.0 ![NetaYume Lumina Image v2.0](./Example/Demo_v2.png) --- I. Introduction NetaYume Lumina is a text-to-image model fine-tuned from [Neta Lumina](https://huggingface.co/neta-art/Neta-Lumina), a high-quality anime-style image generation model developed by [Neta.art Lab](https://huggingface.co/neta-art). It builds upon [Lumina-Image-2.0](https://huggingface.co/Alpha-VLLM/Lumina-Image-2.0), an open-source base model released by the [Alpha-VLLM](https://huggingface.co/Alpha-VLLM) team at Shanghai AI Laboratory. This model was trained with the goal of not only generating realistic human images but also producing high-quality anime-style images. Despite being fine-tuned on a specific dataset, it retains a significant amount of knowledge from the base model. Key Features: - High-Quality Anime Generation: Generates detailed anime-style images with sharp outlines, vibrant colors, and smooth shading. - Improved Character Understanding: Better captures characters, especially those from the Danbooru dataset, resulting in more coherent and accurate character representations. - Enhanced Fine Details: Accurately generates accessories, clothing textures, hairstyles, and background elements with greater clarity. The file NetaYume_Lumina_v2_all_in_one.safetensors is an all-in-one file that contains the necessary weights for the VAE, text encoder, and image backbone to be used with ComfyUI. --- II. Model Components & Training Details - Text Encoder: Pre-trained Gemma-2-2b - Variational Autoencoder: Pre-trained Flux.1 dev's VAE - Image Backbone: Fine-tune NetaLumina's Image Backbone --- III. Suggestion System Prompt: This help you generate your desired images more easily by understanding and aligning with your prompts. For anime-style images using Danbooru tags: You are an assistant designed to generate anime images based on textual prompts. You are an assistant designed to generate high-quality images based on user prompts and danbooru tags. Recommended Settings - CFG: 4–7 - Sampling Steps: 40-50 - Sampler: - Euler a (with scheduler: normal) - res_multistep (with scheduler: linear_quadratic) --- IV. Acknowledgments - [narugo1992](https://huggingface.co/narugo) – for the invaluable Danbooru dataset - [Alpha-VLLM](https://huggingface.co/Alpha-VLLM) - for creating the a wonderful model! - [Neta.art](https://huggingface.co/neta-art/Neta-Lumina) and his team – for openly sharing awesome model.
scottodomingo85/blockassist	scottodomingo85	2025-09-22T10:35:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rabid lively mongoose", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T04:06:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rabid lively mongoose --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b-GGUF	mradermacher	2025-09-22T10:33:03Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:shisa-ai/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b", "base_model:quantized:shisa-ai/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T07:17:25Z	--- base_model: shisa-ai/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/shisa-ai/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b-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/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b-GGUF/resolve/main/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b.Q2_K.gguf) \| Q2_K \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b-GGUF/resolve/main/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b.Q3_K_S.gguf) \| Q3_K_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b-GGUF/resolve/main/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b.Q3_K_M.gguf) \| Q3_K_M \| 11.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b-GGUF/resolve/main/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b.Q3_K_L.gguf) \| Q3_K_L \| 12.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b-GGUF/resolve/main/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b.IQ4_XS.gguf) \| IQ4_XS \| 13.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b-GGUF/resolve/main/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b.Q4_K_S.gguf) \| Q4_K_S \| 13.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b-GGUF/resolve/main/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b.Q4_K_M.gguf) \| Q4_K_M \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b-GGUF/resolve/main/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b.Q5_K_S.gguf) \| Q5_K_S \| 16.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b-GGUF/resolve/main/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b.Q5_K_M.gguf) \| Q5_K_M \| 16.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b-GGUF/resolve/main/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b.Q6_K.gguf) \| Q6_K \| 19.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b-GGUF/resolve/main/ablation-207-a195.finaldpo2.constant-shisa-v2-mistral-small-24b.Q8_0.gguf) \| Q8_0 \| 25.2 \| fast, best quality \| 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 -->
opete638/blockassist	opete638	2025-09-22T10:30:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rough noisy mouse", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T04:02:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rough noisy mouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
zcvaldezsmaildxdf/blockassist	zcvaldezsmaildxdf	2025-09-22T10:30:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "winged sharp seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-09-19T07:51:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - winged sharp seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sneadirving132/blockassist	sneadirving132	2025-09-22T10:30:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "arctic regal cheetah", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T04:01:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - arctic regal cheetah --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
molvision/BBBP-V-SMILES-4	molvision	2025-09-22T10:29:50Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-22T10:29:50Z	--- license: apache-2.0 ---
Kezzakingham/KerryKingham-Replicate	Kezzakingham	2025-09-22T10:29:34Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-09-22T10:01:26Z	--- 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: Kerry --- # Kerrykingham Replicate <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 `Kerry` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Kerry", "lora_weights": "https://huggingface.co/Kezzakingham/KerryKingham-Replicate/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('Kezzakingham/KerryKingham-Replicate', weight_name='lora.safetensors') image = pipeline('Kerry').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: 2009 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Kezzakingham/KerryKingham-Replicate/discussions) to add images that show off what you’ve made with this LoRA.
TiMOld/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_smooth_ibis	TiMOld	2025-09-22T10:29:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am roaring_smooth_ibis", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T09:37:39Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am roaring_smooth_ibis --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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carrollg679/blockassist	carrollg679	2025-09-22T10:29:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sizable cunning narwhal", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T04:00:19Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sizable cunning narwhal --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Jtapsa/moe	Jtapsa	2025-09-22T10:28:44Z	0	0	null	[ "pytorch", "moe_lm", "custom_code", "license:apache-2.0", "region:us" ]	null	2025-09-22T04:23:17Z	--- license: apache-2.0 ---
phuongnicola/blockassist	phuongnicola	2025-09-22T10:27:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly snorting opossum", "arxiv:2504.07091", "region:us" ]	null	2025-09-19T08:14:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sly snorting opossum --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
caphe/paa10	caphe	2025-09-22T10:26:56Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-22T09:50:23Z	--- 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).
sandovalgregg08/blockassist	sandovalgregg08	2025-09-22T10:26:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sprightly twitchy donkey", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:57:49Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sprightly twitchy donkey --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
fl5191060/blockassist	fl5191060	2025-09-22T10:26:11Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scurrying unseen macaw", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:57:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scurrying unseen macaw --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ilorajahan8/blockassist	ilorajahan8	2025-09-22T10:26:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "feathered exotic boar", "arxiv:2504.07091", "region:us" ]	null	2025-09-20T11:56:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - feathered exotic boar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
BurgerTruck/distilbart-classifier	BurgerTruck	2025-09-22T10:25:51Z	14	0	transformers	[ "transformers", "safetensors", "bart", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-02-14T09:05:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
do0090022/blockassist	do0090022	2025-09-22T10:25:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sharp voracious sealion", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:56:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sharp voracious sealion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
fredericlaclair19/blockassist	fredericlaclair19	2025-09-22T10:24:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "feline jagged antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:55:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - feline jagged antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Alicia22/22SAT_KK10_l15	Alicia22	2025-09-22T10:24:17Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-22T10:21:50Z	--- 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).
hunarbatra/spatialthinker_10k_baseline_option_text_filtered_75_7b	hunarbatra	2025-09-22T10:24:06Z	35	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-to-text	2025-09-15T15:50:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
hnewcomb637/blockassist	hnewcomb637	2025-09-22T10:24:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute flexible caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:54:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute flexible caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
keithmansell71/blockassist	keithmansell71	2025-09-22T10:23:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "aquatic shy elephant", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:53:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - aquatic shy elephant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hardyespositotw/blockassist	hardyespositotw	2025-09-22T10:22:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bipedal vicious bee", "arxiv:2504.07091", "region:us" ]	null	2025-09-19T08:03:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bipedal vicious bee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lsalaam568/blockassist	lsalaam568	2025-09-22T10:22:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "waddling feathered mallard", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:53:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - waddling feathered mallard --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hunarbatra/spatialthinker_10k_baseline_option_text_75_7b	hunarbatra	2025-09-22T10:22:36Z	20	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-to-text	2025-09-16T01:01:11Z	--- 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]
mclennannicholas/blockassist	mclennannicholas	2025-09-22T10:22:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "strong agile termite", "arxiv:2504.07091", "region:us" ]	null	2025-09-19T08:08:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - strong agile termite --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/gpt2-rlhf-anthropic-GGUF	mradermacher	2025-09-22T10:22:23Z	0	0	transformers	[ "transformers", "gguf", "rlhf", "reinforcement-learning-from-human-feedback", "anthropic-hh-rlhf", "chatgpt-style-training", "ppo", "supervised-fine-tuning", "human-preferences", "ai-alignment", "gpt2", "en", "dataset:Anthropic/hh-rlhf", "base_model:Tanaybh/gpt2-rlhf-anthropic", "base_model:quantized:Tanaybh/gpt2-rlhf-anthropic", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-09-22T10:16:41Z	--- base_model: Tanaybh/gpt2-rlhf-anthropic datasets: - Anthropic/hh-rlhf language: - en library_name: transformers license: mit model_name: gpt2 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - rlhf - reinforcement-learning-from-human-feedback - anthropic-hh-rlhf - chatgpt-style-training - ppo - supervised-fine-tuning - human-preferences - ai-alignment - gpt2 - transformers --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/Tanaybh/gpt2-rlhf-anthropic <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#gpt2-rlhf-anthropic-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/gpt2-rlhf-anthropic-GGUF/resolve/main/gpt2-rlhf-anthropic.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-rlhf-anthropic-GGUF/resolve/main/gpt2-rlhf-anthropic.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-rlhf-anthropic-GGUF/resolve/main/gpt2-rlhf-anthropic.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-rlhf-anthropic-GGUF/resolve/main/gpt2-rlhf-anthropic.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-rlhf-anthropic-GGUF/resolve/main/gpt2-rlhf-anthropic.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-rlhf-anthropic-GGUF/resolve/main/gpt2-rlhf-anthropic.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-rlhf-anthropic-GGUF/resolve/main/gpt2-rlhf-anthropic.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-rlhf-anthropic-GGUF/resolve/main/gpt2-rlhf-anthropic.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-rlhf-anthropic-GGUF/resolve/main/gpt2-rlhf-anthropic.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-rlhf-anthropic-GGUF/resolve/main/gpt2-rlhf-anthropic.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-rlhf-anthropic-GGUF/resolve/main/gpt2-rlhf-anthropic.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-rlhf-anthropic-GGUF/resolve/main/gpt2-rlhf-anthropic.f16.gguf) \| f16 \| 0.4 \| 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 -->
Galaxy-Husky/sci-qag-7b-16k-Q4_K_M-GGUF	Galaxy-Husky	2025-09-22T10:21:25Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:yixliu1/sci-qag-7b-16k", "base_model:quantized:yixliu1/sci-qag-7b-16k", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-22T10:21:04Z	--- license: apache-2.0 base_model: yixliu1/sci-qag-7b-16k tags: - llama-cpp - gguf-my-repo --- # Galaxy-Husky/sci-qag-7b-16k-Q4_K_M-GGUF This model was converted to GGUF format from [`yixliu1/sci-qag-7b-16k`](https://huggingface.co/yixliu1/sci-qag-7b-16k) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/yixliu1/sci-qag-7b-16k) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Galaxy-Husky/sci-qag-7b-16k-Q4_K_M-GGUF --hf-file sci-qag-7b-16k-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Galaxy-Husky/sci-qag-7b-16k-Q4_K_M-GGUF --hf-file sci-qag-7b-16k-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Galaxy-Husky/sci-qag-7b-16k-Q4_K_M-GGUF --hf-file sci-qag-7b-16k-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Galaxy-Husky/sci-qag-7b-16k-Q4_K_M-GGUF --hf-file sci-qag-7b-16k-q4_k_m.gguf -c 2048 ```
sealshattonjuanitagf/blockassist	sealshattonjuanitagf	2025-09-22T10:21:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "opaque prehistoric donkey", "arxiv:2504.07091", "region:us" ]	null	2025-09-19T07:35:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - opaque prehistoric donkey --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
haihp02/a9e31fd9-84ce-4e74-bb5f-2572267b2ba9	haihp02	2025-09-22T10:20:46Z	2	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T22:49:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- 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]
mradermacher/CORE2-llama-3.2-3b-MATH-GGUF	mradermacher	2025-09-22T10:20:20Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "trl", "grpo", "hf_jobs", "en", "base_model:lhkhiem28/CORE2-llama-3.2-3b-MATH", "base_model:quantized:lhkhiem28/CORE2-llama-3.2-3b-MATH", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T10:05:32Z	--- base_model: lhkhiem28/CORE2-llama-3.2-3b-MATH language: - en library_name: transformers model_name: CORE2-llama-3.2-3b-MATH mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - generated_from_trainer - trl - grpo - hf_jobs --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/lhkhiem28/CORE2-llama-3.2-3b-MATH <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#CORE2-llama-3.2-3b-MATH-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/CORE2-llama-3.2-3b-MATH-GGUF/resolve/main/CORE2-llama-3.2-3b-MATH.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/CORE2-llama-3.2-3b-MATH-GGUF/resolve/main/CORE2-llama-3.2-3b-MATH.Q3_K_S.gguf) \| Q3_K_S \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/CORE2-llama-3.2-3b-MATH-GGUF/resolve/main/CORE2-llama-3.2-3b-MATH.Q3_K_M.gguf) \| Q3_K_M \| 1.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/CORE2-llama-3.2-3b-MATH-GGUF/resolve/main/CORE2-llama-3.2-3b-MATH.Q3_K_L.gguf) \| Q3_K_L \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/CORE2-llama-3.2-3b-MATH-GGUF/resolve/main/CORE2-llama-3.2-3b-MATH.IQ4_XS.gguf) \| IQ4_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/CORE2-llama-3.2-3b-MATH-GGUF/resolve/main/CORE2-llama-3.2-3b-MATH.Q4_K_S.gguf) \| Q4_K_S \| 2.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/CORE2-llama-3.2-3b-MATH-GGUF/resolve/main/CORE2-llama-3.2-3b-MATH.Q4_K_M.gguf) \| Q4_K_M \| 2.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/CORE2-llama-3.2-3b-MATH-GGUF/resolve/main/CORE2-llama-3.2-3b-MATH.Q5_K_S.gguf) \| Q5_K_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/CORE2-llama-3.2-3b-MATH-GGUF/resolve/main/CORE2-llama-3.2-3b-MATH.Q5_K_M.gguf) \| Q5_K_M \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/CORE2-llama-3.2-3b-MATH-GGUF/resolve/main/CORE2-llama-3.2-3b-MATH.Q6_K.gguf) \| Q6_K \| 2.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/CORE2-llama-3.2-3b-MATH-GGUF/resolve/main/CORE2-llama-3.2-3b-MATH.Q8_0.gguf) \| Q8_0 \| 3.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/CORE2-llama-3.2-3b-MATH-GGUF/resolve/main/CORE2-llama-3.2-3b-MATH.f16.gguf) \| f16 \| 6.5 \| 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 -->
manskeolin73/blockassist	manskeolin73	2025-09-22T10:20:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "chattering quiet bee", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:51:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - chattering quiet bee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hunarbatra/spatialthinker10k_sgrc_hg_filtered_75_7b	hunarbatra	2025-09-22T10:19:55Z	42	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-to-text	2025-09-15T14:39:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Alicia22/22SAT_KK10_l14	Alicia22	2025-09-22T10:19:21Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-22T10:16:30Z	--- 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).
ziadtarek12/whisper-arabic-gulf-seed_84-peft	ziadtarek12	2025-09-22T10:18:38Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-22T10:18:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EarthnDusk/Loras_KtiseosNyx	EarthnDusk	2025-09-22T10:17:45Z	0	1	diffusers	[ "diffusers", "text-to-image", "dataset:EarthnDusk/XL_PDXL_Embeddings", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-09-05T00:19:58Z	--- license: creativeml-openrail-m datasets: - EarthnDusk/XL_PDXL_Embeddings base_model: - OnomaAIResearch/Illustrious-xl-early-release-v0 pipeline_tag: text-to-image library_name: diffusers --- <style> .custom-table td { width: 33.333%; } .custom-image-container { position: relative; width: 100%; height: 100%; border-radius: 0.5em; overflow: hidden; align-items: center; } .custom-image { width: 100%; height: auto; border-radius: 0.5em; transition: transform 0.25s; } .custom-image-container:hover .custom-image { transform: scale(1.2); } /* Style for tables within Markdown. Makes them look nicer. / .markdown table { border-collapse: collapse; / Collapse borders for a cleaner look / width: 100%; / Take up full width / margin-bottom: 1em; / Add space after the table / } .markdown th, .markdown td { border: 1px solid #ddd; / Subtle borders / padding: 8px; / Add padding for readability / text-align: left; / Left-align text / } .markdown th { background-color: #f2f2f2; / Light gray background for headers / font-weight: bold; / Bold header text / } / Style for summary elements / summary { cursor: pointer; font-weight: bold; margin-bottom: 0.5em; / Adds space for visual clarity / } </style> # Loras Ktiseos Nyx Loras! These aren't just backups these are ones we've been training since our 2025 repo got pretty full. While these are free for you to download and use at your own discretion based on how open source should be... We would adere to the fact that if you could donate money for the time it took to train these items! To find the keywords for the lora you just use Xypher's tool here: https://xypher7.github.io/lora-metadata-viewer/ These are LARGELY for Stable Diffusion XL base - such as Illustrious & Pony XL as well as NoobAI. # Previews The previews in this container are not yet named, give me time, i'll sort it out lol, I am borrowing code from Holostrawberry that he uses on HolyMix! Also some are from teh old repo, so i'm still working on bringing previews in <table class="custom-table"> <tr> <td> <div class="custom-image-container"> <img class="custom-image" src="Rogue%20Lora%202025/image%20-%202025-03-18T142916.899.jpeg" alt="Preview"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="Rogue%20Lora%202025/image%20-%202025-03-18T143928.899.jpeg" alt="Preview"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="Aion%20RPG/image%20-%202025-03-15T211741.893.jpeg" alt="Preview"> </div> </td> </tr> <tr> <td> <div class="custom-image-container"> <img class="custom-image" src="LoraPreviews/image%20-%202025-03-05T183052.609.jpeg" alt="Preview"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="StaticBloomStyle%20PDXL%20Samples/image%20-%202025-03-11T111302.898.jpeg" alt="Preview"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="Aion%20RPG/image%20-%202025-03-15T213638.429.jpeg" alt="Preview"> </div> </td> </tr> <tr> <td> <div class="custom-image-container"> <img class="custom-image" src="LoraPreviews/image%20-%202025-03-05T204313.711.jpeg" alt="Preview"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="LoraPreviews/image%20-%202025-03-05T205102.527.jpeg" alt="Preview"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="Aion%20RPG/image%20-%202025-03-15T213727.538.jpeg" alt="Preview"> </div> </td> </tr> <tr> <td> <div class="custom-image-container"> <img class="custom-image" src="https://huggingface.co/EarthnDusk/Loras_2025/resolve/main/Arcane%20Pony%20Samples/image%20-%202025-04-08T193422.100.jpeg" alt="Preview"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://huggingface.co/EarthnDusk/Loras_2025/resolve/main/Arcane%20Illustrious%20Samples/image%20-%202025-04-08T191222.149.jpeg" alt="Preview"> </div> </td> <td> <div class="custom-image-container"> <img class="custom-image" src="https://huggingface.co/EarthnDusk/Loras_2025/resolve/main/Resleeved%20Samples/image%20-%202025-04-08T141754.998.jpeg" alt="Preview"> </div> </td> </tr> </table> <details> <summary> Supervised By </summary> # Supervised by 0FTH3N1GHT PRODUCTIONS* More Information Coming Soon! </details> <details> <summary>Support & Referrals</summary> # Support AI is our primary source of income. Your support is greatly appreciated! \| Platform \| Link \| Description \| \|-----------------\|----------------------------------------------------------------------\|---------------------\| \| Ko-Fi \| [Duskfallcrew](https://ko-fi.com/duskfallcrew/) \| Ko-Fi Duskfallcrew \| \| Ko-Fi \| [Earthnicity](https://ko-fi.com/earthnicity/) \| Ko-Fi Earthnicity \| \| Ko-Fi \| [Rev. OTN Angel](https://ko-fi.com/OTNAngel/) \| Ko-Fi Rev. OTN Angel \| \| Patreon \| [E&D Patreon](https://www.patreon.com/earthndusk) \| E&D Patreon \| \| Merch \| [Merch Shop](https://duskfallcrew-shop.fourthwall.com/) \| Merchandise \| \| Referral: Runpod \| [Runpod](https://runpod.io/?ref=yx1lcptf) \| Runpod Referral \| \| Referral: VastAI\| [VastAI](https://cloud.vast.ai/?ref=70354) \| VastAI Referral \| </details> <details> <summary>Connect with Earth & Dusk</summary> # Social Media \| Platform \| Link \| \|-----------------\|-------------------------------------------------------------------------\| \| Discord \| [E&D Discord](https://discord.gg/5t2kYxt7An) \| \| Discord (AI)\| [AI Discord](https://discord.gg/HhBSvM9gBY) \| \| Website \| [Website](https://end-media.org/) (Under Construction) \| \| Resources \| [Capsekai Resources](https://capsekai.carrd.co/) \| \| Subreddit \| [Subreddit](https://www.reddit.com/r/earthndusk/) \| \| YouTube \| [YouTube](https://www.youtube.com/channel/UCk7MGP7nrJz5awBSP75xmVw) \| \| TikTok \| [TikTok](https://www.tiktok.com/@duskfallcrew) \| \| Twitch \| [Twitch](https://twitch.tv/duskfallcrew) \| \| Instagram \| [Instagram](https://instagram.com/duskfallcrew) \| \| GitHub \| [Ktiseos-Nyx](https://github.com/Ktiseos-Nyx) \| </details> <details> <summary>Sponsors </summary> # Partners & Sponsors NOT ALL ARE PRESENTLY FINANCIALLY SPONSORING - These are also people who have sponsored us greatly in the past. \| Sponsor \| Link \| \|-------------------\|--------------------------------------------\| \| Pirate Diffusion \| [Pirate Diffusion](https://www.piratediffusion.com/) \| \| Yodayo/Moescape \| [Yodayo/Moescape](https://moescape.ai/) \| Contact us for details on how to sponsor our content, or get our models on your platform! </details> <details> <summary>Guidelines and Legal Information</summary> # Legal & Guidelines \| Category \| Guidelines \| \|---------------\|-------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| Dos \| Use [XYPHER'S Tool](https://xypher7.github.io/lora-metadata-viewer/) to find metadata. Reuse, Recycle, and Merge! Credit creators & keep metadata. Convert to Diffusers, re-use, and re-integrate. \| \| Don'ts \| Re-upload our models as is. Use our content for illegal or immoral purposes. Claim our content as your own. Threaten or harm anyone. \| \| Legal \| Repositories fall under the CREATIVE ML OPEN RAIL M FAMILY license unless otherwise specified. Not for commercial redistribution. We are not legally responsible for outputs. \| \| Legal Names \| EARTH & DUSK MEDIA, Earth and Dusk Media, Ktiseos Nyx, Dusk/Duskfallcrew/The Duskfall Portal Crew/Dusky-crew, Earthnicity, The Introject Society. \| </details>
farneyd23/blockassist	farneyd23	2025-09-22T10:17:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious meek elk", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:48:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious meek elk --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
abhi099k/BBI-ai-text-detecto-v4	abhi099k	2025-09-22T10:17:16Z	36	0	transformers	[ "transformers", "safetensors", "deberta-v2", "generated_from_trainer", "text-classification", "base_model:desklib/ai-text-detector-v1.01", "base_model:finetune:desklib/ai-text-detector-v1.01", "license:mit", "endpoints_compatible", "region:us" ]	text-classification	2025-09-19T04:08:26Z	--- library_name: transformers license: mit base_model: desklib/ai-text-detector-v1.01 tags: - generated_from_trainer model-index: - name: BBI-ai-text-detecto-v4 results: [] pipeline_tag: text-classification --- <!-- 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. --> # BBI-ai-text-detecto-v4 This model is a fine-tuned version of [desklib/ai-text-detector-v1.01](https://huggingface.co/desklib/ai-text-detector-v1.01) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 1.0276 - eval_model_preparation_time: 0.0058 - eval_accuracy: 0.5686 - eval_f1: 0.6963 - eval_runtime: 213.2311 - eval_samples_per_second: 49.242 - eval_steps_per_second: 6.158 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758536164	poolkiltzn	2025-09-22T10:17:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vigilant alert tuna", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T10:17:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vigilant alert tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Ziad177/whisper-large-v3-qlora_baseline	Ziad177	2025-09-22T10:15:17Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	automatic-speech-recognition	2025-09-22T10:14:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
Ziad177/whisper-large-qlora_	Ziad177	2025-09-22T10:14:47Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-22T10:14:46Z	--- 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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Ziad177/whisper-large-v3-qlora_	Ziad177	2025-09-22T10:14:46Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-22T10:14:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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tc9014233/blockassist	tc9014233	2025-09-22T10:14:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flapping sedate elephant", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:46:12Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flapping sedate elephant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
pepperse077/blockassist	pepperse077	2025-09-22T10:13:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "beaked winged crane", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:45:42Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - beaked winged crane --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
MattBou00/llama-3-2-1b-detox_RETRY_SAMPLING_scale10_Round3	MattBou00	2025-09-22T10:13:36Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T10:12:36Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/final-model") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/final-model") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/final-model") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
spearmanbarry99/blockassist	spearmanbarry99	2025-09-22T10:13:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wild arctic swan", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:45:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wild arctic swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
cornishteddy57/blockassist	cornishteddy57	2025-09-22T10:12:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "noisy freckled camel", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:44:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - noisy freckled camel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rb021938/blockassist	rb021938	2025-09-22T10:12:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous scurrying cheetah", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:44:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous scurrying cheetah --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rb1488926/blockassist	rb1488926	2025-09-22T10:10:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "aquatic bellowing raven", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:42:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - aquatic bellowing raven --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sayannath/gpt-oss-20b-medical-qa	sayannath	2025-09-22T10:10:08Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:openai/gpt-oss-20b", "base_model:finetune:openai/gpt-oss-20b", "endpoints_compatible", "region:us" ]	null	2025-09-21T19:03:18Z	--- base_model: openai/gpt-oss-20b library_name: transformers model_name: gpt-oss-20b-medical-qa tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for gpt-oss-20b-medical-qa This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b). 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="sayannath/gpt-oss-20b-medical-qa", 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/sayannath235/LLM-Recipe/runs/q1iyzxbm) This model was trained with SFT. ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.2 - Pytorch: 2.8.0 - Datasets: 4.1.0 - Tokenizers: 0.22.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
noirchan/Llama-3-8B-JaCode-v1	noirchan	2025-09-22T10:09:51Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2212.04089", "base_model:alfredplpl/Llama-3-8B-Instruct-Ja", "base_model:merge:alfredplpl/Llama-3-8B-Instruct-Ja", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:merge:meta-llama/Meta-Llama-3-8B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T10:09:11Z	--- base_model: - alfredplpl/Llama-3-8B-Instruct-Ja - meta-llama/Meta-Llama-3-8B-Instruct library_name: transformers tags: - mergekit - merge --- # task_arithmetic_v1 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Task Arithmetic](https://arxiv.org/abs/2212.04089) merge method using [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as a base. ### Models Merged The following models were included in the merge: * [alfredplpl/Llama-3-8B-Instruct-Ja](https://huggingface.co/alfredplpl/Llama-3-8B-Instruct-Ja) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: task_arithmetic base_model: meta-llama/Meta-Llama-3-8B-Instruct models: - model: meta-llama/Meta-Llama-3-8B-Instruct parameters: weight: 0.4 - model: alfredplpl/Llama-3-8B-Instruct-Ja parameters: weight: 0.6 parameters: normalize: false dtype: bfloat16 tokenizer_source: union ```
mcdillrobby57/blockassist	mcdillrobby57	2025-09-22T10:09:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "voracious sprightly gazelle", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:41:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - voracious sprightly gazelle --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rodgersearl147/blockassist	rodgersearl147	2025-09-22T10:09:20Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "placid vicious ram", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:41:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - placid vicious ram --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
MattBou00/llama-3-2-1b-detox_RETRY_SAMPLING_scale10_Round3-checkpoint-epoch-80	MattBou00	2025-09-22T10:08:53Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T10:07:54Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-80") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-80") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-80") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
dctellya/distilbert-base-uncased-imdb	dctellya	2025-09-22T10:08:46Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-09-22T09:53:20Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-imdb 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. --> # distilbert-base-uncased-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4892 - Model Preparation Time: 0.0007 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------------:\| \| 2.6732 \| 1.0 \| 157 \| 2.4981 \| 0.0007 \| \| 2.5849 \| 2.0 \| 314 \| 2.4475 \| 0.0007 \| \| 2.5249 \| 3.0 \| 471 \| 2.4815 \| 0.0007 \| ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0 - Datasets 4.0.0 - Tokenizers 0.22.0
nnilayy/dreamer_stride_256-binary-arousal-Kfold-3-stride_256	nnilayy	2025-09-22T10:07:44Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-09-22T10:07:38Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
martijn75/token_voc	martijn75	2025-09-22T10:06:57Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-01-10T10:40:19Z	--- 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]
bs4085350/blockassist	bs4085350	2025-09-22T10:06:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "domestic shy pig", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T03:38:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - domestic shy pig --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/gpt2-medium-tinystories-GGUF	mradermacher	2025-09-22T10:05:41Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:tcmmichaelb139/gpt2-medium-tinystories", "base_model:quantized:tcmmichaelb139/gpt2-medium-tinystories", "endpoints_compatible", "region:us" ]	null	2025-09-22T10:03:33Z	--- base_model: tcmmichaelb139/gpt2-medium-tinystories language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/tcmmichaelb139/gpt2-medium-tinystories <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#gpt2-medium-tinystories-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/gpt2-medium-tinystories-GGUF/resolve/main/gpt2-medium-tinystories.Q2_K.gguf) \| Q2_K \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-tinystories-GGUF/resolve/main/gpt2-medium-tinystories.Q3_K_S.gguf) \| Q3_K_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-tinystories-GGUF/resolve/main/gpt2-medium-tinystories.Q3_K_M.gguf) \| Q3_K_M \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-tinystories-GGUF/resolve/main/gpt2-medium-tinystories.IQ4_XS.gguf) \| IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-tinystories-GGUF/resolve/main/gpt2-medium-tinystories.Q4_K_S.gguf) \| Q4_K_S \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-tinystories-GGUF/resolve/main/gpt2-medium-tinystories.Q3_K_L.gguf) \| Q3_K_L \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-tinystories-GGUF/resolve/main/gpt2-medium-tinystories.Q4_K_M.gguf) \| Q4_K_M \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-tinystories-GGUF/resolve/main/gpt2-medium-tinystories.Q5_K_S.gguf) \| Q5_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-tinystories-GGUF/resolve/main/gpt2-medium-tinystories.Q5_K_M.gguf) \| Q5_K_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-tinystories-GGUF/resolve/main/gpt2-medium-tinystories.Q6_K.gguf) \| Q6_K \| 0.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-tinystories-GGUF/resolve/main/gpt2-medium-tinystories.Q8_0.gguf) \| Q8_0 \| 0.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-tinystories-GGUF/resolve/main/gpt2-medium-tinystories.f16.gguf) \| f16 \| 0.8 \| 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 -->
MattBou00/llama-3-2-1b-detox_RETRY_SAMPLING_scale10_Round3-checkpoint-epoch-60	MattBou00	2025-09-22T10:05:39Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T10:04:39Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-60") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-60") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-60") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
little-john/insurance_doc_classification_model	little-john	2025-09-22T10:05:36Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-classification", "text-generation-inference", "unsloth", "en", "base_model:Skywork/Skywork-Reward-V2-Qwen3-0.6B", "base_model:finetune:Skywork/Skywork-Reward-V2-Qwen3-0.6B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-22T10:04:51Z	--- base_model: Skywork/Skywork-Reward-V2-Qwen3-0.6B tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: little-john - License: apache-2.0 - Finetuned from model : Skywork/Skywork-Reward-V2-Qwen3-0.6B 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)
quablab/SmolLM3-Custom-SFT	quablab	2025-09-22T10:03:41Z	0	0	transformers	[ "transformers", "safetensors", "smollm3", "text-generation", "generated_from_trainer", "smol-course", "instruction-tuning", "sft", "hf_jobs", "trl", "conversational", "base_model:HuggingFaceTB/SmolLM3-3B", "base_model:finetune:HuggingFaceTB/SmolLM3-3B", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T09:53:52Z	--- base_model: HuggingFaceTB/SmolLM3-3B library_name: transformers model_name: SmolLM3-Custom-SFT tags: - generated_from_trainer - smol-course - instruction-tuning - sft - hf_jobs - trl licence: license --- # Model Card for SmolLM3-Custom-SFT This model is a fine-tuned version of [HuggingFaceTB/SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B). 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="quablab/SmolLM3-Custom-SFT", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.2 - Pytorch: 2.5.1 - Datasets: 4.1.1 - Tokenizers: 0.22.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/Koto-Small-7B-IT-ThonkTokens-GGUF	mradermacher	2025-09-22T10:03:39Z	0	0	transformers	[ "transformers", "gguf", "writing", "creative-writing", "roleplay", "en", "base_model:allura-forge/Koto-Small-7B-IT-ThonkTokens", "base_model:quantized:allura-forge/Koto-Small-7B-IT-ThonkTokens", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T09:35:24Z	--- base_model: allura-forge/Koto-Small-7B-IT-ThonkTokens language: - en library_name: transformers license: mit mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - writing - creative-writing - roleplay --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/allura-forge/Koto-Small-7B-IT-ThonkTokens <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Koto-Small-7B-IT-ThonkTokens-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/Koto-Small-7B-IT-ThonkTokens-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.Q2_K.gguf) \| Q2_K \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.Q3_K_M.gguf) \| Q3_K_M \| 4.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Koto-Small-7B-IT-ThonkTokens-GGUF/resolve/main/Koto-Small-7B-IT-ThonkTokens.f16.gguf) \| f16 \| 15.4 \| 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 -->
mradermacher/youtube-comments-distilbert-GGUF	mradermacher	2025-09-22T10:03:39Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:nadakandrew/youtube-comments-distilbert", "base_model:quantized:nadakandrew/youtube-comments-distilbert", "endpoints_compatible", "region:us", "feature-extraction" ]	null	2025-09-22T10:02:17Z	--- base_model: nadakandrew/youtube-comments-distilbert language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/nadakandrew/youtube-comments-distilbert <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#youtube-comments-distilbert-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/youtube-comments-distilbert-GGUF/resolve/main/youtube-comments-distilbert.Q2_K.gguf) \| Q2_K \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/youtube-comments-distilbert-GGUF/resolve/main/youtube-comments-distilbert.Q3_K_S.gguf) \| Q3_K_S \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/youtube-comments-distilbert-GGUF/resolve/main/youtube-comments-distilbert.Q3_K_M.gguf) \| Q3_K_M \| 0.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/youtube-comments-distilbert-GGUF/resolve/main/youtube-comments-distilbert.IQ4_XS.gguf) \| IQ4_XS \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/youtube-comments-distilbert-GGUF/resolve/main/youtube-comments-distilbert.Q3_K_L.gguf) \| Q3_K_L \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/youtube-comments-distilbert-GGUF/resolve/main/youtube-comments-distilbert.Q4_K_S.gguf) \| Q4_K_S \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/youtube-comments-distilbert-GGUF/resolve/main/youtube-comments-distilbert.Q4_K_M.gguf) \| Q4_K_M \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/youtube-comments-distilbert-GGUF/resolve/main/youtube-comments-distilbert.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/youtube-comments-distilbert-GGUF/resolve/main/youtube-comments-distilbert.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/youtube-comments-distilbert-GGUF/resolve/main/youtube-comments-distilbert.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/youtube-comments-distilbert-GGUF/resolve/main/youtube-comments-distilbert.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/youtube-comments-distilbert-GGUF/resolve/main/youtube-comments-distilbert.f16.gguf) \| f16 \| 0.2 \| 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 -->
John6666/illustrious-pixel-art-from-hades-v4-series-v-40-sdxl	John6666	2025-09-22T10:03:33Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "pixel art", "2D", "retro", "indie", "clean lines", "sharp detail", "consistent palettes", "adherence", "perspective", "poses", "consistency", "game assets", "visual fidelity", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-09-22T09:51:57Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - pixel art - 2D - retro - indie - clean lines - sharp detail - consistent palettes - adherence - perspective - poses - consistency - game assets - visual fidelity - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1732312/illustrious-pixelart-from-hades?modelVersionId=2239694). This model created by [DeViLDoNia](https://civitai.com/user/DeViLDoNia).
deckardk09/blockassist	deckardk09	2025-09-22T10:01:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored soft fish", "arxiv:2504.07091", "region:us" ]	null	2025-09-18T17:35:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored soft fish --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/sage-reasoning-3b-GGUF	mradermacher	2025-09-22T10:00:15Z	0	1	transformers	[ "transformers", "gguf", "en", "ko", "fr", "zh", "es", "base_model:sagea-ai/sage-reasoning-3b", "base_model:quantized:sagea-ai/sage-reasoning-3b", "license:llama3.2", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T09:26:26Z	--- base_model: sagea-ai/sage-reasoning-3b language: - en - ko - fr - zh - es library_name: transformers license: llama3.2 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/sagea-ai/sage-reasoning-3b <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#sage-reasoning-3b-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/sage-reasoning-3b-i1-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/sage-reasoning-3b-GGUF/resolve/main/sage-reasoning-3b.Q2_K.gguf) \| Q2_K \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/sage-reasoning-3b-GGUF/resolve/main/sage-reasoning-3b.Q3_K_S.gguf) \| Q3_K_S \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/sage-reasoning-3b-GGUF/resolve/main/sage-reasoning-3b.Q3_K_M.gguf) \| Q3_K_M \| 2.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/sage-reasoning-3b-GGUF/resolve/main/sage-reasoning-3b.Q3_K_L.gguf) \| Q3_K_L \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/sage-reasoning-3b-GGUF/resolve/main/sage-reasoning-3b.IQ4_XS.gguf) \| IQ4_XS \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/sage-reasoning-3b-GGUF/resolve/main/sage-reasoning-3b.Q4_K_S.gguf) \| Q4_K_S \| 2.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/sage-reasoning-3b-GGUF/resolve/main/sage-reasoning-3b.Q4_K_M.gguf) \| Q4_K_M \| 2.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/sage-reasoning-3b-GGUF/resolve/main/sage-reasoning-3b.Q5_K_S.gguf) \| Q5_K_S \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/sage-reasoning-3b-GGUF/resolve/main/sage-reasoning-3b.Q5_K_M.gguf) \| Q5_K_M \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/sage-reasoning-3b-GGUF/resolve/main/sage-reasoning-3b.Q6_K.gguf) \| Q6_K \| 3.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/sage-reasoning-3b-GGUF/resolve/main/sage-reasoning-3b.Q8_0.gguf) \| Q8_0 \| 3.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/sage-reasoning-3b-GGUF/resolve/main/sage-reasoning-3b.f16.gguf) \| f16 \| 7.3 \| 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 -->
deckardkrs/blockassist	deckardkrs	2025-09-22T09:59:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silent colorful ape", "arxiv:2504.07091", "region:us" ]	null	2025-09-18T17:33:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silent colorful ape --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
MattBou00/llama-3-2-1b-detox_RETRY_SAMPLING_scale10_Round3-checkpoint-epoch-20	MattBou00	2025-09-22T09:59:03Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T09:58:05Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-20") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-20") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-20") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
sayouzone25/gemma-3-12b-trans-en-ko	sayouzone25	2025-09-22T09:58:53Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-12b-pt", "base_model:finetune:google/gemma-3-12b-pt", "endpoints_compatible", "region:us" ]	null	2025-09-18T09:32:09Z	--- base_model: google/gemma-3-12b-pt library_name: transformers model_name: gemma-3-12b-trans-en-ko tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-3-12b-trans-en-ko This model is a fine-tuned version of [google/gemma-3-12b-pt](https://huggingface.co/google/gemma-3-12b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="sayouzone25/gemma-3-12b-trans-en-ko", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.55.2 - Pytorch: 2.8.0 - Datasets: 3.3.2 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
jackiemelillo866/blockassist	jackiemelillo866	2025-09-22T09:58:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tiny tricky pelican", "arxiv:2504.07091", "region:us" ]	null	2025-09-18T17:33:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tiny tricky pelican --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Zakaria279/gptoss_translator_2	Zakaria279	2025-09-22T09:54:47Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "base_model:adapter:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "region:us" ]	null	2025-09-22T09:54:44Z	--- base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
dorangao/landify-chatbot-tool-expert-v1	dorangao	2025-09-22T09:54:45Z	0	0	null	[ "safetensors", "text-generation", "conversational", "arxiv:1910.09700", "license:apache-2.0", "region:us" ]	text-generation	2025-09-22T08:40:43Z	--- license: apache-2.0 inference: parameters: temperature: 0.1 max_new_tokens: 1024 stop: - "</s>" - "<\|endoftext\|>" pipeline_tag: text-generation --- --- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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yunyu1258/SD1.5-AX650-Dark_Sushi_Mix	yunyu1258	2025-09-22T09:53:15Z	0	0	null	[ "base_model:stable-diffusion-v1-5/stable-diffusion-v1-5", "base_model:finetune:stable-diffusion-v1-5/stable-diffusion-v1-5", "license:mit", "region:us" ]	null	2025-06-25T04:18:09Z	--- license: mit base_model: - stable-diffusion-v1-5/stable-diffusion-v1-5 --- # Dark-Sushi-Mix-v1.5 on AX650（端侧一键运行版）原模型地址：https://civitai.com/models/24779?modelVersionId=93208 当前为 10 步扩散版本，固定延迟 20×250 ms ≈ 5 s。 --- ## 1. 手动运行（开发调试） \| 步骤 \| 命令 \| \|------\|------\| \| 启动后端 \| `uvicorn api_10steps:app --host 0.0.0.0 --port 7888` \| \| 命令行生图 \| `python gen_img.py` \| \| Web 界面 \| `cd client && python app.py` 后访问 http://127.0.0.1:5000 \| --- ## 2. 端侧一键自启（生产部署） > 开机后自动完成： > ① 启动后端 → ② 启动前端 → ③ 自动打开 Firefox 全屏展示 http://localhost:5000/ ### 2.1 一键启用仓库根目录已内置脚本，直接执行： ```bash # 复制并设置启动脚本 sudo cp sd-launch.sh /opt/sd-launch.sh sudo chmod +x /opt/sd-launch.sh # 创建用户级自启动 mkdir -p ~/.config/autostart cp sd-launch.desktop ~/.config/autostart/ # 确保 Firefox 已安装 sudo apt update && sudo apt install firefox -y ``` ### 2.2 文件说明 - `sd-launch.sh` 统一启动脚本：切目录 → 后台启动后端 → 后台启动前端 → 等待端口 → 使用 Firefox kiosk 模式自动全屏打开。 - `sd-launch.desktop` 用户级自启动配置，在图形界面登录后自动执行脚本。 --- ## 3. 日志与维护 \| 操作 \| 命令 \| \|------\|------\| \| 查看启动日志 \| `tail -f /var/log/sd-launch/startup.log` \| \| 查看后端日志 \| `tail -f /var/log/sd-launch/backend.log` \| \| 查看前端日志 \| `tail -f /var/log/sd-launch/frontend.log` \| \| 查看浏览器日志 \| `tail -f /var/log/sd-launch/browser.log` \| \| 手动重启 \| `pkill -f sd-launch.sh && /opt/sd-launch.sh &` \| \| 停止服务 \| `pkill -f uvicorn && pkill -f "python3 app.py" && pkill firefox` \| --- ## 4. 常见问题 - Firefox 没有自动打开确保已安装 Firefox：`sudo apt install firefox -y` - 重启后服务没启动检查 `~/.config/autostart/sd-launch.desktop` 是否存在 - 端口冲突修改脚本中的 `7888` / `5000` 端口即可 - 手动测试浏览器运行：`firefox --kiosk http://localhost:5000/` --- ## 5. 验证部署重启系统后，应该看到： 1. 自动启动后端服务（端口 7888） 2. 自动启动前端服务（端口 5000） 3. Firefox 自动全屏打开 SD 生图界面至此，AX650 端侧即可实现插电即跑的 Dark-Sushi-Mix-v1.5 体验。
GeraniumCat/bash-seq-to-seq	GeraniumCat	2025-09-22T09:52:52Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "translation", "dataset:aelhalili/bash-commands-dataset", "dataset:darkknight25/Linux_Terminal_Commands_Dataset", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	translation	2025-09-22T09:48:43Z	--- library_name: transformers datasets: - aelhalili/bash-commands-dataset - darkknight25/Linux_Terminal_Commands_Dataset metrics: - bleu pipeline_tag: translation --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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