librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-08-29 00:38:39	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 525 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-29 00:38:28	card stringlengths 11 1.01M
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756322750	ggozzy	2025-08-27T19:27:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T19:26:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mohammadmahdinouri/bert_baseline_30k	mohammadmahdinouri	2025-08-27T19:25:14Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-08-27T19:25:07Z	--- 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]
crystalline7/1531294	crystalline7	2025-08-27T19:24:25Z	0	0	null	[ "region:us" ]	null	2025-08-27T19:24:22Z	[View on Civ Archive](https://civarchive.com/models/1442770?modelVersionId=1630983)
crystalline7/1906119	crystalline7	2025-08-27T19:23:08Z	0	0	null	[ "region:us" ]	null	2025-08-27T19:23:05Z	[View on Civ Archive](https://civarchive.com/models/1775186?modelVersionId=2009103)
Yanzeisi/Llama3.1-8b-Instruct-sft-Aug-27	Yanzeisi	2025-08-27T19:21:52Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-08-27T14:01:31Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: transformers model_name: Llama3.1-8b-Instruct-sft-Aug-27 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for Llama3.1-8b-Instruct-sft-Aug-27 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-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="Yanzeisi/Llama3.1-8b-Instruct-sft-Aug-27", 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/yanzewan-usc/huggingface/runs/7qbj2pd6) This model was trained with SFT. ### Framework versions - TRL: 0.20.0.dev0 - Transformers: 4.53.2 - Pytorch: 2.7.1+cu118 - Datasets: 3.4.1 - Tokenizers: 0.21.0 ## 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}} } ```
fopppyu/blockassist-bc-patterned_monstrous_boar_1756322199	fopppyu	2025-08-27T19:16:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "patterned monstrous boar", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T19:16:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - patterned monstrous boar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AllOfWhich/oil	AllOfWhich	2025-08-27T19:05:53Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-08-27T19:03:55Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/df0r4a3-ffbbd57b-89d6-4dae-9060-9f79d5de1a5e.png text: '-' base_model: black-forest-labs/FLUX.1-dev instance_prompt: null --- # oil <Gallery /> ## Download model [Download](/AllOfWhich/oil/tree/main) them in the Files & versions tab.
motza0025/blockassist-bc-scampering_scaly_salmon_1756318963	motza0025	2025-08-27T18:48:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scampering scaly salmon", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T18:48:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scampering scaly salmon --- # 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_1756320438	liukevin666	2025-08-27T18:48:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T18:48:19Z	--- 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).
pbouda/finetune-cpt-test	pbouda	2025-08-27T18:42:23Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-v0.3", "base_model:finetune:unsloth/mistral-7b-v0.3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-27T08:36:29Z	--- base_model: unsloth/mistral-7b-v0.3 tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: pbouda - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
OksanaB/blockassist-bc-huge_ferocious_chameleon_1756319843	OksanaB	2025-08-27T18:38:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T18:38:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge ferocious chameleon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
michhell/blockassist-bc-stalking_running_albatross_1756317998	michhell	2025-08-27T18:37:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stalking running albatross", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T18:37:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stalking running albatross --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
apriasmoro/28860f3c-5409-4cc2-b2ad-38a891dc636d	apriasmoro	2025-08-27T18:34:41Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", "base_model:adapter:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", "region:us" ]	null	2025-08-27T18:34:15Z	--- base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
happyensworld/blockassist-bc-sleek_scavenging_ram_1756319527	happyensworld	2025-08-27T18:32:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sleek scavenging ram", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T18:32:44Z	--- 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).
brown-girl-Viral-video-original-XX-Clips/New.full.videos.brown.girl.Viral.Video.Official.Tutorial	brown-girl-Viral-video-original-XX-Clips	2025-08-27T18:27:57Z	0	0	null	[ "region:us" ]	null	2025-08-27T18:27:40Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/mdfprj9k?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF	mradermacher	2025-08-27T18:25:34Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:misaeboca/TinyLlama-1.1B-Chat-finetune", "base_model:quantized:misaeboca/TinyLlama-1.1B-Chat-finetune", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-27T18:12:02Z	--- base_model: misaeboca/TinyLlama-1.1B-Chat-finetune language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/misaeboca/TinyLlama-1.1B-Chat-finetune <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#TinyLlama-1.1B-Chat-finetune-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/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q2_K.gguf) \| Q2_K \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q3_K_S.gguf) \| Q3_K_S \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q3_K_M.gguf) \| Q3_K_M \| 0.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q3_K_L.gguf) \| Q3_K_L \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.IQ4_XS.gguf) \| IQ4_XS \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q4_K_S.gguf) \| Q4_K_S \| 0.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q4_K_M.gguf) \| Q4_K_M \| 0.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q5_K_S.gguf) \| Q5_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q5_K_M.gguf) \| Q5_K_M \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q6_K.gguf) \| Q6_K \| 1.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q8_0.gguf) \| Q8_0 \| 1.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.f16.gguf) \| f16 \| 2.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
AnerYubo/blockassist-bc-mangy_quiet_anteater_1756319068	AnerYubo	2025-08-27T18:24:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mangy quiet anteater", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T18:24:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mangy quiet anteater --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Vortex5/StarlitSage-12B-Q6_K-GGUF	Vortex5	2025-08-27T18:20:00Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:Vortex5/StarlitSage-12B", "base_model:quantized:Vortex5/StarlitSage-12B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-27T18:19:18Z	--- base_model: Vortex5/StarlitSage-12B library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Vortex5/StarlitSage-12B-Q6_K-GGUF This model was converted to GGUF format from [`Vortex5/StarlitSage-12B`](https://huggingface.co/Vortex5/StarlitSage-12B) 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/Vortex5/StarlitSage-12B) 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 Vortex5/StarlitSage-12B-Q6_K-GGUF --hf-file starlitsage-12b-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Vortex5/StarlitSage-12B-Q6_K-GGUF --hf-file starlitsage-12b-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Vortex5/StarlitSage-12B-Q6_K-GGUF --hf-file starlitsage-12b-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Vortex5/StarlitSage-12B-Q6_K-GGUF --hf-file starlitsage-12b-q6_k.gguf -c 2048 ```
majid230/gemma-3-1b-finetune	majid230	2025-08-27T18:19:51Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gemma-3-1b-it", "base_model:finetune:unsloth/gemma-3-1b-it", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-27T18:16:18Z	--- base_model: unsloth/gemma-3-1b-it tags: - text-generation-inference - transformers - unsloth - gemma3_text license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: majid230 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-it This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756318515	ggozzy	2025-08-27T18:16:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T18:16:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756317786	Ferdi3425	2025-08-27T18:03:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T18:03:32Z	--- 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).
calegpedia/blockassist-bc-stealthy_slimy_rooster_1756315617	calegpedia	2025-08-27T17:53:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T17:53:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stealthy slimy rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756316998	Ferdi3425	2025-08-27T17:50:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T17:50:27Z	--- 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).
Matt300209/8B-tulu-sft-bf16	Matt300209	2025-08-27T17:42:12Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "dataset:allenai/tulu-3-sft-mixture", "arxiv:2411.15124", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-27T14:12:35Z	--- license: llama3.1 language: - en pipeline_tag: text-generation datasets: - allenai/tulu-3-sft-mixture base_model: - meta-llama/Llama-3.1-8B library_name: transformers --- <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu3/Tulu3-logo.png" alt="Tulu 3 banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Llama-3.1-Tulu-3-8B-SFT Tülu3 is a leading instruction following model family, offering fully open-source data, code, and recipes designed to serve as a comprehensive guide for modern post-training techniques. Tülu3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval. ## Model description - Model type: A model trained on a mix of publicly available, synthetic and human-created datasets. - Language(s) (NLP): Primarily English - License: Llama 3.1 Community License Agreement - Finetuned from model: meta-llama/Llama-3.1-8B ### Model Sources - Training Repository: https://github.com/allenai/open-instruct - Eval Repository: https://github.com/allenai/olmes - Paper: https://arxiv.org/abs/2411.15124 - Demo: https://playground.allenai.org/ ### Model Family \| Stage \| Llama 3.1 8B \| Llama 3.1 70B \| \|----------------------\|----------------------------------------------------------------------------------------------------------\|----------------------------------------------------------------------------------------------------------\| \| Base Model \| [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) \| [meta-llama/Llama-3.1-70B](https://huggingface.co/meta-llama/Llama-3.1-70B) \| \| SFT \| [allenai/Llama-3.1-Tulu-3-8B-SFT](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-SFT) \| [allenai/Llama-3.1-Tulu-3-70B-SFT](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-SFT) \| \| DPO \| [allenai/Llama-3.1-Tulu-3-8B-DPO](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-DPO) \| [allenai/Llama-3.1-Tulu-3-70B-DPO](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-DPO) \| \| Final Models (RLVR) \| [allenai/Llama-3.1-Tulu-3-8B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B) \| [allenai/Llama-3.1-Tulu-3-70B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B) \| \| Reward Model (RM)\| [allenai/Llama-3.1-Tulu-3-8B-RM](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-RM) \| (Same as 8B) \| \| Stage \| Llama 3.1 405B \| \|-----------\|-------------------\| \| Base Model \| [meta-llama/llama-3.1-405B](https://huggingface.co/meta-llama/llama-3.1-405B) \| \| SFT \| [allenai/llama-3.1-Tulu-3-405B-SFT](https://huggingface.co/allenai/llama-3.1-Tulu-3-405B-SFT) \| \| DPO \| [allenai/llama-3.1-Tulu-3-405B-DPO](https://huggingface.co/allenai/llama-3.1-Tulu-3-405B-DPO) \| \| Final Model (RLVR) \| [allenai/llama-3.1-Tulu-3-405B](https://huggingface.co/allenai/llama-3.1-Tulu-3-405B) \| \| Reward Model (RM)\| (Same as 8B) ## Using the model ### Loading with HuggingFace To load the model with HuggingFace, use the following snippet: ``` from transformers import AutoModelForCausalLM tulu_model = AutoModelForCausalLM.from_pretrained("allenai/Llama-3.1-Tulu-3-8B-SFT") ``` ### VLLM As a Llama base model, the model can be easily served with: ``` vllm serve allenai/Llama-3.1-Tulu-3-8B-SFT ``` Note that given the long chat template of Llama, you may want to use `--max_model_len=8192`. ### Chat template The chat template for our models is formatted as: ``` <\|user\|>\nHow are you doing?\n<\|assistant\|>\nI'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<\|endoftext\|> ``` Or with new lines expanded: ``` <\|user\|> How are you doing? <\|assistant\|> I'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<\|endoftext\|> ``` It is embedded within the tokenizer as well, for `tokenizer.apply_chat_template`. ### System prompt In Ai2 demos, we use this system prompt by default: ``` You are Tulu 3, a helpful and harmless AI Assistant built by the Allen Institute for AI. ``` The model has not been trained with a specific system prompt in mind. ### Bias, Risks, and Limitations The Tülu3 models have limited safety training, but are not deployed automatically with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base Llama 3.1 models, however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this. ## Performance \| Benchmark (eval) \| Tülu 3 SFT 8B \| Tülu 3 DPO 8B \| Tülu 3 8B \| Llama 3.1 8B Instruct \| Qwen 2.5 7B Instruct \| Magpie 8B \| Gemma 2 9B Instruct \| Ministral 8B Instruct \| \|---------------------------------\|----------------\|----------------\|------------\|------------------------\|----------------------\|-----------\|---------------------\|-----------------------\| \| Avg. \| 60.4 \| 64.4 \| 64.8 \| 62.2 \| 57.8 \| 44.7 \| 55.2 \| 58.3 \| \| MMLU (0 shot, CoT) \| 65.9 \| 68.7 \| 68.2 \| 71.2 \| 76.6 \| 62.0 \| 74.6 \| 68.5 \| \| PopQA (15 shot) \| 29.3 \| 29.3 \| 29.1 \| 20.2 \| 18.1 \| 22.5 \| 28.3 \| 20.2 \| \| TruthfulQA (6 shot) \| 46.8 \| 56.1 \| 55.0 \| 55.1 \| 63.1 \| 57.0 \| 61.4 \| 55.5 \| \| BigBenchHard (3 shot, CoT) \| 67.9 \| 65.8 \| 66.0 \| 62.8 \| 21.7 \| 0.9 \| 2.5 \| 56.2 \| \| DROP (3 shot) \| 61.3 \| 62.5 \| 62.6 \| 61.5 \| 54.4 \| 49.4 \| 58.8 \| 56.2 \| \| MATH (4 shot CoT, Flex) \| 31.5 \| 42.0 \| 43.7 \| 42.5 \| 14.8 \| 5.1 \| 29.8 \| 40.0 \| \| GSM8K (8 shot, CoT) \| 76.2 \| 84.3 \| 87.6 \| 83.4 \| 83.8 \| 61.2 \| 79.7 \| 80.0 \| \| HumanEval (pass@10) \| 86.2 \| 83.9 \| 83.9 \| 86.3 \| 93.1 \| 75.4 \| 71.7 \| 91.0 \| \| HumanEval+ (pass@10) \| 81.4 \| 78.6 \| 79.2 \| 82.9 \| 89.7 \| 69.1 \| 67.0 \| 88.5 \| \| IFEval (prompt loose) \| 72.8 \| 81.1 \| 82.4 \| 80.6 \| 74.7 \| 38.8 \| 69.9 \| 56.4 \| \| AlpacaEval 2 (LC % win) \| 12.4 \| 33.5 \| 34.5 \| 24.2 \| 29.0 \| 49.0 \| 43.7 \| 31.4 \| \| Safety (6 task avg.) \| 93.1 \| 87.2 \| 85.5 \| 75.2 \| 75.0 \| 46.4 \| 75.5 \| 56.2 \| \| Benchmark (eval) \| Tülu 3 70B SFT \| Tülu 3 DPO 70B \| Tülu 3 70B \| Llama 3.1 70B Instruct \| Qwen 2.5 72B Instruct \| Hermes 3 Llama 3.1 70B \| Nemotron Llama 3.1 70B \| \|---------------------------------\|-----------------\|-----------------\|-------------\|-------------------------\|-----------------------\|------------------------\|-------------------------\| \| Avg. \| 72.6 \| 75.9 \| 76.0 \| 73.4 \| 71.5 \| 68.3 \| 65.5 \| \| MMLU (0 shot, CoT) \| 78.9 \| 83.3 \| 83.1 \| 85.3 \| 85.5 \| 80.4 \| 83.8 \| \| PopQA (15 shot) \| 48.6 \| 46.3 \| 46.5 \| 46.4 \| 30.6 \| 48.1 \| 36.4 \| \| TruthfulQA (6 shot) \| 55.7 \| 67.9 \| 67.6 \| 66.8 \| 69.9 \| 66.5 \| 62.6 \| \| BigBenchHard (3 shot, CoT) \| 82.7 \| 81.8 \| 82.0 \| 73.8 \| 67.2 \| 82.1 \| 0.7 \| \| DROP (3 shot) \| 77.2 \| 74.1 \| 74.3 \| 77.0 \| 34.2 \| 73.2 \| 68.8 \| \| MATH (4 shot CoT, Flex) \| 53.7 \| 62.3 \| 63.0 \| 56.4 \| 74.3 \| 41.9 \| 55.0 \| \| GSM8K (8 shot, CoT) \| 91.1 \| 93.5 \| 93.5 \| 93.7 \| 89.5 \| 90.0 \| 84.7 \| \| HumanEval (pass@10) \| 92.9 \| 92.4 \| 92.4 \| 93.6 \| 94.0 \| 89.6 \| 94.1 \| \| HumanEval+ (pass@10) \| 87.3 \| 88.4 \| 88.0 \| 89.5 \| 90.8 \| 85.9 \| 85.5 \| \| IFEval (prompt loose) \| 82.1 \| 82.6 \| 83.2 \| 88.0 \| 87.6 \| 76.0 \| 79.9 \| \| AlpacaEval 2 (LC % win) \| 26.3 \| 49.6 \| 49.8 \| 33.4 \| 47.7 \| 28.4 \| 66.1 \| \| Safety (6 task avg.) \| 94.4 \| 89.0 \| 88.3 \| 76.5 \| 87.0 \| 57.9 \| 69.0 \| \| Benchmark (eval) \| Tülu 3 405B SFT \| Tülu 3 405B DPO \| Tülu 3 405B \| Llama 3.1 405B Instruct \| Nous Hermes 3 405B \| Deepseek V3 \| GPT 4o (11-24) \| \|-----------------\|----------------\|----------------\|-------------\|------------------------\|-------------------\|-------------\|----------------\| \| Avg w/o Safety \| 76.3 \| 79.0 \| 80.0 \| 78.1 \| 74.4 \| 79.0 \| 80.5 \| \| Avg w/ Safety \| 77.5 \| 79.6 \| 80.7 \| 79.0 \| 73.5 \| 75.9 \| 81.6 \| \| MMLU (5 shot, CoT) \| 84.4 \| 86.6 \| 87.0 \| 88.0 \| 84.9 \| 82.1 \| 87.9 \| \| PopQA (3 shot) \| 55.7 \| 55.4 \| 55.5 \| 52.9 \| 54.2 \| 44.9 \| 53.6 \| \| BigBenchHard (0 shot, CoT) \| 88.0 \| 88.8 \| 88.6 \| 87.1 \| 87.7 \| 89.5 \| 83.3 \| \| MATH (4 shot, Flex) \| 63.4 \| 59.9 \| 67.3 \| 66.6 \| 58.4 \| 72.5 \| 68.8 \| \| GSM8K (8 shot, CoT) \| 93.6 \| 94.2 \| 95.5 \| 95.4 \| 92.7 \| 94.1 \| 91.7 \| \| HumanEval (pass@10) \| 95.7 \| 97.2 \| 95.9 \| 95.9 \| 92.3 \| 94.6 \| 97.0 \| \| HumanEval+ (pass@10) \| 93.3 \| 93.9 \| 92.9 \| 90.3 \| 86.9 \| 91.6 \| 92.7 \| \| IFEval (prompt loose) \| 82.4 \| 85.0 \| 86.0 \| 88.4 \| 81.9 \| 88.0 \| 84.8 \| \| AlpacaEval 2 (LC % win) \| 30.4 \| 49.8 \| 51.4 \| 38.5 \| 30.2 \| 53.5 \| 65.0 \| \| Safety (6 task avg.) \| 87.7 \| 85.5 \| 86.7 \| 86.8 \| 65.8 \| 72.2 \| 90.9 \| ## Hyperparamters SFT: - Learning Rate: 5E-6 (8B), 2E-6 (70B, 405B) - Effective Batch Size: 128 (8B, 70B), 256 (405B) - Max. Sequence Length: 4096 - Loss Accumulation: Sum (see https://unsloth.ai/blog/gradient) - Learning Rate Schedule: Linear - LR Warmup Ratio: 0.03 - Num. Epochs: 2 ## License and use All Llama 3.1 Tülu3 models are released under Meta's [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/). Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. Tülu3 is intended for research and educational use. For more information, please see our [Responsible Use Guidelines](https://allenai.org/responsible-use). ## Citation If Tülu3 or any of the related materials were helpful to your work, please cite: ``` @article{lambert2024tulu3, title = {Tülu 3: Pushing Frontiers in Open Language Model Post-Training}, author = { Nathan Lambert and Jacob Morrison and Valentina Pyatkin and Shengyi Huang and Hamish Ivison and Faeze Brahman and Lester James V. Miranda and Alisa Liu and Nouha Dziri and Shane Lyu and Yuling Gu and Saumya Malik and Victoria Graf and Jena D. Hwang and Jiangjiang Yang and Ronan Le Bras and Oyvind Tafjord and Chris Wilhelm and Luca Soldaini and Noah A. Smith and Yizhong Wang and Pradeep Dasigi and Hannaneh Hajishirzi }, year = {2024}, email = {tulu@allenai.org} } ```
fujiantiiazhraa/blockassist-bc-marine_robust_bee_1756314703	fujiantiiazhraa	2025-08-27T17:36:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "marine robust bee", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T17:36:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - marine robust bee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
tphage/Llama-3B-SFT-250827	tphage	2025-08-27T17:33:33Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-27T17:33:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
amd/Phi-3-mini-4k-instruct-awq-g128-int4-asym-fp16-onnx-hybrid	amd	2025-08-27T17:32:35Z	187	0	null	[ "onnx", "nlp", "code", "amd", "ryzenai-hybrid", "text-generation", "conversational", "en", "fr", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:quantized:microsoft/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]	text-generation	2024-11-30T06:32:13Z	--- license: mit language: - en - fr base_model: - microsoft/Phi-3-mini-4k-instruct pipeline_tag: text-generation tags: - nlp - code - onnx - amd - ryzenai-hybrid --- # microsoft/Phi-3-mini-4k-instruct - ## Introduction This model was prepared using the AMD Quark Quantization tool, followed by necessary post-processing. - ## Quantization Strategy - AWQ / Group 128 / Asymmetric / UINT4 Weights / FP16 activations - Excluded Layers: None - ## Quick Start For quickstart, refer to [Ryzen AI doucmentation](https://ryzenai.docs.amd.com/en/latest/hybrid_oga.html) #### Evaluation scores The perplexity measurement is run on the wikitext-2-raw-v1 (raw data) dataset provided by Hugging Face. Perplexity score measured for prompt length 2k is 6.7532. #### License Modifications copyright(c) 2024 Advanced Micro Devices,Inc. All rights reserved. MIT License Copyright (c) 2024 Advanced Micro Devices, Inc Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. license: MIT license
ASSISTA-VIDEO-DO-SURFISTA-VAZADO-VIDEO/18.VIDEOS.DO.SURFISTA.VAZADO.VIDEO.SEXO.DO.SURFISTA.NO.BANHEIRO.SURFISTA.MANSAO.PRIVILEGE.EROME	ASSISTA-VIDEO-DO-SURFISTA-VAZADO-VIDEO	2025-08-27T17:22:00Z	0	0	null	[ "region:us" ]	null	2025-08-27T17:21:21Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
xinnn32/blockassist-bc-meek_winged_caterpillar_1756315019	xinnn32	2025-08-27T17:17:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T17:17:27Z	--- 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).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756314777	ggozzy	2025-08-27T17:14:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T17:14:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756314734	Ferdi3425	2025-08-27T17:12:42Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T17:12:39Z	--- 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).
Stasonelison/blockassist-bc-howling_powerful_aardvark_1756314422	Stasonelison	2025-08-27T17:07:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "howling powerful aardvark", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T17:07:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - howling powerful aardvark --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
eshanroy5678/blockassist-bc-untamed_dextrous_dingo_1756313676	eshanroy5678	2025-08-27T17:02:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed dextrous dingo", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T16:58:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - untamed dextrous dingo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
OksanaB/blockassist-bc-huge_ferocious_chameleon_1756313979	OksanaB	2025-08-27T17:01:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T17:00:19Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge ferocious chameleon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
OksanaB/blockassist-bc-huge_ferocious_chameleon_1756313672	OksanaB	2025-08-27T16:56:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T16:55:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge ferocious chameleon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
annasoli/gemma-2-9b-it_SV_l20_lr1e-3_a256	annasoli	2025-08-27T16:51:42Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-27T16:51:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756313283	ggozzy	2025-08-27T16:49:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T16:49:07Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/zr-seal-v0.1-GGUF	mradermacher	2025-08-27T16:48:19Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:mfirth/zr-seal-v0.1", "base_model:quantized:mfirth/zr-seal-v0.1", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-27T16:25:11Z	--- base_model: mfirth/zr-seal-v0.1 language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/mfirth/zr-seal-v0.1 <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#zr-seal-v0.1-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/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q2_K.gguf) \| Q2_K \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 0.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 0.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 0.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q6_K.gguf) \| Q6_K \| 1.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q8_0.gguf) \| Q8_0 \| 1.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.f16.gguf) \| f16 \| 2.6 \| 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 -->
vuitton/LouisVuitton_model12	vuitton	2025-08-27T16:42:08Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-27T16:25:58Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
fullclips/O.video.do.surfista.da.Mansao.Privilegio.video.surfista.no.banheiro	fullclips	2025-08-27T16:41:40Z	0	0	null	[ "region:us" ]	null	2025-08-27T16:40:00Z	Watch 🟢 ➤ ➤ ➤ <a href="https://humptydumpty.cfd/surfistad"> 🌐 Click Here To link (Full video) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://humptydumpty.cfd/surfistad"> 🌐 Full video
bdx33/distilbert-base-cased-CoLA	bdx33	2025-08-27T16:41:16Z	3	0	transformers.js	[ "transformers.js", "onnx", "distilbert", "text-classification", "en", "base_model:textattack/distilbert-base-cased-CoLA", "base_model:quantized:textattack/distilbert-base-cased-CoLA", "region:us" ]	text-classification	2024-06-01T11:25:37Z	--- base_model: textattack/distilbert-base-cased-CoLA language: - en library_name: transformers.js pipeline_tag: text-classification --- https://huggingface.co/textattack/distilbert-base-cased-CoLA with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
Putririzqika/blockassist-bc-polished_nimble_chimpanzee_1756312173	Putririzqika	2025-08-27T16:39:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "polished nimble chimpanzee", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T16:39:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - polished nimble chimpanzee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
xinnn32/blockassist-bc-meek_winged_caterpillar_1756312425	xinnn32	2025-08-27T16:34:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T16:34:13Z	--- 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).
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskToken-0.001-v2_2008	luckeciano	2025-08-27T16:28:04Z	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	2025-08-27T10:15:53Z	--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskToken-0.001-v2_2008 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskToken-0.001-v2_2008 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-0.001-v2_2008", 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/0z8h2o91) 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: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
xinnn32/blockassist-bc-meek_winged_caterpillar_1756312037	xinnn32	2025-08-27T16:27:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T16:27:44Z	--- 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).
Yntec/PlayTheGame	Yntec	2025-08-27T16:23:28Z	0	0	diffusers	[ "diffusers", "safetensors", "Base Model", "Realistic", "Fantasy", "RPG", "World of Warcraft", "Style", "53rt5355iz", "Anashel", "stable-diffusion", "stable-diffusion-1.5", "stable-diffusion-diffusers", "text-to-image", "base_model:Yntec/RPG_Remix", "base_model:merge:Yntec/RPG_Remix", "base_model:digiplay/BeautyFoolReality_4", "base_model:merge:digiplay/BeautyFoolReality_4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-08-27T14:27:41Z	--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Base Model - Realistic - Fantasy - RPG - World of Warcraft - Style - 53rt5355iz - Anashel - stable-diffusion - stable-diffusion-1.5 - stable-diffusion-diffusers - diffusers - text-to-image base_model: - Yntec/RPG_Remix - digiplay/BeautyFoolReality_4 base_model_relation: merge --- # Play The Game RPG Remix (which includes RPG v5 by Anashel and RPG v3 Canditate 16 by Anashel) mixed with BeautyFoolReality4 by 53rt5355iz so it can make what both can make! Showcase and prompts (all use seed 9119): ![Pink haired witch](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/-dpnAhm7a7sZbjKHuR56G.png) photo, best quality, masterpiece, gameplay, heads-up display, a pretty young pink witch's brew causes chaos, detailed purple eyes ![Baldur's gate](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/Ga1ronIHFNpjZC37r1uDn.png) Baldur's gate ![old man with girls on subway](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/z1aC_6dltNctZdW4QgnHv.png) cute girl dozing on the subway didn't know that her wide collar showed a bit of her cleavage, best quality, the bearded man sitting next to her stared intently at her, masterpiece, by Jessie Willcox Smith, bright colors, hair, ((box art, logo, English text, 1980s /(style/), copyright name, retro artstyle)), powerful magical traveler, ![pirate ship painting](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/6iTEdFBgVD3Twn1P_vjyP.png) oil painting with heavy impasto of a pirate ship and its captain, cosmic horror painting, elegant intricate artstation concept art by craig mullins detailed # Recipe: - SuperMerger Weight Sum Use MBW 1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,1,1,1 Model A: BeautyfoolRealityV4 Model B: RPG Remix Then the 840KVAE was baked in, and then it was converted into no-ema version so diffusers can create high quality images with it. Output: PlayTheGame
OrdalieTech/Solon-embeddings-mini-beta-1.1	OrdalieTech	2025-08-27T16:21:43Z	0	1	sentence-transformers	[ "sentence-transformers", "safetensors", "eurobert", "embeddings", "multilingual", "feature-extraction", "sentence-similarity", "custom_code", "fr", "en", "base_model:EuroBERT/EuroBERT-210m", "base_model:finetune:EuroBERT/EuroBERT-210m", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-08-27T16:19:14Z	--- license: apache-2.0 language: - fr - en library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - embeddings - eurobert - multilingual - feature-extraction base_model: EuroBERT/EuroBERT-210m --- # OrdalieTech/Solon-embeddings-mini-beta-1.1 Le modèle d'origine a été créé à partir de `EuroBERT/EuroBERT-210m`, puis entraîné avec la technique InfoNCE sur des paires de très haute qualité générées par LLM ## Points clés - Backbone : `EuroBERT/EuroBERT-210m` - Pooling : moyenne des tokens (CLS désactivé, max désactivé) - Dimensions : 768 - Langues : multilingue dont le français et l'anglais ## Exemples d'usage ### Avec `sentence-transformers` ```python pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("OrdalieTech/Solon-embeddings-mini-beta-1.1") sentences = ["Ceci est une phrase d'exemple", "Chaque phrase est convertie en vecteur"] embeddings = model.encode(sentences, convert_to_tensor=False, normalize_embeddings=True) print(embeddings[0].shape) # (768,) ``` ### Avec `transformers` (feature extraction) ```python pip install -U transformers torch ``` ```python from transformers import AutoTokenizer, AutoModel import torch tok = AutoTokenizer.from_pretrained("EuroBERT/EuroBERT-210m", trust_remote_code=True) enc = AutoModel.from_pretrained("EuroBERT/EuroBERT-210m", trust_remote_code=True) inputs = tok(["Ceci est une phrase d'exemple"], padding=True, truncation=True, return_tensors="pt") with torch.no_grad(): out = enc(*inputs).last_hidden_state # (batch, seq, 768) mask = inputs["attention_mask"].unsqueeze(-1) # (batch, seq, 1) mean_emb = (out mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1) ``` ## Cas d'usage - Recherche sémantique - Reranking - Similarité sémantique de phrases (STS) - Recommandation de contenu - Classification basée sur des embeddings ## Crédit et licence - Modèle de base : [`EuroBERT/EuroBERT-210m`](https://huggingface.co/EuroBERT/EuroBERT-210m) • licence Apache-2.0 - Cette publication reprend la licence Apache-2.0 et respecte les conditions de redistribution du modèle de base - Merci aux auteurs d'EuroBERT pour leur travail et l'ouverture du modèle - Création : @matheoqtb
OksanaB/blockassist-bc-huge_ferocious_chameleon_1756311277	OksanaB	2025-08-27T16:16:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T16:15:15Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge ferocious chameleon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hakimjustbao/blockassist-bc-raging_subtle_wasp_1756309361	hakimjustbao	2025-08-27T16:13:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T16:12:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
xinnn32/blockassist-bc-meek_winged_caterpillar_1756310953	xinnn32	2025-08-27T16:09:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T16:09:41Z	--- 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).
Orginal-Jhoselyn-Maura-viral-video-links/NEW.FULL.VIDEO.Jhoselyn.Maura.Viral.Video.Official.Tutorial	Orginal-Jhoselyn-Maura-viral-video-links	2025-08-27T16:05:40Z	0	0	null	[ "region:us" ]	null	2025-08-27T16:05:06Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/mdfprj9k?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
capungmerah627/blockassist-bc-stinging_soaring_porcupine_1756309163	capungmerah627	2025-08-27T16:05:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stinging soaring porcupine", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T16:05:30Z	--- 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).
licyk/comfyui-extension-models	licyk	2025-08-27T16:05:20Z	0	5	diffusers	[ "diffusers", "onnx", "safetensors", "license:openrail", "region:us" ]	null	2024-01-20T13:33:21Z	--- license: openrail --- 这是储存stable-diffusion-webui/ComfyUI扩展所需的部分模型文件 ## 仓库列表 [sd-extensions-model](https://huggingface.co/licyk/sd-extensions-model) 存放stable-diffusion-webui扩展的模型文件 [comfyui-extension-models](https://huggingface.co/licyk/comfyui-extension-models) 存放ComfyUI扩展的模型文件
OksanaB/blockassist-bc-huge_ferocious_chameleon_1756310570	OksanaB	2025-08-27T16:04:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T16:03:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge ferocious chameleon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
MultivexAI/gemma-style-20m-dolly-pretrained	MultivexAI	2025-08-27T15:58:35Z	0	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-27T15:19:54Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: gemma-style-20m-dolly-pretrained 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. --> # gemma-style-20m-dolly-pretrained This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.6593 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 7.4028 \| 0.9191 \| 250 \| 7.4364 \| \| 7.0051 \| 1.8382 \| 500 \| 7.0592 \| \| 6.7136 \| 2.7574 \| 750 \| 6.8416 \| \| 6.5304 \| 3.6765 \| 1000 \| 6.7170 \| \| 6.4196 \| 4.5956 \| 1250 \| 6.6593 \| ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
hasanbasbunar/an-adapter	hasanbasbunar	2025-08-27T15:58:14Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gpt_oss", "trl", "en", "base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-27T15:57:57Z	--- base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gpt_oss - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: hasanbasbunar - License: apache-2.0 - Finetuned from model : unsloth/gpt-oss-20b-unsloth-bnb-4bit This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
chainway9/blockassist-bc-untamed_quick_eel_1756308025	chainway9	2025-08-27T15:50:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed quick eel", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:50:08Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - untamed quick eel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Bheri/labse-en-sa-v1	Bheri	2025-08-27T15:44:12Z	0	1	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:257886", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:sentence-transformers/LaBSE", "base_model:finetune:sentence-transformers/LaBSE", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-08-27T15:43:01Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:257886 - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/LaBSE widget: - source_sentence: 'Karwa Chauth is a festival celebrated by Hindu women of Northern and Western India on the fourth day after Purnima in the month of Kartika. ' sentences: - 'तस्याः युग्मभ्रातुः वंशानुगत-राजकुमारस्य जाक् इत्यस्य निमेषद्वयात् प्राक् सा अजायत। ' - '"तथापि, Internet Explorer नोपयोक्तव्यम् । यतो हि तत् सम्यक् डिस्प्ले न करोति ।"' - 'कर्वा-चौथ् इति उत्सवः उत्तर-पश्चिम-भारतस्य हिन्दु-महिलाभिः कार्तिकमासे पूर्णिमायाः अनन्तरं चतुर्थदिने आचर्यते। ' - source_sentence: '"""And if any man will hurt them, fire proceedeth out of their mouth, and devoureth their enemies: and if any man will hurt them, he must in this manner be killed."""' sentences: - '"C तथा C++ उभयोः मध्येऽपि, इदं समानं मार्गं इम्प्लिमेण्ट् कर्तुमनुसरति ।"' - यदि केचित् तौ हिंसितुं चेष्टन्ते तर्हि तयो र्वदनाभ्याम् अग्नि र्निर्गत्य तयोः शत्रून् भस्मीकरिष्यति। यः कश्चित् तौ हिंसितुं चेष्टते तेनैवमेव विनष्टव्यं। - यवक्रीत उवाच नायं शक्यस्त्वया बड़े महानोघस्तपोधन। अशक्याद् विनिवर्तस्व शक्यमर्थं समारभ॥ - source_sentence: 'It tarnishes in air to produce a whitish oxidized layer on the surface. ' sentences: - उपस्थितानां रत्नानां श्रेष्ठानामर्घहारिणाम्। नादृश्यत परः पारो नापरस्तत्र भारत॥ - 'इदं वायौ कलङ्कितं भवति, येन तले श्वेतवर्णीयं आक्सिडैस्ड्-आस्तरणं निर्मीयते। ' - आचार्येणाभ्यनुज्ञातश्चतुर्णामेकमाश्रमम्। आविमोक्षाच्छरीरस्य सोऽवतिष्ठेद् यथाविधि॥ - source_sentence: 'If you''re planning to fund part or all of your child''s higher education, it''s best to start saving early on. ' sentences: - समयं वाजिमेधस्य विदित्वा पुरुषर्षभः। यथोक्तो धर्मपुत्रेण प्रव्रजन् स्वपुरी प्रति॥ - 'यदि भवान् भवतः सन्ततेः उच्चशिक्षायाः कृते, आंशिकं वा सम्पूर्णं वा शुल्कं दातुम् इच्छति तर्हि तदर्थं पूर्वमेव धनसञ्चयस्य आरम्भः क्षेमकरः भवेत्। ' - '"""तदनन्तरं तेषां सप्तकंसधारिणां सप्तदूतानाम् एक आगत्य मां सम्भाष्यावदत्, अत्रागच्छ, मेदिन्या नरपतयो यया वेश्यया सार्द्धं व्यभिचारकर्म्म कृतवन्तः,"""' - source_sentence: In spite of these, Dhananjaya made Drona's son carless by cutting off the out-stretched bow of his foe with three shafts, killing his driver with a razor like shaft and making away with his banner with three and his four horses with four other shafts. sentences: - तथापि तं प्रस्फुरदात्तकार्मुकं त्रिभिः शरैर्यन्तृशिरः क्षुरेणा हयांश्चतुर्भिश्च पुनस्त्रिभिर्ध्वज धनंजयो द्रौणिरथादपातयत्॥ - एकवारं पूरितं चेत् एतां प्रक्रियां undo कर्तुं न शक्नुमः । - क्रीडां तथा कूर्दनं विना शिक्षा अपूर्णा अस्ति । pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - src2trg_accuracy - trg2src_accuracy - mean_accuracy model-index: - name: SentenceTransformer based on sentence-transformers/LaBSE results: - task: type: translation name: Translation dataset: name: eval en sa type: eval-en-sa metrics: - type: src2trg_accuracy value: 0.944 name: Src2Trg Accuracy - type: trg2src_accuracy value: 0.947 name: Trg2Src Accuracy - type: mean_accuracy value: 0.9455 name: Mean Accuracy --- # SentenceTransformer based on sentence-transformers/LaBSE This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). It maps sentences & paragraphs to a 768-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: [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision 836121a0533e5664b21c7aacc5d22951f2b8b25b --> - Maximum Sequence Length: 128 tokens - Output Dimensionality: 768 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': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, '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): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) (3): 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("sentence_transformers_model_id") # Run inference sentences = [ "In spite of these, Dhananjaya made Drona's son carless by cutting off the out-stretched bow of his foe with three shafts, killing his driver with a razor like shaft and making away with his banner with three and his four horses with four other shafts.", 'तथापि तं प्रस्फुरदात्तकार्मुकं त्रिभिः शरैर्यन्तृशिरः क्षुरेणा हयांश्चतुर्भिश्च पुनस्त्रिभिर्ध्वज धनंजयो द्रौणिरथादपातयत्॥', 'क्रीडां तथा कूर्दनं विना शिक्षा अपूर्णा अस्ति ।', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### 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. --> ## Evaluation ### Metrics #### Translation * Dataset: `eval-en-sa` * Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator) \| Metric \| Value \| \|:------------------\|:-----------\| \| src2trg_accuracy \| 0.944 \| \| trg2src_accuracy \| 0.947 \| \| mean_accuracy \| 0.9455 \| <!-- ## 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: 257,886 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 1000 samples: \| \| sentence_0 \| sentence_1 \| \|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 6 tokens</li><li>mean: 31.6 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 7 tokens</li><li>mean: 40.18 tokens</li><li>max: 128 tokens</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| \|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>It normally connects to port 80 on a computer.<br></code> \| <code>इदं सामान्यतः एकस्मिन् सङ्गणके पोर्ट् ८० इत्यनेन सम्पर्कं साधयति।<br></code> \| \| <code>He who gives to a Brahmana a good bed perfumed with fragrant scents, covered with an excellent sheet, and pillows, gets without any effort on his part a beautiful wife, belonging to a respectable family and of agreeable manners.</code> \| <code>सुगन्धचित्रास्तरणोपधानं दद्यान्नरो यः शयनं द्विजाय। रूपान्वितां पक्षवती मनोज्ञां भार्यामयत्नोपगतां लभेत् सः।</code> \| \| <code>By mid-1665, with the fortress at Purandar besieged and near capture, Shivaji was forced to come to terms with Jai Singh.<br></code> \| <code>१६६५ तमवर्षस्य मध्यभागे यावत् पुरन्दरस्थस्य दुर्गस्य परिवेष्टनं कृत्वा, ग्रहणस्य समीपे, शिवाजी जयसिङ्घेन सह सन्धानं कर्तुं बाध्यः अभवत्।<br></code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `num_train_epochs`: 15 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `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`: 15 - `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 - `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`: False - `hub_always_push`: False - `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 - `dispatch_batches`: None - `split_batches`: 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 - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs <details><summary>Click to expand</summary> \| Epoch \| Step \| Training Loss \| eval-en-sa_mean_accuracy \| \|:-------:\|:------:\|:-------------:\|:------------------------:\| \| 0.0310 \| 500 \| 0.4289 \| - \| \| 0.0620 \| 1000 \| 0.182 \| - \| \| 0.0931 \| 1500 \| 0.1405 \| - \| \| 0.1241 \| 2000 \| 0.1097 \| - \| \| 0.1551 \| 2500 \| 0.0911 \| - \| \| 0.1861 \| 3000 \| 0.0791 \| - \| \| 0.2171 \| 3500 \| 0.0725 \| - \| \| 0.2482 \| 4000 \| 0.067 \| - \| \| 0.2792 \| 4500 \| 0.0594 \| - \| \| 0.3102 \| 5000 \| 0.0629 \| - \| \| 0.3412 \| 5500 \| 0.0535 \| - \| \| 0.3723 \| 6000 \| 0.0512 \| - \| \| 0.4033 \| 6500 \| 0.0456 \| - \| \| 0.4343 \| 7000 \| 0.0462 \| - \| \| 0.4653 \| 7500 \| 0.043 \| - \| \| 0.4963 \| 8000 \| 0.0425 \| - \| \| 0.5274 \| 8500 \| 0.0412 \| - \| \| 0.5584 \| 9000 \| 0.0418 \| - \| \| 0.5894 \| 9500 \| 0.0415 \| - \| \| 0.6204 \| 10000 \| 0.0409 \| - \| \| 0.6514 \| 10500 \| 0.04 \| - \| \| 0.6825 \| 11000 \| 0.032 \| - \| \| 0.7135 \| 11500 \| 0.0323 \| - \| \| 0.7445 \| 12000 \| 0.0325 \| - \| \| 0.7755 \| 12500 \| 0.0355 \| - \| \| 0.8066 \| 13000 \| 0.0285 \| - \| \| 0.8376 \| 13500 \| 0.0281 \| - \| \| 0.8686 \| 14000 \| 0.0289 \| - \| \| 0.8996 \| 14500 \| 0.033 \| - \| \| 0.9306 \| 15000 \| 0.0336 \| - \| \| 0.9617 \| 15500 \| 0.0335 \| - \| \| 0.9927 \| 16000 \| 0.0278 \| - \| \| 1.0 \| 16118 \| - \| 0.913 \| \| 1.0237 \| 16500 \| 0.0312 \| - \| \| 1.0547 \| 17000 \| 0.0294 \| - \| \| 1.0857 \| 17500 \| 0.0288 \| - \| \| 1.1168 \| 18000 \| 0.0287 \| - \| \| 1.1478 \| 18500 \| 0.0245 \| - \| \| 1.1788 \| 19000 \| 0.0243 \| - \| \| 1.2098 \| 19500 \| 0.022 \| - \| \| 1.2408 \| 20000 \| 0.0266 \| - \| \| 1.2719 \| 20500 \| 0.0224 \| - \| \| 1.3029 \| 21000 \| 0.0283 \| - \| \| 1.3339 \| 21500 \| 0.02 \| - \| \| 1.3649 \| 22000 \| 0.0212 \| - \| \| 1.3960 \| 22500 \| 0.0197 \| - \| \| 1.4270 \| 23000 \| 0.0174 \| - \| \| 1.4580 \| 23500 \| 0.0179 \| - \| \| 1.4890 \| 24000 \| 0.0187 \| - \| \| 1.5200 \| 24500 \| 0.0191 \| - \| \| 1.5511 \| 25000 \| 0.0151 \| - \| \| 1.5821 \| 25500 \| 0.0161 \| - \| \| 1.6131 \| 26000 \| 0.0182 \| - \| \| 1.6441 \| 26500 \| 0.0155 \| - \| \| 1.6751 \| 27000 \| 0.013 \| - \| \| 1.7062 \| 27500 \| 0.0119 \| - \| \| 1.7372 \| 28000 \| 0.0119 \| - \| \| 1.7682 \| 28500 \| 0.0133 \| - \| \| 1.7992 \| 29000 \| 0.0113 \| - \| \| 1.8303 \| 29500 \| 0.011 \| - \| \| 1.8613 \| 30000 \| 0.0133 \| - \| \| 1.8923 \| 30500 \| 0.0114 \| - \| \| 1.9233 \| 31000 \| 0.0139 \| - \| \| 1.9543 \| 31500 \| 0.0131 \| - \| \| 1.9854 \| 32000 \| 0.0115 \| - \| \| 2.0 \| 32236 \| - \| 0.9345 \| \| 2.0164 \| 32500 \| 0.01 \| - \| \| 2.0474 \| 33000 \| 0.01 \| - \| \| 2.0784 \| 33500 \| 0.0091 \| - \| \| 2.1094 \| 34000 \| 0.0131 \| - \| \| 2.1405 \| 34500 \| 0.0096 \| - \| \| 2.1715 \| 35000 \| 0.0095 \| - \| \| 2.2025 \| 35500 \| 0.0103 \| - \| \| 2.2335 \| 36000 \| 0.0101 \| - \| \| 2.2645 \| 36500 \| 0.0102 \| - \| \| 2.2956 \| 37000 \| 0.0102 \| - \| \| 2.3266 \| 37500 \| 0.0085 \| - \| \| 2.3576 \| 38000 \| 0.0087 \| - \| \| 2.3886 \| 38500 \| 0.0103 \| - \| \| 2.4197 \| 39000 \| 0.0058 \| - \| \| 2.4507 \| 39500 \| 0.0086 \| - \| \| 2.4817 \| 40000 \| 0.0088 \| - \| \| 2.5127 \| 40500 \| 0.0088 \| - \| \| 2.5437 \| 41000 \| 0.007 \| - \| \| 2.5748 \| 41500 \| 0.0082 \| - \| \| 2.6058 \| 42000 \| 0.0069 \| - \| \| 2.6368 \| 42500 \| 0.0071 \| - \| \| 2.6678 \| 43000 \| 0.0058 \| - \| \| 2.6988 \| 43500 \| 0.0075 \| - \| \| 2.7299 \| 44000 \| 0.0064 \| - \| \| 2.7609 \| 44500 \| 0.0053 \| - \| \| 2.7919 \| 45000 \| 0.0055 \| - \| \| 2.8229 \| 45500 \| 0.0061 \| - \| \| 2.8540 \| 46000 \| 0.0059 \| - \| \| 2.8850 \| 46500 \| 0.0062 \| - \| \| 2.9160 \| 47000 \| 0.0046 \| - \| \| 2.9470 \| 47500 \| 0.0064 \| - \| \| 2.9780 \| 48000 \| 0.0053 \| - \| \| 3.0 \| 48354 \| - \| 0.941 \| \| 3.0091 \| 48500 \| 0.0048 \| - \| \| 3.0401 \| 49000 \| 0.0059 \| - \| \| 3.0711 \| 49500 \| 0.005 \| - \| \| 3.1021 \| 50000 \| 0.005 \| 0.9415 \| \| 3.1331 \| 50500 \| 0.0046 \| - \| \| 3.1642 \| 51000 \| 0.005 \| - \| \| 3.1952 \| 51500 \| 0.0051 \| - \| \| 3.2262 \| 52000 \| 0.0041 \| - \| \| 3.2572 \| 52500 \| 0.0052 \| - \| \| 3.2882 \| 53000 \| 0.0052 \| - \| \| 3.3193 \| 53500 \| 0.0053 \| - \| \| 3.3503 \| 54000 \| 0.0041 \| - \| \| 3.3813 \| 54500 \| 0.0042 \| - \| \| 3.4123 \| 55000 \| 0.0026 \| - \| \| 3.4434 \| 55500 \| 0.0045 \| - \| \| 3.4744 \| 56000 \| 0.0045 \| - \| \| 3.5054 \| 56500 \| 0.0054 \| - \| \| 3.5364 \| 57000 \| 0.0055 \| - \| \| 3.5674 \| 57500 \| 0.0046 \| - \| \| 3.5985 \| 58000 \| 0.0045 \| - \| \| 3.6295 \| 58500 \| 0.0041 \| - \| \| 3.6605 \| 59000 \| 0.0037 \| - \| \| 3.6915 \| 59500 \| 0.003 \| - \| \| 3.7225 \| 60000 \| 0.0039 \| - \| \| 3.7536 \| 60500 \| 0.0027 \| - \| \| 3.7846 \| 61000 \| 0.0041 \| - \| \| 3.8156 \| 61500 \| 0.003 \| - \| \| 3.8466 \| 62000 \| 0.0027 \| - \| \| 3.8777 \| 62500 \| 0.0039 \| - \| \| 3.9087 \| 63000 \| 0.0038 \| - \| \| 3.9397 \| 63500 \| 0.0029 \| - \| \| 3.9707 \| 64000 \| 0.0037 \| - \| \| 4.0 \| 64472 \| - \| 0.9365 \| \| 4.0017 \| 64500 \| 0.0023 \| - \| \| 4.0328 \| 65000 \| 0.0034 \| - \| \| 4.0638 \| 65500 \| 0.0033 \| - \| \| 4.0948 \| 66000 \| 0.0033 \| - \| \| 4.1258 \| 66500 \| 0.004 \| - \| \| 4.1568 \| 67000 \| 0.0026 \| - \| \| 4.1879 \| 67500 \| 0.0026 \| - \| \| 4.2189 \| 68000 \| 0.0025 \| - \| \| 4.2499 \| 68500 \| 0.0037 \| - \| \| 4.2809 \| 69000 \| 0.0041 \| - \| \| 4.3119 \| 69500 \| 0.0031 \| - \| \| 4.3430 \| 70000 \| 0.0025 \| - \| \| 4.3740 \| 70500 \| 0.0025 \| - \| \| 4.4050 \| 71000 \| 0.0022 \| - \| \| 4.4360 \| 71500 \| 0.0016 \| - \| \| 4.4671 \| 72000 \| 0.003 \| - \| \| 4.4981 \| 72500 \| 0.0029 \| - \| \| 4.5291 \| 73000 \| 0.003 \| - \| \| 4.5601 \| 73500 \| 0.0025 \| - \| \| 4.5911 \| 74000 \| 0.0027 \| - \| \| 4.6222 \| 74500 \| 0.0028 \| - \| \| 4.6532 \| 75000 \| 0.003 \| - \| \| 4.6842 \| 75500 \| 0.002 \| - \| \| 4.7152 \| 76000 \| 0.0028 \| - \| \| 4.7462 \| 76500 \| 0.0016 \| - \| \| 4.7773 \| 77000 \| 0.0022 \| - \| \| 4.8083 \| 77500 \| 0.0019 \| - \| \| 4.8393 \| 78000 \| 0.0019 \| - \| \| 4.8703 \| 78500 \| 0.0026 \| - \| \| 4.9014 \| 79000 \| 0.0023 \| - \| \| 4.9324 \| 79500 \| 0.0016 \| - \| \| 4.9634 \| 80000 \| 0.0019 \| - \| \| 4.9944 \| 80500 \| 0.0018 \| - \| \| 5.0 \| 80590 \| - \| 0.937 \| \| 5.0254 \| 81000 \| 0.0028 \| - \| \| 5.0565 \| 81500 \| 0.0019 \| - \| \| 5.0875 \| 82000 \| 0.0024 \| - \| \| 5.1185 \| 82500 \| 0.0016 \| - \| \| 5.1495 \| 83000 \| 0.0015 \| - \| \| 5.1805 \| 83500 \| 0.0017 \| - \| \| 5.2116 \| 84000 \| 0.0016 \| - \| \| 5.2426 \| 84500 \| 0.0026 \| - \| \| 5.2736 \| 85000 \| 0.0029 \| - \| \| 5.3046 \| 85500 \| 0.0027 \| - \| \| 5.3356 \| 86000 \| 0.002 \| - \| \| 5.3667 \| 86500 \| 0.002 \| - \| \| 5.3977 \| 87000 \| 0.0021 \| - \| \| 5.4287 \| 87500 \| 0.0011 \| - \| \| 5.4597 \| 88000 \| 0.0016 \| - \| \| 5.4908 \| 88500 \| 0.0019 \| - \| \| 5.5218 \| 89000 \| 0.0027 \| - \| \| 5.5528 \| 89500 \| 0.0012 \| - \| \| 5.5838 \| 90000 \| 0.0012 \| - \| \| 5.6148 \| 90500 \| 0.0016 \| - \| \| 5.6459 \| 91000 \| 0.0019 \| - \| \| 5.6769 \| 91500 \| 0.0016 \| - \| \| 5.7079 \| 92000 \| 0.0027 \| - \| \| 5.7389 \| 92500 \| 0.0013 \| - \| \| 5.7699 \| 93000 \| 0.0013 \| - \| \| 5.8010 \| 93500 \| 0.0015 \| - \| \| 5.8320 \| 94000 \| 0.0016 \| - \| \| 5.8630 \| 94500 \| 0.002 \| - \| \| 5.8940 \| 95000 \| 0.001 \| - \| \| 5.9251 \| 95500 \| 0.0014 \| - \| \| 5.9561 \| 96000 \| 0.0021 \| - \| \| 5.9871 \| 96500 \| 0.0022 \| - \| \| 6.0 \| 96708 \| - \| 0.933 \| \| 6.0181 \| 97000 \| 0.0016 \| - \| \| 6.0491 \| 97500 \| 0.0015 \| - \| \| 6.0802 \| 98000 \| 0.0011 \| - \| \| 6.1112 \| 98500 \| 0.0016 \| - \| \| 6.1422 \| 99000 \| 0.001 \| - \| \| 6.1732 \| 99500 \| 0.0013 \| - \| \| 6.2042 \| 100000 \| 0.0015 \| 0.9365 \| \| 6.2353 \| 100500 \| 0.0017 \| - \| \| 6.2663 \| 101000 \| 0.0015 \| - \| \| 6.2973 \| 101500 \| 0.0016 \| - \| \| 6.3283 \| 102000 \| 0.001 \| - \| \| 6.3593 \| 102500 \| 0.0013 \| - \| \| 6.3904 \| 103000 \| 0.0013 \| - \| \| 6.4214 \| 103500 \| 0.0011 \| - \| \| 6.4524 \| 104000 \| 0.0007 \| - \| \| 6.4834 \| 104500 \| 0.0013 \| - \| \| 6.5145 \| 105000 \| 0.0011 \| - \| \| 6.5455 \| 105500 \| 0.0011 \| - \| \| 6.5765 \| 106000 \| 0.0015 \| - \| \| 6.6075 \| 106500 \| 0.002 \| - \| \| 6.6385 \| 107000 \| 0.0011 \| - \| \| 6.6696 \| 107500 \| 0.0013 \| - \| \| 6.7006 \| 108000 \| 0.0017 \| - \| \| 6.7316 \| 108500 \| 0.0008 \| - \| \| 6.7626 \| 109000 \| 0.0011 \| - \| \| 6.7936 \| 109500 \| 0.0008 \| - \| \| 6.8247 \| 110000 \| 0.0009 \| - \| \| 6.8557 \| 110500 \| 0.0014 \| - \| \| 6.8867 \| 111000 \| 0.0014 \| - \| \| 6.9177 \| 111500 \| 0.0014 \| - \| \| 6.9488 \| 112000 \| 0.0014 \| - \| \| 6.9798 \| 112500 \| 0.0013 \| - \| \| 7.0 \| 112826 \| - \| 0.9390 \| \| 7.0108 \| 113000 \| 0.0011 \| - \| \| 7.0418 \| 113500 \| 0.0013 \| - \| \| 7.0728 \| 114000 \| 0.0012 \| - \| \| 7.1039 \| 114500 \| 0.001 \| - \| \| 7.1349 \| 115000 \| 0.0016 \| - \| \| 7.1659 \| 115500 \| 0.0009 \| - \| \| 7.1969 \| 116000 \| 0.0009 \| - \| \| 7.2279 \| 116500 \| 0.0007 \| - \| \| 7.2590 \| 117000 \| 0.0008 \| - \| \| 7.2900 \| 117500 \| 0.0014 \| - \| \| 7.3210 \| 118000 \| 0.0012 \| - \| \| 7.3520 \| 118500 \| 0.0007 \| - \| \| 7.3831 \| 119000 \| 0.001 \| - \| \| 7.4141 \| 119500 \| 0.001 \| - \| \| 7.4451 \| 120000 \| 0.0007 \| - \| \| 7.4761 \| 120500 \| 0.0008 \| - \| \| 7.5071 \| 121000 \| 0.0009 \| - \| \| 7.5382 \| 121500 \| 0.0009 \| - \| \| 7.5692 \| 122000 \| 0.001 \| - \| \| 7.6002 \| 122500 \| 0.0009 \| - \| \| 7.6312 \| 123000 \| 0.0007 \| - \| \| 7.6622 \| 123500 \| 0.0009 \| - \| \| 7.6933 \| 124000 \| 0.0007 \| - \| \| 7.7243 \| 124500 \| 0.0012 \| - \| \| 7.7553 \| 125000 \| 0.001 \| - \| \| 7.7863 \| 125500 \| 0.0005 \| - \| \| 7.8173 \| 126000 \| 0.0005 \| - \| \| 7.8484 \| 126500 \| 0.0008 \| - \| \| 7.8794 \| 127000 \| 0.0014 \| - \| \| 7.9104 \| 127500 \| 0.0014 \| - \| \| 7.9414 \| 128000 \| 0.0009 \| - \| \| 7.9725 \| 128500 \| 0.0008 \| - \| \| 8.0 \| 128944 \| - \| 0.94 \| \| 8.0035 \| 129000 \| 0.0013 \| - \| \| 8.0345 \| 129500 \| 0.0007 \| - \| \| 8.0655 \| 130000 \| 0.0007 \| - \| \| 8.0965 \| 130500 \| 0.0008 \| - \| \| 8.1276 \| 131000 \| 0.0009 \| - \| \| 8.1586 \| 131500 \| 0.0009 \| - \| \| 8.1896 \| 132000 \| 0.0007 \| - \| \| 8.2206 \| 132500 \| 0.0008 \| - \| \| 8.2516 \| 133000 \| 0.0008 \| - \| \| 8.2827 \| 133500 \| 0.0006 \| - \| \| 8.3137 \| 134000 \| 0.0008 \| - \| \| 8.3447 \| 134500 \| 0.001 \| - \| \| 8.3757 \| 135000 \| 0.0006 \| - \| \| 8.4068 \| 135500 \| 0.0007 \| - \| \| 8.4378 \| 136000 \| 0.0007 \| - \| \| 8.4688 \| 136500 \| 0.0009 \| - \| \| 8.4998 \| 137000 \| 0.0008 \| - \| \| 8.5308 \| 137500 \| 0.0006 \| - \| \| 8.5619 \| 138000 \| 0.0008 \| - \| \| 8.5929 \| 138500 \| 0.0007 \| - \| \| 8.6239 \| 139000 \| 0.0008 \| - \| \| 8.6549 \| 139500 \| 0.0006 \| - \| \| 8.6859 \| 140000 \| 0.0005 \| - \| \| 8.7170 \| 140500 \| 0.0006 \| - \| \| 8.7480 \| 141000 \| 0.0006 \| - \| \| 8.7790 \| 141500 \| 0.0006 \| - \| \| 8.8100 \| 142000 \| 0.0005 \| - \| \| 8.8410 \| 142500 \| 0.0006 \| - \| \| 8.8721 \| 143000 \| 0.0005 \| - \| \| 8.9031 \| 143500 \| 0.0006 \| - \| \| 8.9341 \| 144000 \| 0.0009 \| - \| \| 8.9651 \| 144500 \| 0.0007 \| - \| \| 8.9962 \| 145000 \| 0.0007 \| - \| \| 9.0 \| 145062 \| - \| 0.938 \| \| 9.0272 \| 145500 \| 0.0007 \| - \| \| 9.0582 \| 146000 \| 0.0007 \| - \| \| 9.0892 \| 146500 \| 0.0007 \| - \| \| 9.1202 \| 147000 \| 0.0007 \| - \| \| 9.1513 \| 147500 \| 0.0005 \| - \| \| 9.1823 \| 148000 \| 0.0005 \| - \| \| 9.2133 \| 148500 \| 0.0005 \| - \| \| 9.2443 \| 149000 \| 0.0007 \| - \| \| 9.2753 \| 149500 \| 0.0006 \| - \| \| 9.3064 \| 150000 \| 0.0005 \| 0.938 \| \| 9.3374 \| 150500 \| 0.0005 \| - \| \| 9.3684 \| 151000 \| 0.0004 \| - \| \| 9.3994 \| 151500 \| 0.0007 \| - \| \| 9.4305 \| 152000 \| 0.0006 \| - \| \| 9.4615 \| 152500 \| 0.0006 \| - \| \| 9.4925 \| 153000 \| 0.0012 \| - \| \| 9.5235 \| 153500 \| 0.0015 \| - \| \| 9.5545 \| 154000 \| 0.0006 \| - \| \| 9.5856 \| 154500 \| 0.0004 \| - \| \| 9.6166 \| 155000 \| 0.0004 \| - \| \| 9.6476 \| 155500 \| 0.0007 \| - \| \| 9.6786 \| 156000 \| 0.0005 \| - \| \| 9.7096 \| 156500 \| 0.0006 \| - \| \| 9.7407 \| 157000 \| 0.0004 \| - \| \| 9.7717 \| 157500 \| 0.0004 \| - \| \| 9.8027 \| 158000 \| 0.0006 \| - \| \| 9.8337 \| 158500 \| 0.0004 \| - \| \| 9.8647 \| 159000 \| 0.0005 \| - \| \| 9.8958 \| 159500 \| 0.0005 \| - \| \| 9.9268 \| 160000 \| 0.0004 \| - \| \| 9.9578 \| 160500 \| 0.0007 \| - \| \| 9.9888 \| 161000 \| 0.0008 \| - \| \| 10.0 \| 161180 \| - \| 0.9405 \| \| 10.0199 \| 161500 \| 0.0009 \| - \| \| 10.0509 \| 162000 \| 0.0007 \| - \| \| 10.0819 \| 162500 \| 0.0007 \| - \| \| 10.1129 \| 163000 \| 0.0007 \| - \| \| 10.1439 \| 163500 \| 0.0005 \| - \| \| 10.1750 \| 164000 \| 0.0005 \| - \| \| 10.2060 \| 164500 \| 0.0004 \| - \| \| 10.2370 \| 165000 \| 0.0006 \| - \| \| 10.2680 \| 165500 \| 0.0006 \| - \| \| 10.2990 \| 166000 \| 0.0005 \| - \| \| 10.3301 \| 166500 \| 0.0005 \| - \| \| 10.3611 \| 167000 \| 0.0006 \| - \| \| 10.3921 \| 167500 \| 0.0006 \| - \| \| 10.4231 \| 168000 \| 0.0003 \| - \| \| 10.4542 \| 168500 \| 0.0005 \| - \| \| 10.4852 \| 169000 \| 0.001 \| - \| \| 10.5162 \| 169500 \| 0.0007 \| - \| \| 10.5472 \| 170000 \| 0.0003 \| - \| \| 10.5782 \| 170500 \| 0.0005 \| - \| \| 10.6093 \| 171000 \| 0.0003 \| - \| \| 10.6403 \| 171500 \| 0.0004 \| - \| \| 10.6713 \| 172000 \| 0.0006 \| - \| \| 10.7023 \| 172500 \| 0.0006 \| - \| \| 10.7333 \| 173000 \| 0.0005 \| - \| \| 10.7644 \| 173500 \| 0.0004 \| - \| \| 10.7954 \| 174000 \| 0.0003 \| - \| \| 10.8264 \| 174500 \| 0.0007 \| - \| \| 10.8574 \| 175000 \| 0.0005 \| - \| \| 10.8884 \| 175500 \| 0.0003 \| - \| \| 10.9195 \| 176000 \| 0.0006 \| - \| \| 10.9505 \| 176500 \| 0.001 \| - \| \| 10.9815 \| 177000 \| 0.0007 \| - \| \| 11.0 \| 177298 \| - \| 0.9345 \| \| 11.0125 \| 177500 \| 0.0003 \| - \| \| 11.0436 \| 178000 \| 0.0003 \| - \| \| 11.0746 \| 178500 \| 0.0005 \| - \| \| 11.1056 \| 179000 \| 0.0005 \| - \| \| 11.1366 \| 179500 \| 0.0007 \| - \| \| 11.1676 \| 180000 \| 0.0008 \| - \| \| 11.1987 \| 180500 \| 0.0004 \| - \| \| 11.2297 \| 181000 \| 0.0006 \| - \| \| 11.2607 \| 181500 \| 0.0006 \| - \| \| 11.2917 \| 182000 \| 0.0009 \| - \| \| 11.3227 \| 182500 \| 0.0005 \| - \| \| 11.3538 \| 183000 \| 0.0004 \| - \| \| 11.3848 \| 183500 \| 0.0004 \| - \| \| 11.4158 \| 184000 \| 0.0005 \| - \| \| 11.4468 \| 184500 \| 0.0003 \| - \| \| 11.4779 \| 185000 \| 0.0002 \| - \| \| 11.5089 \| 185500 \| 0.0003 \| - \| \| 11.5399 \| 186000 \| 0.0007 \| - \| \| 11.5709 \| 186500 \| 0.0003 \| - \| \| 11.6019 \| 187000 \| 0.0003 \| - \| \| 11.6330 \| 187500 \| 0.0004 \| - \| \| 11.6640 \| 188000 \| 0.0007 \| - \| \| 11.6950 \| 188500 \| 0.0003 \| - \| \| 11.7260 \| 189000 \| 0.0003 \| - \| \| 11.7570 \| 189500 \| 0.0004 \| - \| \| 11.7881 \| 190000 \| 0.0004 \| - \| \| 11.8191 \| 190500 \| 0.0003 \| - \| \| 11.8501 \| 191000 \| 0.0003 \| - \| \| 11.8811 \| 191500 \| 0.0003 \| - \| \| 11.9121 \| 192000 \| 0.0002 \| - \| \| 11.9432 \| 192500 \| 0.0008 \| - \| \| 11.9742 \| 193000 \| 0.0004 \| - \| \| 12.0 \| 193416 \| - \| 0.944 \| \| 12.0052 \| 193500 \| 0.0005 \| - \| \| 12.0362 \| 194000 \| 0.0002 \| - \| \| 12.0673 \| 194500 \| 0.0003 \| - \| \| 12.0983 \| 195000 \| 0.0004 \| - \| \| 12.1293 \| 195500 \| 0.0005 \| - \| \| 12.1603 \| 196000 \| 0.0004 \| - \| \| 12.1913 \| 196500 \| 0.0002 \| - \| \| 12.2224 \| 197000 \| 0.0002 \| - \| \| 12.2534 \| 197500 \| 0.0003 \| - \| \| 12.2844 \| 198000 \| 0.0003 \| - \| \| 12.3154 \| 198500 \| 0.0005 \| - \| \| 12.3464 \| 199000 \| 0.0004 \| - \| \| 12.3775 \| 199500 \| 0.0004 \| - \| \| 12.4085 \| 200000 \| 0.0003 \| 0.9435 \| \| 12.4395 \| 200500 \| 0.0003 \| - \| \| 12.4705 \| 201000 \| 0.0004 \| - \| \| 12.5016 \| 201500 \| 0.0009 \| - \| \| 12.5326 \| 202000 \| 0.0005 \| - \| \| 12.5636 \| 202500 \| 0.0003 \| - \| \| 12.5946 \| 203000 \| 0.0003 \| - \| \| 12.6256 \| 203500 \| 0.0002 \| - \| \| 12.6567 \| 204000 \| 0.0003 \| - \| \| 12.6877 \| 204500 \| 0.0002 \| - \| \| 12.7187 \| 205000 \| 0.0005 \| - \| \| 12.7497 \| 205500 \| 0.0003 \| - \| \| 12.7807 \| 206000 \| 0.0004 \| - \| \| 12.8118 \| 206500 \| 0.0003 \| - \| \| 12.8428 \| 207000 \| 0.0003 \| - \| \| 12.8738 \| 207500 \| 0.0003 \| - \| \| 12.9048 \| 208000 \| 0.0003 \| - \| \| 12.9358 \| 208500 \| 0.0006 \| - \| \| 12.9669 \| 209000 \| 0.0004 \| - \| \| 12.9979 \| 209500 \| 0.0004 \| - \| \| 13.0 \| 209534 \| - \| 0.9455 \| </details> ### Framework Versions - Python: 3.10.17 - Sentence Transformers: 4.1.0 - Transformers: 4.46.3 - PyTorch: 2.2.0+cu121 - Accelerate: 1.1.1 - Datasets: 2.18.0 - Tokenizers: 0.20.3 ## 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", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## 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. -->
OksanaB/blockassist-bc-huge_ferocious_chameleon_1756309051	OksanaB	2025-08-27T15:39:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:38:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge ferocious chameleon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dejiat/blockassist-bc-savage_unseen_bobcat_1756309043	Dejiat	2025-08-27T15:37:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:37:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # 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_1756307121	koloni	2025-08-27T15:31:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:31:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
dr-wong-lu-yang-cctv-Viral-videoss/New.full.videos.Dr.wong.Viral.Video.Official.Tutorial	dr-wong-lu-yang-cctv-Viral-videoss	2025-08-27T15:26:12Z	0	0	null	[ "region:us" ]	null	2025-08-27T15:25:42Z	<animated-image data-catalyst=""><a href="https://fubotv24.com/Leaked/?v=video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Dejiat/blockassist-bc-savage_unseen_bobcat_1756308294	Dejiat	2025-08-27T15:25:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:25:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
dereklvlv/embedding	dereklvlv	2025-08-27T15:25:03Z	0	0	pyannote-audio	[ "pyannote-audio", "pytorch", "tensorboard", "pyannote", "pyannote-audio-model", "audio", "voice", "speech", "speaker", "speaker-recognition", "speaker-verification", "speaker-identification", "speaker-embedding", "dataset:voxceleb", "license:mit", "region:us" ]	null	2025-08-27T15:23:35Z	--- tags: - pyannote - pyannote-audio - pyannote-audio-model - audio - voice - speech - speaker - speaker-recognition - speaker-verification - speaker-identification - speaker-embedding datasets: - voxceleb license: mit inference: false extra_gated_prompt: "The collected information will help acquire a better knowledge of pyannote.audio userbase and help its maintainers apply for grants to improve it further. If you are an academic researcher, please cite the relevant papers in your own publications using the model. If you work for a company, please consider contributing back to pyannote.audio development (e.g. through unrestricted gifts). We also provide scientific consulting services around speaker diarization and machine listening." extra_gated_fields: Company/university: text Website: text I plan to use this model for (task, type of audio data, etc): text --- Using this open-source model in production? Consider switching to [pyannoteAI](https://www.pyannote.ai) for better and faster options. # 🎹 Speaker embedding Relies on pyannote.audio 2.1: see [installation instructions](https://github.com/pyannote/pyannote-audio/). This model is based on the [canonical x-vector TDNN-based architecture](https://ieeexplore.ieee.org/abstract/document/8461375), but with filter banks replaced with [trainable SincNet features](https://ieeexplore.ieee.org/document/8639585). See [`XVectorSincNet`](https://github.com/pyannote/pyannote-audio/blob/3c988c028dc505c64fe776720372f6fe816b585a/pyannote/audio/models/embedding/xvector.py#L104-L169) architecture for implementation details. ## Basic usage ```python # 1. visit hf.co/pyannote/embedding and accept user conditions # 2. visit hf.co/settings/tokens to create an access token # 3. instantiate pretrained model from pyannote.audio import Model model = Model.from_pretrained("pyannote/embedding", use_auth_token="ACCESS_TOKEN_GOES_HERE") ``` ```python from pyannote.audio import Inference inference = Inference(model, window="whole") embedding1 = inference("speaker1.wav") embedding2 = inference("speaker2.wav") # `embeddingX` is (1 x D) numpy array extracted from the file as a whole. from scipy.spatial.distance import cdist distance = cdist(embedding1, embedding2, metric="cosine")[0,0] # `distance` is a `float` describing how dissimilar speakers 1 and 2 are. ``` Using cosine distance directly, this model reaches 2.8% equal error rate (EER) on VoxCeleb 1 test set. This is without voice activity detection (VAD) nor probabilistic linear discriminant analysis (PLDA). Expect even better results when adding one of those. ## Advanced usage ### Running on GPU ```python import torch inference.to(torch.device("cuda")) embedding = inference("audio.wav") ``` ### Extract embedding from an excerpt ```python from pyannote.audio import Inference from pyannote.core import Segment inference = Inference(model, window="whole") excerpt = Segment(13.37, 19.81) embedding = inference.crop("audio.wav", excerpt) # `embedding` is (1 x D) numpy array extracted from the file excerpt. ``` ### Extract embeddings using a sliding window ```python from pyannote.audio import Inference inference = Inference(model, window="sliding", duration=3.0, step=1.0) embeddings = inference("audio.wav") # `embeddings` is a (N x D) pyannote.core.SlidingWindowFeature # `embeddings[i]` is the embedding of the ith position of the # sliding window, i.e. from [i * step, i * step + duration]. ``` ## Citation ```bibtex @inproceedings{Bredin2020, Title = {{pyannote.audio: neural building blocks for speaker diarization}}, Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe}, Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing}, Address = {Barcelona, Spain}, Month = {May}, Year = {2020}, } ``` ```bibtex @inproceedings{Coria2020, author="Coria, Juan M. and Bredin, Herv{\'e} and Ghannay, Sahar and Rosset, Sophie", editor="Espinosa-Anke, Luis and Mart{\'i}n-Vide, Carlos and Spasi{\'{c}}, Irena", title="{A Comparison of Metric Learning Loss Functions for End-To-End Speaker Verification}", booktitle="Statistical Language and Speech Processing", year="2020", publisher="Springer International Publishing", pages="137--148", isbn="978-3-030-59430-5" } ```
thorejaya/omega_U6BU5iI	thorejaya	2025-08-27T15:25:02Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-27T15:25:01Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
jibrilorradv/blockassist-bc-shaggy_pale_tortoise_1756306406	jibrilorradv	2025-08-27T15:21:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "shaggy pale tortoise", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:21:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - shaggy pale tortoise --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
xinnn32/blockassist-bc-meek_winged_caterpillar_1756307934	xinnn32	2025-08-27T15:19:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:19:20Z	--- 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).
Dejiat/blockassist-bc-savage_unseen_bobcat_1756307175	Dejiat	2025-08-27T15:06:42Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:06:38Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
alvdansen/upload-models	alvdansen	2025-08-27T15:01:10Z	0	0	null	[ "license:other", "region:us" ]	null	2025-02-03T12:12:03Z	--- license: other license_name: limited license_link: LICENSE ---
koloni/blockassist-bc-deadly_graceful_stingray_1756305136	koloni	2025-08-27T14:59:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:59:11Z	--- 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).
hflqf88888/SWIRL_GUI	hflqf88888	2025-08-27T14:57:32Z	0	0	null	[ "safetensors", "dataset:hflqf88888/SWIRL_GUI_data", "base_model:Qwen/Qwen2.5-VL-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct", "region:us" ]	null	2025-08-25T06:04:57Z	--- datasets: - hflqf88888/SWIRL_GUI_data base_model: - Qwen/Qwen2.5-VL-3B-Instruct --- The instantiation of SWIRL's dual-agent architecture in mobile GUI control. The Navigator translates instructions, history, and screenshots into structured low-level instructions (LLI), while the Interactor executes them as atomic actions (click, scroll, text input) with precise grounding. This hierarchical design enhances robustness, generalization, and interpretability. For more details, please refer to our [project repository](https://github.com/Lqf-HFNJU/SWIRL).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756305902	Ferdi3425	2025-08-27T14:45:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:45:25Z	--- 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).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756305352	Ferdi3425	2025-08-27T14:36:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:36:17Z	--- 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).
camilasfeijoo/my_smolvla_drawertapefinale	camilasfeijoo	2025-08-27T14:33:52Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "smolvla", "dataset:camilasfeijoo/drawertape", "arxiv:2506.01844", "base_model:lerobot/smolvla_base", "base_model:finetune:lerobot/smolvla_base", "license:apache-2.0", "region:us" ]	robotics	2025-08-27T14:33:48Z	--- base_model: lerobot/smolvla_base datasets: camilasfeijoo/drawertape library_name: lerobot license: apache-2.0 model_name: smolvla pipeline_tag: robotics tags: - lerobot - robotics - smolvla --- # Model Card for smolvla <!-- Provide a quick summary of what the model is/does. --> [SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1756303522	helmutsukocok	2025-08-27T14:30:42Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:30:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - loud scavenging kangaroo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Vivek20052/blockassist-bc-howling_domestic_puffin_1756304817	Vivek20052	2025-08-27T14:27:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "howling domestic puffin", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:27:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - howling domestic puffin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dejiat/blockassist-bc-savage_unseen_bobcat_1756304799	Dejiat	2025-08-27T14:27:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:27:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756304563	ggozzy	2025-08-27T14:23:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:23:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
NahedDom/blockassist-bc-flapping_stocky_leopard_1756302683	NahedDom	2025-08-27T14:17:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flapping stocky leopard", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:17:19Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flapping stocky leopard --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hothihongthanh2016/blockassist-bc-raging_tropical_anaconda_1756303327	hothihongthanh2016	2025-08-27T14:16:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging tropical anaconda", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:16:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging tropical anaconda --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1756302380	lisaozill03	2025-08-27T14:13:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:13:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jonlizardo/affine-grpo-03	jonlizardo	2025-08-27T14:13:24Z	0	0	transformers	[ "transformers", "safetensors", "smollm3", "text-generation", "conversational", "en", "fr", "es", "it", "pt", "zh", "ar", "ru", "base_model:HuggingFaceTB/SmolLM3-3B-Base", "base_model:finetune:HuggingFaceTB/SmolLM3-3B-Base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-27T14:12:12Z	--- library_name: transformers license: apache-2.0 language: - en - fr - es - it - pt - zh - ar - ru base_model: - HuggingFaceTB/SmolLM3-3B-Base --- # SmolLM3 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/zy0dqTCCt5IHmuzwoqtJ9.png) ## Table of Contents 1. [Model Summary](#model-summary) 2. [How to use](#how-to-use) 3. [Evaluation](#evaluation) 4. [Training](#training) 5. [Limitations](#limitations) 6. [License](#license) ## Model Summary SmolLM3 is a 3B parameter language model designed to push the boundaries of small models. It supports dual mode reasoning, 6 languages and long context. SmolLM3 is a fully open model that offers strong performance at the 3B–4B scale. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/db3az7eGzs-Sb-8yUj-ff.png) The model is a decoder-only transformer using GQA and NoPE (with 3:1 ratio), it was pretrained on 11.2T tokens with a staged curriculum of web, code, math and reasoning data. Post-training included midtraining on 140B reasoning tokens followed by supervised fine-tuning and alignment via Anchored Preference Optimization (APO). ### Key features - Instruct model optimized for hybrid reasoning - Fully open model: open weights + full training details including public data mixture and training configs - Long context: Trained on 64k context and supports up to 128k tokens using YARN extrapolation - Multilingual: 6 natively supported (English, French, Spanish, German, Italian, and Portuguese) For more details refer to our blog post: https://hf.co/blog/smollm3 ## How to use The modeling code for SmolLM3 is available in transformers `v4.53.0`, so make sure to upgrade your transformers version. You can also load the model with the latest `vllm` which uses transformers as a backend. ```bash pip install -U transformers ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "HuggingFaceTB/SmolLM3-3B" device = "cuda" # for GPU usage or "cpu" for CPU usage # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, ).to(device) # prepare the model input prompt = "Give me a brief explanation of gravity in simple terms." messages_think = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages_think, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # Generate the output generated_ids = model.generate(model_inputs, max_new_tokens=32768) # Get and decode the output output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :] print(tokenizer.decode(output_ids, skip_special_tokens=True)) ``` >[!TIP] > We recommend setting `temperature=0.6` and `top_p=0.95` in the sampling parameters. ### Long context processing The current `config.json` is set for context length up to 65,536 tokens. To handle longer inputs (128k or 256k), we utilize YaRN you can change the `max_position_embeddings` and rope_scaling` to: ``` { ..., "rope_scaling": { "factor": 2.0, #2x65536=131 072 "original_max_position_embeddings": 65536, "type": "yarn" } } ``` ### Enabling and Disabling Extended Thinking Mode We enable extended thinking by default, so the example above generates the output with a reasoning trace. For choosing between enabling, you can provide the `/think` and `/no_think` flags through the system prompt as shown in the snippet below for extended thinking disabled. The code for generating the response with extended thinking would be the same except that the system prompt should have `/think` instead of `/no_think`. ```python prompt = "Give me a brief explanation of gravity in simple terms." messages = [ {"role": "system", "content": "/no_think"}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) ``` We also provide the option of specifying the whether to use extended thinking through the `enable_thinking` kwarg as in the example below. You do not need to set the `/no_think` or `/think` flags through the system prompt if using the kwarg, but keep in mind that the flag in the system prompt overwrites the setting in the kwarg. ```python prompt = "Give me a brief explanation of gravity in simple terms." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False ) ``` ### Agentic Usage SmolLM3 supports tool calling! Just pass your list of tools: - Under the argument `xml_tools` for standard tool-calling: these tools will be called as JSON blobs within XML tags, like `<tool_call>{"name": "get_weather", "arguments": {"city": "Copenhagen"}}</tool_call>` - Or under `python_tools`: then the model will call tools like python functions in a `<code>` snippet, like `<code>get_weather(city="Copenhagen")</code>` ```python from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "HuggingFaceTB/SmolLM3-3B" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint) tools = [ { "name": "get_weather", "description": "Get the weather in a city", "parameters": {"type": "object", "properties": {"city": {"type": "string", "description": "The city to get the weather for"}}}} ] messages = [ { "role": "user", "content": "Hello! How is the weather today in Copenhagen?" } ] inputs = tokenizer.apply_chat_template( messages, enable_thinking=False, # True works as well, your choice! xml_tools=tools, add_generation_prompt=True, tokenize=True, return_tensors="pt" ) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ### Using Custom System Instructions. You can specify custom instruction through the system prompt while controlling whether to use extended thinking. For example, the snippet below shows how to make the model speak like a pirate while enabling extended thinking. ```python prompt = "Give me a brief explanation of gravity in simple terms." messages = [ {"role": "system", "content": "Speak like a pirate./think"}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) ``` For local inference, you can use `llama.cpp`, `ONNX`, `MLX`, `MLC` and `ExecuTorch`. You can find quantized checkpoints in this collection (https://huggingface.co/collections/HuggingFaceTB/smollm3-686d33c1fdffe8e635317e23) ### vLLM and SGLang You can use vLLM and SGLang to deploy the model in an API compatible with OpenAI format. #### SGLang ```bash python -m sglang.launch_server --model-path HuggingFaceTB/SmolLM3-3B ``` #### vLLM ```bash vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes ``` #### Setting `chat_template_kwargs` You can specify `chat_template_kwargs` such as `enable_thinking` to a deployed model by passing the `chat_template_kwargs` parameter in the API request. ```bash curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "HuggingFaceTB/SmolLM3-3B", "messages": [ {"role": "user", "content": "Give me a brief explanation of gravity in simple terms."} ], "temperature": 0.6, "top_p": 0.95, "max_tokens": 16384, "chat_template_kwargs": {"enable_thinking": false} }' ``` ## Evaluation In this section, we report the evaluation results of SmolLM3 model. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them. We highlight the best score in bold and underline the second-best score. ### Instruction Model #### No Extended Thinking Evaluation results of non reasoning models and reasoning models in no thinking mode. We highlight the best and second-best scores in bold. \| Category \| Metric \| SmoLLM3-3B \| Qwen2.5-3B \| Llama3.1-3B \| Qwen3-1.7B \| Qwen3-4B \| \|---------\|--------\|------------\|------------\|-------------\|------------\|----------\| \| High school math competition \| AIME 2025 \| <u>9.3</u> \| 2.9 \| 0.3 \| 8.0 \| 17.1 \| \| Math problem-solving \| GSM-Plus \| 72.8 \| <u>74.1</u> \| 59.2 \| 68.3 \| 82.1 \| \| Competitive programming \| LiveCodeBench v4 \| <u>15.2</u> \| 10.5 \| 3.4 \| 15.0 \| 24.9 \| \| Graduate-level reasoning \| GPQA Diamond \| <u>35.7</u> \| 32.2 \| 29.4 \| 31.8 \| 44.4 \| \| Instruction following \| IFEval \| 76.7 \| 65.6 \| 71.6 \| <u>74.0</u> \| 68.9 \| \| Alignment \| MixEval Hard \| 26.9 \| <u>27.6</u> \| 24.9 \| 24.3 \| 31.6** \| \| Tool Calling \| BFCL\| <u>92.3</u> \| - \| <u>92.3</u> * \| 89.5 \| 95.0 \| \| Multilingual Q&A \| Global MMLU \| <u>53.5</u> \| 50.54 \| 46.8 \| 49.5 \| 65.1 \| (): this is a tool calling finetune #### Extended Thinking Evaluation results in reasoning mode for SmolLM3 and Qwen3 models: \| Category \| Metric \| SmoLLM3-3B \| Qwen3-1.7B \| Qwen3-4B \| \|---------\|--------\|------------\|------------\|----------\| \| High school math competition \| AIME 2025 \| <u>36.7</u> \| 30.7 \| 58.8* \| \| Math problem-solving \| GSM-Plus \| <u>83.4</u> \| 79.4 \| 88.2 \| \| Competitive programming \| LiveCodeBench v4 \| 30.0 \| <u>34.4</u> \| 52.9 \| \| Graduate-level reasoning \| GPQA Diamond \| <u>41.7</u> \| 39.9 \| 55.3 \| \| Instruction following \| IFEval \| 71.2 \| <u>74.2</u> \| 85.4 \| \| Alignment \| MixEval Hard \| 30.8 \| <u>33.9</u> \| 38.0 \| \| Tool Calling \| BFCL \| <u>88.8</u> \| <u>88.8</u> \| 95.5 \| \| Multilingual Q&A \| Global MMLU \| <u>64.1</u> \| 62.3 \| 73.3 \| ### Base Pre-Trained Model #### English benchmarks Note: All evaluations are zero-shot unless stated otherwise. For Ruler 64k evaluation, we apply YaRN to the Qwen models with 32k context to extrapolate the context length. \| Category \| Metric \| SmolLM3-3B \| Qwen2.5-3B \| Llama3-3.2B \| Qwen3-1.7B-Base \| Qwen3-4B-Base \| \|---------\|--------\|---------------------\|------------\|--------------\|------------------\|---------------\| \| Reasoning & Commonsense\| HellaSwag \| 76.15 \| 74.19 \|<u>75.52</u> \| 60.52 \| 74.37 \| \| \| ARC-CF (Average) \| 65.61 \| 59.81 \| 58.58 \| 55.88 \| <u>62.11</u> \| \| \| Winogrande \| 58.88 \| 61.41 \| 58.72 \| 57.06 \| <u>59.59</u> \| \| \| CommonsenseQA \| <u>55.28</u> \| 49.14 \| 60.60 \| 48.98 \| 52.99 \| \| Knowledge & Understanding \| MMLU-CF (Average) \| <u>44.13</u> \| 42.93 \| 41.32 \| 39.11 \| 47.65 \| \| \| MMLU Pro CF \| <u>19.61</u> \| 16.66 \| 16.42 \| 18.04 \| 24.92 \| \| \| MMLU Pro MCF \| <u>32.70</u> \| 31.32 \| 25.07 \| 30.39 \| 41.07 \| \| \| PIQA \| 78.89 \| 78.35 \| <u>78.51</u> \| 75.35 \| 77.58 \| \| \| OpenBookQA \| 40.60 \| 40.20 \| <u>42.00</u> \| 36.40 \| 42.40 \| \| \| BoolQ \| 78.99 \| 73.61 \| <u>75.33</u> \| 74.46 \| 74.28 \| \| Math & Code \| \| \| \| \| \| \| \| Coding & math \| HumanEval+ \| 30.48 \| 34.14\| 25.00 \| <u>43.29</u>\| 54.87 \| \| \| MBPP+ \| 52.91 \| 52.11 \| 38.88\| <u>59.25</u> \| 63.75 \| \| \| MATH (4-shot) \| <u>46.10</u> \| 40.10 \| 7.44 \| 41.64 \| 51.20 \| \| \| GSM8k (5-shot) \| 67.63 \| <u>70.13</u> \| 25.92 \| 65.88 \| 74.14 \| \| Long context \| \| \| \| \| \| \| \| \| Ruler 32k \| 76.35 \| 75.93 \| <u>77.58</u> \| 70.63 \| 83.98 \| \| \| Ruler 64k \| <u>67.85</u> \| 64.90 \| 72.93 \| 57.18 \| 60.29 \| \| \| Ruler 128k \| 61.03 \| <u>62.23</u> \| 71.30 \| 43.03 \| 47.23 \| #### Multilingual benchmarks \| Category \| Metric \| SmolLM3 3B Base \| Qwen2.5-3B \| Llama3.2 3B \| Qwen3 1.7B Base \| Qwen3 4B Base \| \|---------\|--------\|---------------------\|------------\|--------------\|------------------\|---------------\| \| Main supported languages \| \| \| \| \| \| \| \| \| French\| MLMM Hellaswag \| 63.94 \| 57.47 \| 57.66 \| 51.26 \| <u>61.00</u> \| \| \| Belebele \| 51.00 \| <u>51.55</u> \| 49.22 \|49.44\| 55.00 \| \| \| Global MMLU (CF) \| <u>38.37</u> \| 34.22 \| 33.71 \| 34.94 \|41.80 \| \| \| Flores-200 (5-shot) \| 62.85\| 61.38\| <u>62.89</u> \| 58.68 \| 65.76 \| \| Spanish\| MLMM Hellaswag \| 65.85 \| 58.25 \| 59.39 \| 52.40 \| <u>61.85</u> \| \| \| Belebele \| 47.00 \| <u>48.88</u> \| 47.00 \| 47.56 \| 50.33 \| \| \| Global MMLU (CF) \| <u>38.51</u> \| 35.84 \| 35.60 \| 34.79 \|41.22 \| \| \| Flores-200 (5-shot) \| <u>48.25</u>\| 50.00\| 44.45 \| 46.93 \| 50.16 \| \| German\| MLMM Hellaswag \| 59.56 \| 49.99\| 53.19\|46.10\| <u>56.43</u>\| \| \| Belebele \| <u>48.44</u> \| 47.88 \| 46.22 \| 48.00 \| 53.44\| \| \| Global MMLU (CF) \| <u>35.10</u> \| 33.19 \| 32.60 \| 32.73 \|38.70 \| \| \| Flores-200 (5-shot) \| 56.60\| 50.63\| <u>54.95</u> \| 52.58 \| 50.48 \| \| Italian\| MLMM Hellaswag \| 62.49 \| 53.21 \| 54.96 \| 48.72 \| <u>58.76</u> \| \| \| Belebele \| <u>46.44</u> \| 44.77 \| 43.88 \| 44.00 \| 48.78 \| 44.88 \| \| \| Global MMLU (CF) \| <u>36.99</u> \| 33.91 \| 32.79 \| 35.37 \|39.26 \| \| \| Flores-200 (5-shot) \| <u>52.65<u/>\| 54.87\| 48.83 \| 48.37 \| 49.11 \| \| Portuguese\| MLMM Hellaswag \| 63.22 \| 57.38 \| 56.84 \| 50.73 \| <u>59.89</u> \| \| \| Belebele \| 47.67 \| 49.22 \| 45.00 \| 44.00 \| 50.00 \| <u>49.00</U> \| \| \| Global MMLU (CF) \| <u>36.88</u> \| 34.72 \| 33.05 \| 35.26 \|40.66 \| \| \| Flores-200 (5-shot) \| <u>60.93</u> \|57.68\| 54.28 \| 56.58 \| 63.43 \| The model has also been trained on Arabic (standard), Chinese and Russian data, but has seen fewer tokens in these languages compared to the 6 above. We report the performance on these langages for information. \| Category \| Metric \| SmolLM3 3B Base \| Qwen2.5-3B \| Llama3.2 3B \| Qwen3 1.7B Base \| Qwen3 4B Base \| \|---------\|--------\|---------------------\|------------\|--------------\|------------------\|---------------\| \| Other supported languages \| \| \| \| \| \| \| \| \| Arabic\| Belebele \| 40.22 \| 44.22 \| <u>45.33</u> \| 42.33 \| 51.78 \| \| \| Global MMLU (CF) \| 28.57 \| 28.81 \| 27.67 \| <u>29.37</u> \| 31.85 \| \| \| Flores-200 (5-shot) \| <u>40.22</u> \| 39.44 \| 44.43 \| 35.82 \| 39.76 \| \| Chinese\| Belebele \| 43.78 \| 44.56 \| <u>49.56</u> \| 48.78 \| 53.22 \| \| \| Global MMLU (CF) \| 36.16 \| 33.79 \| <u>39.57</u> \| 38.56 \| 44.55 \| \| \| Flores-200 (5-shot) \| 29.17 \| 33.21 \| 31.89 \| 25.70 \| <u>32.50</u> \| \| Russian\| Belebele \| <u>47.44</u> \| 45.89 \| <u>47.44</u> \| 45.22 \| 51.44 \| \| \| Global MMLU (CF) \| <u>36.51</u> \| 32.47 \| 34.52 \| 34.83 \| 38.80 \| \| \| Flores-200 (5-shot) \| 47.13 \| 48.74 \| 50.74 \| <u>54.70</u> \| 60.53 \| ## Training ### Model - Architecture: Transformer decoder - Pretraining tokens: 11T - Precision: bfloat16 ### Software & hardware - GPUs: 384 H100 - Training Framework: [nanotron](https://github.com/huggingface/nanotron/tree/smollm3) - Data processing framework: [datatrove](https://github.com/huggingface/datatrove) - Evaluation framework: [lighteval](https://github.com/huggingface/lighteval) - Post-training Framework: [TRL](https://github.com/huggingface/trl) ### Open resources Here is an infographic with all the training details - The datasets used for pretraining can be found in this [collection](https://huggingface.co/collections/HuggingFaceTB/smollm3-pretraining-datasets-685a7353fdc01aecde51b1d9) and those used in mid-training and post-training will be uploaded later - The training and evaluation configs and code can be found in the [huggingface/smollm](https://github.com/huggingface/smollm) repository. - The training intermediate checkpoints (including the mid-training and SFT checkpoints) are available at [HuggingFaceTB/SmolLM3-3B-checkpoints](https://huggingface.co/HuggingFaceTB/SmolLM3-3B-checkpoints) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/651e96991b97c9f33d26bde6/qiE5ZYr9SD1CIAtfEfuC8.png) ### EU Summary of Public Content The EU AI Act requires all GPAI models to provide a Public Summary of Training Content according to a [given template](https://digital-strategy.ec.europa.eu/en/library/explanatory-notice-and-template-public-summary-training-content-general-purpose-ai-models). You can find the summary for this model below, as well as in its [development Space](https://huggingface.co/spaces/hfmlsoc/smollm3-eu-data-transparency). <iframe src="https://hfmlsoc-smollm3-eu-data-transparency.hf.space" frameborder="0" width="850" height="350" ></iframe> ## Limitations SmolLM3 can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. ## License [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) ## Citation ```bash @misc{bakouch2025smollm3, title={{SmolLM3: smol, multilingual, long-context reasoner}}, author={Bakouch, Elie and Ben Allal, Loubna and Lozhkov, Anton and Tazi, Nouamane and Tunstall, Lewis and Patiño, Carlos Miguel and Beeching, Edward and Roucher, Aymeric and Reedi, Aksel Joonas and Gallouédec, Quentin and Rasul, Kashif and Habib, Nathan and Fourrier, Clémentine and Kydlicek, Hynek and Penedo, Guilherme and Larcher, Hugo and Morlon, Mathieu and Srivastav, Vaibhav and Lochner, Joshua and Nguyen, Xuan-Son and Raffel, Colin and von Werra, Leandro and Wolf, Thomas}, year={2025}, howpublished={\url{https://huggingface.co/blog/smollm3}} } ```
Dejiat/blockassist-bc-savage_unseen_bobcat_1756303378	Dejiat	2025-08-27T14:03:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:03:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
dwoprer/blockassist-bc-mammalian_foxy_squirrel_1756303225	dwoprer	2025-08-27T14:00:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mammalian foxy squirrel", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:00:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mammalian foxy squirrel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
qwersdfvg/blockassist-bc-tricky_mottled_whale_1756302922	qwersdfvg	2025-08-27T13:55:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tricky mottled whale", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T13:55:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tricky mottled whale --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1756301272	coelacanthxyz	2025-08-27T13:55:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T13:55:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
qwersdfvg/blockassist-bc-reclusive_scruffy_gibbon_1756302649	qwersdfvg	2025-08-27T13:51:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "reclusive scruffy gibbon", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T13:50:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - reclusive scruffy gibbon --- # 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-pawing_downy_anaconda_1756302643	AnerYubo	2025-08-27T13:50:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pawing downy anaconda", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T13:50:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pawing downy anaconda --- # 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_1756302393	Ferdi3425	2025-08-27T13:47:11Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T13:47:00Z	--- 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).
hoanganhvutk31/mrpc-bert-finetuned	hoanganhvutk31	2025-08-27T13:44:12Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-27T13:43:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
JiHBijou/SAFE_0826	JiHBijou	2025-08-27T13:43:03Z	0	0	null	[ "region:us" ]	null	2025-08-26T04:02:18Z	# SAFE Video Challenge Example Submission The key requirements is to have a `script.py` file in the top level directory of the repo and optionally a `requirements.txt` file For more details: https://safe-video-2025.dsri.org/#-model-submission
annasoli/gemma-2-9b-it_SV_l20_lr5e-4_a256_nKL	annasoli	2025-08-27T13:34:54Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-27T13:34:25Z	--- 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]
yaelahnal/blockassist-bc-mute_clawed_crab_1756301108	yaelahnal	2025-08-27T13:30:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T13:25:56Z	--- 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).
liukevin666/blockassist-bc-yawning_striped_cassowary_1756301195	liukevin666	2025-08-27T13:28:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T13:27:31Z	--- 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).
QuantTrio/DeepSeek-V3.1-AWQ-Fp16Mix	QuantTrio	2025-08-27T12:31:16Z	0	0	transformers	[ "transformers", "safetensors", "deepseek_v3", "text-generation", "vLLM", "GPTQ", "conversational", "custom_code", "zh", "en", "arxiv:2412.19437", "base_model:deepseek-ai/DeepSeek-V3.1", "base_model:quantized:deepseek-ai/DeepSeek-V3.1", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2025-08-27T07:31:11Z	--- license: mit library_name: transformers pipeline_tag: text-generation tags: - vLLM - GPTQ language: - zh - en base_model: - deepseek-ai/DeepSeek-V3.1 base_model_relation: quantized --- # DeepSeek-V3.1-AWQ-Fp16Mix Base model: [DeepSeek-V3.1](https://www.modelscope.cn/models/deepseek-ai/DeepSeek-V3.1) ### 【Dependencies / Installation】 As of 2025-08-27, create a fresh Python environment and run: ```bash # ❗there are glitches with vllm 0.10.1.1, still looking for resolutions❗ # ❗downgrade vllm for now ❗ pip install vllm==0.9.2 transformers==4.53.0 SITE_PACKAGES=$(pip -V \| awk '{print $4}' \| sed 's/\/pip$//') # ❗patch up AWQ MoE quant config, otherwise some modules cannot be properly loaded❗ cp awq_marlin.py "$SITE_PACKAGES/vllm/model_executor/layers/quantization/awq_marlin.py" # ❗patch up for fp32 e_score_correction_bias, see https://www.github.com/vllm-project/vllm/pull/23640❗ cp deepseek_v2.py "$SITE_PACKAGES/vllm/model_executor/models/deepseek_v2.py" ``` ### 【vLLM Single Node with 8 GPUs — Startup Command】 ``` CONTEXT_LENGTH=32768 vllm serve \ tclf90/DeepSeek-V3.1-AWQ-Fp16Mix \ --served-model-name DeepSeek-V3.1-AWQ-Fp16Mix \ --swap-space 16 \ --max-num-seqs 512 \ --max-model-len $CONTEXT_LENGTH \ --max-seq-len-to-capture $CONTEXT_LENGTH \ --gpu-memory-utilization 0.8 \ --tensor-parallel-size 8 \ --trust-remote-code \ --disable-log-requests \ --host 0.0.0.0 \ --port 8000 ``` ### 【Logs】 ``` 2025-08-27 1. new installation instuction for stable/correct performance (a) use vllm 0.9.2 instead of 0.9.0: there is unidentified issue with 0.9.0 🥹 which causes numerical error (b) patch up deepseek_v2.py for fp32 e_score_correction_bias 2025-08-25 1. Initial commit ``` ### 【Model Files】 \| File Size \| Last Updated \| \|-----------\|--------------\| \| `435GB` \| `2025-08-25` \| ### 【Model Download】 ```python from modelscope import snapshot_download snapshot_download('tclf90/DeepSeek-V3.1-AWQ-Fp16Mix', cache_dir="your_local_path") ``` ### 【Overview】 <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%20V3-536af5?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="LICENSE" style="margin: 2px;"> <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div> ## Introduction DeepSeek-V3.1 is a hybrid model that supports both thinking mode and non-thinking mode. Compared to the previous version, this upgrade brings improvements in multiple aspects: - Hybrid thinking mode: One model supports both thinking mode and non-thinking mode by changing the chat template. - Smarter tool calling: Through post-training optimization, the model's performance in tool usage and agent tasks has significantly improved. - Higher thinking efficiency: DeepSeek-V3.1-Think achieves comparable answer quality to DeepSeek-R1-0528, while responding more quickly. DeepSeek-V3.1 is post-trained on the top of DeepSeek-V3.1-Base, which is built upon the original V3 base checkpoint through a two-phase long context extension approach, following the methodology outlined in the original DeepSeek-V3 report. We have expanded our dataset by collecting additional long documents and substantially extending both training phases. The 32K extension phase has been increased 10-fold to 630B tokens, while the 128K extension phase has been extended by 3.3x to 209B tokens. Additionally, DeepSeek-V3.1 is trained using the UE8M0 FP8 scale data format to ensure compatibility with microscaling data formats. ## Model Downloads <div align="center"> \| Model \| #Total Params \| #Activated Params \| Context Length \| Download \| \| :------------: \| :------------: \| :------------: \| :------------: \| :------------: \| \| DeepSeek-V3.1-Base \| 671B \| 37B \| 128K \| [HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Base) \\| [ModelScope](https://modelscope.cn/models/deepseek-ai/DeepSeek-V3.1-Base) \| \| DeepSeek-V3.1 \| 671B \| 37B \| 128K \| [HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V3.1) \\| [ModelScope](https://modelscope.cn/models/deepseek-ai/DeepSeek-V3.1) \| </div> ## Chat Template The details of our chat template is described in `tokenizer_config.json` and `assets/chat_template.jinja`. Here is a brief description. ### Non-Thinking #### First-Turn Prefix: `<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜></think>` With the given prefix, DeepSeek V3.1 generates responses to queries in non-thinking mode. Unlike DeepSeek V3, it introduces an additional token `</think>`. #### Multi-Turn Context: `<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>...<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>` Prefix: `<｜User｜>{query}<｜Assistant｜></think>` By concatenating the context and the prefix, we obtain the correct prompt for the query. ### Thinking #### First-Turn Prefix: `<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜><think>` The prefix of thinking mode is similar to DeepSeek-R1. #### Multi-Turn Context: `<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>...<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>` Prefix: `<｜User｜>{query}<｜Assistant｜><think>` The multi-turn template is the same with non-thinking multi-turn chat template. It means the thinking token in the last turn will be dropped but the `</think>` is retained in every turn of context. ### ToolCall Toolcall is supported in non-thinking mode. The format is: `<｜begin▁of▁sentence｜>{system prompt}{tool_description}<｜User｜>{query}<｜Assistant｜></think>` where the tool_description is ``` ## Tools You have access to the following tools: ### {tool_name1} Description: {description} Parameters: {json.dumps(parameters)} IMPORTANT: ALWAYS adhere to this exact format for tool use: <｜tool▁calls▁begin｜><｜tool▁call▁begin｜>tool_call_name<｜tool▁sep｜>tool_call_arguments<｜tool▁call▁end｜>{{additional_tool_calls}}<｜tool▁calls▁end｜> Where: - `tool_call_name` must be an exact match to one of the available tools - `tool_call_arguments` must be valid JSON that strictly follows the tool's Parameters Schema - For multiple tool calls, chain them directly without separators or spaces ``` ### Code-Agent We support various code agent frameworks. Please refer to the above toolcall format to create your own code agents. An example is shown in `assets/code_agent_trajectory.html`. ### Search-Agent We design a specific format for searching toolcall in thinking mode, to support search agent. For complex questions that require accessing external or up-to-date information, DeepSeek-V3.1 can leverage a user-provided search tool through a multi-turn tool-calling process. Please refer to the `assets/search_tool_trajectory.html` and `assets/search_python_tool_trajectory.html` for the detailed template. ## Evaluation \| Category \| Benchmark (Metric) \| DeepSeek V3.1-NonThinking \| DeepSeek V3 0324 \| DeepSeek V3.1-Thinking \| DeepSeek R1 0528 \|----------\|----------------------------------\|-----------------\|---\|---\|---\| \| General \| \| \| MMLU-Redux (EM) \| 91.8 \| 90.5 \| 93.7 \| 93.4 \| \| MMLU-Pro (EM) \| 83.7 \| 81.2 \| 84.8 \| 85.0 \| \| GPQA-Diamond (Pass@1) \| 74.9 \| 68.4 \| 80.1 \| 81.0 \| \| Humanity's Last Exam (Pass@1) \| - \| - \| 15.9 \| 17.7 \|Search Agent\| \| \| BrowseComp \| - \| - \| 30.0 \| 8.9 \| \| BrowseComp_zh \| - \| - \| 49.2 \| 35.7 \| \| Humanity's Last Exam (Python + Search) \|- \| - \| 29.8 \| 24.8 \| \| SimpleQA \| - \| - \| 93.4 \| 92.3 \| Code \| \| \| LiveCodeBench (2408-2505) (Pass@1) \| 56.4 \| 43.0 \| 74.8 \| 73.3 \| \| Codeforces-Div1 (Rating) \| - \| - \| 2091 \| 1930 \| \| Aider-Polyglot (Acc.) \| 68.4 \| 55.1 \| 76.3 \| 71.6 \| Code Agent\| \| \| SWE Verified (Agent mode) \| 66.0 \| 45.4 \| - \| 44.6 \| \| SWE-bench Multilingual (Agent mode) \| 54.5 \| 29.3 \| - \| 30.5 \| \| Terminal-bench (Terminus 1 framework) \| 31.3 \| 13.3 \| - \| 5.7 \| Math \| \| \| AIME 2024 (Pass@1) \| 66.3 \| 59.4 \| 93.1 \| 91.4 \| \| AIME 2025 (Pass@1) \| 49.8 \| 51.3 \| 88.4 \| 87.5 \| \| HMMT 2025 (Pass@1) \| 33.5 \| 29.2 \| 84.2 \| 79.4 \| Note: - Search agents are evaluated with our internal search framework, which uses a commercial search API + webpage filter + 128K context window. Seach agent results of R1-0528 are evaluated with a pre-defined workflow. - SWE-bench is evaluated with our internal code agent framework. - HLE is evaluated with the text-only subset. ### Usage Example ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V3.1") messages = [ {"role": "system", "content": "You are a helpful assistant"}, {"role": "user", "content": "Who are you?"}, {"role": "assistant", "content": "<think>Hmm</think>I am DeepSeek"}, {"role": "user", "content": "1+1=?"} ] tokenizer.apply_chat_template(messages, tokenize=False, thinking=True, add_generation_prompt=True) # '<｜begin▁of▁sentence｜>You are a helpful assistant<｜User｜>Who are you?<｜Assistant｜></think>I am DeepSeek<｜end▁of▁sentence｜><｜User｜>1+1=?<｜Assistant｜><think>' tokenizer.apply_chat_template(messages, tokenize=False, thinking=False, add_generation_prompt=True) # '<｜begin▁of▁sentence｜>You are a helpful assistant<｜User｜>Who are you?<｜Assistant｜></think>I am DeepSeek<｜end▁of▁sentence｜><｜User｜>1+1=?<｜Assistant｜></think>' ``` ## How to Run Locally The model structure of DeepSeek-V3.1 is the same as DeepSeek-V3. Please visit [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repo for more information about running this model locally. ## License This repository and the model weights are licensed under the [MIT License](LICENSE). ## Citation ``` @misc{deepseekai2024deepseekv3technicalreport, title={DeepSeek-V3 Technical Report}, author={DeepSeek-AI}, year={2024}, eprint={2412.19437}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.19437}, } ``` ## Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).
felixZzz/student_sft_len32k_sub1k_multiZ_acc_mixw8-0827	felixZzz	2025-08-27T12:29:32Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-27T12:29:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BaekSeungJu/Ophtimus-8B-Reasoning	BaekSeungJu	2025-08-27T12:20:14Z	15	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-21T13:10:23Z	--- 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]
capungmerah627/blockassist-bc-stinging_soaring_porcupine_1756295398	capungmerah627	2025-08-27T12:18:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stinging soaring porcupine", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T12:18:10Z	--- 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).
Dejiat/blockassist-bc-savage_unseen_bobcat_1756296147	Dejiat	2025-08-27T12:02:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T12:02:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dejiat/blockassist-bc-savage_unseen_bobcat_1756295791	Dejiat	2025-08-27T11:56:55Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T11:56:53Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

ggozzy/blockassist-bc-stubby_yapping_mandrill_1756322750

ggozzy

2025-08-27T19:27:02Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T19:26:55Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

mohammadmahdinouri/bert_baseline_30k

mohammadmahdinouri

2025-08-27T19:25:14Z

0

transformers

[ "transformers", "safetensors", "modernbert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2025-08-27T19:25:07Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

crystalline7/1531294

crystalline7

2025-08-27T19:24:25Z

0

null

[ "region:us" ]

null

2025-08-27T19:24:22Z

[View on Civ Archive](https://civarchive.com/models/1442770?modelVersionId=1630983)

crystalline7/1906119

crystalline7

2025-08-27T19:23:08Z

0

null

[ "region:us" ]

null

2025-08-27T19:23:05Z

[View on Civ Archive](https://civarchive.com/models/1775186?modelVersionId=2009103)

Yanzeisi/Llama3.1-8b-Instruct-sft-Aug-27

Yanzeisi

2025-08-27T19:21:52Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us" ]

null

2025-08-27T14:01:31Z

--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: transformers model_name: Llama3.1-8b-Instruct-sft-Aug-27 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for Llama3.1-8b-Instruct-sft-Aug-27 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-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="Yanzeisi/Llama3.1-8b-Instruct-sft-Aug-27", 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/yanzewan-usc/huggingface/runs/7qbj2pd6) This model was trained with SFT. ### Framework versions - TRL: 0.20.0.dev0 - Transformers: 4.53.2 - Pytorch: 2.7.1+cu118 - Datasets: 3.4.1 - Tokenizers: 0.21.0 ## 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}} } ```

fopppyu/blockassist-bc-patterned_monstrous_boar_1756322199

fopppyu

2025-08-27T19:16:51Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "patterned monstrous boar", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T19:16:39Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - patterned monstrous boar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

AllOfWhich/oil

AllOfWhich

2025-08-27T19:05:53Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]

text-to-image

2025-08-27T19:03:55Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/df0r4a3-ffbbd57b-89d6-4dae-9060-9f79d5de1a5e.png text: '-' base_model: black-forest-labs/FLUX.1-dev instance_prompt: null --- # oil <Gallery /> ## Download model [Download](/AllOfWhich/oil/tree/main) them in the Files & versions tab.

motza0025/blockassist-bc-scampering_scaly_salmon_1756318963

motza0025

2025-08-27T18:48:35Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scampering scaly salmon", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T18:48:20Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scampering scaly salmon --- # 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_1756320438

liukevin666

2025-08-27T18:48:27Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T18:48:19Z

--- 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).

pbouda/finetune-cpt-test

pbouda

2025-08-27T18:42:23Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-v0.3", "base_model:finetune:unsloth/mistral-7b-v0.3", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-08-27T08:36:29Z

--- base_model: unsloth/mistral-7b-v0.3 tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** pbouda - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-v0.3 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

OksanaB/blockassist-bc-huge_ferocious_chameleon_1756319843

OksanaB

2025-08-27T18:38:56Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T18:38:01Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge ferocious chameleon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

michhell/blockassist-bc-stalking_running_albatross_1756317998

michhell

2025-08-27T18:37:30Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stalking running albatross", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T18:37:25Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stalking running albatross --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

apriasmoro/28860f3c-5409-4cc2-b2ad-38a891dc636d

apriasmoro

2025-08-27T18:34:41Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", "base_model:adapter:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", "region:us" ]

null

2025-08-27T18:34:15Z

--- base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1

happyensworld/blockassist-bc-sleek_scavenging_ram_1756319527

happyensworld

2025-08-27T18:32:50Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sleek scavenging ram", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T18:32:44Z

--- 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).

brown-girl-Viral-video-original-XX-Clips/New.full.videos.brown.girl.Viral.Video.Official.Tutorial

brown-girl-Viral-video-original-XX-Clips

2025-08-27T18:27:57Z

0

null

[ "region:us" ]

null

2025-08-27T18:27:40Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/mdfprj9k?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF

mradermacher

2025-08-27T18:25:34Z

0

transformers

[ "transformers", "gguf", "en", "base_model:misaeboca/TinyLlama-1.1B-Chat-finetune", "base_model:quantized:misaeboca/TinyLlama-1.1B-Chat-finetune", "endpoints_compatible", "region:us", "conversational" ]

null

2025-08-27T18:12:02Z

--- base_model: misaeboca/TinyLlama-1.1B-Chat-finetune language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About         static quants of https://huggingface.co/misaeboca/TinyLlama-1.1B-Chat-finetune  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#TinyLlama-1.1B-Chat-finetune-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/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q2_K.gguf) | Q2_K | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q3_K_S.gguf) | Q3_K_S | 0.6 | | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q3_K_M.gguf) | Q3_K_M | 0.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q3_K_L.gguf) | Q3_K_L | 0.7 | | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.IQ4_XS.gguf) | IQ4_XS | 0.7 | | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q4_K_S.gguf) | Q4_K_S | 0.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q4_K_M.gguf) | Q4_K_M | 0.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q5_K_S.gguf) | Q5_K_S | 0.9 | | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q5_K_M.gguf) | Q5_K_M | 0.9 | | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q6_K.gguf) | Q6_K | 1.0 | very good quality | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.Q8_0.gguf) | Q8_0 | 1.3 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-Chat-finetune-GGUF/resolve/main/TinyLlama-1.1B-Chat-finetune.f16.gguf) | f16 | 2.3 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

AnerYubo/blockassist-bc-mangy_quiet_anteater_1756319068

AnerYubo

2025-08-27T18:24:32Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mangy quiet anteater", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T18:24:29Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mangy quiet anteater --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Vortex5/StarlitSage-12B-Q6_K-GGUF

Vortex5

2025-08-27T18:20:00Z

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:Vortex5/StarlitSage-12B", "base_model:quantized:Vortex5/StarlitSage-12B", "endpoints_compatible", "region:us", "conversational" ]

null

2025-08-27T18:19:18Z

--- base_model: Vortex5/StarlitSage-12B library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Vortex5/StarlitSage-12B-Q6_K-GGUF This model was converted to GGUF format from [`Vortex5/StarlitSage-12B`](https://huggingface.co/Vortex5/StarlitSage-12B) 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/Vortex5/StarlitSage-12B) 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 Vortex5/StarlitSage-12B-Q6_K-GGUF --hf-file starlitsage-12b-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Vortex5/StarlitSage-12B-Q6_K-GGUF --hf-file starlitsage-12b-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Vortex5/StarlitSage-12B-Q6_K-GGUF --hf-file starlitsage-12b-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Vortex5/StarlitSage-12B-Q6_K-GGUF --hf-file starlitsage-12b-q6_k.gguf -c 2048 ```

majid230/gemma-3-1b-finetune

majid230

2025-08-27T18:19:51Z

0

transformers

[ "transformers", "safetensors", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gemma-3-1b-it", "base_model:finetune:unsloth/gemma-3-1b-it", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-08-27T18:16:18Z

--- base_model: unsloth/gemma-3-1b-it tags: - text-generation-inference - transformers - unsloth - gemma3_text license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** majid230 - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-3-1b-it This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

ggozzy/blockassist-bc-stubby_yapping_mandrill_1756318515

ggozzy

2025-08-27T18:16:16Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T18:16:11Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756317786

Ferdi3425

2025-08-27T18:03:43Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T18:03:32Z

--- 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).

calegpedia/blockassist-bc-stealthy_slimy_rooster_1756315617

calegpedia

2025-08-27T17:53:27Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T17:53:23Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stealthy slimy rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756316998

Ferdi3425

2025-08-27T17:50:32Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T17:50:27Z

--- 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).

Matt300209/8B-tulu-sft-bf16

Matt300209

2025-08-27T17:42:12Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "dataset:allenai/tulu-3-sft-mixture", "arxiv:2411.15124", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-27T14:12:35Z

--- license: llama3.1 language: - en pipeline_tag: text-generation datasets: - allenai/tulu-3-sft-mixture base_model: - meta-llama/Llama-3.1-8B library_name: transformers --- <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu3/Tulu3-logo.png" alt="Tulu 3 banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Llama-3.1-Tulu-3-8B-SFT Tülu3 is a leading instruction following model family, offering fully open-source data, code, and recipes designed to serve as a comprehensive guide for modern post-training techniques. Tülu3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval. ## Model description - **Model type:** A model trained on a mix of publicly available, synthetic and human-created datasets. - **Language(s) (NLP):** Primarily English - **License:** Llama 3.1 Community License Agreement - **Finetuned from model:** meta-llama/Llama-3.1-8B ### Model Sources - **Training Repository:** https://github.com/allenai/open-instruct - **Eval Repository:** https://github.com/allenai/olmes - **Paper:** https://arxiv.org/abs/2411.15124 - **Demo:** https://playground.allenai.org/ ### Model Family | **Stage** | **Llama 3.1 8B** | **Llama 3.1 70B** | |----------------------|----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------| | **Base Model** | [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) | [meta-llama/Llama-3.1-70B](https://huggingface.co/meta-llama/Llama-3.1-70B) | | **SFT** | [allenai/Llama-3.1-Tulu-3-8B-SFT](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-SFT) | [allenai/Llama-3.1-Tulu-3-70B-SFT](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-SFT) | | **DPO** | [allenai/Llama-3.1-Tulu-3-8B-DPO](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-DPO) | [allenai/Llama-3.1-Tulu-3-70B-DPO](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-DPO) | | **Final Models (RLVR)** | [allenai/Llama-3.1-Tulu-3-8B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B) | [allenai/Llama-3.1-Tulu-3-70B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B) | | **Reward Model (RM)**| [allenai/Llama-3.1-Tulu-3-8B-RM](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-RM) | (Same as 8B) | | **Stage** | **Llama 3.1 405B** | |-----------|-------------------| | **Base Model** | [meta-llama/llama-3.1-405B](https://huggingface.co/meta-llama/llama-3.1-405B) | | **SFT** | [allenai/llama-3.1-Tulu-3-405B-SFT](https://huggingface.co/allenai/llama-3.1-Tulu-3-405B-SFT) | | **DPO** | [allenai/llama-3.1-Tulu-3-405B-DPO](https://huggingface.co/allenai/llama-3.1-Tulu-3-405B-DPO) | | **Final Model (RLVR)** | [allenai/llama-3.1-Tulu-3-405B](https://huggingface.co/allenai/llama-3.1-Tulu-3-405B) | | **Reward Model (RM)**| (Same as 8B) ## Using the model ### Loading with HuggingFace To load the model with HuggingFace, use the following snippet: ``` from transformers import AutoModelForCausalLM tulu_model = AutoModelForCausalLM.from_pretrained("allenai/Llama-3.1-Tulu-3-8B-SFT") ``` ### VLLM As a Llama base model, the model can be easily served with: ``` vllm serve allenai/Llama-3.1-Tulu-3-8B-SFT ``` Note that given the long chat template of Llama, you may want to use `--max_model_len=8192`. ### Chat template The chat template for our models is formatted as: ``` <|user|>\nHow are you doing?\n<|assistant|>\nI'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|> ``` Or with new lines expanded: ``` <|user|> How are you doing? <|assistant|> I'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|> ``` It is embedded within the tokenizer as well, for `tokenizer.apply_chat_template`. ### System prompt In Ai2 demos, we use this system prompt by default: ``` You are Tulu 3, a helpful and harmless AI Assistant built by the Allen Institute for AI. ``` The model has not been trained with a specific system prompt in mind. ### Bias, Risks, and Limitations The Tülu3 models have limited safety training, but are not deployed automatically with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base Llama 3.1 models, however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this. ## Performance | Benchmark (eval) | Tülu 3 SFT 8B | Tülu 3 DPO 8B | Tülu 3 8B | Llama 3.1 8B Instruct | Qwen 2.5 7B Instruct | Magpie 8B | Gemma 2 9B Instruct | Ministral 8B Instruct | |---------------------------------|----------------|----------------|------------|------------------------|----------------------|-----------|---------------------|-----------------------| | **Avg.** | 60.4 | 64.4 | **64.8** | 62.2 | 57.8 | 44.7 | 55.2 | 58.3 | | **MMLU (0 shot, CoT)** | 65.9 | 68.7 | 68.2 | 71.2 | **76.6** | 62.0 | 74.6 | 68.5 | | **PopQA (15 shot)** | **29.3** | 29.3 | 29.1 | 20.2 | 18.1 | 22.5 | 28.3 | 20.2 | | **TruthfulQA (6 shot)** | 46.8 | 56.1 | 55.0 | 55.1 | **63.1** | 57.0 | 61.4 | 55.5 | | **BigBenchHard (3 shot, CoT)** | **67.9** | 65.8 | 66.0 | 62.8 | 21.7 | 0.9 | 2.5 | 56.2 | | **DROP (3 shot)** | 61.3 | 62.5 | **62.6** | 61.5 | 54.4 | 49.4 | 58.8 | 56.2 | | **MATH (4 shot CoT, Flex)** | 31.5 | 42.0 | **43.7** | 42.5 | 14.8 | 5.1 | 29.8 | 40.0 | | **GSM8K (8 shot, CoT)** | 76.2 | 84.3 | **87.6** | 83.4 | 83.8 | 61.2 | 79.7 | 80.0 | | **HumanEval (pass@10)** | 86.2 | 83.9 | 83.9 | 86.3 | **93.1** | 75.4 | 71.7 | 91.0 | | **HumanEval+ (pass@10)** | 81.4 | 78.6 | 79.2 | 82.9 | **89.7** | 69.1 | 67.0 | 88.5 | | **IFEval (prompt loose)** | 72.8 | 81.1 | **82.4** | 80.6 | 74.7 | 38.8 | 69.9 | 56.4 | | **AlpacaEval 2 (LC % win)** | 12.4 | 33.5 | 34.5 | 24.2 | 29.0 | **49.0** | 43.7 | 31.4 | | **Safety (6 task avg.)** | **93.1** | 87.2 | 85.5 | 75.2 | 75.0 | 46.4 | 75.5 | 56.2 | | Benchmark (eval) | Tülu 3 70B SFT | Tülu 3 DPO 70B | Tülu 3 70B | Llama 3.1 70B Instruct | Qwen 2.5 72B Instruct | Hermes 3 Llama 3.1 70B | Nemotron Llama 3.1 70B | |---------------------------------|-----------------|-----------------|-------------|-------------------------|-----------------------|------------------------|-------------------------| | **Avg.** | 72.6 | 75.9 | **76.0** | 73.4 | 71.5 | 68.3 | 65.5 | | **MMLU (0 shot, CoT)** | 78.9 | 83.3 | 83.1 | 85.3 | **85.5** | 80.4 | 83.8 | | **PopQA (15 shot)** | **48.6** | 46.3 | 46.5 | 46.4 | 30.6 | 48.1 | 36.4 | | **TruthfulQA (6 shot)** | 55.7 | 67.9 | 67.6 | 66.8 | **69.9** | 66.5 | 62.6 | | **BigBenchHard (3 shot, CoT)** | **82.7** | 81.8 | 82.0 | 73.8 | 67.2 | 82.1 | 0.7 | | **DROP (3 shot)** | **77.2** | 74.1 | 74.3 | 77.0 | 34.2 | 73.2 | 68.8 | | **MATH (4 shot CoT, Flex)** | 53.7 | 62.3 | 63.0 | 56.4 | **74.3** | 41.9 | 55.0 | | **GSM8K (8 shot, CoT)** | 91.1 | 93.5 | 93.5 | **93.7** | 89.5 | 90.0 | 84.7 | | **HumanEval (pass@10)** | 92.9 | 92.4 | 92.4 | 93.6 | 94.0 | 89.6 | **94.1** | | **HumanEval+ (pass@10)** | 87.3 | 88.4 | 88.0 | 89.5 | **90.8** | 85.9 | 85.5 | | **IFEval (prompt loose)** | 82.1 | 82.6 | 83.2 | **88.0** | 87.6 | 76.0 | 79.9 | | **AlpacaEval 2 (LC % win)** | 26.3 | 49.6 | 49.8 | 33.4 | 47.7 | 28.4 | **66.1** | | **Safety (6 task avg.)** | **94.4** | 89.0 | 88.3 | 76.5 | 87.0 | 57.9 | 69.0 | | Benchmark (eval) | Tülu 3 405B SFT | Tülu 3 405B DPO | Tülu 3 405B | Llama 3.1 405B Instruct | Nous Hermes 3 405B | Deepseek V3 | GPT 4o (11-24) | |-----------------|----------------|----------------|-------------|------------------------|-------------------|-------------|----------------| | **Avg w/o Safety** | 76.3 | 79.0 | 80.0 | 78.1 | 74.4 | 79.0 | **80.5** | | **Avg w/ Safety** | 77.5 | 79.6 | 80.7 | 79.0 | 73.5 | 75.9 | **81.6** | | **MMLU (5 shot, CoT)** | 84.4 | 86.6 | 87.0 | **88.0** | 84.9 | 82.1 | 87.9 | | **PopQA (3 shot)** | **55.7** | 55.4 | 55.5 | 52.9 | 54.2 | 44.9 | 53.6 | | **BigBenchHard (0 shot, CoT)** | 88.0 | 88.8 | 88.6 | 87.1 | 87.7 | **89.5** | 83.3 | | **MATH (4 shot, Flex)** | 63.4 | 59.9 | 67.3 | 66.6 | 58.4 | **72.5** | 68.8 | | **GSM8K (8 shot, CoT)** | 93.6 | 94.2 | **95.5** | 95.4 | 92.7 | 94.1 | 91.7 | | **HumanEval (pass@10)** | 95.7 | **97.2** | 95.9 | 95.9 | 92.3 | 94.6 | 97.0 | | **HumanEval+ (pass@10)** | 93.3 | **93.9** | 92.9 | 90.3 | 86.9 | 91.6 | 92.7 | | **IFEval (prompt loose)** | 82.4 | 85.0 | 86.0 | **88.4** | 81.9 | 88.0 | 84.8 | | **AlpacaEval 2 (LC % win)** | 30.4 | 49.8 | 51.4 | 38.5 | 30.2 | 53.5 | **65.0** | | **Safety (6 task avg.)** | 87.7 | 85.5 | 86.7 | 86.8 | 65.8 | 72.2 | **90.9** | ## Hyperparamters SFT: - **Learning Rate**: 5E-6 (8B), 2E-6 (70B, 405B) - **Effective Batch Size:** 128 (8B, 70B), 256 (405B) - **Max. Sequence Length:** 4096 - **Loss Accumulation:** Sum (see https://unsloth.ai/blog/gradient) - **Learning Rate Schedule:** Linear - **LR Warmup Ratio:** 0.03 - **Num. Epochs:** 2 ## License and use All Llama 3.1 Tülu3 models are released under Meta's [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/). Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. Tülu3 is intended for research and educational use. For more information, please see our [Responsible Use Guidelines](https://allenai.org/responsible-use). ## Citation If Tülu3 or any of the related materials were helpful to your work, please cite: ``` @article{lambert2024tulu3, title = {Tülu 3: Pushing Frontiers in Open Language Model Post-Training}, author = { Nathan Lambert and Jacob Morrison and Valentina Pyatkin and Shengyi Huang and Hamish Ivison and Faeze Brahman and Lester James V. Miranda and Alisa Liu and Nouha Dziri and Shane Lyu and Yuling Gu and Saumya Malik and Victoria Graf and Jena D. Hwang and Jiangjiang Yang and Ronan Le Bras and Oyvind Tafjord and Chris Wilhelm and Luca Soldaini and Noah A. Smith and Yizhong Wang and Pradeep Dasigi and Hannaneh Hajishirzi }, year = {2024}, email = {tulu@allenai.org} } ```

fujiantiiazhraa/blockassist-bc-marine_robust_bee_1756314703

fujiantiiazhraa

2025-08-27T17:36:33Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "marine robust bee", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T17:36:29Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - marine robust bee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

tphage/Llama-3B-SFT-250827

tphage

2025-08-27T17:33:33Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-08-27T17:33:19Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

amd/Phi-3-mini-4k-instruct-awq-g128-int4-asym-fp16-onnx-hybrid

amd

2025-08-27T17:32:35Z

187

0

null

[ "onnx", "nlp", "code", "amd", "ryzenai-hybrid", "text-generation", "conversational", "en", "fr", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:quantized:microsoft/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]

text-generation

2024-11-30T06:32:13Z

--- license: mit language: - en - fr base_model: - microsoft/Phi-3-mini-4k-instruct pipeline_tag: text-generation tags: - nlp - code - onnx - amd - ryzenai-hybrid --- # microsoft/Phi-3-mini-4k-instruct - ## Introduction This model was prepared using the AMD Quark Quantization tool, followed by necessary post-processing. - ## Quantization Strategy - AWQ / Group 128 / Asymmetric / UINT4 Weights / FP16 activations - Excluded Layers: None - ## Quick Start For quickstart, refer to [Ryzen AI doucmentation](https://ryzenai.docs.amd.com/en/latest/hybrid_oga.html) #### Evaluation scores The perplexity measurement is run on the wikitext-2-raw-v1 (raw data) dataset provided by Hugging Face. Perplexity score measured for prompt length 2k is 6.7532. #### License Modifications copyright(c) 2024 Advanced Micro Devices,Inc. All rights reserved. MIT License Copyright (c) 2024 Advanced Micro Devices, Inc Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. license: MIT license

ASSISTA-VIDEO-DO-SURFISTA-VAZADO-VIDEO/18.VIDEOS.DO.SURFISTA.VAZADO.VIDEO.SEXO.DO.SURFISTA.NO.BANHEIRO.SURFISTA.MANSAO.PRIVILEGE.EROME

ASSISTA-VIDEO-DO-SURFISTA-VAZADO-VIDEO

2025-08-27T17:22:00Z

0

null

[ "region:us" ]

null

2025-08-27T17:21:21Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

xinnn32/blockassist-bc-meek_winged_caterpillar_1756315019

xinnn32

2025-08-27T17:17:34Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T17:17:27Z

--- 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).

ggozzy/blockassist-bc-stubby_yapping_mandrill_1756314777

ggozzy

2025-08-27T17:14:06Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T17:14:00Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756314734

Ferdi3425

2025-08-27T17:12:42Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T17:12:39Z

--- 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).

Stasonelison/blockassist-bc-howling_powerful_aardvark_1756314422

Stasonelison

2025-08-27T17:07:43Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "howling powerful aardvark", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T17:07:34Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - howling powerful aardvark --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

eshanroy5678/blockassist-bc-untamed_dextrous_dingo_1756313676

eshanroy5678

2025-08-27T17:02:31Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed dextrous dingo", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T16:58:33Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - untamed dextrous dingo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

OksanaB/blockassist-bc-huge_ferocious_chameleon_1756313979

OksanaB

2025-08-27T17:01:25Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T17:00:19Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge ferocious chameleon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

OksanaB/blockassist-bc-huge_ferocious_chameleon_1756313672

OksanaB

2025-08-27T16:56:09Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T16:55:14Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge ferocious chameleon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

annasoli/gemma-2-9b-it_SV_l20_lr1e-3_a256

annasoli

2025-08-27T16:51:42Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-08-27T16:51:11Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ggozzy/blockassist-bc-stubby_yapping_mandrill_1756313283

ggozzy

2025-08-27T16:49:13Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T16:49:07Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

mradermacher/zr-seal-v0.1-GGUF

mradermacher

2025-08-27T16:48:19Z

0

transformers

[ "transformers", "gguf", "en", "base_model:mfirth/zr-seal-v0.1", "base_model:quantized:mfirth/zr-seal-v0.1", "endpoints_compatible", "region:us", "conversational" ]

null

2025-08-27T16:25:11Z

--- base_model: mfirth/zr-seal-v0.1 language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About         static quants of https://huggingface.co/mfirth/zr-seal-v0.1  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#zr-seal-v0.1-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/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q2_K.gguf) | Q2_K | 0.7 | | | [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q3_K_S.gguf) | Q3_K_S | 0.7 | | | [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q3_K_M.gguf) | Q3_K_M | 0.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q3_K_L.gguf) | Q3_K_L | 0.8 | | | [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.IQ4_XS.gguf) | IQ4_XS | 0.8 | | | [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q4_K_S.gguf) | Q4_K_S | 0.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q4_K_M.gguf) | Q4_K_M | 0.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q5_K_S.gguf) | Q5_K_S | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q5_K_M.gguf) | Q5_K_M | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q6_K.gguf) | Q6_K | 1.1 | very good quality | | [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.Q8_0.gguf) | Q8_0 | 1.4 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/zr-seal-v0.1-GGUF/resolve/main/zr-seal-v0.1.f16.gguf) | f16 | 2.6 | 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.

vuitton/LouisVuitton_model12

vuitton

2025-08-27T16:42:08Z

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

2025-08-27T16:25:58Z

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

fullclips/O.video.do.surfista.da.Mansao.Privilegio.video.surfista.no.banheiro

fullclips

2025-08-27T16:41:40Z

0

null

[ "region:us" ]

null

2025-08-27T16:40:00Z

Watch 🟢 ➤ ➤ ➤ <a href="https://humptydumpty.cfd/surfistad"> 🌐 Click Here To link (Full video) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://humptydumpty.cfd/surfistad"> 🌐 Full video

bdx33/distilbert-base-cased-CoLA

bdx33

2025-08-27T16:41:16Z

3

0

transformers.js

[ "transformers.js", "onnx", "distilbert", "text-classification", "en", "base_model:textattack/distilbert-base-cased-CoLA", "base_model:quantized:textattack/distilbert-base-cased-CoLA", "region:us" ]

text-classification

2024-06-01T11:25:37Z

--- base_model: textattack/distilbert-base-cased-CoLA language: - en library_name: transformers.js pipeline_tag: text-classification --- https://huggingface.co/textattack/distilbert-base-cased-CoLA with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

Putririzqika/blockassist-bc-polished_nimble_chimpanzee_1756312173

Putririzqika

2025-08-27T16:39:56Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "polished nimble chimpanzee", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T16:39:18Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - polished nimble chimpanzee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

xinnn32/blockassist-bc-meek_winged_caterpillar_1756312425

xinnn32

2025-08-27T16:34:17Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T16:34:13Z

--- 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).

luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskToken-0.001-v2_2008

luckeciano

2025-08-27T16:28:04Z

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

2025-08-27T10:15:53Z

--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskToken-0.001-v2_2008 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskToken-0.001-v2_2008 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-0.001-v2_2008", 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/0z8h2o91) 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: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

xinnn32/blockassist-bc-meek_winged_caterpillar_1756312037

xinnn32

2025-08-27T16:27:52Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T16:27:44Z

--- 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).

Yntec/PlayTheGame

Yntec

2025-08-27T16:23:28Z

0

diffusers

[ "diffusers", "safetensors", "Base Model", "Realistic", "Fantasy", "RPG", "World of Warcraft", "Style", "53rt5355iz", "Anashel", "stable-diffusion", "stable-diffusion-1.5", "stable-diffusion-diffusers", "text-to-image", "base_model:Yntec/RPG_Remix", "base_model:merge:Yntec/RPG_Remix", "base_model:digiplay/BeautyFoolReality_4", "base_model:merge:digiplay/BeautyFoolReality_4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2025-08-27T14:27:41Z

--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Base Model - Realistic - Fantasy - RPG - World of Warcraft - Style - 53rt5355iz - Anashel - stable-diffusion - stable-diffusion-1.5 - stable-diffusion-diffusers - diffusers - text-to-image base_model: - Yntec/RPG_Remix - digiplay/BeautyFoolReality_4 base_model_relation: merge --- # Play The Game RPG Remix (which includes RPG v5 by Anashel and RPG v3 Canditate 16 by Anashel) mixed with BeautyFoolReality4 by 53rt5355iz so it can make what both can make! Showcase and prompts (all use seed 9119): ![Pink haired witch](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/-dpnAhm7a7sZbjKHuR56G.png) photo, best quality, masterpiece, gameplay, heads-up display, a pretty young pink witch's brew causes chaos, detailed purple eyes ![Baldur's gate](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/Ga1ronIHFNpjZC37r1uDn.png) Baldur's gate ![old man with girls on subway](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/z1aC_6dltNctZdW4QgnHv.png) cute girl dozing on the subway didn't know that her wide collar showed a bit of her cleavage, best quality, the bearded man sitting next to her stared intently at her, masterpiece, by Jessie Willcox Smith, bright colors, hair, ((box art, logo, English text, 1980s /(style/), copyright name, retro artstyle)), powerful magical traveler, ![pirate ship painting](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/6iTEdFBgVD3Twn1P_vjyP.png) oil painting with heavy impasto of a pirate ship and its captain, cosmic horror painting, elegant intricate artstation concept art by craig mullins detailed # Recipe: - SuperMerger Weight Sum Use MBW 1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,1,1,1 Model A: BeautyfoolRealityV4 Model B: RPG Remix Then the 840KVAE was baked in, and then it was converted into no-ema version so diffusers can create high quality images with it. Output: PlayTheGame

OrdalieTech/Solon-embeddings-mini-beta-1.1

OrdalieTech

2025-08-27T16:21:43Z

0

1

sentence-transformers

[ "sentence-transformers", "safetensors", "eurobert", "embeddings", "multilingual", "feature-extraction", "sentence-similarity", "custom_code", "fr", "en", "base_model:EuroBERT/EuroBERT-210m", "base_model:finetune:EuroBERT/EuroBERT-210m", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-08-27T16:19:14Z

--- license: apache-2.0 language: - fr - en library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - embeddings - eurobert - multilingual - feature-extraction base_model: EuroBERT/EuroBERT-210m --- # OrdalieTech/Solon-embeddings-mini-beta-1.1 Le modèle d'origine a été créé à partir de `EuroBERT/EuroBERT-210m`, puis entraîné avec la technique **InfoNCE** sur des **paires de très haute qualité générées par LLM** ## Points clés - **Backbone** : `EuroBERT/EuroBERT-210m` - **Pooling** : moyenne des tokens (CLS désactivé, max désactivé) - **Dimensions** : 768 - **Langues** : multilingue dont le français et l'anglais ## Exemples d'usage ### Avec `sentence-transformers` ```python pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("OrdalieTech/Solon-embeddings-mini-beta-1.1") sentences = ["Ceci est une phrase d'exemple", "Chaque phrase est convertie en vecteur"] embeddings = model.encode(sentences, convert_to_tensor=False, normalize_embeddings=True) print(embeddings[0].shape) # (768,) ``` ### Avec `transformers` (feature extraction) ```python pip install -U transformers torch ``` ```python from transformers import AutoTokenizer, AutoModel import torch tok = AutoTokenizer.from_pretrained("EuroBERT/EuroBERT-210m", trust_remote_code=True) enc = AutoModel.from_pretrained("EuroBERT/EuroBERT-210m", trust_remote_code=True) inputs = tok(["Ceci est une phrase d'exemple"], padding=True, truncation=True, return_tensors="pt") with torch.no_grad(): out = enc(**inputs).last_hidden_state # (batch, seq, 768) mask = inputs["attention_mask"].unsqueeze(-1) # (batch, seq, 1) mean_emb = (out * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1) ``` ## Cas d'usage - Recherche sémantique - Reranking - Similarité sémantique de phrases (STS) - Recommandation de contenu - Classification basée sur des embeddings ## Crédit et licence - Modèle de base : [`EuroBERT/EuroBERT-210m`](https://huggingface.co/EuroBERT/EuroBERT-210m) • licence Apache-2.0 - Cette publication reprend la licence Apache-2.0 et respecte les conditions de redistribution du modèle de base - Merci aux auteurs d'EuroBERT pour leur travail et l'ouverture du modèle - Création : @matheoqtb

OksanaB/blockassist-bc-huge_ferocious_chameleon_1756311277

OksanaB

2025-08-27T16:16:12Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T16:15:15Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge ferocious chameleon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

hakimjustbao/blockassist-bc-raging_subtle_wasp_1756309361

hakimjustbao

2025-08-27T16:13:01Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T16:12:57Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

xinnn32/blockassist-bc-meek_winged_caterpillar_1756310953

xinnn32

2025-08-27T16:09:46Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T16:09:41Z

--- 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).

Orginal-Jhoselyn-Maura-viral-video-links/NEW.FULL.VIDEO.Jhoselyn.Maura.Viral.Video.Official.Tutorial

Orginal-Jhoselyn-Maura-viral-video-links

2025-08-27T16:05:40Z

0

null

[ "region:us" ]

null

2025-08-27T16:05:06Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/mdfprj9k?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

capungmerah627/blockassist-bc-stinging_soaring_porcupine_1756309163

capungmerah627

2025-08-27T16:05:33Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stinging soaring porcupine", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T16:05:30Z

--- 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).

licyk/comfyui-extension-models

licyk

2025-08-27T16:05:20Z

0

5

diffusers

[ "diffusers", "onnx", "safetensors", "license:openrail", "region:us" ]

null

2024-01-20T13:33:21Z

--- license: openrail --- 这是储存stable-diffusion-webui/ComfyUI扩展所需的部分模型文件 ## 仓库列表 [sd-extensions-model](https://huggingface.co/licyk/sd-extensions-model) 存放stable-diffusion-webui扩展的模型文件 [comfyui-extension-models](https://huggingface.co/licyk/comfyui-extension-models) 存放ComfyUI扩展的模型文件

OksanaB/blockassist-bc-huge_ferocious_chameleon_1756310570

OksanaB

2025-08-27T16:04:23Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T16:03:26Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge ferocious chameleon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

MultivexAI/gemma-style-20m-dolly-pretrained

MultivexAI

2025-08-27T15:58:35Z

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-27T15:19:54Z

--- library_name: transformers tags: - generated_from_trainer model-index: - name: gemma-style-20m-dolly-pretrained results: [] ---  # gemma-style-20m-dolly-pretrained This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.6593 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 7.4028 | 0.9191 | 250 | 7.4364 | | 7.0051 | 1.8382 | 500 | 7.0592 | | 6.7136 | 2.7574 | 750 | 6.8416 | | 6.5304 | 3.6765 | 1000 | 6.7170 | | 6.4196 | 4.5956 | 1250 | 6.6593 | ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

hasanbasbunar/an-adapter

hasanbasbunar

2025-08-27T15:58:14Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gpt_oss", "trl", "en", "base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-08-27T15:57:57Z

--- base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gpt_oss - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** hasanbasbunar - **License:** apache-2.0 - **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

chainway9/blockassist-bc-untamed_quick_eel_1756308025

chainway9

2025-08-27T15:50:12Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed quick eel", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:50:08Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - untamed quick eel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Bheri/labse-en-sa-v1

Bheri

2025-08-27T15:44:12Z

0

1

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:257886", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:sentence-transformers/LaBSE", "base_model:finetune:sentence-transformers/LaBSE", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-08-27T15:43:01Z

--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:257886 - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/LaBSE widget: - source_sentence: 'Karwa Chauth is a festival celebrated by Hindu women of Northern and Western India on the fourth day after Purnima in the month of Kartika. ' sentences: - 'तस्याः युग्मभ्रातुः वंशानुगत-राजकुमारस्य जाक् इत्यस्य निमेषद्वयात् प्राक् सा अजायत। ' - '"तथापि, Internet Explorer नोपयोक्तव्यम् । यतो हि तत् सम्यक् डिस्प्ले न करोति ।"' - 'कर्वा-चौथ् इति उत्सवः उत्तर-पश्चिम-भारतस्य हिन्दु-महिलाभिः कार्तिकमासे पूर्णिमायाः अनन्तरं चतुर्थदिने आचर्यते। ' - source_sentence: '"""And if any man will hurt them, fire proceedeth out of their mouth, and devoureth their enemies: and if any man will hurt them, he must in this manner be killed."""' sentences: - '"C तथा C++ उभयोः मध्येऽपि, इदं समानं मार्गं इम्प्लिमेण्ट् कर्तुमनुसरति ।"' - यदि केचित् तौ हिंसितुं चेष्टन्ते तर्हि तयो र्वदनाभ्याम् अग्नि र्निर्गत्य तयोः शत्रून् भस्मीकरिष्यति। यः कश्चित् तौ हिंसितुं चेष्टते तेनैवमेव विनष्टव्यं। - यवक्रीत उवाच नायं शक्यस्त्वया बड़े महानोघस्तपोधन। अशक्याद् विनिवर्तस्व शक्यमर्थं समारभ॥ - source_sentence: 'It tarnishes in air to produce a whitish oxidized layer on the surface. ' sentences: - उपस्थितानां रत्नानां श्रेष्ठानामर्घहारिणाम्। नादृश्यत परः पारो नापरस्तत्र भारत॥ - 'इदं वायौ कलङ्कितं भवति, येन तले श्वेतवर्णीयं आक्सिडैस्ड्-आस्तरणं निर्मीयते। ' - आचार्येणाभ्यनुज्ञातश्चतुर्णामेकमाश्रमम्। आविमोक्षाच्छरीरस्य सोऽवतिष्ठेद् यथाविधि॥ - source_sentence: 'If you''re planning to fund part or all of your child''s higher education, it''s best to start saving early on. ' sentences: - समयं वाजिमेधस्य विदित्वा पुरुषर्षभः। यथोक्तो धर्मपुत्रेण प्रव्रजन् स्वपुरी प्रति॥ - 'यदि भवान् भवतः सन्ततेः उच्चशिक्षायाः कृते, आंशिकं वा सम्पूर्णं वा शुल्कं दातुम् इच्छति तर्हि तदर्थं पूर्वमेव धनसञ्चयस्य आरम्भः क्षेमकरः भवेत्। ' - '"""तदनन्तरं तेषां सप्तकंसधारिणां सप्तदूतानाम् एक आगत्य मां सम्भाष्यावदत्, अत्रागच्छ, मेदिन्या नरपतयो यया वेश्यया सार्द्धं व्यभिचारकर्म्म कृतवन्तः,"""' - source_sentence: In spite of these, Dhananjaya made Drona's son carless by cutting off the out-stretched bow of his foe with three shafts, killing his driver with a razor like shaft and making away with his banner with three and his four horses with four other shafts. sentences: - तथापि तं प्रस्फुरदात्तकार्मुकं त्रिभिः शरैर्यन्तृशिरः क्षुरेणा हयांश्चतुर्भिश्च पुनस्त्रिभिर्ध्वज धनंजयो द्रौणिरथादपातयत्॥ - एकवारं पूरितं चेत् एतां प्रक्रियां undo कर्तुं न शक्नुमः । - क्रीडां तथा कूर्दनं विना शिक्षा अपूर्णा अस्ति । pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - src2trg_accuracy - trg2src_accuracy - mean_accuracy model-index: - name: SentenceTransformer based on sentence-transformers/LaBSE results: - task: type: translation name: Translation dataset: name: eval en sa type: eval-en-sa metrics: - type: src2trg_accuracy value: 0.944 name: Src2Trg Accuracy - type: trg2src_accuracy value: 0.947 name: Trg2Src Accuracy - type: mean_accuracy value: 0.9455 name: Mean Accuracy --- # SentenceTransformer based on sentence-transformers/LaBSE This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). It maps sentences & paragraphs to a 768-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:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE)  - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity    ### 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': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, '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): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) (3): 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("sentence_transformers_model_id") # Run inference sentences = [ "In spite of these, Dhananjaya made Drona's son carless by cutting off the out-stretched bow of his foe with three shafts, killing his driver with a razor like shaft and making away with his banner with three and his four horses with four other shafts.", 'तथापि तं प्रस्फुरदात्तकार्मुकं त्रिभिः शरैर्यन्तृशिरः क्षुरेणा हयांश्चतुर्भिश्च पुनस्त्रिभिर्ध्वज धनंजयो द्रौणिरथादपातयत्॥', 'क्रीडां तथा कूर्दनं विना शिक्षा अपूर्णा अस्ति ।', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Translation * Dataset: `eval-en-sa` * Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator) | Metric | Value | |:------------------|:-----------| | src2trg_accuracy | 0.944 | | trg2src_accuracy | 0.947 | | **mean_accuracy** | **0.9455** |   ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 257,886 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 6 tokens</li><li>mean: 31.6 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 40.18 tokens</li><li>max: 128 tokens</li></ul> | * Samples: | sentence_0 | sentence_1 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>It normally connects to port 80 on a computer. </code> | <code>इदं सामान्यतः एकस्मिन् सङ्गणके पोर्ट् ८० इत्यनेन सम्पर्कं साधयति। </code> | | <code>He who gives to a Brahmana a good bed perfumed with fragrant scents, covered with an excellent sheet, and pillows, gets without any effort on his part a beautiful wife, belonging to a respectable family and of agreeable manners.</code> | <code>सुगन्धचित्रास्तरणोपधानं दद्यान्नरो यः शयनं द्विजाय। रूपान्वितां पक्षवती मनोज्ञां भार्यामयत्नोपगतां लभेत् सः।</code> | | <code>By mid-1665, with the fortress at Purandar besieged and near capture, Shivaji was forced to come to terms with Jai Singh. </code> | <code>१६६५ तमवर्षस्य मध्यभागे यावत् पुरन्दरस्थस्य दुर्गस्य परिवेष्टनं कृत्वा, ग्रहणस्य समीपे, शिवाजी जयसिङ्घेन सह सन्धानं कर्तुं बाध्यः अभवत्। </code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `num_train_epochs`: 15 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `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`: 15 - `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 - `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`: False - `hub_always_push`: False - `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 - `dispatch_batches`: None - `split_batches`: 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 - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | eval-en-sa_mean_accuracy | |:-------:|:------:|:-------------:|:------------------------:| | 0.0310 | 500 | 0.4289 | - | | 0.0620 | 1000 | 0.182 | - | | 0.0931 | 1500 | 0.1405 | - | | 0.1241 | 2000 | 0.1097 | - | | 0.1551 | 2500 | 0.0911 | - | | 0.1861 | 3000 | 0.0791 | - | | 0.2171 | 3500 | 0.0725 | - | | 0.2482 | 4000 | 0.067 | - | | 0.2792 | 4500 | 0.0594 | - | | 0.3102 | 5000 | 0.0629 | - | | 0.3412 | 5500 | 0.0535 | - | | 0.3723 | 6000 | 0.0512 | - | | 0.4033 | 6500 | 0.0456 | - | | 0.4343 | 7000 | 0.0462 | - | | 0.4653 | 7500 | 0.043 | - | | 0.4963 | 8000 | 0.0425 | - | | 0.5274 | 8500 | 0.0412 | - | | 0.5584 | 9000 | 0.0418 | - | | 0.5894 | 9500 | 0.0415 | - | | 0.6204 | 10000 | 0.0409 | - | | 0.6514 | 10500 | 0.04 | - | | 0.6825 | 11000 | 0.032 | - | | 0.7135 | 11500 | 0.0323 | - | | 0.7445 | 12000 | 0.0325 | - | | 0.7755 | 12500 | 0.0355 | - | | 0.8066 | 13000 | 0.0285 | - | | 0.8376 | 13500 | 0.0281 | - | | 0.8686 | 14000 | 0.0289 | - | | 0.8996 | 14500 | 0.033 | - | | 0.9306 | 15000 | 0.0336 | - | | 0.9617 | 15500 | 0.0335 | - | | 0.9927 | 16000 | 0.0278 | - | | 1.0 | 16118 | - | 0.913 | | 1.0237 | 16500 | 0.0312 | - | | 1.0547 | 17000 | 0.0294 | - | | 1.0857 | 17500 | 0.0288 | - | | 1.1168 | 18000 | 0.0287 | - | | 1.1478 | 18500 | 0.0245 | - | | 1.1788 | 19000 | 0.0243 | - | | 1.2098 | 19500 | 0.022 | - | | 1.2408 | 20000 | 0.0266 | - | | 1.2719 | 20500 | 0.0224 | - | | 1.3029 | 21000 | 0.0283 | - | | 1.3339 | 21500 | 0.02 | - | | 1.3649 | 22000 | 0.0212 | - | | 1.3960 | 22500 | 0.0197 | - | | 1.4270 | 23000 | 0.0174 | - | | 1.4580 | 23500 | 0.0179 | - | | 1.4890 | 24000 | 0.0187 | - | | 1.5200 | 24500 | 0.0191 | - | | 1.5511 | 25000 | 0.0151 | - | | 1.5821 | 25500 | 0.0161 | - | | 1.6131 | 26000 | 0.0182 | - | | 1.6441 | 26500 | 0.0155 | - | | 1.6751 | 27000 | 0.013 | - | | 1.7062 | 27500 | 0.0119 | - | | 1.7372 | 28000 | 0.0119 | - | | 1.7682 | 28500 | 0.0133 | - | | 1.7992 | 29000 | 0.0113 | - | | 1.8303 | 29500 | 0.011 | - | | 1.8613 | 30000 | 0.0133 | - | | 1.8923 | 30500 | 0.0114 | - | | 1.9233 | 31000 | 0.0139 | - | | 1.9543 | 31500 | 0.0131 | - | | 1.9854 | 32000 | 0.0115 | - | | 2.0 | 32236 | - | 0.9345 | | 2.0164 | 32500 | 0.01 | - | | 2.0474 | 33000 | 0.01 | - | | 2.0784 | 33500 | 0.0091 | - | | 2.1094 | 34000 | 0.0131 | - | | 2.1405 | 34500 | 0.0096 | - | | 2.1715 | 35000 | 0.0095 | - | | 2.2025 | 35500 | 0.0103 | - | | 2.2335 | 36000 | 0.0101 | - | | 2.2645 | 36500 | 0.0102 | - | | 2.2956 | 37000 | 0.0102 | - | | 2.3266 | 37500 | 0.0085 | - | | 2.3576 | 38000 | 0.0087 | - | | 2.3886 | 38500 | 0.0103 | - | | 2.4197 | 39000 | 0.0058 | - | | 2.4507 | 39500 | 0.0086 | - | | 2.4817 | 40000 | 0.0088 | - | | 2.5127 | 40500 | 0.0088 | - | | 2.5437 | 41000 | 0.007 | - | | 2.5748 | 41500 | 0.0082 | - | | 2.6058 | 42000 | 0.0069 | - | | 2.6368 | 42500 | 0.0071 | - | | 2.6678 | 43000 | 0.0058 | - | | 2.6988 | 43500 | 0.0075 | - | | 2.7299 | 44000 | 0.0064 | - | | 2.7609 | 44500 | 0.0053 | - | | 2.7919 | 45000 | 0.0055 | - | | 2.8229 | 45500 | 0.0061 | - | | 2.8540 | 46000 | 0.0059 | - | | 2.8850 | 46500 | 0.0062 | - | | 2.9160 | 47000 | 0.0046 | - | | 2.9470 | 47500 | 0.0064 | - | | 2.9780 | 48000 | 0.0053 | - | | 3.0 | 48354 | - | 0.941 | | 3.0091 | 48500 | 0.0048 | - | | 3.0401 | 49000 | 0.0059 | - | | 3.0711 | 49500 | 0.005 | - | | 3.1021 | 50000 | 0.005 | 0.9415 | | 3.1331 | 50500 | 0.0046 | - | | 3.1642 | 51000 | 0.005 | - | | 3.1952 | 51500 | 0.0051 | - | | 3.2262 | 52000 | 0.0041 | - | | 3.2572 | 52500 | 0.0052 | - | | 3.2882 | 53000 | 0.0052 | - | | 3.3193 | 53500 | 0.0053 | - | | 3.3503 | 54000 | 0.0041 | - | | 3.3813 | 54500 | 0.0042 | - | | 3.4123 | 55000 | 0.0026 | - | | 3.4434 | 55500 | 0.0045 | - | | 3.4744 | 56000 | 0.0045 | - | | 3.5054 | 56500 | 0.0054 | - | | 3.5364 | 57000 | 0.0055 | - | | 3.5674 | 57500 | 0.0046 | - | | 3.5985 | 58000 | 0.0045 | - | | 3.6295 | 58500 | 0.0041 | - | | 3.6605 | 59000 | 0.0037 | - | | 3.6915 | 59500 | 0.003 | - | | 3.7225 | 60000 | 0.0039 | - | | 3.7536 | 60500 | 0.0027 | - | | 3.7846 | 61000 | 0.0041 | - | | 3.8156 | 61500 | 0.003 | - | | 3.8466 | 62000 | 0.0027 | - | | 3.8777 | 62500 | 0.0039 | - | | 3.9087 | 63000 | 0.0038 | - | | 3.9397 | 63500 | 0.0029 | - | | 3.9707 | 64000 | 0.0037 | - | | 4.0 | 64472 | - | 0.9365 | | 4.0017 | 64500 | 0.0023 | - | | 4.0328 | 65000 | 0.0034 | - | | 4.0638 | 65500 | 0.0033 | - | | 4.0948 | 66000 | 0.0033 | - | | 4.1258 | 66500 | 0.004 | - | | 4.1568 | 67000 | 0.0026 | - | | 4.1879 | 67500 | 0.0026 | - | | 4.2189 | 68000 | 0.0025 | - | | 4.2499 | 68500 | 0.0037 | - | | 4.2809 | 69000 | 0.0041 | - | | 4.3119 | 69500 | 0.0031 | - | | 4.3430 | 70000 | 0.0025 | - | | 4.3740 | 70500 | 0.0025 | - | | 4.4050 | 71000 | 0.0022 | - | | 4.4360 | 71500 | 0.0016 | - | | 4.4671 | 72000 | 0.003 | - | | 4.4981 | 72500 | 0.0029 | - | | 4.5291 | 73000 | 0.003 | - | | 4.5601 | 73500 | 0.0025 | - | | 4.5911 | 74000 | 0.0027 | - | | 4.6222 | 74500 | 0.0028 | - | | 4.6532 | 75000 | 0.003 | - | | 4.6842 | 75500 | 0.002 | - | | 4.7152 | 76000 | 0.0028 | - | | 4.7462 | 76500 | 0.0016 | - | | 4.7773 | 77000 | 0.0022 | - | | 4.8083 | 77500 | 0.0019 | - | | 4.8393 | 78000 | 0.0019 | - | | 4.8703 | 78500 | 0.0026 | - | | 4.9014 | 79000 | 0.0023 | - | | 4.9324 | 79500 | 0.0016 | - | | 4.9634 | 80000 | 0.0019 | - | | 4.9944 | 80500 | 0.0018 | - | | 5.0 | 80590 | - | 0.937 | | 5.0254 | 81000 | 0.0028 | - | | 5.0565 | 81500 | 0.0019 | - | | 5.0875 | 82000 | 0.0024 | - | | 5.1185 | 82500 | 0.0016 | - | | 5.1495 | 83000 | 0.0015 | - | | 5.1805 | 83500 | 0.0017 | - | | 5.2116 | 84000 | 0.0016 | - | | 5.2426 | 84500 | 0.0026 | - | | 5.2736 | 85000 | 0.0029 | - | | 5.3046 | 85500 | 0.0027 | - | | 5.3356 | 86000 | 0.002 | - | | 5.3667 | 86500 | 0.002 | - | | 5.3977 | 87000 | 0.0021 | - | | 5.4287 | 87500 | 0.0011 | - | | 5.4597 | 88000 | 0.0016 | - | | 5.4908 | 88500 | 0.0019 | - | | 5.5218 | 89000 | 0.0027 | - | | 5.5528 | 89500 | 0.0012 | - | | 5.5838 | 90000 | 0.0012 | - | | 5.6148 | 90500 | 0.0016 | - | | 5.6459 | 91000 | 0.0019 | - | | 5.6769 | 91500 | 0.0016 | - | | 5.7079 | 92000 | 0.0027 | - | | 5.7389 | 92500 | 0.0013 | - | | 5.7699 | 93000 | 0.0013 | - | | 5.8010 | 93500 | 0.0015 | - | | 5.8320 | 94000 | 0.0016 | - | | 5.8630 | 94500 | 0.002 | - | | 5.8940 | 95000 | 0.001 | - | | 5.9251 | 95500 | 0.0014 | - | | 5.9561 | 96000 | 0.0021 | - | | 5.9871 | 96500 | 0.0022 | - | | 6.0 | 96708 | - | 0.933 | | 6.0181 | 97000 | 0.0016 | - | | 6.0491 | 97500 | 0.0015 | - | | 6.0802 | 98000 | 0.0011 | - | | 6.1112 | 98500 | 0.0016 | - | | 6.1422 | 99000 | 0.001 | - | | 6.1732 | 99500 | 0.0013 | - | | 6.2042 | 100000 | 0.0015 | 0.9365 | | 6.2353 | 100500 | 0.0017 | - | | 6.2663 | 101000 | 0.0015 | - | | 6.2973 | 101500 | 0.0016 | - | | 6.3283 | 102000 | 0.001 | - | | 6.3593 | 102500 | 0.0013 | - | | 6.3904 | 103000 | 0.0013 | - | | 6.4214 | 103500 | 0.0011 | - | | 6.4524 | 104000 | 0.0007 | - | | 6.4834 | 104500 | 0.0013 | - | | 6.5145 | 105000 | 0.0011 | - | | 6.5455 | 105500 | 0.0011 | - | | 6.5765 | 106000 | 0.0015 | - | | 6.6075 | 106500 | 0.002 | - | | 6.6385 | 107000 | 0.0011 | - | | 6.6696 | 107500 | 0.0013 | - | | 6.7006 | 108000 | 0.0017 | - | | 6.7316 | 108500 | 0.0008 | - | | 6.7626 | 109000 | 0.0011 | - | | 6.7936 | 109500 | 0.0008 | - | | 6.8247 | 110000 | 0.0009 | - | | 6.8557 | 110500 | 0.0014 | - | | 6.8867 | 111000 | 0.0014 | - | | 6.9177 | 111500 | 0.0014 | - | | 6.9488 | 112000 | 0.0014 | - | | 6.9798 | 112500 | 0.0013 | - | | 7.0 | 112826 | - | 0.9390 | | 7.0108 | 113000 | 0.0011 | - | | 7.0418 | 113500 | 0.0013 | - | | 7.0728 | 114000 | 0.0012 | - | | 7.1039 | 114500 | 0.001 | - | | 7.1349 | 115000 | 0.0016 | - | | 7.1659 | 115500 | 0.0009 | - | | 7.1969 | 116000 | 0.0009 | - | | 7.2279 | 116500 | 0.0007 | - | | 7.2590 | 117000 | 0.0008 | - | | 7.2900 | 117500 | 0.0014 | - | | 7.3210 | 118000 | 0.0012 | - | | 7.3520 | 118500 | 0.0007 | - | | 7.3831 | 119000 | 0.001 | - | | 7.4141 | 119500 | 0.001 | - | | 7.4451 | 120000 | 0.0007 | - | | 7.4761 | 120500 | 0.0008 | - | | 7.5071 | 121000 | 0.0009 | - | | 7.5382 | 121500 | 0.0009 | - | | 7.5692 | 122000 | 0.001 | - | | 7.6002 | 122500 | 0.0009 | - | | 7.6312 | 123000 | 0.0007 | - | | 7.6622 | 123500 | 0.0009 | - | | 7.6933 | 124000 | 0.0007 | - | | 7.7243 | 124500 | 0.0012 | - | | 7.7553 | 125000 | 0.001 | - | | 7.7863 | 125500 | 0.0005 | - | | 7.8173 | 126000 | 0.0005 | - | | 7.8484 | 126500 | 0.0008 | - | | 7.8794 | 127000 | 0.0014 | - | | 7.9104 | 127500 | 0.0014 | - | | 7.9414 | 128000 | 0.0009 | - | | 7.9725 | 128500 | 0.0008 | - | | 8.0 | 128944 | - | 0.94 | | 8.0035 | 129000 | 0.0013 | - | | 8.0345 | 129500 | 0.0007 | - | | 8.0655 | 130000 | 0.0007 | - | | 8.0965 | 130500 | 0.0008 | - | | 8.1276 | 131000 | 0.0009 | - | | 8.1586 | 131500 | 0.0009 | - | | 8.1896 | 132000 | 0.0007 | - | | 8.2206 | 132500 | 0.0008 | - | | 8.2516 | 133000 | 0.0008 | - | | 8.2827 | 133500 | 0.0006 | - | | 8.3137 | 134000 | 0.0008 | - | | 8.3447 | 134500 | 0.001 | - | | 8.3757 | 135000 | 0.0006 | - | | 8.4068 | 135500 | 0.0007 | - | | 8.4378 | 136000 | 0.0007 | - | | 8.4688 | 136500 | 0.0009 | - | | 8.4998 | 137000 | 0.0008 | - | | 8.5308 | 137500 | 0.0006 | - | | 8.5619 | 138000 | 0.0008 | - | | 8.5929 | 138500 | 0.0007 | - | | 8.6239 | 139000 | 0.0008 | - | | 8.6549 | 139500 | 0.0006 | - | | 8.6859 | 140000 | 0.0005 | - | | 8.7170 | 140500 | 0.0006 | - | | 8.7480 | 141000 | 0.0006 | - | | 8.7790 | 141500 | 0.0006 | - | | 8.8100 | 142000 | 0.0005 | - | | 8.8410 | 142500 | 0.0006 | - | | 8.8721 | 143000 | 0.0005 | - | | 8.9031 | 143500 | 0.0006 | - | | 8.9341 | 144000 | 0.0009 | - | | 8.9651 | 144500 | 0.0007 | - | | 8.9962 | 145000 | 0.0007 | - | | 9.0 | 145062 | - | 0.938 | | 9.0272 | 145500 | 0.0007 | - | | 9.0582 | 146000 | 0.0007 | - | | 9.0892 | 146500 | 0.0007 | - | | 9.1202 | 147000 | 0.0007 | - | | 9.1513 | 147500 | 0.0005 | - | | 9.1823 | 148000 | 0.0005 | - | | 9.2133 | 148500 | 0.0005 | - | | 9.2443 | 149000 | 0.0007 | - | | 9.2753 | 149500 | 0.0006 | - | | 9.3064 | 150000 | 0.0005 | 0.938 | | 9.3374 | 150500 | 0.0005 | - | | 9.3684 | 151000 | 0.0004 | - | | 9.3994 | 151500 | 0.0007 | - | | 9.4305 | 152000 | 0.0006 | - | | 9.4615 | 152500 | 0.0006 | - | | 9.4925 | 153000 | 0.0012 | - | | 9.5235 | 153500 | 0.0015 | - | | 9.5545 | 154000 | 0.0006 | - | | 9.5856 | 154500 | 0.0004 | - | | 9.6166 | 155000 | 0.0004 | - | | 9.6476 | 155500 | 0.0007 | - | | 9.6786 | 156000 | 0.0005 | - | | 9.7096 | 156500 | 0.0006 | - | | 9.7407 | 157000 | 0.0004 | - | | 9.7717 | 157500 | 0.0004 | - | | 9.8027 | 158000 | 0.0006 | - | | 9.8337 | 158500 | 0.0004 | - | | 9.8647 | 159000 | 0.0005 | - | | 9.8958 | 159500 | 0.0005 | - | | 9.9268 | 160000 | 0.0004 | - | | 9.9578 | 160500 | 0.0007 | - | | 9.9888 | 161000 | 0.0008 | - | | 10.0 | 161180 | - | 0.9405 | | 10.0199 | 161500 | 0.0009 | - | | 10.0509 | 162000 | 0.0007 | - | | 10.0819 | 162500 | 0.0007 | - | | 10.1129 | 163000 | 0.0007 | - | | 10.1439 | 163500 | 0.0005 | - | | 10.1750 | 164000 | 0.0005 | - | | 10.2060 | 164500 | 0.0004 | - | | 10.2370 | 165000 | 0.0006 | - | | 10.2680 | 165500 | 0.0006 | - | | 10.2990 | 166000 | 0.0005 | - | | 10.3301 | 166500 | 0.0005 | - | | 10.3611 | 167000 | 0.0006 | - | | 10.3921 | 167500 | 0.0006 | - | | 10.4231 | 168000 | 0.0003 | - | | 10.4542 | 168500 | 0.0005 | - | | 10.4852 | 169000 | 0.001 | - | | 10.5162 | 169500 | 0.0007 | - | | 10.5472 | 170000 | 0.0003 | - | | 10.5782 | 170500 | 0.0005 | - | | 10.6093 | 171000 | 0.0003 | - | | 10.6403 | 171500 | 0.0004 | - | | 10.6713 | 172000 | 0.0006 | - | | 10.7023 | 172500 | 0.0006 | - | | 10.7333 | 173000 | 0.0005 | - | | 10.7644 | 173500 | 0.0004 | - | | 10.7954 | 174000 | 0.0003 | - | | 10.8264 | 174500 | 0.0007 | - | | 10.8574 | 175000 | 0.0005 | - | | 10.8884 | 175500 | 0.0003 | - | | 10.9195 | 176000 | 0.0006 | - | | 10.9505 | 176500 | 0.001 | - | | 10.9815 | 177000 | 0.0007 | - | | 11.0 | 177298 | - | 0.9345 | | 11.0125 | 177500 | 0.0003 | - | | 11.0436 | 178000 | 0.0003 | - | | 11.0746 | 178500 | 0.0005 | - | | 11.1056 | 179000 | 0.0005 | - | | 11.1366 | 179500 | 0.0007 | - | | 11.1676 | 180000 | 0.0008 | - | | 11.1987 | 180500 | 0.0004 | - | | 11.2297 | 181000 | 0.0006 | - | | 11.2607 | 181500 | 0.0006 | - | | 11.2917 | 182000 | 0.0009 | - | | 11.3227 | 182500 | 0.0005 | - | | 11.3538 | 183000 | 0.0004 | - | | 11.3848 | 183500 | 0.0004 | - | | 11.4158 | 184000 | 0.0005 | - | | 11.4468 | 184500 | 0.0003 | - | | 11.4779 | 185000 | 0.0002 | - | | 11.5089 | 185500 | 0.0003 | - | | 11.5399 | 186000 | 0.0007 | - | | 11.5709 | 186500 | 0.0003 | - | | 11.6019 | 187000 | 0.0003 | - | | 11.6330 | 187500 | 0.0004 | - | | 11.6640 | 188000 | 0.0007 | - | | 11.6950 | 188500 | 0.0003 | - | | 11.7260 | 189000 | 0.0003 | - | | 11.7570 | 189500 | 0.0004 | - | | 11.7881 | 190000 | 0.0004 | - | | 11.8191 | 190500 | 0.0003 | - | | 11.8501 | 191000 | 0.0003 | - | | 11.8811 | 191500 | 0.0003 | - | | 11.9121 | 192000 | 0.0002 | - | | 11.9432 | 192500 | 0.0008 | - | | 11.9742 | 193000 | 0.0004 | - | | 12.0 | 193416 | - | 0.944 | | 12.0052 | 193500 | 0.0005 | - | | 12.0362 | 194000 | 0.0002 | - | | 12.0673 | 194500 | 0.0003 | - | | 12.0983 | 195000 | 0.0004 | - | | 12.1293 | 195500 | 0.0005 | - | | 12.1603 | 196000 | 0.0004 | - | | 12.1913 | 196500 | 0.0002 | - | | 12.2224 | 197000 | 0.0002 | - | | 12.2534 | 197500 | 0.0003 | - | | 12.2844 | 198000 | 0.0003 | - | | 12.3154 | 198500 | 0.0005 | - | | 12.3464 | 199000 | 0.0004 | - | | 12.3775 | 199500 | 0.0004 | - | | 12.4085 | 200000 | 0.0003 | 0.9435 | | 12.4395 | 200500 | 0.0003 | - | | 12.4705 | 201000 | 0.0004 | - | | 12.5016 | 201500 | 0.0009 | - | | 12.5326 | 202000 | 0.0005 | - | | 12.5636 | 202500 | 0.0003 | - | | 12.5946 | 203000 | 0.0003 | - | | 12.6256 | 203500 | 0.0002 | - | | 12.6567 | 204000 | 0.0003 | - | | 12.6877 | 204500 | 0.0002 | - | | 12.7187 | 205000 | 0.0005 | - | | 12.7497 | 205500 | 0.0003 | - | | 12.7807 | 206000 | 0.0004 | - | | 12.8118 | 206500 | 0.0003 | - | | 12.8428 | 207000 | 0.0003 | - | | 12.8738 | 207500 | 0.0003 | - | | 12.9048 | 208000 | 0.0003 | - | | 12.9358 | 208500 | 0.0006 | - | | 12.9669 | 209000 | 0.0004 | - | | 12.9979 | 209500 | 0.0004 | - | | 13.0 | 209534 | - | 0.9455 | </details> ### Framework Versions - Python: 3.10.17 - Sentence Transformers: 4.1.0 - Transformers: 4.46.3 - PyTorch: 2.2.0+cu121 - Accelerate: 1.1.1 - Datasets: 2.18.0 - Tokenizers: 0.20.3 ## 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", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

OksanaB/blockassist-bc-huge_ferocious_chameleon_1756309051

OksanaB

2025-08-27T15:39:06Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:38:11Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge ferocious chameleon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Dejiat/blockassist-bc-savage_unseen_bobcat_1756309043

Dejiat

2025-08-27T15:37:47Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:37:45Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # 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_1756307121

koloni

2025-08-27T15:31:52Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:31:48Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

dr-wong-lu-yang-cctv-Viral-videoss/New.full.videos.Dr.wong.Viral.Video.Official.Tutorial

dr-wong-lu-yang-cctv-Viral-videoss

2025-08-27T15:26:12Z

0

null

[ "region:us" ]

null

2025-08-27T15:25:42Z

<animated-image data-catalyst=""><a href="https://fubotv24.com/Leaked/?v=video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

Dejiat/blockassist-bc-savage_unseen_bobcat_1756308294

Dejiat

2025-08-27T15:25:21Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:25:17Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

dereklvlv/embedding

dereklvlv

2025-08-27T15:25:03Z

0

pyannote-audio

[ "pyannote-audio", "pytorch", "tensorboard", "pyannote", "pyannote-audio-model", "audio", "voice", "speech", "speaker", "speaker-recognition", "speaker-verification", "speaker-identification", "speaker-embedding", "dataset:voxceleb", "license:mit", "region:us" ]

null

2025-08-27T15:23:35Z

--- tags: - pyannote - pyannote-audio - pyannote-audio-model - audio - voice - speech - speaker - speaker-recognition - speaker-verification - speaker-identification - speaker-embedding datasets: - voxceleb license: mit inference: false extra_gated_prompt: "The collected information will help acquire a better knowledge of pyannote.audio userbase and help its maintainers apply for grants to improve it further. If you are an academic researcher, please cite the relevant papers in your own publications using the model. If you work for a company, please consider contributing back to pyannote.audio development (e.g. through unrestricted gifts). We also provide scientific consulting services around speaker diarization and machine listening." extra_gated_fields: Company/university: text Website: text I plan to use this model for (task, type of audio data, etc): text --- Using this open-source model in production? Consider switching to [pyannoteAI](https://www.pyannote.ai) for better and faster options. # 🎹 Speaker embedding Relies on pyannote.audio 2.1: see [installation instructions](https://github.com/pyannote/pyannote-audio/). This model is based on the [canonical x-vector TDNN-based architecture](https://ieeexplore.ieee.org/abstract/document/8461375), but with filter banks replaced with [trainable SincNet features](https://ieeexplore.ieee.org/document/8639585). See [`XVectorSincNet`](https://github.com/pyannote/pyannote-audio/blob/3c988c028dc505c64fe776720372f6fe816b585a/pyannote/audio/models/embedding/xvector.py#L104-L169) architecture for implementation details. ## Basic usage ```python # 1. visit hf.co/pyannote/embedding and accept user conditions # 2. visit hf.co/settings/tokens to create an access token # 3. instantiate pretrained model from pyannote.audio import Model model = Model.from_pretrained("pyannote/embedding", use_auth_token="ACCESS_TOKEN_GOES_HERE") ``` ```python from pyannote.audio import Inference inference = Inference(model, window="whole") embedding1 = inference("speaker1.wav") embedding2 = inference("speaker2.wav") # `embeddingX` is (1 x D) numpy array extracted from the file as a whole. from scipy.spatial.distance import cdist distance = cdist(embedding1, embedding2, metric="cosine")[0,0] # `distance` is a `float` describing how dissimilar speakers 1 and 2 are. ``` Using cosine distance directly, this model reaches 2.8% equal error rate (EER) on VoxCeleb 1 test set. This is without voice activity detection (VAD) nor probabilistic linear discriminant analysis (PLDA). Expect even better results when adding one of those. ## Advanced usage ### Running on GPU ```python import torch inference.to(torch.device("cuda")) embedding = inference("audio.wav") ``` ### Extract embedding from an excerpt ```python from pyannote.audio import Inference from pyannote.core import Segment inference = Inference(model, window="whole") excerpt = Segment(13.37, 19.81) embedding = inference.crop("audio.wav", excerpt) # `embedding` is (1 x D) numpy array extracted from the file excerpt. ``` ### Extract embeddings using a sliding window ```python from pyannote.audio import Inference inference = Inference(model, window="sliding", duration=3.0, step=1.0) embeddings = inference("audio.wav") # `embeddings` is a (N x D) pyannote.core.SlidingWindowFeature # `embeddings[i]` is the embedding of the ith position of the # sliding window, i.e. from [i * step, i * step + duration]. ``` ## Citation ```bibtex @inproceedings{Bredin2020, Title = {{pyannote.audio: neural building blocks for speaker diarization}}, Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe}, Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing}, Address = {Barcelona, Spain}, Month = {May}, Year = {2020}, } ``` ```bibtex @inproceedings{Coria2020, author="Coria, Juan M. and Bredin, Herv{\'e} and Ghannay, Sahar and Rosset, Sophie", editor="Espinosa-Anke, Luis and Mart{\'i}n-Vide, Carlos and Spasi{\'{c}}, Irena", title="{A Comparison of Metric Learning Loss Functions for End-To-End Speaker Verification}", booktitle="Statistical Language and Speech Processing", year="2020", publisher="Springer International Publishing", pages="137--148", isbn="978-3-030-59430-5" } ```

thorejaya/omega_U6BU5iI

thorejaya

2025-08-27T15:25:02Z

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

2025-08-27T15:25:01Z

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

jibrilorradv/blockassist-bc-shaggy_pale_tortoise_1756306406

jibrilorradv

2025-08-27T15:21:17Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "shaggy pale tortoise", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:21:14Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - shaggy pale tortoise --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

xinnn32/blockassist-bc-meek_winged_caterpillar_1756307934

xinnn32

2025-08-27T15:19:28Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:19:20Z

--- 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).

Dejiat/blockassist-bc-savage_unseen_bobcat_1756307175

Dejiat

2025-08-27T15:06:42Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:06:38Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

alvdansen/upload-models

alvdansen

2025-08-27T15:01:10Z

0

null

[ "license:other", "region:us" ]

null

2025-02-03T12:12:03Z

--- license: other license_name: limited license_link: LICENSE ---

koloni/blockassist-bc-deadly_graceful_stingray_1756305136

koloni

2025-08-27T14:59:15Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:59:11Z

--- 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).

hflqf88888/SWIRL_GUI

hflqf88888

2025-08-27T14:57:32Z

0

null

[ "safetensors", "dataset:hflqf88888/SWIRL_GUI_data", "base_model:Qwen/Qwen2.5-VL-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct", "region:us" ]

null

2025-08-25T06:04:57Z

--- datasets: - hflqf88888/SWIRL_GUI_data base_model: - Qwen/Qwen2.5-VL-3B-Instruct --- The instantiation of SWIRL's dual-agent architecture in mobile GUI control. The Navigator translates instructions, history, and screenshots into structured low-level instructions (LLI), while the Interactor executes them as atomic actions (click, scroll, text input) with precise grounding. This hierarchical design enhances robustness, generalization, and interpretability. For more details, please refer to our [project repository](https://github.com/Lqf-HFNJU/SWIRL).

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756305902

Ferdi3425

2025-08-27T14:45:36Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:45:25Z

--- 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).

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756305352

Ferdi3425

2025-08-27T14:36:28Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:36:17Z

--- 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).

camilasfeijoo/my_smolvla_drawertapefinale

camilasfeijoo

2025-08-27T14:33:52Z

0

lerobot

[ "lerobot", "safetensors", "robotics", "smolvla", "dataset:camilasfeijoo/drawertape", "arxiv:2506.01844", "base_model:lerobot/smolvla_base", "base_model:finetune:lerobot/smolvla_base", "license:apache-2.0", "region:us" ]

robotics

2025-08-27T14:33:48Z

--- base_model: lerobot/smolvla_base datasets: camilasfeijoo/drawertape library_name: lerobot license: apache-2.0 model_name: smolvla pipeline_tag: robotics tags: - lerobot - robotics - smolvla --- # Model Card for smolvla  [SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - **License:** apache-2.0

helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1756303522

helmutsukocok

2025-08-27T14:30:42Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:30:39Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - loud scavenging kangaroo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Vivek20052/blockassist-bc-howling_domestic_puffin_1756304817

Vivek20052

2025-08-27T14:27:35Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "howling domestic puffin", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:27:31Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - howling domestic puffin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Dejiat/blockassist-bc-savage_unseen_bobcat_1756304799

Dejiat

2025-08-27T14:27:09Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:27:05Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

ggozzy/blockassist-bc-stubby_yapping_mandrill_1756304563

ggozzy

2025-08-27T14:23:52Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:23:46Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

NahedDom/blockassist-bc-flapping_stocky_leopard_1756302683

NahedDom

2025-08-27T14:17:22Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flapping stocky leopard", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:17:19Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flapping stocky leopard --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

hothihongthanh2016/blockassist-bc-raging_tropical_anaconda_1756303327

hothihongthanh2016

2025-08-27T14:16:35Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging tropical anaconda", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:16:32Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging tropical anaconda --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

lisaozill03/blockassist-bc-rugged_prickly_alpaca_1756302380

lisaozill03

2025-08-27T14:13:39Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:13:35Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

jonlizardo/affine-grpo-03

jonlizardo

2025-08-27T14:13:24Z

0

transformers

[ "transformers", "safetensors", "smollm3", "text-generation", "conversational", "en", "fr", "es", "it", "pt", "zh", "ar", "ru", "base_model:HuggingFaceTB/SmolLM3-3B-Base", "base_model:finetune:HuggingFaceTB/SmolLM3-3B-Base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-08-27T14:12:12Z

--- library_name: transformers license: apache-2.0 language: - en - fr - es - it - pt - zh - ar - ru base_model: - HuggingFaceTB/SmolLM3-3B-Base --- # SmolLM3 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/zy0dqTCCt5IHmuzwoqtJ9.png) ## Table of Contents 1. [Model Summary](#model-summary) 2. [How to use](#how-to-use) 3. [Evaluation](#evaluation) 4. [Training](#training) 5. [Limitations](#limitations) 6. [License](#license) ## Model Summary SmolLM3 is a 3B parameter language model designed to push the boundaries of small models. It supports dual mode reasoning, 6 languages and long context. SmolLM3 is a fully open model that offers strong performance at the 3B–4B scale. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/db3az7eGzs-Sb-8yUj-ff.png) The model is a decoder-only transformer using GQA and NoPE (with 3:1 ratio), it was pretrained on 11.2T tokens with a staged curriculum of web, code, math and reasoning data. Post-training included midtraining on 140B reasoning tokens followed by supervised fine-tuning and alignment via Anchored Preference Optimization (APO). ### Key features - Instruct model optimized for **hybrid reasoning** - **Fully open model**: open weights + full training details including public data mixture and training configs - **Long context:** Trained on 64k context and supports up to **128k tokens** using YARN extrapolation - **Multilingual**: 6 natively supported (English, French, Spanish, German, Italian, and Portuguese) For more details refer to our blog post: https://hf.co/blog/smollm3 ## How to use The modeling code for SmolLM3 is available in transformers `v4.53.0`, so make sure to upgrade your transformers version. You can also load the model with the latest `vllm` which uses transformers as a backend. ```bash pip install -U transformers ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "HuggingFaceTB/SmolLM3-3B" device = "cuda" # for GPU usage or "cpu" for CPU usage # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, ).to(device) # prepare the model input prompt = "Give me a brief explanation of gravity in simple terms." messages_think = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages_think, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # Generate the output generated_ids = model.generate(**model_inputs, max_new_tokens=32768) # Get and decode the output output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :] print(tokenizer.decode(output_ids, skip_special_tokens=True)) ``` >[!TIP] > We recommend setting `temperature=0.6` and `top_p=0.95` in the sampling parameters. ### Long context processing The current `config.json` is set for context length up to 65,536 tokens. To handle longer inputs (128k or 256k), we utilize YaRN you can change the `max_position_embeddings` and rope_scaling` to: ``` { ..., "rope_scaling": { "factor": 2.0, #2x65536=131 072 "original_max_position_embeddings": 65536, "type": "yarn" } } ``` ### Enabling and Disabling Extended Thinking Mode We enable extended thinking by default, so the example above generates the output with a reasoning trace. For choosing between enabling, you can provide the `/think` and `/no_think` flags through the system prompt as shown in the snippet below for extended thinking disabled. The code for generating the response with extended thinking would be the same except that the system prompt should have `/think` instead of `/no_think`. ```python prompt = "Give me a brief explanation of gravity in simple terms." messages = [ {"role": "system", "content": "/no_think"}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) ``` We also provide the option of specifying the whether to use extended thinking through the `enable_thinking` kwarg as in the example below. You do not need to set the `/no_think` or `/think` flags through the system prompt if using the kwarg, but keep in mind that the flag in the system prompt overwrites the setting in the kwarg. ```python prompt = "Give me a brief explanation of gravity in simple terms." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False ) ``` ### Agentic Usage SmolLM3 supports tool calling! Just pass your list of tools: - Under the argument `xml_tools` for standard tool-calling: these tools will be called as JSON blobs within XML tags, like `<tool_call>{"name": "get_weather", "arguments": {"city": "Copenhagen"}}</tool_call>` - Or under `python_tools`: then the model will call tools like python functions in a `<code>` snippet, like `<code>get_weather(city="Copenhagen")</code>` ```python from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "HuggingFaceTB/SmolLM3-3B" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint) tools = [ { "name": "get_weather", "description": "Get the weather in a city", "parameters": {"type": "object", "properties": {"city": {"type": "string", "description": "The city to get the weather for"}}}} ] messages = [ { "role": "user", "content": "Hello! How is the weather today in Copenhagen?" } ] inputs = tokenizer.apply_chat_template( messages, enable_thinking=False, # True works as well, your choice! xml_tools=tools, add_generation_prompt=True, tokenize=True, return_tensors="pt" ) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ### Using Custom System Instructions. You can specify custom instruction through the system prompt while controlling whether to use extended thinking. For example, the snippet below shows how to make the model speak like a pirate while enabling extended thinking. ```python prompt = "Give me a brief explanation of gravity in simple terms." messages = [ {"role": "system", "content": "Speak like a pirate./think"}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) ``` For local inference, you can use `llama.cpp`, `ONNX`, `MLX`, `MLC` and `ExecuTorch`. You can find quantized checkpoints in this collection (https://huggingface.co/collections/HuggingFaceTB/smollm3-686d33c1fdffe8e635317e23) ### vLLM and SGLang You can use vLLM and SGLang to deploy the model in an API compatible with OpenAI format. #### SGLang ```bash python -m sglang.launch_server --model-path HuggingFaceTB/SmolLM3-3B ``` #### vLLM ```bash vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes ``` #### Setting `chat_template_kwargs` You can specify `chat_template_kwargs` such as `enable_thinking` to a deployed model by passing the `chat_template_kwargs` parameter in the API request. ```bash curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "HuggingFaceTB/SmolLM3-3B", "messages": [ {"role": "user", "content": "Give me a brief explanation of gravity in simple terms."} ], "temperature": 0.6, "top_p": 0.95, "max_tokens": 16384, "chat_template_kwargs": {"enable_thinking": false} }' ``` ## Evaluation In this section, we report the evaluation results of SmolLM3 model. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them. We highlight the best score in bold and underline the second-best score. ### Instruction Model #### No Extended Thinking Evaluation results of non reasoning models and reasoning models in no thinking mode. We highlight the best and second-best scores in bold. | Category | Metric | SmoLLM3-3B | Qwen2.5-3B | Llama3.1-3B | Qwen3-1.7B | Qwen3-4B | |---------|--------|------------|------------|-------------|------------|----------| | High school math competition | AIME 2025 | 9.3 | 2.9 | 0.3 | 8.0 | **17.1** | | Math problem-solving | GSM-Plus | 72.8 | 74.1 | 59.2 | 68.3 | **82.1** | | Competitive programming | LiveCodeBench v4 | 15.2 | 10.5 | 3.4 | 15.0 | **24.9** | | Graduate-level reasoning | GPQA Diamond | 35.7 | 32.2 | 29.4 | 31.8 | **44.4** | | Instruction following | IFEval | **76.7** | 65.6 | 71.6 | 74.0 | 68.9 | | Alignment | MixEval Hard | 26.9 | 27.6 | 24.9 | 24.3 | **31.6** | | Tool Calling | BFCL| 92.3 | - | 92.3 * | 89.5 | **95.0** | | Multilingual Q&A | Global MMLU | 53.5 | 50.54 | 46.8 | 49.5 | **65.1** | (*): this is a tool calling finetune #### Extended Thinking Evaluation results in reasoning mode for SmolLM3 and Qwen3 models: | Category | Metric | SmoLLM3-3B | Qwen3-1.7B | Qwen3-4B | |---------|--------|------------|------------|----------| | High school math competition | AIME 2025 | 36.7 | 30.7 | **58.8** | | Math problem-solving | GSM-Plus | 83.4 | 79.4 | **88.2** | | Competitive programming | LiveCodeBench v4 | 30.0 | 34.4 | **52.9** | | Graduate-level reasoning | GPQA Diamond | 41.7 | 39.9 | **55.3** | | Instruction following | IFEval | 71.2 | 74.2 | **85.4** | | Alignment | MixEval Hard | 30.8 | 33.9 | **38.0** | | Tool Calling | BFCL | 88.8 | 88.8 | **95.5** | | Multilingual Q&A | Global MMLU | 64.1 | 62.3 | **73.3** | ### Base Pre-Trained Model #### English benchmarks Note: All evaluations are zero-shot unless stated otherwise. For Ruler 64k evaluation, we apply YaRN to the Qwen models with 32k context to extrapolate the context length. | Category | Metric | SmolLM3-3B | Qwen2.5-3B | Llama3-3.2B | Qwen3-1.7B-Base | Qwen3-4B-Base | |---------|--------|---------------------|------------|--------------|------------------|---------------| | Reasoning & Commonsense| HellaSwag | **76.15** | 74.19 |75.52 | 60.52 | 74.37 | | | ARC-CF (Average) | **65.61** | 59.81 | 58.58 | 55.88 | 62.11 | | | Winogrande | 58.88 | **61.41** | 58.72 | 57.06 | 59.59 | | | CommonsenseQA | 55.28 | 49.14 | **60.60** | 48.98 | 52.99 | | Knowledge & Understanding | MMLU-CF (Average) | 44.13 | 42.93 | 41.32 | 39.11 | **47.65** | | | MMLU Pro CF | 19.61 | 16.66 | 16.42 | 18.04 | **24.92** | | | MMLU Pro MCF | 32.70 | 31.32 | 25.07 | 30.39 | **41.07** | | | PIQA | **78.89** | 78.35 | 78.51 | 75.35 | 77.58 | | | OpenBookQA | 40.60 | 40.20 | 42.00 | 36.40 | **42.40** | | | BoolQ | **78.99** | 73.61 | 75.33 | 74.46 | 74.28 | | **Math & Code** | | | | | | | | Coding & math | HumanEval+ | 30.48 | 34.14| 25.00 | 43.29| **54.87** | | | MBPP+ | 52.91 | 52.11 | 38.88| 59.25 | **63.75** | | | MATH (4-shot) | 46.10 | 40.10 | 7.44 | 41.64 | **51.20** | | | GSM8k (5-shot) | 67.63 | 70.13 | 25.92 | 65.88 | **74.14** | | **Long context** | | | | | | | | | Ruler 32k | 76.35 | 75.93 | 77.58 | 70.63 | **83.98** | | | Ruler 64k | 67.85 | 64.90 | **72.93** | 57.18 | 60.29 | | | Ruler 128k | 61.03 | 62.23 | **71.30** | 43.03 | 47.23 | #### Multilingual benchmarks | Category | Metric | SmolLM3 3B Base | Qwen2.5-3B | Llama3.2 3B | Qwen3 1.7B Base | Qwen3 4B Base | |---------|--------|---------------------|------------|--------------|------------------|---------------| | Main supported languages | | | | | | | | | French| MLMM Hellaswag | **63.94** | 57.47 | 57.66 | 51.26 | 61.00 | | | Belebele | 51.00 | 51.55 | 49.22 |49.44| **55.00** | | | Global MMLU (CF) | 38.37 | 34.22 | 33.71 | 34.94 |**41.80** | | | Flores-200 (5-shot) | 62.85| 61.38| 62.89 | 58.68 | **65.76** | | Spanish| MLMM Hellaswag | **65.85** | 58.25 | 59.39 | 52.40 | 61.85 | | | Belebele | 47.00 | 48.88 | 47.00 | 47.56 | **50.33** | | | Global MMLU (CF) | 38.51 | 35.84 | 35.60 | 34.79 |**41.22** | | | Flores-200 (5-shot) | 48.25| 50.00| 44.45 | 46.93 | **50.16** | | German| MLMM Hellaswag | **59.56** | 49.99| 53.19|46.10| 56.43| | | Belebele | 48.44 | 47.88 | 46.22 | 48.00 | **53.44**| | | Global MMLU (CF) | 35.10 | 33.19 | 32.60 | 32.73 |**38.70** | | | Flores-200 (5-shot) | **56.60**| 50.63| 54.95 | 52.58 | 50.48 | | Italian| MLMM Hellaswag | **62.49** | 53.21 | 54.96 | 48.72 | 58.76 | | | Belebele | 46.44 | 44.77 | 43.88 | 44.00 | **48.78** | 44.88 | | | Global MMLU (CF) | 36.99 | 33.91 | 32.79 | 35.37 |**39.26** | | | Flores-200 (5-shot) | 52.65| **54.87**| 48.83 | 48.37 | 49.11 | | Portuguese| MLMM Hellaswag | **63.22** | 57.38 | 56.84 | 50.73 | 59.89 | | | Belebele | 47.67 | **49.22** | 45.00 | 44.00 | 50.00 | 49.00 | | | Global MMLU (CF) | 36.88 | 34.72 | 33.05 | 35.26 |**40.66** | | | Flores-200 (5-shot) | 60.93 |57.68| 54.28 | 56.58 | **63.43** | The model has also been trained on Arabic (standard), Chinese and Russian data, but has seen fewer tokens in these languages compared to the 6 above. We report the performance on these langages for information. | Category | Metric | SmolLM3 3B Base | Qwen2.5-3B | Llama3.2 3B | Qwen3 1.7B Base | Qwen3 4B Base | |---------|--------|---------------------|------------|--------------|------------------|---------------| | Other supported languages | | | | | | | | | Arabic| Belebele | 40.22 | 44.22 | 45.33 | 42.33 | **51.78** | | | Global MMLU (CF) | 28.57 | 28.81 | 27.67 | 29.37 | **31.85** | | | Flores-200 (5-shot) | 40.22 | 39.44 | **44.43** | 35.82 | 39.76 | | Chinese| Belebele | 43.78 | 44.56 | 49.56 | 48.78 | **53.22** | | | Global MMLU (CF) | 36.16 | 33.79 | 39.57 | 38.56 | **44.55** | | | Flores-200 (5-shot) | 29.17 | **33.21** | 31.89 | 25.70 | 32.50 | | Russian| Belebele | 47.44 | 45.89 | 47.44 | 45.22 | **51.44** | | | Global MMLU (CF) | 36.51 | 32.47 | 34.52 | 34.83 | **38.80** | | | Flores-200 (5-shot) | 47.13 | 48.74 | 50.74 | 54.70 | **60.53** | ## Training ### Model - **Architecture:** Transformer decoder - **Pretraining tokens:** 11T - **Precision:** bfloat16 ### Software & hardware - **GPUs:** 384 H100 - **Training Framework:** [nanotron](https://github.com/huggingface/nanotron/tree/smollm3) - **Data processing framework:** [datatrove](https://github.com/huggingface/datatrove) - **Evaluation framework:** [lighteval](https://github.com/huggingface/lighteval) - **Post-training Framework:** [TRL](https://github.com/huggingface/trl) ### Open resources Here is an infographic with all the training details - The datasets used for pretraining can be found in this [collection](https://huggingface.co/collections/HuggingFaceTB/smollm3-pretraining-datasets-685a7353fdc01aecde51b1d9) and those used in mid-training and post-training will be uploaded later - The training and evaluation configs and code can be found in the [huggingface/smollm](https://github.com/huggingface/smollm) repository. - The training intermediate checkpoints (including the mid-training and SFT checkpoints) are available at [HuggingFaceTB/SmolLM3-3B-checkpoints](https://huggingface.co/HuggingFaceTB/SmolLM3-3B-checkpoints) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/651e96991b97c9f33d26bde6/qiE5ZYr9SD1CIAtfEfuC8.png) ### EU Summary of Public Content The EU AI Act requires all GPAI models to provide a Public Summary of Training Content according to a [given template](https://digital-strategy.ec.europa.eu/en/library/explanatory-notice-and-template-public-summary-training-content-general-purpose-ai-models). You can find the summary for this model below, as well as in its [development Space](https://huggingface.co/spaces/hfmlsoc/smollm3-eu-data-transparency). <iframe src="https://hfmlsoc-smollm3-eu-data-transparency.hf.space" frameborder="0" width="850" height="350" ></iframe> ## Limitations SmolLM3 can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. ## License [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) ## Citation ```bash @misc{bakouch2025smollm3, title={{SmolLM3: smol, multilingual, long-context reasoner}}, author={Bakouch, Elie and Ben Allal, Loubna and Lozhkov, Anton and Tazi, Nouamane and Tunstall, Lewis and Patiño, Carlos Miguel and Beeching, Edward and Roucher, Aymeric and Reedi, Aksel Joonas and Gallouédec, Quentin and Rasul, Kashif and Habib, Nathan and Fourrier, Clémentine and Kydlicek, Hynek and Penedo, Guilherme and Larcher, Hugo and Morlon, Mathieu and Srivastav, Vaibhav and Lochner, Joshua and Nguyen, Xuan-Son and Raffel, Colin and von Werra, Leandro and Wolf, Thomas}, year={2025}, howpublished={\url{https://huggingface.co/blog/smollm3}} } ```

Dejiat/blockassist-bc-savage_unseen_bobcat_1756303378

Dejiat

2025-08-27T14:03:21Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:03:18Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

dwoprer/blockassist-bc-mammalian_foxy_squirrel_1756303225

dwoprer

2025-08-27T14:00:58Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mammalian foxy squirrel", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:00:27Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mammalian foxy squirrel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

qwersdfvg/blockassist-bc-tricky_mottled_whale_1756302922

qwersdfvg

2025-08-27T13:55:47Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tricky mottled whale", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T13:55:23Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tricky mottled whale --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1756301272

coelacanthxyz

2025-08-27T13:55:27Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T13:55:20Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

qwersdfvg/blockassist-bc-reclusive_scruffy_gibbon_1756302649

qwersdfvg

2025-08-27T13:51:33Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "reclusive scruffy gibbon", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T13:50:52Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - reclusive scruffy gibbon --- # 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-pawing_downy_anaconda_1756302643

AnerYubo

2025-08-27T13:50:46Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pawing downy anaconda", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T13:50:43Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pawing downy anaconda --- # 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_1756302393

Ferdi3425

2025-08-27T13:47:11Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T13:47:00Z

--- 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).

hoanganhvutk31/mrpc-bert-finetuned

hoanganhvutk31

2025-08-27T13:44:12Z

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-08-27T13:43:40Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

JiHBijou/SAFE_0826

JiHBijou

2025-08-27T13:43:03Z

0

null

[ "region:us" ]

null

2025-08-26T04:02:18Z

# SAFE Video Challenge Example Submission The key requirements is to have a `script.py` file in the top level directory of the repo and optionally a `requirements.txt` file For more details: https://safe-video-2025.dsri.org/#-model-submission

annasoli/gemma-2-9b-it_SV_l20_lr5e-4_a256_nKL

annasoli

2025-08-27T13:34:54Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-08-27T13:34:25Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

yaelahnal/blockassist-bc-mute_clawed_crab_1756301108

yaelahnal

2025-08-27T13:30:29Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T13:25:56Z

--- 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).

liukevin666/blockassist-bc-yawning_striped_cassowary_1756301195

liukevin666

2025-08-27T13:28:40Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T13:27:31Z

--- 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).

QuantTrio/DeepSeek-V3.1-AWQ-Fp16Mix

QuantTrio

2025-08-27T12:31:16Z

0

transformers

[ "transformers", "safetensors", "deepseek_v3", "text-generation", "vLLM", "GPTQ", "conversational", "custom_code", "zh", "en", "arxiv:2412.19437", "base_model:deepseek-ai/DeepSeek-V3.1", "base_model:quantized:deepseek-ai/DeepSeek-V3.1", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]

text-generation

2025-08-27T07:31:11Z

--- license: mit library_name: transformers pipeline_tag: text-generation tags: - vLLM - GPTQ language: - zh - en base_model: - deepseek-ai/DeepSeek-V3.1 base_model_relation: quantized --- # DeepSeek-V3.1-AWQ-Fp16Mix Base model: [DeepSeek-V3.1](https://www.modelscope.cn/models/deepseek-ai/DeepSeek-V3.1) ### 【Dependencies / Installation】 As of **2025-08-27**, create a fresh Python environment and run: ```bash # ❗there are glitches with vllm 0.10.1.1, still looking for resolutions❗ # ❗downgrade vllm for now ❗ pip install vllm==0.9.2 transformers==4.53.0 SITE_PACKAGES=$(pip -V | awk '{print $4}' | sed 's/\/pip$//') # ❗patch up AWQ MoE quant config, otherwise some modules cannot be properly loaded❗ cp awq_marlin.py "$SITE_PACKAGES/vllm/model_executor/layers/quantization/awq_marlin.py" # ❗patch up for fp32 e_score_correction_bias, see https://www.github.com/vllm-project/vllm/pull/23640❗ cp deepseek_v2.py "$SITE_PACKAGES/vllm/model_executor/models/deepseek_v2.py" ``` ### 【vLLM Single Node with 8 GPUs — Startup Command】 ``` CONTEXT_LENGTH=32768 vllm serve \ tclf90/DeepSeek-V3.1-AWQ-Fp16Mix \ --served-model-name DeepSeek-V3.1-AWQ-Fp16Mix \ --swap-space 16 \ --max-num-seqs 512 \ --max-model-len $CONTEXT_LENGTH \ --max-seq-len-to-capture $CONTEXT_LENGTH \ --gpu-memory-utilization 0.8 \ --tensor-parallel-size 8 \ --trust-remote-code \ --disable-log-requests \ --host 0.0.0.0 \ --port 8000 ``` ### 【Logs】 ``` 2025-08-27 1. new installation instuction for stable/correct performance (a) use vllm 0.9.2 instead of 0.9.0: there is unidentified issue with 0.9.0 🥹 which causes numerical error (b) patch up deepseek_v2.py for fp32 e_score_correction_bias 2025-08-25 1. Initial commit ``` ### 【Model Files】 | File Size | Last Updated | |-----------|--------------| | `435GB` | `2025-08-25` | ### 【Model Download】 ```python from modelscope import snapshot_download snapshot_download('tclf90/DeepSeek-V3.1-AWQ-Fp16Mix', cache_dir="your_local_path") ``` ### 【Overview】 <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%20V3-536af5?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="LICENSE" style="margin: 2px;"> <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div> ## Introduction DeepSeek-V3.1 is a hybrid model that supports both thinking mode and non-thinking mode. Compared to the previous version, this upgrade brings improvements in multiple aspects: - **Hybrid thinking mode**: One model supports both thinking mode and non-thinking mode by changing the chat template. - **Smarter tool calling**: Through post-training optimization, the model's performance in tool usage and agent tasks has significantly improved. - **Higher thinking efficiency**: DeepSeek-V3.1-Think achieves comparable answer quality to DeepSeek-R1-0528, while responding more quickly. DeepSeek-V3.1 is post-trained on the top of DeepSeek-V3.1-Base, which is built upon the original V3 base checkpoint through a two-phase long context extension approach, following the methodology outlined in the original DeepSeek-V3 report. We have expanded our dataset by collecting additional long documents and substantially extending both training phases. The 32K extension phase has been increased 10-fold to 630B tokens, while the 128K extension phase has been extended by 3.3x to 209B tokens. Additionally, DeepSeek-V3.1 is trained using the UE8M0 FP8 scale data format to ensure compatibility with microscaling data formats. ## Model Downloads <div align="center"> | **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** | | :------------: | :------------: | :------------: | :------------: | :------------: | | DeepSeek-V3.1-Base | 671B | 37B | 128K | [HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Base) \| [ModelScope](https://modelscope.cn/models/deepseek-ai/DeepSeek-V3.1-Base) | | DeepSeek-V3.1 | 671B | 37B | 128K | [HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V3.1) \| [ModelScope](https://modelscope.cn/models/deepseek-ai/DeepSeek-V3.1) | </div> ## Chat Template The details of our chat template is described in `tokenizer_config.json` and `assets/chat_template.jinja`. Here is a brief description. ### Non-Thinking #### First-Turn Prefix: `<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜></think>` With the given prefix, DeepSeek V3.1 generates responses to queries in non-thinking mode. Unlike DeepSeek V3, it introduces an additional token `</think>`. #### Multi-Turn Context: `<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>...<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>` Prefix: `<｜User｜>{query}<｜Assistant｜></think>` By concatenating the context and the prefix, we obtain the correct prompt for the query. ### Thinking #### First-Turn Prefix: `<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜><think>` The prefix of thinking mode is similar to DeepSeek-R1. #### Multi-Turn Context: `<｜begin▁of▁sentence｜>{system prompt}<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>...<｜User｜>{query}<｜Assistant｜></think>{response}<｜end▁of▁sentence｜>` Prefix: `<｜User｜>{query}<｜Assistant｜><think>` The multi-turn template is the same with non-thinking multi-turn chat template. It means the thinking token in the last turn will be dropped but the `</think>` is retained in every turn of context. ### ToolCall Toolcall is supported in non-thinking mode. The format is: `<｜begin▁of▁sentence｜>{system prompt}{tool_description}<｜User｜>{query}<｜Assistant｜></think>` where the tool_description is ``` ## Tools You have access to the following tools: ### {tool_name1} Description: {description} Parameters: {json.dumps(parameters)} IMPORTANT: ALWAYS adhere to this exact format for tool use: <｜tool▁calls▁begin｜><｜tool▁call▁begin｜>tool_call_name<｜tool▁sep｜>tool_call_arguments<｜tool▁call▁end｜>{{additional_tool_calls}}<｜tool▁calls▁end｜> Where: - `tool_call_name` must be an exact match to one of the available tools - `tool_call_arguments` must be valid JSON that strictly follows the tool's Parameters Schema - For multiple tool calls, chain them directly without separators or spaces ``` ### Code-Agent We support various code agent frameworks. Please refer to the above toolcall format to create your own code agents. An example is shown in `assets/code_agent_trajectory.html`. ### Search-Agent We design a specific format for searching toolcall in thinking mode, to support search agent. For complex questions that require accessing external or up-to-date information, DeepSeek-V3.1 can leverage a user-provided search tool through a multi-turn tool-calling process. Please refer to the `assets/search_tool_trajectory.html` and `assets/search_python_tool_trajectory.html` for the detailed template. ## Evaluation | Category | Benchmark (Metric) | DeepSeek V3.1-NonThinking | DeepSeek V3 0324 | DeepSeek V3.1-Thinking | DeepSeek R1 0528 |----------|----------------------------------|-----------------|---|---|---| | General | | | MMLU-Redux (EM) | 91.8 | 90.5 | 93.7 | 93.4 | | MMLU-Pro (EM) | 83.7 | 81.2 | 84.8 | 85.0 | | GPQA-Diamond (Pass@1) | 74.9 | 68.4 | 80.1 | 81.0 | | Humanity's Last Exam (Pass@1) | - | - | 15.9 | 17.7 |Search Agent| | | BrowseComp | - | - | 30.0 | 8.9 | | BrowseComp_zh | - | - | 49.2 | 35.7 | | Humanity's Last Exam (Python + Search) |- | - | 29.8 | 24.8 | | SimpleQA | - | - | 93.4 | 92.3 | Code | | | LiveCodeBench (2408-2505) (Pass@1) | 56.4 | 43.0 | 74.8 | 73.3 | | Codeforces-Div1 (Rating) | - | - | 2091 | 1930 | | Aider-Polyglot (Acc.) | 68.4 | 55.1 | 76.3 | 71.6 | Code Agent| | | SWE Verified (Agent mode) | 66.0 | 45.4 | - | 44.6 | | SWE-bench Multilingual (Agent mode) | 54.5 | 29.3 | - | 30.5 | | Terminal-bench (Terminus 1 framework) | 31.3 | 13.3 | - | 5.7 | Math | | | AIME 2024 (Pass@1) | 66.3 | 59.4 | 93.1 | 91.4 | | AIME 2025 (Pass@1) | 49.8 | 51.3 | 88.4 | 87.5 | | HMMT 2025 (Pass@1) | 33.5 | 29.2 | 84.2 | 79.4 | Note: - Search agents are evaluated with our internal search framework, which uses a commercial search API + webpage filter + 128K context window. Seach agent results of R1-0528 are evaluated with a pre-defined workflow. - SWE-bench is evaluated with our internal code agent framework. - HLE is evaluated with the text-only subset. ### Usage Example ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V3.1") messages = [ {"role": "system", "content": "You are a helpful assistant"}, {"role": "user", "content": "Who are you?"}, {"role": "assistant", "content": "<think>Hmm</think>I am DeepSeek"}, {"role": "user", "content": "1+1=?"} ] tokenizer.apply_chat_template(messages, tokenize=False, thinking=True, add_generation_prompt=True) # '<｜begin▁of▁sentence｜>You are a helpful assistant<｜User｜>Who are you?<｜Assistant｜></think>I am DeepSeek<｜end▁of▁sentence｜><｜User｜>1+1=?<｜Assistant｜><think>' tokenizer.apply_chat_template(messages, tokenize=False, thinking=False, add_generation_prompt=True) # '<｜begin▁of▁sentence｜>You are a helpful assistant<｜User｜>Who are you?<｜Assistant｜></think>I am DeepSeek<｜end▁of▁sentence｜><｜User｜>1+1=?<｜Assistant｜></think>' ``` ## How to Run Locally The model structure of DeepSeek-V3.1 is the same as DeepSeek-V3. Please visit [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repo for more information about running this model locally. ## License This repository and the model weights are licensed under the [MIT License](LICENSE). ## Citation ``` @misc{deepseekai2024deepseekv3technicalreport, title={DeepSeek-V3 Technical Report}, author={DeepSeek-AI}, year={2024}, eprint={2412.19437}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.19437}, } ``` ## Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).

felixZzz/student_sft_len32k_sub1k_multiZ_acc_mixw8-0827

felixZzz

2025-08-27T12:29:32Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-08-27T12:29:22Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

BaekSeungJu/Ophtimus-8B-Reasoning

BaekSeungJu

2025-08-27T12:20:14Z

15

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-21T13:10:23Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

capungmerah627/blockassist-bc-stinging_soaring_porcupine_1756295398

capungmerah627

2025-08-27T12:18:14Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stinging soaring porcupine", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T12:18:10Z

--- 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).

Dejiat/blockassist-bc-savage_unseen_bobcat_1756296147

Dejiat

2025-08-27T12:02:54Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T12:02:52Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Dejiat/blockassist-bc-savage_unseen_bobcat_1756295791

Dejiat

2025-08-27T11:56:55Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T11:56:53Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).