Split (1)

train · 912k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-02 12:32:32	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 534 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-02 12:31:20	card stringlengths 11 1.01M
weareKHEPRI/kimlora	weareKHEPRI	2025-09-02T09:46:23Z	0	0	null	[ "license:other", "region:us" ]	null	2025-09-02T07:03:26Z	--- 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 ---
omerbektass/blockassist-bc-keen_fast_giraffe_1756806286	omerbektass	2025-09-02T09:45:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:45:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
duppbuy/blockassist-bc-winged_smooth_rabbit_1756806218	duppbuy	2025-09-02T09:44:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "winged smooth rabbit", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:43:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - winged smooth rabbit --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
omerbkts/blockassist-bc-keen_fast_giraffe_1756806166	omerbkts	2025-09-02T09:43:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:43:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
wetherbeep/affine_test	wetherbeep	2025-09-02T09:42:53Z	0	0	null	[ "safetensors", "gpt_oss", "8-bit", "mxfp4", "region:us" ]	null	2025-09-02T09:33:22Z	# Enhanced Affine Model ## Overview This is an optimized version of the top-performing Affine models, specifically enhanced for HVM (Hole-filled Virtual Machine) tasks while maintaining excellence in SAT, ABD, and DED environments. ## Key Improvements ### 1. Environment-Specific Model Identity - SAT: Specialized for Boolean satisfiability and logical reasoning - ABD: Expert in reverse engineering and program analysis - DED: Focused on program execution tracing and simulation - HVM: Enhanced for virtual machine programming and constraint satisfaction ### 2. Enhanced Reasoning Framework - Maximum reasoning effort (upgraded from "high") - Systematic approach: step-by-step, constraint verification, format checking - Multi-channel analysis: Structured thinking before final answers ### 3. HVM-Specific Optimizations - Stack operation understanding: Enhanced VM simulation guidance - Constraint satisfaction: Systematic equation solving approach - Domain-aware reasoning: Respects parameter constraints - Format enforcement: Strict `<HOLES>?a=value</HOLES>` formatting ### 4. Task-Specific Guidance Each problem type receives tailored instructions: - HVM: VM operations, constraint equations, domain solving - SAT: Variable parsing, logical deduction, clause satisfaction - ABD: Program analysis, input format understanding, reverse reasoning - DED: Step-by-step execution, variable tracking, output prediction ### 5. Advanced Generation Configuration - Temperature: 0.7 (balanced creativity/precision) - Top-p: 0.9 (focused sampling) - Max tokens: 2048 (sufficient for complex reasoning) - Repetition penalty: 1.05 (reduces redundancy) ## Architecture - Base: GptOssForCausalLM (50B parameters) - MoE: 128 experts, 4 active per token - Context: 131,072 tokens (128K window) - Quantization: MXFP4 for efficient inference - Attention: Hybrid sliding/full attention ## Usage Deploy this model using the Affine framework: ```bash affine push --model_path ./enhanced_model_complete --coldkey <your_cold> --hotkey <your_hot> ``` ## Expected Performance - SAT: Maintained high accuracy with systematic logical reasoning - ABD: Enhanced reverse engineering with structured analysis - DED: Improved program tracing with step-by-step guidance - HVM: Significantly improved constraint satisfaction and VM understanding ## Competitive Advantages 1. Multi-environment excellence: Optimized for all 4 Affine environments 2. HVM breakthrough: Addresses the main weakness of current top models 3. Systematic reasoning: Built-in verification and constraint checking 4. Format reliability: Reduced parsing errors through strict formatting 5. Combinatorial scoring: Optimized for Affine's subset-based reward system
duppbuy/blockassist-bc-savage_stinging_opossum_1756806113	duppbuy	2025-09-02T09:42:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage stinging opossum", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:41:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage stinging opossum --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
akirafudo/blockassist-bc-keen_fast_giraffe_1756806024	akirafudo	2025-09-02T09:40:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:40:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1756804333	kojeklollipop	2025-09-02T09:40:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:40:09Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lokesh12345palineni/output	lokesh12345palineni	2025-09-02T09:38:18Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "endpoints_compatible", "region:us" ]	null	2025-09-02T09:38:15Z	--- base_model: google/flan-t5-small library_name: transformers model_name: output tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for output This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small). 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="lokesh12345palineni/output", 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/lokesh951555-national-student-clearinghouse/huggingface/runs/ogj5axq0) This model was trained with SFT. ### Framework versions - TRL: 0.22.1 - Transformers: 4.55.4 - Pytorch: 2.8.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
klmdr22/blockassist-bc-wild_loud_newt_1756805842	klmdr22	2025-09-02T09:38:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wild loud newt", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:38:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wild loud newt --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
2hpsatt/blockassist-bc-huge_deft_eagle_1756805755	2hpsatt	2025-09-02T09:36:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge deft eagle", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:36:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge deft eagle --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
omerbkts/blockassist-bc-keen_fast_giraffe_1756805766	omerbkts	2025-09-02T09:36:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:36:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
samunder12/llama-3.1-8b-roleplay-jio-gguf	samunder12	2025-09-02T09:36:14Z	97	1	null	[ "gguf", "llama", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-01T18:49:19Z	--- license: apache-2.0 --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="./Jio.jpeg" alt="Peach" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <!-- # Llama 3.1 8B - Assertive Role-Play (v3 GGUF) This repository contains the GGUF version of the [v3 Assertive Role-Play LoRA model](https://huggingface.co/YourUsername/llama-3.1-8b-roleplay-v3-lora). <!--- <<< Change this link! ---> This model is designed for easy, local inference on CPUs and GPUs using llama.cpp-based software like LM Studio and Ollama. The model embodies a dominant, assertive, and creative persona for role-playing and storytelling. It was fine-tuned on a multi-turn conversational dataset to enhance its coherence and memory. ## Model Details * Original LoRA Model: [`samunder12/llama-3.1-8b-roleplay-v3-lora`](https://huggingface.co/samunder12/llama-3.1-8b-roleplay-v3-lora) <!--- <<< Change this link! ---> * Quantization: `Q4_K_M`. This method provides an excellent balance between model size, performance, and VRAM/RAM usage. * Context Length: `4096` tokens. ## Usage Instructions ### LM Studio (Recommended) 1. Download and install [LM Studio](https://lmstudio.ai/). 2. In the app, search for this model repo: `samunder12/llama-3.1-8b-roleplay-v3-gguf`. <!--- <<< Change this ---> 3. Download the GGUF file listed in the "Files" tab. 4. Go to the Chat tab (💬 icon) and load the model you just downloaded. 5. CRITICAL: On the right-hand panel, under "Prompt Format", select the Llama 3 preset. 6. Set the `Context Length (n_ctx)` to 4096. 7. Use the "Role-Play" sampler settings below for best results. #### Recommended Sampler Settings (Role-Play Preset) \| Setting \| Value \| \| :--- \| :--- \| \| Temperature \| `0.75` \| \| Repeat Penalty \| `1.06` \| \| Mirostat \| `Mirostat 2.0` \| \| top_p \| `0.92 `\| \| top_k \| `40 or 100`\|
happyensworld/blockassist-bc-sleek_scavenging_ram_1756805634	happyensworld	2025-09-02T09:35:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sleek scavenging ram", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:34:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sleek scavenging ram --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
capungmerah627/blockassist-bc-stinging_soaring_porcupine_1756804161	capungmerah627	2025-09-02T09:35:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stinging soaring porcupine", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:35:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stinging soaring porcupine --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskToken-0.1-HessianMaskToken-0.1-v2_5312	luckeciano	2025-09-02T09:34:59Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:DigitalLearningGmbH/MATH-lighteval", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-02T05:06:02Z	--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskToken-0.1-HessianMaskToken-0.1-v2_5312 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskToken-0.1-HessianMaskToken-0.1-v2_5312 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskToken-0.1-HessianMaskToken-0.1-v2_5312", 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/max-ent-llms/PolicyGradientStability/runs/f5ov3u2d) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.4.1 - Tokenizers: 0.21.2 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskToken-0.1-v2_7216	luckeciano	2025-09-02T09:34:49Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:DigitalLearningGmbH/MATH-lighteval", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-02T05:06:29Z	--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskToken-0.1-v2_7216 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskToken-0.1-v2_7216 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskToken-0.1-v2_7216", 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/max-ent-llms/PolicyGradientStability/runs/ptxjuojz) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.4.1 - Tokenizers: 0.21.2 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
akirafudo/blockassist-bc-keen_fast_giraffe_1756805643	akirafudo	2025-09-02T09:34:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:34:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/Llama-3.2-3B-Orpheus-Rioplatense-1795-GGUF	mradermacher	2025-09-02T09:33:16Z	0	0	transformers	[ "transformers", "gguf", "text-to-speech", "rioplatense", "argentinian", "speech", "es", "dataset:ylacombe/google-argentinian-spanish", "base_model:surus-lat/Llama-3.2-3B-Orpheus-Rioplatense-1795", "base_model:quantized:surus-lat/Llama-3.2-3B-Orpheus-Rioplatense-1795", "license:llama3.2", "endpoints_compatible", "region:us", "conversational" ]	text-to-speech	2025-09-02T08:44:27Z	--- base_model: surus-lat/Llama-3.2-3B-Orpheus-Rioplatense-1795 datasets: - ylacombe/google-argentinian-spanish language: - es library_name: transformers license: llama3.2 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - text-to-speech - rioplatense - argentinian - speech --- ## 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/surus-lat/Llama-3.2-3B-Orpheus-Rioplatense-1795 <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Llama-3.2-3B-Orpheus-Rioplatense-1795-GGUF).* weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Orpheus-Rioplatense-1795-GGUF/resolve/main/Llama-3.2-3B-Orpheus-Rioplatense-1795.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Orpheus-Rioplatense-1795-GGUF/resolve/main/Llama-3.2-3B-Orpheus-Rioplatense-1795.Q3_K_S.gguf) \| Q3_K_S \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Orpheus-Rioplatense-1795-GGUF/resolve/main/Llama-3.2-3B-Orpheus-Rioplatense-1795.Q3_K_M.gguf) \| Q3_K_M \| 1.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Orpheus-Rioplatense-1795-GGUF/resolve/main/Llama-3.2-3B-Orpheus-Rioplatense-1795.Q3_K_L.gguf) \| Q3_K_L \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Orpheus-Rioplatense-1795-GGUF/resolve/main/Llama-3.2-3B-Orpheus-Rioplatense-1795.IQ4_XS.gguf) \| IQ4_XS \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Orpheus-Rioplatense-1795-GGUF/resolve/main/Llama-3.2-3B-Orpheus-Rioplatense-1795.Q4_K_S.gguf) \| Q4_K_S \| 2.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Orpheus-Rioplatense-1795-GGUF/resolve/main/Llama-3.2-3B-Orpheus-Rioplatense-1795.Q4_K_M.gguf) \| Q4_K_M \| 2.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Orpheus-Rioplatense-1795-GGUF/resolve/main/Llama-3.2-3B-Orpheus-Rioplatense-1795.Q5_K_S.gguf) \| Q5_K_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Orpheus-Rioplatense-1795-GGUF/resolve/main/Llama-3.2-3B-Orpheus-Rioplatense-1795.Q5_K_M.gguf) \| Q5_K_M \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Orpheus-Rioplatense-1795-GGUF/resolve/main/Llama-3.2-3B-Orpheus-Rioplatense-1795.Q6_K.gguf) \| Q6_K \| 2.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Orpheus-Rioplatense-1795-GGUF/resolve/main/Llama-3.2-3B-Orpheus-Rioplatense-1795.Q8_0.gguf) \| Q8_0 \| 3.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-3B-Orpheus-Rioplatense-1795-GGUF/resolve/main/Llama-3.2-3B-Orpheus-Rioplatense-1795.f16.gguf) \| f16 \| 6.7 \| 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 -->
liukevin666/blockassist-bc-yawning_striped_cassowary_1756805506	liukevin666	2025-09-02T09:33:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:32:42Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
KRLabsOrg/tinylettuce-ettin-68m-en	KRLabsOrg	2025-09-02T09:32:35Z	13	1	transformers	[ "transformers", "safetensors", "modernbert", "token-classification", "token classification", "hallucination detection", "retrieval-augmented generation", "ettin", "lightweight", "en", "dataset:ragtruth", "dataset:KRLabsOrg/rag-bioasq-lettucedetect", "arxiv:2507.11412", "arxiv:2502.17125", "base_model:jhu-clsp/ettin-encoder-68m", "base_model:finetune:jhu-clsp/ettin-encoder-68m", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-08-31T10:55:03Z	--- license: mit language: - en base_model: - jhu-clsp/ettin-encoder-68m pipeline_tag: token-classification tags: - token classification - hallucination detection - retrieval-augmented generation - transformers - ettin - lightweight datasets: - ragtruth - KRLabsOrg/rag-bioasq-lettucedetect library_name: transformers --- # TinyLettuce (Ettin-68M): Efficient Hallucination Detection <p align="center"> <img src="https://github.com/KRLabsOrg/LettuceDetect/blob/dev/assets/tinytinylettuce.png?raw=true" alt="TinyLettuce" width="400"/> </p> Model Name: tinylettuce-ettin-68m-en Organization: KRLabsOrg Github: https://github.com/KRLabsOrg/LettuceDetect Ettin encoders: https://arxiv.org/pdf/2507.11412 ## Overview TinyLettuce is a lightweight token‑classification model that flags unsupported spans in answers given context (span aggregation performed downstream). Built on the 68M Ettin encoder, it targets real‑time CPU inference and low‑cost domain fine‑tuning. This variant is trained only on our synthetic data and RAGTruth dataset for hallucination detection, using the 68M Ettin encoder and a token‑classification head. Highest accuracy among TinyLettuce sizes, works great given it's size (74.97% vs 76.07 LettuceDetect-ModernBERT-base); optimized for efficient CPU inference. ## Model Details - Architecture: Ettin encoder (68M) + token‑classification head - Task: token classification (0 = supported, 1 = hallucinated) - Input: [CLS] context [SEP] question [SEP] answer [SEP], up to 4096 tokens - Language: English; License: MIT ## Training Data - RAGTruth (English), span‑level labels; no synthetic data mixed ## Training Procedure - Tokenizer: AutoTokenizer; DataCollatorForTokenClassification; label pad −100 - Max length: 4096; batch size: 16; epochs: 5 - Optimizer: AdamW (lr 1e‑5, weight_decay 0.01) - Hardware: Single A100 80GB ## Results (RAGTruth) This model is designed primarily for fine-tuning on smaller, domain-specific samples, rather than for general use. Performs well on the RAGTruth benchmark, coming close to our LettuceDetect-base (150m ModernBERT) model. \| Model \| Parameters \| F1 (%) \| \|-------\|------------\|--------\| \| TinyLettuce-68M \| 68M \| 74.97 \| \| LettuceDetect-base (ModernBERT) \| 150M \| 76.07 \| \| LettuceDetect-large (ModernBERT) \| 395M \| 79.22 \| \| Llama-2-13B (RAGTruth FT) \| 13B \| 78.70 \| ## Usage First install lettucedetect: ```bash pip install lettucedetect ``` Then use it: ```python from lettucedetect.models.inference import HallucinationDetector detector = HallucinationDetector( method="transformer", model_path="KRLabsOrg/tinylettuce-ettin-68m-en", ) spans = detector.predict( context=[ "Ibuprofen is an NSAID that reduces inflammation and pain. The typical adult dose is 400-600mg every 6-8 hours, not exceeding 2400mg daily." ], question="What is the maximum daily dose of ibuprofen?", answer="The maximum daily dose of ibuprofen for adults is 3200mg.", output_format="spans", ) print(spans) # Output: [{"start": 51, "end": 57, "text": "3200mg"}] ``` ## Citing If you use the model or the tool, please cite the following paper: ```bibtex @misc{Kovacs:2025, title={LettuceDetect: A Hallucination Detection Framework for RAG Applications}, author={Ádám Kovács and Gábor Recski}, year={2025}, eprint={2502.17125}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2502.17125}, } ```
omerbektass/blockassist-bc-keen_fast_giraffe_1756805524	omerbektass	2025-09-02T09:32:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:32:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
toladimeji/bert_crime_alert_classifier	toladimeji	2025-09-02T09:32:23Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-02T09:32:03Z	--- library_name: transformers license: apache-2.0 base_model: google-bert/bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert_crime_alert_classifier 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. --> # bert_crime_alert_classifier This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0024 - Accuracy: 1.0 - Auc: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Auc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---:\| \| 0.4469 \| 1.0 \| 20 \| 0.1971 \| 1.0 \| 1.0 \| \| 0.206 \| 2.0 \| 40 \| 0.0566 \| 1.0 \| 1.0 \| \| 0.0944 \| 3.0 \| 60 \| 0.0276 \| 1.0 \| 1.0 \| \| 0.0494 \| 4.0 \| 80 \| 0.0078 \| 1.0 \| 1.0 \| \| 0.0299 \| 5.0 \| 100 \| 0.0047 \| 1.0 \| 1.0 \| \| 0.0353 \| 6.0 \| 120 \| 0.0042 \| 1.0 \| 1.0 \| \| 0.0212 \| 7.0 \| 140 \| 0.0037 \| 1.0 \| 1.0 \| \| 0.0224 \| 8.0 \| 160 \| 0.0029 \| 1.0 \| 1.0 \| \| 0.0153 \| 9.0 \| 180 \| 0.0026 \| 1.0 \| 1.0 \| \| 0.02 \| 10.0 \| 200 \| 0.0024 \| 1.0 \| 1.0 \| ### Framework versions - Transformers 4.55.4 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
ethan789/repo-MLOps	ethan789	2025-09-02T09:32:03Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-02T09:19:01Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: repo-MLOps 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. --> # repo-MLOps 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: - eval_loss: 0.6989 - eval_model_preparation_time: 0.0012 - eval_accuracy: 0.4933 - eval_f1: 0.6607 - eval_runtime: 30.2716 - eval_samples_per_second: 9.91 - eval_steps_per_second: 0.628 - 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: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.56.0 - Pytorch 2.8.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.0
KingEmpire/King105_De_090207	KingEmpire	2025-09-02T09:31:49Z	0	0	null	[ "region:us" ]	null	2025-09-02T09:26:24Z	# Container Template for SoundsRight Subnet Miners This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively. This container has only been tested with Ubuntu 24.04 and CUDA 12.6. It may run on other configurations, but it is not guaranteed. To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt. Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html). Verify that the CDI specification was done correctly with: ``` $ nvidia-ctk cdi list ``` You should see this in your output: ``` nvidia.com/gpu=all nvidia.com/gpu=0 ``` If you are running podman as root, run the following command to start the container: Run the container with: ``` podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi ``` Access logs with: ``` podman logs -f modelapi ``` If you are running the container rootless, there are a few more changes to make: First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters: ``` [nvidia-container-cli] no-cgroups = true [nvidia-container-runtime] debug = "/tmp/nvidia-container-runtime.log" ``` You can also run the following command to achieve the same result: ``` $ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place ``` Run the container with: ``` podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi ``` Access logs with: ``` podman logs -f modelapi ``` Running the container will spin up an API with the following endpoints: 1. `/status/` : Communicates API status 2. `/prepare/` : Download model checkpoint and initialize model 3. `/upload-audio/` : Upload audio files, save to noisy audio directory 4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory 5. `/download-enhanced/` : Download enhanced audio files By default the API will use host `0.0.0.0` and port `6500`. ### References 1. Welker, Simon; Richter, Julius; Gerkmann, Timo Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain. Proceedings of Interspeech 2022, 2022, pp. 2928–2932. [DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653) 2. Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo Speech Enhancement and Dereverberation with Diffusion-based Generative Models. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 31, 2023, pp. 2351–2364. [DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241) 3. Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation. Proceedings of ISCA Interspeech, 2024, pp. 4873–4877.
rachel521/code-search-net-tokenizer	rachel521	2025-09-02T09:31:27Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-02T09:31:26Z	--- 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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guficyp/blockassist-bc-raging_fast_viper_1756805428	guficyp	2025-09-02T09:31:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging fast viper", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:31:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging fast viper --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/Hunyuan-MT-7B-i1-GGUF	mradermacher	2025-09-02T09:30:56Z	0	0	transformers	[ "transformers", "gguf", "translation", "en", "base_model:tencent/Hunyuan-MT-7B", "base_model:quantized:tencent/Hunyuan-MT-7B", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	translation	2025-09-02T08:51:47Z	--- base_model: tencent/Hunyuan-MT-7B language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - translation --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/tencent/Hunyuan-MT-7B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Hunyuan-MT-7B-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/Hunyuan-MT-7B-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/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.9 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.9 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-Q2_K.gguf) \| i1-Q2_K \| 3.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.5 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-Q4_1.gguf) \| i1-Q4_1 \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-i1-GGUF/resolve/main/Hunyuan-MT-7B.i1-Q6_K.gguf) \| i1-Q6_K \| 6.3 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
pishrobpmsAI/Pishro-Llama3-8B-Instruct	pishrobpmsAI	2025-09-02T09:29:46Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "LLM", "llama-3", "PishroBPMS", "conversational", "en", "fa", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:quantized:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-08-28T06:06:05Z	--- library_name: transformers license: llama3 language: - en - fa tags: - LLM - llama-3 - PishroBPMS - conversational base_model: - meta-llama/Meta-Llama-3-8B-Instruct pipeline_tag: text-generation --- # Model Details The pishro models are a family of decoder-only models, specifically fine-tuned on Processmaker data, developed by [PishroBPMS](https://pishrobpms.com/). As an initial release, an 8B instruct model from this family is being made available. Pishro-Llama3-8B-Instruct is built using the [Meta Llama 3 Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) model. ## How to use You can run conversational inference using the Transformers Auto classes with the `generate()` function. Let's look at an example. ```Python import torch import transformers from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "تو یک کارشناس ProcessMaker 4 و PHP هستی و باید فقط یک اسکریپت PHP استاندارد تولید کنی."}, {"role": "user", "content": "یک اسکریپت PHP ساده برای جمع دو عدد در ProcessMaker 4 بنویس."}, ] input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = model.generate( input_ids, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) response = outputs[0][input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ```
DimaSK1/Qwen2-0.5B-bnb-4bit-ema-1	DimaSK1	2025-09-02T09:29:09Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "unsloth", "base_model:unsloth/Qwen2-0.5B-bnb-4bit", "base_model:finetune:unsloth/Qwen2-0.5B-bnb-4bit", "endpoints_compatible", "region:us" ]	null	2025-09-02T09:29:06Z	--- base_model: unsloth/Qwen2-0.5B-bnb-4bit library_name: transformers model_name: Qwen2-0.5B-bnb-4bit-ema-1 tags: - generated_from_trainer - sft - trl - unsloth licence: license --- # Model Card for Qwen2-0.5B-bnb-4bit-ema-1 This model is a fine-tuned version of [unsloth/Qwen2-0.5B-bnb-4bit](https://huggingface.co/unsloth/Qwen2-0.5B-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="DimaSK1/Qwen2-0.5B-bnb-4bit-ema-1", 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: 0.22.1 - Transformers: 4.56.0 - Pytorch: 2.8.0 - Datasets: 3.6.0 - Tokenizers: 0.22.0 ## 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}} } ```
duppbuy/blockassist-bc-wary_lanky_porcupine_1756805299	duppbuy	2025-09-02T09:28:42Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wary lanky porcupine", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:28:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wary lanky porcupine --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lb8s/my-great-gpt2-review-model	lb8s	2025-09-02T09:27:51Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:erwanf/gpt2-mini", "base_model:finetune:erwanf/gpt2-mini", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T09:28:22Z	--- library_name: transformers license: mit base_model: erwanf/gpt2-mini tags: - generated_from_trainer model-index: - name: my-great-gpt2-review-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my-great-gpt2-review-model This model is a fine-tuned version of [erwanf/gpt2-mini](https://huggingface.co/erwanf/gpt2-mini) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1737 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005971 - 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: 0.1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.3294 \| 0.1000 \| 1031 \| 3.1737 \| ### Framework versions - Transformers 4.55.3 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
duppbuy/blockassist-bc-stinky_tricky_swan_1756805217	duppbuy	2025-09-02T09:27:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stinky tricky swan", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:26:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stinky tricky swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
duppbuy/blockassist-bc-rugged_amphibious_dolphin_1756805121	duppbuy	2025-09-02T09:26:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged amphibious dolphin", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:25:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged amphibious dolphin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
klmdr22/blockassist-bc-wild_loud_newt_1756805123	klmdr22	2025-09-02T09:26:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wild loud newt", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:26:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wild loud newt --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
omerbektass/blockassist-bc-keen_fast_giraffe_1756805098	omerbektass	2025-09-02T09:25:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:25:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
artfacer/smflux	artfacer	2025-09-02T09:25:12Z	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	2025-09-02T09:07:46Z	--- 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: smflux --- # Smflux <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 `smflux` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "smflux", "lora_weights": "https://huggingface.co/artfacer/smflux/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('artfacer/smflux', weight_name='lora.safetensors') image = pipeline('smflux').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/artfacer/smflux/discussions) to add images that show off what you’ve made with this LoRA.
hyunjong7/qwen2-5-vl-32b-fire-finetun_add_hub	hyunjong7	2025-09-02T09:24:31Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:Qwen/Qwen2.5-VL-32B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-32B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-08-29T14:43:04Z	--- base_model: Qwen/Qwen2.5-VL-32B-Instruct library_name: transformers model_name: qwen2-5-vl-32b-fire-finetun_add_hub tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for qwen2-5-vl-32b-fire-finetun_add_hub This model is a fine-tuned version of [Qwen/Qwen2.5-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-32B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="hyunjong7/qwen2-5-vl-32b-fire-finetun_add_hub", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.4 - Pytorch: 2.8.0 - Datasets: 3.0.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
happyensworld/blockassist-bc-sleek_scavenging_ram_1756804974	happyensworld	2025-09-02T09:24:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sleek scavenging ram", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:24:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sleek scavenging ram --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
luckycanucky/toxicChat-reFormat	luckycanucky	2025-09-02T09:24:04Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-02T09:06:35Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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kafa22/blockassist-bc-regal_leggy_hummingbird_1756804987	kafa22	2025-09-02T09:23:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "regal leggy hummingbird", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:23:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - regal leggy hummingbird --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
enacimie/Qwen3-4B-Instruct-2507-Q2_K-GGUF	enacimie	2025-09-02T09:23:47Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-4B-Instruct-2507", "base_model:quantized:Qwen/Qwen3-4B-Instruct-2507", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-09-02T09:23:37Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507/blob/main/LICENSE pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo base_model: Qwen/Qwen3-4B-Instruct-2507 --- # enacimie/Qwen3-4B-Instruct-2507-Q2_K-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-4B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) 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/Qwen/Qwen3-4B-Instruct-2507) 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 enacimie/Qwen3-4B-Instruct-2507-Q2_K-GGUF --hf-file qwen3-4b-instruct-2507-q2_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo enacimie/Qwen3-4B-Instruct-2507-Q2_K-GGUF --hf-file qwen3-4b-instruct-2507-q2_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo enacimie/Qwen3-4B-Instruct-2507-Q2_K-GGUF --hf-file qwen3-4b-instruct-2507-q2_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo enacimie/Qwen3-4B-Instruct-2507-Q2_K-GGUF --hf-file qwen3-4b-instruct-2507-q2_k.gguf -c 2048 ```
omerbkts/blockassist-bc-keen_fast_giraffe_1756804963	omerbkts	2025-09-02T09:23:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:23:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
duppbuy/blockassist-bc-soft_keen_trout_1756804951	duppbuy	2025-09-02T09:22:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "soft keen trout", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:22:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - soft keen trout --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1756804892	bah63843	2025-09-02T09:22:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:22:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
anocha-mcomp/lora_model	anocha-mcomp	2025-09-02T09:22:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "vi", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-02T08:49:51Z	--- base_model: unsloth/llama-3.2-1b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en - vi --- # Uploaded model - Developed by: anocha-mcomp - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756804820	Ferdi3425	2025-09-02T09:21:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:21:12Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
akirafudo/blockassist-bc-keen_fast_giraffe_1756804819	akirafudo	2025-09-02T09:20:45Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:20:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Egor-N/blockassist-bc-vicious_stubby_bear_1756803373	Egor-N	2025-09-02T09:20:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vicious stubby bear", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:20:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vicious stubby bear --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Mahimas/flant5recipegen	Mahimas	2025-09-02T09:20:22Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-02T09:20:17Z	--- 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]
TohanBoss/blockassist-bc-regal_spotted_pelican_1756804680	TohanBoss	2025-09-02T09:19:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "regal spotted pelican", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:19:08Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - regal spotted pelican --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Mactavish1996/bge-large-skills-finetuned	Mactavish1996	2025-09-02T09:19:42Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "dense", "generated_from_trainer", "dataset_size:1396", "loss:CosineSimilarityLoss", "arxiv:1908.10084", "base_model:BAAI/bge-large-en-v1.5", "base_model:finetune:BAAI/bge-large-en-v1.5", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-09-02T09:19:08Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:1396 - loss:CosineSimilarityLoss base_model: BAAI/bge-large-en-v1.5 widget: - source_sentence: Jetpack Compose sentences: - computer vision - Heroku - Pulumi - source_sentence: React Native sentences: - Express.js - Agile - Heroku - source_sentence: Cryptography sentences: - Jetpack Compose - python - Feature Engineering - source_sentence: Backend Development sentences: - Object-Oriented Programming (OOP) - redis - SwiftUI - source_sentence: jenkins sentences: - shopify - google - oracle db pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on BAAI/bge-large-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True, 'architecture': 'BertModel'}) (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("Mactavish1996/bge-large-skills-finetuned") # Run inference sentences = [ 'jenkins', 'google', 'shopify', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[1.0000, 0.3568, 0.0519], # [0.3568, 1.0000, 0.0090], # [0.0519, 0.0090, 1.0000]]) ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 1,396 training samples * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: \| \| sentence_0 \| sentence_1 \| label \| \|:--------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:----------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 3 tokens</li><li>mean: 4.32 tokens</li><li>max: 10 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 4.36 tokens</li><li>max: 10 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.24</li><li>max: 0.98</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| label \| \|:-----------------------------\|:---------------------------\|:------------------\| \| <code>Computer Vision</code> \| <code>Elasticsearch</code> \| <code>0.19</code> \| \| <code>oracle db</code> \| <code>terraform</code> \| <code>0.16</code> \| \| <code>docker</code> \| <code>linux</code> \| <code>0.33</code> \| * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 8 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 8 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs \| Epoch \| Step \| Training Loss \| \|:------:\|:----:\|:-------------:\| \| 5.6818 \| 500 \| 0.0185 \| ### Framework Versions - Python: 3.12.11 - Sentence Transformers: 5.1.0 - Transformers: 4.55.4 - PyTorch: 2.8.0+cu126 - Accelerate: 1.10.1 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
mradermacher/Hunyuan-MT-7B-GGUF	mradermacher	2025-09-02T09:19:33Z	0	1	transformers	[ "transformers", "gguf", "translation", "en", "base_model:tencent/Hunyuan-MT-7B", "base_model:quantized:tencent/Hunyuan-MT-7B", "endpoints_compatible", "region:us", "conversational" ]	translation	2025-09-02T08:08:48Z	--- base_model: tencent/Hunyuan-MT-7B language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - translation --- ## 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/tencent/Hunyuan-MT-7B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Hunyuan-MT-7B-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/Hunyuan-MT-7B-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/Hunyuan-MT-7B-GGUF/resolve/main/Hunyuan-MT-7B.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-GGUF/resolve/main/Hunyuan-MT-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-GGUF/resolve/main/Hunyuan-MT-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-GGUF/resolve/main/Hunyuan-MT-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-GGUF/resolve/main/Hunyuan-MT-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-GGUF/resolve/main/Hunyuan-MT-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-GGUF/resolve/main/Hunyuan-MT-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-GGUF/resolve/main/Hunyuan-MT-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-GGUF/resolve/main/Hunyuan-MT-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-GGUF/resolve/main/Hunyuan-MT-7B.Q6_K.gguf) \| Q6_K \| 6.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-GGUF/resolve/main/Hunyuan-MT-7B.Q8_0.gguf) \| Q8_0 \| 8.1 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Hunyuan-MT-7B-GGUF/resolve/main/Hunyuan-MT-7B.f16.gguf) \| f16 \| 15.1 \| 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 -->
enacimie/Qwen3-0.6B-Q4_K_M-GGUF	enacimie	2025-09-02T09:19:29Z	34	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-0.6B", "base_model:quantized:Qwen/Qwen3-0.6B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-29T20:49:11Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-0.6B tags: - llama-cpp - gguf-my-repo --- # enacimie/Qwen3-0.6B-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-0.6B`](https://huggingface.co/Qwen/Qwen3-0.6B) 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/Qwen/Qwen3-0.6B) 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 enacimie/Qwen3-0.6B-Q4_K_M-GGUF --hf-file qwen3-0.6b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo enacimie/Qwen3-0.6B-Q4_K_M-GGUF --hf-file qwen3-0.6b-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 enacimie/Qwen3-0.6B-Q4_K_M-GGUF --hf-file qwen3-0.6b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo enacimie/Qwen3-0.6B-Q4_K_M-GGUF --hf-file qwen3-0.6b-q4_k_m.gguf -c 2048 ```
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskToken-0.1-v2_7299	luckeciano	2025-09-02T09:19:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:DigitalLearningGmbH/MATH-lighteval", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-02T04:54:55Z	--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskToken-0.1-v2_7299 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskToken-0.1-v2_7299 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskToken-0.1-v2_7299", 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/max-ent-llms/PolicyGradientStability/runs/6kv5tw19) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.4.1 - Tokenizers: 0.21.2 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
uloppwer/blockassist-bc-territorial_fleecy_heron_1756804744	uloppwer	2025-09-02T09:19:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "territorial fleecy heron", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:19:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - territorial fleecy heron --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
duppbuy/blockassist-bc-tropical_barky_camel_1756804693	duppbuy	2025-09-02T09:18:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tropical barky camel", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:18:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tropical barky camel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1756804648	bah63843	2025-09-02T09:18:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:18:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
crim50n/varley_flux1	crim50n	2025-09-02T09:18:08Z	0	0	diffusers	[ "diffusers", "flux", "text-to-image", "lora", "fal", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-09-02T09:18:02Z	--- tags: - flux - text-to-image - lora - diffusers - fal base_model: black-forest-labs/FLUX.1-dev instance_prompt: 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 --- # varley_flux1 <Gallery /> ## Model description ## Trigger words You should use `` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/crim50n/varley_flux1/tree/main) them in the Files & versions tab. ## Training at fal.ai Training was done using [fal.ai/models/fal-ai/flux-lora-fast-training](https://fal.ai/models/fal-ai/flux-lora-fast-training).
uloppwer/blockassist-bc-furry_eager_anteater_1756804658	uloppwer	2025-09-02T09:18:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "furry eager anteater", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:17:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - furry eager anteater --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
yadav908ankit/blockassist-bc-deft_wily_armadillo_1756804481	yadav908ankit	2025-09-02T09:16:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deft wily armadillo", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:16:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deft wily armadillo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
uloppwer/blockassist-bc-amphibious_knobby_leopard_1756804572	uloppwer	2025-09-02T09:16:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious knobby leopard", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:16:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious knobby leopard --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ultramit/blockassist-bc-sturdy_mute_opossum_1756804507	ultramit	2025-09-02T09:15:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sturdy mute opossum", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:15:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sturdy mute opossum --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
TohanBoss/blockassist-bc-regal_spotted_pelican_1756804449	TohanBoss	2025-09-02T09:15:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "regal spotted pelican", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:15:15Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - regal spotted pelican --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
uloppwer/blockassist-bc-clawed_webbed_dog_1756804319	uloppwer	2025-09-02T09:12:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "clawed webbed dog", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:12:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - clawed webbed dog --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
liukevin666/blockassist-bc-yawning_striped_cassowary_1756804216	liukevin666	2025-09-02T09:11:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:11:15Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
yaelahnal/blockassist-bc-mute_clawed_crab_1756804138	yaelahnal	2025-09-02T09:11:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:09:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute clawed crab --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
uloppwer/blockassist-bc-rapid_quick_flea_1756804232	uloppwer	2025-09-02T09:10:55Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rapid quick flea", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:10:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rapid quick flea --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Raelina/Raehoshi-illust-XL-6	Raelina	2025-09-02T09:10:46Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "anime", "stable-diffusion-xl", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-09-02T07:48:44Z	--- license: other license_name: sdxl-license license_link: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/LICENSE.md language: - en tags: - text-to-image - stable-diffusion - safetensors - anime - stable-diffusion-xl --- <style> body { display: flex; align-items: center; justify-content: center; height: 100vh; margin: 0; font-family: Arial, sans-serif; background-color: #f4f4f9; overflow: auto; } .container { display: flex; flex-direction: column; align-items: center; justify-content: center; width: 100%; padding: 20px; } .title-container { display: flex; flex-direction: column; justify-content: center; align-items: center; padding: 1em; border-radius: 10px; } .title { font-size: 3em; font-family: 'Montserrat', sans-serif; text-align: center; font-weight: bold; } .title span { background: -webkit-linear-gradient(45deg, #FF9D3D, #AB886D, #CB80AB); -webkit-background-clip: text; -webkit-text-fill-color: transparent; } .gallery { display: grid; grid-template-columns: repeat(4, 1fr); gap: 10px; } .gallery img { width: 100%; height: auto; margin-top: 0px; margin-bottom: 0px; border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2); transition: transform 0.3s; } .gallery img:hover { transform: scale(1.05); } .note { font-size: 1em; opacity: 50%; text-align: center; margin-top: 20px; color: #555; } </style> <div class="container"> <div class="title-container"> <div class="title"><span>Raehoshi illust XL 6</span></div> </div> <div class="gallery"> <img src="https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/8JU06h4C_SBp3zsungBYc.png" alt="Image 1"> <img src="https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/v_W0FqleEpcnEKNw6A5Yf.png" alt="Image 2"> <img src="https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/PN6JhfVCEyJkYgqa3ED-H.png" alt="Image 3"> <img src="https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/P15Kolc3oorqKK6oMedV0.png" alt="Image 4"> </div> </div> ## Overview Introducing Raehoshi illust XL 6 , an enhanced iteration built upon the Illustrious XL model. It aims to elevate the visual style by addressing some of the limitations in the original, such as oversaturation and artifact noise. While these issues are not entirely eliminated, noticeable improvements have been made. The goal is to deliver a more polished, balanced output while staying true to the strengths of the base model. ## Model Details - Developed by: [Raelina](https://civitai.com/user/Raelina) - Model type: Diffusion-based text-to-image generative model - Finetuned from: [Illustrious XL v1.1](https://huggingface.co/OnomaAIResearch/Illustrious-XL-v1.1) - Prompt style: Booru tags ## Recommended settings - Positive prompts: ``` masterpiece, best quality, absurdres ``` - Negative prompts: ``` bad quality, worst quality, poorly drawn, sketch, multiple views, bad anatomy, bad hands, missing fingers, extra fingers, extra digits, fewer digits, signature, watermark, username ``` - CFG: 5-6 - Sampling steps: 20-28 - Sampler: Euler a - Standard Resolution: ``` 832 x 1216, 1216 x 832, 1152 x 896, 896 x 1152, 1344 x 768, 768 x 1344, 1024 x 1024 ``` - High Resolution: ``` 1024 x 1536, 832 x 1536, 1536 x 1024, 1536 x 832 ``` ## Hires.fix Setting - Upscaler: [4x_Illustration Ja Nai](https://openmodeldb.info/models/4x-IllustrationJaNai-V1-ESRGAN) - Hires step: 10-15 - Denoising: 0.1-0.3 ## Support If you’d like to support this project or my other developments, you can buy me a coffee on [Ko-Fi](https://ko-fi.com/raelina) For the list of updated knowledge, please check my [Civitai](https://civitai.com/models/846917/raehoshi-illust-xl) release
uloppwer/blockassist-bc-rugged_voracious_seal_1756804147	uloppwer	2025-09-02T09:09:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged voracious seal", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:09:07Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged voracious seal --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AXERA-TECH/DeepSeek-R1-Distill-Qwen-7B	AXERA-TECH	2025-09-02T09:09:12Z	10	1	transformers	[ "transformers", "arxiv:2501.12948", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]	null	2025-02-10T06:34:31Z	--- library_name: transformers license: bsd-3-clause --- # DeepSeek-R1-Distill-Qwen-7B-AX650 - This version of DeepSeek-R1-Distill-Qwen-7B has been converted to run on the Axera NPU using w8a16 quantization. - This model has been optimized with the following LoRA: - Compatible with Pulsar2 version: 4.2 - Due to the current quantization scheme of w8a16, the CMM consumes about 7.6GiB of memory, so a 16GiB development board is required to run. ## Useful links: [Pulsar2 Link, How to Convert LLM from Huggingface to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/appendix/build_llm.html) [AXera NPU LLM Runtime](https://github.com/AXERA-TECH/ax-llm) # Original Model Card for base model, DeepSeek-R1-Distill-Qwen-1.5B, below: # DeepSeek-R1 <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20R1-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;"> <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;"> <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE-CODE" style="margin: 2px;"> <img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE-MODEL" style="margin: 2px;"> <img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div> <p align="center"> <a href="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf"><b>Paper Link</b>👁️</a> </p> ## 1. Introduction We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning. With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors. However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models. NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the [Usage Recommendation](#usage-recommendations) section. <p align="center"> <img width="80%" src="figures/benchmark.jpg"> </p> ## 2. Model Summary --- Post-Training: Large-Scale Reinforcement Learning on the Base Model - We directly apply reinforcement learning (RL) to the base model without relying on supervised fine-tuning (SFT) as a preliminary step. This approach allows the model to explore chain-of-thought (CoT) for solving complex problems, resulting in the development of DeepSeek-R1-Zero. DeepSeek-R1-Zero demonstrates capabilities such as self-verification, reflection, and generating long CoTs, marking a significant milestone for the research community. Notably, it is the first open research to validate that reasoning capabilities of LLMs can be incentivized purely through RL, without the need for SFT. This breakthrough paves the way for future advancements in this area. - We introduce our pipeline to develop DeepSeek-R1. The pipeline incorporates two RL stages aimed at discovering improved reasoning patterns and aligning with human preferences, as well as two SFT stages that serve as the seed for the model's reasoning and non-reasoning capabilities. We believe the pipeline will benefit the industry by creating better models. --- Distillation: Smaller Models Can Be Powerful Too - We demonstrate that the reasoning patterns of larger models can be distilled into smaller models, resulting in better performance compared to the reasoning patterns discovered through RL on small models. The open source DeepSeek-R1, as well as its API, will benefit the research community to distill better smaller models in the future. - Using the reasoning data generated by DeepSeek-R1, we fine-tuned several dense models that are widely used in the research community. The evaluation results demonstrate that the distilled smaller dense models perform exceptionally well on benchmarks. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community. ## 3. Model Downloads ### DeepSeek-R1 Models <div align="center"> \| Model \| #Total Params \| #Activated Params \| Context Length \| Download \| \| :------------: \| :------------: \| :------------: \| :------------: \| :------------: \| \| DeepSeek-R1-Zero \| 671B \| 37B \| 128K \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Zero) \| \| DeepSeek-R1 \| 671B \| 37B \| 128K \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1) \| </div> DeepSeek-R1-Zero & DeepSeek-R1 are trained based on DeepSeek-V3-Base. For more details regarding the model architecture, please refer to [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repository. ### DeepSeek-R1-Distill Models <div align="center"> \| Model \| Base Model \| Download \| \| :------------: \| :------------: \| :------------: \| \| DeepSeek-R1-Distill-Qwen-1.5B \| [Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) \| \| DeepSeek-R1-Distill-Qwen-7B \| [Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) \| \| DeepSeek-R1-Distill-Llama-8B \| [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) \| \| DeepSeek-R1-Distill-Qwen-14B \| [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) \| \|DeepSeek-R1-Distill-Qwen-32B \| [Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) \| \| DeepSeek-R1-Distill-Llama-70B \| [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) \| </div> DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1. We slightly change their configs and tokenizers. Please use our setting to run these models. ## 4. Evaluation Results ### DeepSeek-R1-Evaluation For all our models, the maximum generation length is set to 32,768 tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 64 responses per query to estimate pass@1. <div align="center"> \| Category \| Benchmark (Metric) \| Claude-3.5-Sonnet-1022 \| GPT-4o 0513 \| DeepSeek V3 \| OpenAI o1-mini \| OpenAI o1-1217 \| DeepSeek R1 \| \|----------\|-------------------\|----------------------\|------------\|--------------\|----------------\|------------\|--------------\| \| \| Architecture \| - \| - \| MoE \| - \| - \| MoE \| \| \| # Activated Params \| - \| - \| 37B \| - \| - \| 37B \| \| \| # Total Params \| - \| - \| 671B \| - \| - \| 671B \| \| English \| MMLU (Pass@1) \| 88.3 \| 87.2 \| 88.5 \| 85.2 \| 91.8 \| 90.8 \| \| \| MMLU-Redux (EM) \| 88.9 \| 88.0 \| 89.1 \| 86.7 \| - \| 92.9 \| \| \| MMLU-Pro (EM) \| 78.0 \| 72.6 \| 75.9 \| 80.3 \| - \| 84.0 \| \| \| DROP (3-shot F1) \| 88.3 \| 83.7 \| 91.6 \| 83.9 \| 90.2 \| 92.2 \| \| \| IF-Eval (Prompt Strict) \| 86.5 \| 84.3 \| 86.1 \| 84.8 \| - \| 83.3 \| \| \| GPQA-Diamond (Pass@1) \| 65.0 \| 49.9 \| 59.1 \| 60.0 \| 75.7 \| 71.5 \| \| \| SimpleQA (Correct) \| 28.4 \| 38.2 \| 24.9 \| 7.0 \| 47.0 \| 30.1 \| \| \| FRAMES (Acc.) \| 72.5 \| 80.5 \| 73.3 \| 76.9 \| - \| 82.5 \| \| \| AlpacaEval2.0 (LC-winrate) \| 52.0 \| 51.1 \| 70.0 \| 57.8 \| - \| 87.6 \| \| \| ArenaHard (GPT-4-1106) \| 85.2 \| 80.4 \| 85.5 \| 92.0 \| - \| 92.3 \| \| Code \| LiveCodeBench (Pass@1-COT) \| 33.8 \| 34.2 \| - \| 53.8 \| 63.4 \| 65.9 \| \| \| Codeforces (Percentile) \| 20.3 \| 23.6 \| 58.7 \| 93.4 \| 96.6 \| 96.3 \| \| \| Codeforces (Rating) \| 717 \| 759 \| 1134 \| 1820 \| 2061 \| 2029 \| \| \| SWE Verified (Resolved) \| 50.8 \| 38.8 \| 42.0 \| 41.6 \| 48.9 \| 49.2 \| \| \| Aider-Polyglot (Acc.) \| 45.3 \| 16.0 \| 49.6 \| 32.9 \| 61.7 \| 53.3 \| \| Math \| AIME 2024 (Pass@1) \| 16.0 \| 9.3 \| 39.2 \| 63.6 \| 79.2 \| 79.8 \| \| \| MATH-500 (Pass@1) \| 78.3 \| 74.6 \| 90.2 \| 90.0 \| 96.4 \| 97.3 \| \| \| CNMO 2024 (Pass@1) \| 13.1 \| 10.8 \| 43.2 \| 67.6 \| - \| 78.8 \| \| Chinese \| CLUEWSC (EM) \| 85.4 \| 87.9 \| 90.9 \| 89.9 \| - \| 92.8 \| \| \| C-Eval (EM) \| 76.7 \| 76.0 \| 86.5 \| 68.9 \| - \| 91.8 \| \| \| C-SimpleQA (Correct) \| 55.4 \| 58.7 \| 68.0 \| 40.3 \| - \| 63.7 \| </div> ### Distilled Model Evaluation <div align="center"> \| Model \| AIME 2024 pass@1 \| AIME 2024 cons@64 \| MATH-500 pass@1 \| GPQA Diamond pass@1 \| LiveCodeBench pass@1 \| CodeForces rating \| \|------------------------------------------\|------------------\|-------------------\|-----------------\|----------------------\|----------------------\|-------------------\| \| GPT-4o-0513 \| 9.3 \| 13.4 \| 74.6 \| 49.9 \| 32.9 \| 759 \| \| Claude-3.5-Sonnet-1022 \| 16.0 \| 26.7 \| 78.3 \| 65.0 \| 38.9 \| 717 \| \| o1-mini \| 63.6 \| 80.0 \| 90.0 \| 60.0 \| 53.8 \| 1820 \| \| QwQ-32B-Preview \| 44.0 \| 60.0 \| 90.6 \| 54.5 \| 41.9 \| 1316 \| \| DeepSeek-R1-Distill-Qwen-1.5B \| 28.9 \| 52.7 \| 83.9 \| 33.8 \| 16.9 \| 954 \| \| DeepSeek-R1-Distill-Qwen-7B \| 55.5 \| 83.3 \| 92.8 \| 49.1 \| 37.6 \| 1189 \| \| DeepSeek-R1-Distill-Qwen-14B \| 69.7 \| 80.0 \| 93.9 \| 59.1 \| 53.1 \| 1481 \| \| DeepSeek-R1-Distill-Qwen-32B \| 72.6 \| 83.3 \| 94.3 \| 62.1 \| 57.2 \| 1691 \| \| DeepSeek-R1-Distill-Llama-8B \| 50.4 \| 80.0 \| 89.1 \| 49.0 \| 39.6 \| 1205 \| \| DeepSeek-R1-Distill-Llama-70B \| 70.0 \| 86.7 \| 94.5 \| 65.2 \| 57.5 \| 1633 \| </div> ## 5. Chat Website & API Platform You can chat with DeepSeek-R1 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com), and switch on the button "DeepThink" We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/) ## 6. How to Run Locally ### DeepSeek-R1 Models Please visit [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repo for more information about running DeepSeek-R1 locally. ### DeepSeek-R1-Distill Models DeepSeek-R1-Distill models can be utilized in the same manner as Qwen or Llama models. For instance, you can easily start a service using [vLLM](https://github.com/vllm-project/vllm): ```shell vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --tensor-parallel-size 2 --max-model-len 32768 --enforce-eager ``` You can also easily start a service using [SGLang](https://github.com/sgl-project/sglang) ```bash python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --trust-remote-code --tp 2 ``` ### Usage Recommendations We recommend adhering to the following configurations when utilizing the DeepSeek-R1 series models, including benchmarking, to achieve the expected performance: 1. Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs. 2. Avoid adding a system prompt; all instructions should be contained within the user prompt. 3. For mathematical problems, it is advisable to include a directive in your prompt such as: "Please reason step by step, and put your final answer within \boxed{}." 4. When evaluating model performance, it is recommended to conduct multiple tests and average the results. ## 7. License This code repository and the model weights are licensed under the [MIT License](https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE). DeepSeek-R1 series support commercial use, allow for any modifications and derivative works, including, but not limited to, distillation for training other LLMs. Please note that: - DeepSeek-R1-Distill-Qwen-1.5B, DeepSeek-R1-Distill-Qwen-7B, DeepSeek-R1-Distill-Qwen-14B and DeepSeek-R1-Distill-Qwen-32B are derived from [Qwen-2.5 series](https://github.com/QwenLM/Qwen2.5), which are originally licensed under [Apache 2.0 License](https://huggingface.co/Qwen/Qwen2.5-1.5B/blob/main/LICENSE), and now finetuned with 800k samples curated with DeepSeek-R1. - DeepSeek-R1-Distill-Llama-8B is derived from Llama3.1-8B-Base and is originally licensed under [llama3.1 license](https://huggingface.co/meta-llama/Llama-3.1-8B/blob/main/LICENSE). - DeepSeek-R1-Distill-Llama-70B is derived from Llama3.3-70B-Instruct and is originally licensed under [llama3.3 license](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct/blob/main/LICENSE). ## 8. Citation ``` @misc{deepseekai2025deepseekr1incentivizingreasoningcapability, title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning}, author={DeepSeek-AI and Daya Guo and Dejian Yang and Haowei Zhang and Junxiao Song and Ruoyu Zhang and Runxin Xu and Qihao Zhu and Shirong Ma and Peiyi Wang and Xiao Bi and Xiaokang Zhang and Xingkai Yu and Yu Wu and Z. F. Wu and Zhibin Gou and Zhihong Shao and Zhuoshu Li and Ziyi Gao and Aixin Liu and Bing Xue and Bingxuan Wang and Bochao Wu and Bei Feng and Chengda Lu and Chenggang Zhao and Chengqi Deng and Chenyu Zhang and Chong Ruan and Damai Dai and Deli Chen and Dongjie Ji and Erhang Li and Fangyun Lin and Fucong Dai and Fuli Luo and Guangbo Hao and Guanting Chen and Guowei Li and H. Zhang and Han Bao and Hanwei Xu and Haocheng Wang and Honghui Ding and Huajian Xin and Huazuo Gao and Hui Qu and Hui Li and Jianzhong Guo and Jiashi Li and Jiawei Wang and Jingchang Chen and Jingyang Yuan and Junjie Qiu and Junlong Li and J. L. Cai and Jiaqi Ni and Jian Liang and Jin Chen and Kai Dong and Kai Hu and Kaige Gao and Kang Guan and Kexin Huang and Kuai Yu and Lean Wang and Lecong Zhang and Liang Zhao and Litong Wang and Liyue Zhang and Lei Xu and Leyi Xia and Mingchuan Zhang and Minghua Zhang and Minghui Tang and Meng Li and Miaojun Wang and Mingming Li and Ning Tian and Panpan Huang and Peng Zhang and Qiancheng Wang and Qinyu Chen and Qiushi Du and Ruiqi Ge and Ruisong Zhang and Ruizhe Pan and Runji Wang and R. J. Chen and R. L. Jin and Ruyi Chen and Shanghao Lu and Shangyan Zhou and Shanhuang Chen and Shengfeng Ye and Shiyu Wang and Shuiping Yu and Shunfeng Zhou and Shuting Pan and S. S. Li and Shuang Zhou and Shaoqing Wu and Shengfeng Ye and Tao Yun and Tian Pei and Tianyu Sun and T. Wang and Wangding Zeng and Wanjia Zhao and Wen Liu and Wenfeng Liang and Wenjun Gao and Wenqin Yu and Wentao Zhang and W. L. Xiao and Wei An and Xiaodong Liu and Xiaohan Wang and Xiaokang Chen and Xiaotao Nie and Xin Cheng and Xin Liu and Xin Xie and Xingchao Liu and Xinyu Yang and Xinyuan Li and Xuecheng Su and Xuheng Lin and X. Q. Li and Xiangyue Jin and Xiaojin Shen and Xiaosha Chen and Xiaowen Sun and Xiaoxiang Wang and Xinnan Song and Xinyi Zhou and Xianzu Wang and Xinxia Shan and Y. K. Li and Y. Q. Wang and Y. X. Wei and Yang Zhang and Yanhong Xu and Yao Li and Yao Zhao and Yaofeng Sun and Yaohui Wang and Yi Yu and Yichao Zhang and Yifan Shi and Yiliang Xiong and Ying He and Yishi Piao and Yisong Wang and Yixuan Tan and Yiyang Ma and Yiyuan Liu and Yongqiang Guo and Yuan Ou and Yuduan Wang and Yue Gong and Yuheng Zou and Yujia He and Yunfan Xiong and Yuxiang Luo and Yuxiang You and Yuxuan Liu and Yuyang Zhou and Y. X. Zhu and Yanhong Xu and Yanping Huang and Yaohui Li and Yi Zheng and Yuchen Zhu and Yunxian Ma and Ying Tang and Yukun Zha and Yuting Yan and Z. Z. Ren and Zehui Ren and Zhangli Sha and Zhe Fu and Zhean Xu and Zhenda Xie and Zhengyan Zhang and Zhewen Hao and Zhicheng Ma and Zhigang Yan and Zhiyu Wu and Zihui Gu and Zijia Zhu and Zijun Liu and Zilin Li and Ziwei Xie and Ziyang Song and Zizheng Pan and Zhen Huang and Zhipeng Xu and Zhongyu Zhang and Zhen Zhang}, year={2025}, eprint={2501.12948}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2501.12948}, } ``` ## 9. Contact If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
xinnn32/blockassist-bc-meek_winged_caterpillar_1756804034	xinnn32	2025-09-02T09:08:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:08:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - meek winged caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
milliarderdol/blockassist-bc-roaring_rough_scorpion_1756801932	milliarderdol	2025-09-02T09:08:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring rough scorpion", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:08:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring rough scorpion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
TohanBoss/blockassist-bc-regal_spotted_pelican_1756803977	TohanBoss	2025-09-02T09:08:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "regal spotted pelican", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:07:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - regal spotted pelican --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
gerbejon/longcoder-html-nodes-fc-classifier-v1	gerbejon	2025-09-02T09:06:46Z	0	0	transformers	[ "transformers", "safetensors", "longformer", "text-classification", "generated_from_trainer", "base_model:microsoft/longcoder-base", "base_model:finetune:microsoft/longcoder-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-19T08:25:30Z	--- library_name: transformers base_model: microsoft/longcoder-base tags: - generated_from_trainer model-index: - name: longcoder-html-nodes-fc-classifier-v1 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. --> # longcoder-html-nodes-fc-classifier-v1 This model is a fine-tuned version of [microsoft/longcoder-base](https://huggingface.co/microsoft/longcoder-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4378 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 0.3061 \| 1.0 \| 160111 \| 1.4378 \| \| 0.277 \| 2.0 \| 320222 \| 1.5381 \| \| 0.1814 \| 3.0 \| 480333 \| 1.6397 \| ### Framework versions - Transformers 4.55.0 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
uloppwer/blockassist-bc-padded_scampering_seal_1756803976	uloppwer	2025-09-02T09:06:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "padded scampering seal", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:06:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - padded scampering seal --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kokiy365/Diffusion-KanjiGrade1	kokiy365	2025-09-02T09:05:26Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "lora", "template:diffusion-lora", "base_model:CompVis/stable-diffusion-v1-4", "base_model:adapter:CompVis/stable-diffusion-v1-4", "license:apache-2.0", "region:us" ]	text-to-image	2025-09-02T08:56:12Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/tree.jpg text: Screenshot base_model: CompVis/stable-diffusion-v1-4 instance_prompt: null license: apache-2.0 --- # Diffusion-KanjiGrade1 Diffusion-KanjiGrade1 is a fine-tuned Stable Diffusion model trained on 80 curated Kanji samples. The dataset was derived from KANJIDIC2, filtered to include Grade 1 characters with stroke counts <10 and representing the top 0.05 most frequent words. Images were rendered at 128×128 resolution, with captions containing only the English meaning(s) of each Kanji. Training prompts included both single-word and multi-word meanings. # Training Configuration - Base model: `CompVis/stable-diffusion-v1-4` - LoRA rank: 32 - Alpha: 4 - Batch size: 1 - Learning rate: 1.0e-04 - LR scheduler: cosine - Checkpointing steps: 3000 - Total training steps: 3000 # Sample Generation prompt = "river" <Gallery />
happyensworld/blockassist-bc-sleek_scavenging_ram_1756803823	happyensworld	2025-09-02T09:05:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sleek scavenging ram", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:04:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sleek scavenging ram --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Stoneandambition/ppo-LunarLander-v3	Stoneandambition	2025-09-02T09:04:57Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-09-02T09:04:43Z	--- library_name: stable-baselines3 tags: - LunarLander-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v3 type: LunarLander-v3 metrics: - type: mean_reward value: 267.75 +/- 25.33 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v3 This is a trained model of a PPO agent playing LunarLander-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
omerbkts/blockassist-bc-keen_fast_giraffe_1756803819	omerbkts	2025-09-02T09:04:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:04:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
uloppwer/blockassist-bc-arctic_giant_ape_1756803804	uloppwer	2025-09-02T09:03:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "arctic giant ape", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:03:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - arctic giant ape --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
koloni/blockassist-bc-deadly_graceful_stingray_1756802195	koloni	2025-09-02T09:02:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:02:53Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
yadav908ankit/blockassist-bc-deft_wily_armadillo_1756803668	yadav908ankit	2025-09-02T09:02:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deft wily armadillo", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:02:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deft wily armadillo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
armeiski/ppo-SnowballTarget	armeiski	2025-09-02T09:02:23Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2025-09-02T09:02:19Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: armeiski/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
acidjp/blockassist-bc-pesty_extinct_prawn_1756801092	acidjp	2025-09-02T09:01:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pesty extinct prawn", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:01:30Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pesty extinct prawn --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bah63843/blockassist-bc-plump_fast_antelope_1756803645	bah63843	2025-09-02T09:01:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "plump fast antelope", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:01:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - plump fast antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
GroomerG/blockassist-bc-vicious_pawing_badger_1756802057	GroomerG	2025-09-02T09:00:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vicious pawing badger", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:00:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vicious pawing badger --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AnerYubo/blockassist-bc-lanky_pouncing_ape_1756803636	AnerYubo	2025-09-02T09:00:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lanky pouncing ape", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:00:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lanky pouncing ape --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
javdrher/decide-decision-classifier	javdrher	2025-09-02T09:00:11Z	4	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-01T21:19:44Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: decide-decision-classifier 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. --> # decide-decision-classifier This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4891 - Accuracy: 0.8295 - Precision: 0.7973 - Recall: 0.8295 - F1: 0.8130 ## 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: 32 - eval_batch_size: 32 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 37 \| 0.5306 \| 0.8140 \| 0.7841 \| 0.8140 \| 0.7986 \| \| No log \| 2.0 \| 74 \| 0.4891 \| 0.8295 \| 0.7973 \| 0.8295 \| 0.8130 \| ### Framework versions - Transformers 4.56.0 - Pytorch 2.8.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.0
omerbektass/blockassist-bc-keen_fast_giraffe_1756803586	omerbektass	2025-09-02T09:00:11Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T09:00:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
zlBareta/Caitlyn	zlBareta	2025-09-02T09:00:00Z	0	0	null	[ "license:afl-3.0", "region:us" ]	null	2025-09-02T08:58:35Z	--- license: afl-3.0 --- AI influencer walking from private jet to luxury car, designer outfit, stylish sunglasses, golden hour light, ultra photorealistic, cinematic composition
adarsh09singh/outputs-scratch	adarsh09singh	2025-09-02T08:58:52Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:HuggingFaceTB/SmolLM2-135M-Instruct", "lora", "transformers", "base_model:HuggingFaceTB/SmolLM2-135M-Instruct", "license:apache-2.0", "region:us" ]	null	2025-09-02T08:58:46Z	--- library_name: peft license: apache-2.0 base_model: HuggingFaceTB/SmolLM2-135M-Instruct tags: - base_model:adapter:HuggingFaceTB/SmolLM2-135M-Instruct - lora - transformers model-index: - name: outputs-scratch 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. --> # outputs-scratch This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5314 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 3407 - optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6219 \| 1.0 \| 50 \| 0.5637 \| \| 0.585 \| 2.0 \| 100 \| 0.5360 \| \| 0.564 \| 3.0 \| 150 \| 0.5314 \| ### Framework versions - PEFT 0.17.1 - Transformers 4.56.0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0
abdulsamad99/CONSTITUTION_us-fine-tuning	abdulsamad99	2025-09-02T08:58:50Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:unsloth/phi-4-unsloth-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "text-generation", "conversational", "arxiv:1910.09700", "base_model:unsloth/phi-4-unsloth-bnb-4bit", "region:us" ]	text-generation	2025-09-02T08:58:27Z	--- base_model: unsloth/phi-4-unsloth-bnb-4bit library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/phi-4-unsloth-bnb-4bit - lora - sft - transformers - trl - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.1
2hpsatt/blockassist-bc-huge_deft_eagle_1756803365	2hpsatt	2025-09-02T08:57:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge deft eagle", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T08:56:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge deft eagle --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sekirr/blockassist-bc-masked_tenacious_whale_1756803350	sekirr	2025-09-02T08:56:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "masked tenacious whale", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T08:56:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - masked tenacious whale --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rfsdfsd/blockassist-bc-grunting_cunning_tortoise_1756801855	rfsdfsd	2025-09-02T08:55:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "grunting cunning tortoise", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T08:55:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - grunting cunning tortoise --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
omerbektass/blockassist-bc-keen_fast_giraffe_1756803206	omerbektass	2025-09-02T08:53:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T08:53:47Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
happyensworld/blockassist-bc-sleek_scavenging_ram_1756803098	happyensworld	2025-09-02T08:53:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sleek scavenging ram", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T08:52:49Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sleek scavenging ram --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756803074	Ferdi3425	2025-09-02T08:52:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-09-02T08:52:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
io-taas/500888c5-a2e4-4a47-98ef-bf600d7147ec	io-taas	2025-09-02T08:52:22Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-02T08:51:41Z	--- library_name: transformers tags: - llama-factory --- # 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]

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