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 2025-09-13 06:30:42	downloads int64 0 223M	library_name stringclasses 556 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-13 06:27:56	card stringlengths 11 1.01M
aleebaster/blockassist-bc-sly_eager_boar_1755678942	aleebaster	2025-08-20T09:01:20Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly eager boar", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T09:01:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sly eager boar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/Rubicon-Preview-i1-GGUF	mradermacher	2025-08-20T09:00:58Z	0	transformers	[ "transformers", "gguf", "en", "base_model:inclusionAI/Rubicon-Preview", "base_model:quantized:inclusionAI/Rubicon-Preview", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-08-20T04:05:45Z	--- base_model: inclusionAI/Rubicon-Preview language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/inclusionAI/Rubicon-Preview <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Rubicon-Preview-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/Rubicon-Preview-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.imatrix.gguf) \| imatrix \| 0.2 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ1_S.gguf) \| i1-IQ1_S \| 6.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ1_M.gguf) \| i1-IQ1_M \| 7.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 9.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ2_S.gguf) \| i1-IQ2_S \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ2_M.gguf) \| i1-IQ2_M \| 10.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 10.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q2_K.gguf) \| i1-Q2_K \| 11.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 11.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 12.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 13.4 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ3_S.gguf) \| i1-IQ3_S \| 13.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ3_M.gguf) \| i1-IQ3_M \| 13.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 14.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 16.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 16.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q4_0.gguf) \| i1-Q4_0 \| 17.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 17.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 18.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q4_1.gguf) \| i1-Q4_1 \| 19.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 21.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 21.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q6_K.gguf) \| i1-Q6_K \| 25.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
aralper18/blockassist-bc-gilded_tangled_albatross_1755680287	aralper18	2025-08-20T08:58:53Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gilded tangled albatross", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:58:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gilded tangled albatross --- # 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_1755680202	liukevin666	2025-08-20T08:58:16Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:57:56Z	--- 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).
dongjuu/gemma-3-12b-it-Rude-LORA	dongjuu	2025-08-20T08:56:57Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-20T08:56:48Z	--- 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]
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755678475	katanyasekolah	2025-08-20T08:56:09Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:56:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silky sprightly cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
prithivMLmods/SimpleChat-4B-V1-f32-GGUF	prithivMLmods	2025-08-20T08:55:56Z	0	transformers	[ "transformers", "gguf", "qwen3", "text-generation-inference", "Non-CoT", "text-generation", "en", "base_model:OpenBuddy/SimpleChat-4B-V1", "base_model:quantized:OpenBuddy/SimpleChat-4B-V1", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-08-20T06:00:32Z	--- license: apache-2.0 language: - en base_model: - OpenBuddy/SimpleChat-4B-V1 pipeline_tag: text-generation library_name: transformers tags: - text-generation-inference - Non-CoT --- # SimpleChat-4B-V1-f32-GGUF > OpenBuddy’s SimpleChat-4B-V1 is a 4B-parameter conversational AI model built for streamlined, casual everyday interactions, emphasizing concise, rational, and creative responses without relying on complex chain-of-thought reasoning. Optimized for natural dialogue, SimpleChat-4B-V1 is part of the SimpleChat series exploring Non-Chain-of-Thought (Non-CoT) models, making it especially suitable for direct, engaging conversations that prioritize clarity and brevity while retaining the ability to enhance creativity and expressive capacity in outputs. ## Model Files \| File Name \| Quant Type \| File Size \| \| - \| - \| - \| \| SimpleChat-4B-V1.BF16.gguf \| BF16 \| 8.05 GB \| \| SimpleChat-4B-V1.F16.gguf \| F16 \| 8.05 GB \| \| SimpleChat-4B-V1.F32.gguf \| F32 \| 16.1 GB \| \| SimpleChat-4B-V1.Q2_K.gguf \| Q2_K \| 1.67 GB \| \| SimpleChat-4B-V1.Q3_K_L.gguf \| Q3_K_L \| 2.24 GB \| \| SimpleChat-4B-V1.Q3_K_M.gguf \| Q3_K_M \| 2.08 GB \| \| SimpleChat-4B-V1.Q3_K_S.gguf \| Q3_K_S \| 1.89 GB \| \| SimpleChat-4B-V1.Q4_K_M.gguf \| Q4_K_M \| 2.5 GB \| \| SimpleChat-4B-V1.Q4_K_S.gguf \| Q4_K_S \| 2.38 GB \| \| SimpleChat-4B-V1.Q5_K_M.gguf \| Q5_K_M \| 2.89 GB \| \| SimpleChat-4B-V1.Q5_K_S.gguf \| Q5_K_S \| 2.82 GB \| \| SimpleChat-4B-V1.Q6_K.gguf \| Q6_K \| 3.31 GB \| \| SimpleChat-4B-V1.Q8_0.gguf \| Q8_0 \| 4.28 GB \| ## Quants Usage (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) 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)
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755678400	kojeklollipop	2025-08-20T08:55:09Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:55:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
LSummer/my_awesome_video_cls_model	LSummer	2025-08-20T08:54:22Z	0	transformers	[ "transformers", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-08-20T08:54:03Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer model-index: - name: my_awesome_video_cls_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_video_cls_model This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 300 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755678318	vwzyrraz7l	2025-08-20T08:54:03Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:54:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755678244	manusiaperahu2012	2025-08-20T08:52:10Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring long tuna", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:52:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring long tuna --- # 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_1755678290	koloni	2025-08-20T08:51:01Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:50:57Z	--- 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).
indoempatnol/blockassist-bc-fishy_wary_swan_1755678078	indoempatnol	2025-08-20T08:48:18Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:48:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy wary swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
18-Arovi-Nusrat-Ridhi-Viral-Video-links/New.full.videos.Arovi.Nusrat.Ridhi.Viral.Video.Official.Tutorial	18-Arovi-Nusrat-Ridhi-Viral-Video-links	2025-08-20T08:45:30Z	0	null	[ "region:us" ]	null	2025-08-20T08:45:20Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>
liukevin666/blockassist-bc-yawning_striped_cassowary_1755679191	liukevin666	2025-08-20T08:43:59Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:41:47Z	--- 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).
goodragon/gemma-3-12b-it-Rude-LORA	goodragon	2025-08-20T08:43:04Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-20T08:42:54Z	--- 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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Donchocho/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_tricky_dolphin	Donchocho	2025-08-20T08:41:05Z	106	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am graceful_tricky_dolphin", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-07-23T09:20:04Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am graceful_tricky_dolphin --- # 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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sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755677562	sampingkaca72	2025-08-20T08:38:45Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored stealthy elephant", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:38:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored stealthy elephant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
yaelahnal/blockassist-bc-mute_clawed_crab_1755679016	yaelahnal	2025-08-20T08:38:05Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:37:47Z	--- 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).
VIDEOS-19-Nayeon-Sana-viral-video-Clips/New.full.videos.Nayeon.Sana.Viral.Video.Official.Tutorial	VIDEOS-19-Nayeon-Sana-viral-video-Clips	2025-08-20T08:37:28Z	0	null	[ "region:us" ]	null	2025-08-20T08:37:16Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>
lostinjamal/053c182c-e9ca-420e-b9fd-22199c23b1cb	lostinjamal	2025-08-20T08:37:25Z	0	peft	[ "peft", "safetensors", "base_model:adapter:unsloth/smollm-1.7b-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "text-generation", "arxiv:1910.09700", "region:us" ]	text-generation	2025-08-20T08:37:12Z	--- base_model: unsloth/smollm-1.7b-bnb-4bit library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/smollm-1.7b-bnb-4bit - lora - sft - transformers - trl - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755677398	ihsanridzi	2025-08-20T08:36:56Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:36:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sdagsadgd/blockassist-bc-sedate_squeaky_salamander_1755675680	sdagsadgd	2025-08-20T08:35:36Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sedate squeaky salamander", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:35:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sedate squeaky salamander --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VIDEOS-19-Kim-Mariya-Viral-Video-Clips/New.full.videos.Kim.Mariya.Viral.Video.Official.Tutorial	VIDEOS-19-Kim-Mariya-Viral-Video-Clips	2025-08-20T08:34:56Z	0	null	[ "region:us" ]	null	2025-08-20T08:34:45Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755677742	Sayemahsjn	2025-08-20T08:34:52Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:34:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kongleehan/my_awesome_video_cls_model	kongleehan	2025-08-20T08:34:36Z	0	transformers	[ "transformers", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-08-20T08:34:14Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_video_cls_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_video_cls_model This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2957 - Accuracy: 0.9143 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1200 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.0926 \| 0.25 \| 300 \| 1.9448 \| 0.3286 \| \| 0.2963 \| 1.25 \| 600 \| 0.7159 \| 0.7429 \| \| 0.0149 \| 2.25 \| 900 \| 0.5006 \| 0.8571 \| \| 0.0027 \| 3.25 \| 1200 \| 0.2957 \| 0.9143 \| ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
AmirRghp/distilbert-base-uncasedimdb-text-classification	AmirRghp	2025-08-20T08:33:33Z	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "code", "movie", "en", "dataset:shawhin/imdb-truncated", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-18T12:38:24Z	--- library_name: transformers tags: - code - movie license: mit datasets: - shawhin/imdb-truncated language: - en metrics: - accuracy - f1 - precision - recall - confusion_matrix base_model: - distilbert/distilbert-base-uncased pipeline_tag: text-classification --- # Model Card for IMDB Movie Review Classifier This model is built for classifying movie reviews from the IMDB dataset into positive or negative categories. It uses the Hugging Face's `DistilBERT` model, a lighter version of BERT, for text classification tasks. ## Model Details ### Model Description This model is fine-tuned for binary classification, trained on the IMDb dataset, and can predict whether a given review is positive or negative. It utilizes the `distilbert-base-uncased` model, a pre-trained transformer-based architecture. - Developed by: Amirreza Gholipour - Funded by: Amirreza Gholipour - Model type: Transformer-based Text Classifier - Language(s) (NLP): English - License: MIT - Finetuned from model: distilbert-base-uncased ## Uses ### Direct Use This model can be used directly for classifying movie reviews. Given an IMDB review, the model will return a sentiment classification: positive or negative. The input text is tokenized, passed through the `DistilBERT` model, and the output is processed to classify the review sentiment. ### Downstream Use [optional] The model can be fine-tuned further on other similar datasets to specialize it for different domains, such as classifying product reviews, news sentiment, or other text-based sentiment analysis tasks. ### Out-of-Scope Use This model is not intended for use in detecting sarcasm, irony, or more nuanced sentiment expressions that require deeper contextual understanding. It may not perform well on non-English reviews. ## Bias, Risks, and Limitations The model is trained on the IMDb dataset, which may introduce bias due to the nature of the content of movie reviews. It might not generalize well to domains outside of movie review sentiment classification. The dataset could be biased in terms of the types of movies reviewed (e.g., biased toward Hollywood blockbusters). ### Recommendations Users should ensure they are aware of potential biases in the training data. The model should not be relied on for applications requiring high accuracy in specialized domains or nuanced text understanding. ## How to Get Started with the Model ```python from transformers import pipeline # Load the pre-trained model classifier = pipeline('text-classification', model='AmirRghp/distilbert-base-uncasedimdb-text-classification') # Classify a sample text text = "The movie was absolutely amazing and I loved every minute of it!" result = classifier(text) print(result) ``` ## Training Details ### Training Data The model was trained on the IMDb dataset, a collection of 50,000 movie reviews categorized as either positive or negative. - Dataset: IMDb dataset - Number of samples: 50,000 - Categories: Positive, Negative - Data Preprocessing: Tokenization and padding were applied to the raw text data to ensure compatibility with the DistilBERT model. ### Training Procedure #### Preprocessing [optional] The text data is tokenized using the DistilBERT tokenizer. #### Training Hyperparameters - Learning rate: 2e-5 - Batch size: 4 - Epochs: 5 #### Speeds, Sizes, Times [optional] Training was conducted on a GPU with the following specifications: - Hardware Type: Nvidia RTX 5060 TI - Training Time: 5 Min ## Evaluation The model was evaluated on accuracy, F1 score, precision, recall, and confusion matrix metrics. Here are the key evaluation results: - Accuracy: 89.2% - F1 Score: 0.89 - Precision: 0.88 - Recall: 0.90 #### Summary The model performs well on the IMDb dataset, with a high accuracy rate and strong performance across other metrics. It’s ready for use in practical sentiment analysis tasks. ## Technical Specifications [optional] ### Model Architecture and Objective The model is based on the DistilBERT architecture, which is a smaller and faster variant of the BERT model, designed to provide similar performance with fewer parameters. - Architecture: Transformer-based encoder-decoder model - Objective: Binary classification of text (positive or negative sentiment) ### Compute Infrastructure - Hardware: Nvidia RTX 5060 TI - Libraries: Hugging Face Transformers, PyTorch ## More Information [optional] For more details on the model architecture and training, please refer to the Hugging Face documentation ## Model Card Authors [optional] - Author: Amirreza Gholipour - Contact: [](https://www.linkedin.com/in/amirreza-gholipour-11a05a323/)
Sunbird/qwen3-14b-ug40-sft-translation-plus-multilingual-tasks-merged	Sunbird	2025-08-20T08:32:27Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T08:15:06Z	--- 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]
bimabk/053c182c-e9ca-420e-b9fd-22199c23b1cb	bimabk	2025-08-20T08:32:26Z	0	peft	[ "peft", "safetensors", "base_model:adapter:unsloth/smollm-1.7b-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "text-generation", "arxiv:1910.09700", "region:us" ]	text-generation	2025-08-20T08:32:20Z	--- base_model: unsloth/smollm-1.7b-bnb-4bit library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/smollm-1.7b-bnb-4bit - lora - sft - transformers - trl - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0
Milica-y-Angel-David/Milica.y.Angel.David.Video.Debut.Erome.Video.de.Milica.y.Angel.David.ybanez.Jugar.y.descargar	Milica-y-Angel-David	2025-08-20T08:32:06Z	0	null	[ "region:us" ]	null	2025-08-20T08:31:49Z	<a href="https://sdu.sk/AyL"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="fsd" /></a> <a href="https://sdu.sk/AyL" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
vuitton/LouisVuitton_model5	vuitton	2025-08-20T08:30:43Z	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-20T08:24:45Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
jo-mengr/geneformer-fork	jo-mengr	2025-08-20T08:30:21Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-20T08:30:21Z	--- license: apache-2.0 ---
BootesVoid/cm8tb7xkk0000wzj24pkk2m5g_cmejoukqo0u4jrts8h12d5rpm	BootesVoid	2025-08-20T08:29:07Z	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-08-20T08:29:05Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: G3RMNGRL --- # Cm8Tb7Xkk0000Wzj24Pkk2M5G_Cmejoukqo0U4Jrts8H12D5Rpm <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `G3RMNGRL` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "G3RMNGRL", "lora_weights": "https://huggingface.co/BootesVoid/cm8tb7xkk0000wzj24pkk2m5g_cmejoukqo0u4jrts8h12d5rpm/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cm8tb7xkk0000wzj24pkk2m5g_cmejoukqo0u4jrts8h12d5rpm', weight_name='lora.safetensors') image = pipeline('G3RMNGRL').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cm8tb7xkk0000wzj24pkk2m5g_cmejoukqo0u4jrts8h12d5rpm/discussions) to add images that show off what you’ve made with this LoRA.
sehun96/gemma-3-12b-it-Rude-LORA	sehun96	2025-08-20T08:28:42Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-20T08:28:36Z	--- 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]
Official-Archita-Phukan-first-film-video/Archita.Phukan.first.film.video.Clip	Official-Archita-Phukan-first-film-video	2025-08-20T08:28:03Z	0	null	[ "region:us" ]	null	2025-08-20T08:27:52Z	<a href="https://sdu.sk/AyL"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="fsd" /></a> <a href="https://sdu.sk/AyL" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
playdatakoo/omar-ax-lora-skt-4.0	playdatakoo	2025-08-20T08:27:02Z	0	peft	[ "peft", "safetensors", "lora", "skt", "korean", "ax", "ko", "base_model:skt/A.X-4.0-Light", "base_model:adapter:skt/A.X-4.0-Light", "region:us" ]	null	2025-08-20T08:26:41Z	--- base_model: skt/A.X-4.0-Light tags: - lora - peft - skt - korean - ax language: - ko library_name: peft --- # Omar AX LoRA Adapter for SKT A.X-4.0-Light This is a LoRA adapter fine-tuned for the Korean language model. ## Model Details - Base Model: [skt/A.X-4.0-Light](https://huggingface.co/skt/A.X-4.0-Light) - LoRA Rank: 24 - LoRA Alpha: 12 - LoRA Dropout: 0.1 - Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj - Task Type: CAUSAL_LM ## Usage with vLLM INFO 08-20 08:26:21 [__init__.py:241] Automatically detected platform cuda. [1;36m(APIServer pid=15975)[0;0m INFO 08-20 08:26:24 [api_server.py:1805] vLLM API server version 0.10.1 [1;36m(APIServer pid=15975)[0;0m INFO 08-20 08:26:24 [utils.py:326] non-default args: {'model_tag': 'skt/A.X-4.0-Light', 'lora_modules': [LoRAModulePath(name='omar_ax', path='playdatakoo/omar-ax-lora-skt-4.0', base_model_name=None)], 'model': 'skt/A.X-4.0-Light', 'enable_lora': True} [1;36m(APIServer pid=15975)[0;0m INFO 08-20 08:26:30 [__init__.py:711] Resolved architecture: Qwen2ForCausalLM [1;36m(APIServer pid=15975)[0;0m INFO 08-20 08:26:30 [__init__.py:1750] Using max model len 16384 [1;36m(APIServer pid=15975)[0;0m INFO 08-20 08:26:31 [scheduler.py:222] Chunked prefill is enabled with max_num_batched_tokens=2048. INFO 08-20 08:26:36 [__init__.py:241] Automatically detected platform cuda. [1;36m(EngineCore_0 pid=16412)[0;0m INFO 08-20 08:26:37 [core.py:636] Waiting for init message from front-end. [1;36m(EngineCore_0 pid=16412)[0;0m INFO 08-20 08:26:37 [core.py:74] Initializing a V1 LLM engine (v0.10.1) with config: model='skt/A.X-4.0-Light', speculative_config=None, tokenizer='skt/A.X-4.0-Light', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config={}, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=16384, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, decoding_config=DecodingConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_backend=''), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=skt/A.X-4.0-Light, enable_prefix_caching=True, chunked_prefill_enabled=True, use_async_output_proc=True, pooler_config=None, compilation_config={"level":3,"debug_dump_path":"","cache_dir":"","backend":"","custom_ops":[],"splitting_ops":["vllm.unified_attention","vllm.unified_attention_with_output","vllm.mamba_mixer2"],"use_inductor":true,"compile_sizes":[],"inductor_compile_config":{"enable_auto_functionalized_v2":false},"inductor_passes":{},"cudagraph_mode":1,"use_cudagraph":true,"cudagraph_num_of_warmups":1,"cudagraph_capture_sizes":[512,504,496,488,480,472,464,456,448,440,432,424,416,408,400,392,384,376,368,360,352,344,336,328,320,312,304,296,288,280,272,264,256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"cudagraph_copy_inputs":false,"full_cuda_graph":false,"pass_config":{},"max_capture_size":512,"local_cache_dir":null} [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] EngineCore failed to start. [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] Traceback (most recent call last): [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/v1/engine/core.py", line 691, in run_engine_core [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] engine_core = EngineCoreProc(args, kwargs) [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/v1/engine/core.py", line 492, in __init__ [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] super().__init__(vllm_config, executor_class, log_stats, [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/v1/engine/core.py", line 80, in __init__ [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] self.model_executor = executor_class(vllm_config) [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] ^^^^^^^^^^^^^^^^^^^^^^^^^^^ [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/executor/executor_base.py", line 54, in __init__ [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] self._init_executor() [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/executor/uniproc_executor.py", line 48, in _init_executor [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] self.collective_rpc("init_device") [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/executor/uniproc_executor.py", line 58, in collective_rpc [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] answer = run_method(self.driver_worker, method, args, kwargs) [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/utils/__init__.py", line 3007, in run_method [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] return func(args, kwargs) [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] ^^^^^^^^^^^^^^^^^^^^^ [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/worker/worker_base.py", line 603, in init_device [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] self.worker.init_device() # type: ignore [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] ^^^^^^^^^^^^^^^^^^^^^^^^^ [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/v1/worker/gpu_worker.py", line 179, in init_device [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] raise ValueError( [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] ValueError: Free memory on device (2.96/44.34 GiB) on startup is less than desired GPU memory utilization (0.9, 39.91 GiB). Decrease GPU memory utilization or reduce GPU memory used by other processes. ### API Usage ## Usage with PEFT ## Performance - Hot-swap Speed: 0.18s (switching between base and LoRA) - GPU Memory: ~14.8 GiB - Inference Speed**: 0.18-0.19s per request after warmup ## Files - : LoRA configuration - : LoRA weights (SafeTensors format) - Tokenizer files included for convenience
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755676741	quantumxnode	2025-08-20T08:25:05Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:25:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Abigail-video-viral-original/New.full.videos.Abigail.Viral.Video.Official.Tutorial	Abigail-video-viral-original	2025-08-20T08:24:44Z	0	null	[ "region:us" ]	null	2025-08-20T08:23:27Z	[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://the-amarican-dream.blogspot.com/2025/08/han-ty.html) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️ ](https://the-amarican-dream.blogspot.com/2025/08/han-ty.html) [<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://the-amarican-dream.blogspot.com/2025/08/han-ty.html)
thomasht86/Qwen3-Embedding-0.6B-ONNX-Vespa	thomasht86	2025-08-20T08:24:19Z	0	null	[ "onnx", "license:apache-2.0", "region:us" ]	null	2025-08-20T08:19:41Z	--- license: apache-2.0 ---
Archita-Phukan-Viral-full-Video-hq/New.full.Videos.Archita.Phukan.Viral.Video.New.MMS.Original	Archita-Phukan-Viral-full-Video-hq	2025-08-20T08:23:41Z	0	null	[ "region:us" ]	null	2025-08-20T08:23:36Z	<a href="https://sdu.sk/AyL"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="fsd" /></a> <a href="https://sdu.sk/AyL" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
evanurasyifa-Official-video-Clip/New.full.videos.evanurasyifa.Viral.Video.Official.Tutorial	evanurasyifa-Official-video-Clip	2025-08-20T08:23:05Z	0	null	[ "region:us" ]	null	2025-08-20T08:22:50Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>
evanurasyifa-Official-video-Clip/New.full.videos.Bindura.University.Viral.Video.Official.Tutorial	evanurasyifa-Official-video-Clip	2025-08-20T08:21:49Z	0	null	[ "region:us" ]	null	2025-08-20T08:21:37Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>
mang3dd/blockassist-bc-tangled_slithering_alligator_1755676496	mang3dd	2025-08-20T08:20:59Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:20:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
18-Clip-Sophie-Rain-Viral-video-original/New.full.videos.Sophie.Rain.Spiderman.Viral.Video.Official.Tutorial	18-Clip-Sophie-Rain-Viral-video-original	2025-08-20T08:20:50Z	0	null	[ "region:us" ]	null	2025-08-20T08:20:45Z	<a href="https://sdu.sk/AyL"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="fsd" /></a> <a href="https://sdu.sk/AyL" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
koloni/blockassist-bc-deadly_graceful_stingray_1755676489	koloni	2025-08-20T08:20:14Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:20:11Z	--- 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).
Medved444/blockassist-bc-bellowing_finicky_manatee_1755676845	Medved444	2025-08-20T08:19:43Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bellowing finicky manatee", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:19:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bellowing finicky manatee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755676360	kojeklollipop	2025-08-20T08:18:48Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:18:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Uppal-Farm-Girl-Viral-Video-Link-Orginal/New.full.videos.Uppal.Farm.Girl.Viral.Video.Official.Tutorial	Uppal-Farm-Girl-Viral-Video-Link-Orginal	2025-08-20T08:18:45Z	0	null	[ "region:us" ]	null	2025-08-20T08:18:37Z	<a href="https://sdu.sk/AyL"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="fsd" /></a> <a href="https://sdu.sk/AyL" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755676082	vwzyrraz7l	2025-08-20T08:17:40Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:17:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VIDEOS-19-afrin-apu-viral-links/New.full.videos.afrin.apu.Viral.Video.Official.Tutorial	VIDEOS-19-afrin-apu-viral-links	2025-08-20T08:17:20Z	0	null	[ "region:us" ]	null	2025-08-20T08:17:03Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>
VIDEOS-19-fooni-fun-Viral-Video-Clip-XX/New.full.videos.fooni.fun.Viral.Video.Official.Tutorial	VIDEOS-19-fooni-fun-Viral-Video-Clip-XX	2025-08-20T08:16:18Z	0	null	[ "region:us" ]	null	2025-08-20T08:16:11Z	<a href="https://sdu.sk/AyL"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="fsd" /></a> <a href="https://sdu.sk/AyL" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755676003	manusiaperahu2012	2025-08-20T08:16:07Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring long tuna", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:16:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring long tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
unitova/blockassist-bc-zealous_sneaky_raven_1755676050	unitova	2025-08-20T08:15:52Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:15:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - zealous sneaky raven --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
armonio/my-bert-fine-tuned1	armonio	2025-08-20T08:15:46Z	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T08:15:30Z	--- 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]
Orginal-afrin-apu-viral-video-link/New.full.videos.afrin.apu.Viral.Video.Official.Tutorial	Orginal-afrin-apu-viral-video-link	2025-08-20T08:15:38Z	0	null	[ "region:us" ]	null	2025-08-20T08:15:25Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>
coppertoy/blockassist-bc-dappled_purring_bobcat_1755677667	coppertoy	2025-08-20T08:14:36Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dappled purring bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:14:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dappled purring bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
TonevitItaly/TonevitItaly	TonevitItaly	2025-08-20T08:14:28Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-20T08:13:40Z	--- license: apache-2.0 --- Cos'è Tonevit? Tonevit Capsula è una capsula per l'ipertensione progettata per aiutare a mantenere livelli di pressione sanguigna sani in modo sicuro ed efficace. È sviluppata per le persone che desiderano prendersi cura della propria salute cardiovascolare riducendo al contempo i rischi legati all'ipertensione. A differenza dei trattamenti chimici intensivi che a volte possono avere effetti collaterali, Tonevit Pillole è formulato per agire delicatamente sull'organismo, rendendolo una scelta affidabile per l'uso quotidiano. Offre un supporto a lungo termine per le persone che desiderano bilanciare la pressione sanguigna e proteggere la salute del cuore in modo naturale. Sito ufficiale:<a href="https://www.nutritionsee.com/tonevitaly">www.Tonevit.com</a> <p><a href="https://www.nutritionsee.com/tonevitaly"> <img src="https://www.nutritionsee.com/wp-content/uploads/2025/08/Tonevit-Italy.png" alt="enter image description here"> </a></p> <a href="https://www.nutritionsee.com/tonevitaly">Acquista ora!! Clicca sul link qui sotto per maggiori informazioni e ottieni subito il 50% di sconto... Affrettati</a> Sito ufficiale:<a href="https://www.nutritionsee.com/tonevitaly">www.Tonevit.com</a>
Danisalah/Homefixsolutions	Danisalah	2025-08-20T08:13:11Z	0	null	[ "region:us" ]	null	2025-08-20T07:52:25Z	import React, { useState, useEffect } from 'react'; import { View, Text, TouchableOpacity, StyleSheet, Linking, Image } from 'react-native'; export default function App() { const [showMain, setShowMain] = useState(false); useEffect(() => { // Splash Screen مدة 2 ثانية const timer = setTimeout(() => { setShowMain(true); }, 2000); return () => clearTimeout(timer); }, []); const openWhatsApp = () => { Linking.openURL('https://wa.me/213666061838'); }; const sendEmail = () => { Linking.openURL('mailto:Dendanisalah30@gmail.com'); }; const openPayPal = () => { Linking.openURL('https://www.paypal.com/paypalme/dendanisalah30/5'); }; if (!showMain) { // Splash Screen return ( <View style={styles.splash}> <Text style={styles.splashText}>Home Fix Solutions</Text> <Image source={{ uri: 'https://i.postimg.cc/Fzf9X6d6/homefix-icon.png' }} style={styles.logo} /> </View> ); } // Main Screen return ( <View style={styles.container}> <TouchableOpacity style={styles.mainButton} onPress={() => setShowMain('buttons')}> <Text style={styles.mainButtonText}>Start Now</Text> </TouchableOpacity> {showMain === 'buttons' && ( <View style={styles.buttonContainer}> <TouchableOpacity style={styles.button} onPress={openWhatsApp}> <Text style={styles.buttonText}>WhatsApp 🟢</Text> </TouchableOpacity> <TouchableOpacity style={styles.button} onPress={sendEmail}> <Text style={styles.buttonText}>Email ✉️</Text> </TouchableOpacity> <TouchableOpacity style={styles.button} onPress={openPayPal}> <Text style={styles.buttonText}>PayPal 💳</Text> </TouchableOpacity> </View> )} </View> ); } const styles = StyleSheet.create({ splash: { flex: 1, justifyContent: 'center', alignItems: 'center', backgroundColor: '#007bff', }, splashText: { fontSize: 28, fontWeight: 'bold', color: '#fff', marginBottom: 20, }, logo: { width: 100, height: 100, }, container: { flex: 1, justifyContent: 'center', alignItems: 'center', backgroundColor: '#f2f2f2', padding: 20, }, mainButton: { backgroundColor: '#007bff', padding: 20, borderRadius: 15, width: '70%', alignItems: 'center', }, mainButtonText: { color: '#fff', fontSize: 22, fontWeight: 'bold', }, buttonContainer: { marginTop: 30, width: '80%', }, button: { backgroundColor: '#007bff', padding: 15, borderRadius: 12, marginBottom: 15, alignItems: 'center', }, buttonText: { color: '#fff', fontSize: 18, fontWeight: 'bold', }, });
AnerYubo/blockassist-bc-dense_savage_lynx_1755677585	AnerYubo	2025-08-20T08:13:08Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dense savage lynx", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:13:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dense savage lynx --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VIDEOS-18-Sister-Hong-viral-Telegram/New.full.videos.Sister.Hong.Viral.Video.Official.Tutorial	VIDEOS-18-Sister-Hong-viral-Telegram	2025-08-20T08:12:36Z	0	null	[ "region:us" ]	null	2025-08-20T08:12:24Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>
ChavyvAkvar/Liquid-Thinking	ChavyvAkvar	2025-08-20T08:12:20Z	0	transformers	[ "transformers", "safetensors", "lfm2", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T16:28:34Z	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
clairedhx/mistral7b-labels2codes-lora	clairedhx	2025-08-20T08:12:07Z	0	peft	[ "peft", "safetensors", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3", "lora", "sft", "transformers", "trl", "text-generation", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "region:us" ]	text-generation	2025-08-20T07:57:35Z	--- base_model: mistralai/Mistral-7B-Instruct-v0.3 library_name: peft model_name: mistral7b_labels2codes_lora tags: - base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3 - lora - sft - transformers - trl licence: license pipeline_tag: text-generation --- # Model Card for mistral7b_labels2codes_lora This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3). It has been trained using [TRL](https://github.com/huggingface/trl). Mistral-7B Instruct — LoRA (ICD-10 labels → codes) This LoRA adapter fine-tunes mistralai/Mistral-7B-Instruct-v0.3 to map French ICD-10 diagnostic labels (synonyms) to their corresponding codes (dot-less, up to 5 chars). ## Quick start ```python from transformers import pipeline question = "Libellé: Antécédent bronchite chronique" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure It was trained via supervised fine-tuning (QLoRA) on an instruction dataset built from (label, code) pairs (instruct_labels2codes) derived from a curated ICD-10 synonyms table created from the webscrapinng of aideaucodage.fr . ### Framework versions - PEFT 0.17.0 - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.7.1 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
AnerYubo/blockassist-bc-fishy_endangered_antelope_1755677506	AnerYubo	2025-08-20T08:11:49Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy endangered antelope", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:11:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy endangered antelope --- # 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_1755675981	calegpedia	2025-08-20T08:11:25Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:11:22Z	--- 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).
New-Clip-prabh-viral-video-Orginal/New.full.videos.prabh.Viral.Video.Official.Tutorial	New-Clip-prabh-viral-video-Orginal	2025-08-20T08:08:10Z	0	null	[ "region:us" ]	null	2025-08-20T08:07:58Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>
jakehsv/blockassist-bc-flexible_waddling_peacock_1755675452	jakehsv	2025-08-20T08:07:42Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flexible waddling peacock", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:07:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - flexible waddling peacock --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jayssaa/my_awesome_video_cls_model	jayssaa	2025-08-20T08:07:30Z	0	transformers	[ "transformers", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-08-20T08:06:45Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_video_cls_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_video_cls_model This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2870 - Accuracy: 0.4857 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 300 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.2211 \| 1.0 \| 300 \| 1.2870 \| 0.4857 \| ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755675560	helmutsukocok	2025-08-20T08:07:12Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:07:09Z	--- 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).
zzyyuu/distilbert-base-uncased-finetuned-imdb	zzyyuu	2025-08-20T08:06:33Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-08-20T07:47:21Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4892 - Model Preparation Time: 0.0016 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------------:\| \| 2.6814 \| 1.0 \| 157 \| 2.4929 \| 0.0016 \| \| 2.5825 \| 2.0 \| 314 \| 2.4480 \| 0.0016 \| \| 2.5258 \| 3.0 \| 471 \| 2.4823 \| 0.0016 \| ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
TomeroSama07/act_single_lego_pp_420p_no_blur_compressed	TomeroSama07	2025-08-20T08:03:30Z	0	lerobot	[ "lerobot", "safetensors", "robotics", "act", "dataset:TomeroSama07/single_lego_pp_420p_no_blur_compressed", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-20T08:02:48Z	--- datasets: TomeroSama07/single_lego_pp_420p_no_blur_compressed library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - lerobot - robotics - act --- # Model Card for act <!-- Provide a quick summary of what the model is/does. --> [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
ypszn/blockassist-bc-yapping_pawing_worm_1755676956	ypszn	2025-08-20T08:03:28Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping pawing worm", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:03:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping pawing worm --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
yaelahnal/blockassist-bc-mute_clawed_crab_1755676912	yaelahnal	2025-08-20T08:03:24Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:02:43Z	--- 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).
Kokoutou/soundsright_dn_2008_5	Kokoutou	2025-08-20T08:02:43Z	0	null	[ "region:us" ]	null	2025-08-20T07:02:42Z	# Container Template for SoundsRight Subnet Miners This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively. This container has only been tested with Ubuntu 24.04 and CUDA 12.6. It may run on other configurations, but it is not guaranteed. To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt. Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html). Verify that the CDI specification was done correctly with: ``` $ nvidia-ctk cdi list ``` You should see this in your output: ``` nvidia.com/gpu=all nvidia.com/gpu=0 ``` If you are running podman as root, run the following command to start the container: Run the container with: ``` podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi ``` Access logs with: ``` podman logs -f modelapi ``` If you are running the container rootless, there are a few more changes to make: First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters: ``` [nvidia-container-cli] no-cgroups = true [nvidia-container-runtime] debug = "/tmp/nvidia-container-runtime.log" ``` You can also run the following command to achieve the same result: ``` $ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place ``` Run the container with: ``` podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi ``` Access logs with: ``` podman logs -f modelapi ``` Running the container will spin up an API with the following endpoints: 1. `/status/` : Communicates API status 2. `/prepare/` : Download model checkpoint and initialize model 3. `/upload-audio/` : Upload audio files, save to noisy audio directory 4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory 5. `/download-enhanced/` : Download enhanced audio files By default the API will use host `0.0.0.0` and port `6500`. ### References 1. Welker, Simon; Richter, Julius; Gerkmann, Timo Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain. Proceedings of Interspeech 2022, 2022, pp. 2928–2932. [DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653) 2. Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo Speech Enhancement and Dereverberation with Diffusion-based Generative Models. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 31, 2023, pp. 2351–2364. [DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241) 3. Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation. Proceedings of ISCA Interspeech, 2024, pp. 4873–4877.
Kokoutou/soundsright_dn_2008_6	Kokoutou	2025-08-20T08:02:35Z	0	null	[ "region:us" ]	null	2025-08-20T07:02:42Z	# Container Template for SoundsRight Subnet Miners This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively. This container has only been tested with Ubuntu 24.04 and CUDA 12.6. It may run on other configurations, but it is not guaranteed. To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt. Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html). Verify that the CDI specification was done correctly with: ``` $ nvidia-ctk cdi list ``` You should see this in your output: ``` nvidia.com/gpu=all nvidia.com/gpu=0 ``` If you are running podman as root, run the following command to start the container: Run the container with: ``` podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi ``` Access logs with: ``` podman logs -f modelapi ``` If you are running the container rootless, there are a few more changes to make: First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters: ``` [nvidia-container-cli] no-cgroups = true [nvidia-container-runtime] debug = "/tmp/nvidia-container-runtime.log" ``` You can also run the following command to achieve the same result: ``` $ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place ``` Run the container with: ``` podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi ``` Access logs with: ``` podman logs -f modelapi ``` Running the container will spin up an API with the following endpoints: 1. `/status/` : Communicates API status 2. `/prepare/` : Download model checkpoint and initialize model 3. `/upload-audio/` : Upload audio files, save to noisy audio directory 4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory 5. `/download-enhanced/` : Download enhanced audio files By default the API will use host `0.0.0.0` and port `6500`. ### References 1. Welker, Simon; Richter, Julius; Gerkmann, Timo Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain. Proceedings of Interspeech 2022, 2022, pp. 2928–2932. [DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653) 2. Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo Speech Enhancement and Dereverberation with Diffusion-based Generative Models. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 31, 2023, pp. 2351–2364. [DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241) 3. Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation. Proceedings of ISCA Interspeech, 2024, pp. 4873–4877.
milliarderdol/blockassist-bc-roaring_rough_scorpion_1755674877	milliarderdol	2025-08-20T08:01:59Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring rough scorpion", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T08:01:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring rough scorpion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
thedeba/debai	thedeba	2025-08-20T08:01:51Z	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T07:46:34Z	--- 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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Singhzaman/qwen-2.5-vl-lora-sroie	Singhzaman	2025-08-20T08:00:51Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-19T21:21:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mradermacher/BiggerCoQ-Qwen3-10b-GGUF	mradermacher	2025-08-20T08:00:48Z	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:KaraKaraWitch/BiggerCoQ-Qwen3-10b", "base_model:quantized:KaraKaraWitch/BiggerCoQ-Qwen3-10b", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-20T06:17:16Z	--- base_model: KaraKaraWitch/BiggerCoQ-Qwen3-10b language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - mergekit - merge --- ## 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/KaraKaraWitch/BiggerCoQ-Qwen3-10b <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#BiggerCoQ-Qwen3-10b-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q2_K.gguf) \| Q2_K \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q3_K_S.gguf) \| Q3_K_S \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q3_K_M.gguf) \| Q3_K_M \| 5.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q3_K_L.gguf) \| Q3_K_L \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.IQ4_XS.gguf) \| IQ4_XS \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q4_K_S.gguf) \| Q4_K_S \| 6.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q4_K_M.gguf) \| Q4_K_M \| 6.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q5_K_S.gguf) \| Q5_K_S \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q5_K_M.gguf) \| Q5_K_M \| 7.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q6_K.gguf) \| Q6_K \| 9.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q8_0.gguf) \| Q8_0 \| 11.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.f16.gguf) \| f16 \| 21.9 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
CoDiEmb/CoDi-MiniCPM_sentence_transformers	CoDiEmb	2025-08-20T07:59:30Z	0	sentence-transformers	[ "sentence-transformers", "safetensors", "minicpm", "sentence-similarity", "feature-extraction", "custom_code", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-08-20T07:48:03Z	--- language: [] tags: - sentence-transformers - sentence-similarity - feature-extraction widget: [] datasets: [] pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 2304-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: [Unknown](https://huggingface.co/unknown) --> - Maximum Sequence Length: 1024 tokens - Output Dimensionality: 2304 tokens - 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': 1024, 'do_lower_case': False}) with Transformer model: MiniCPMModel (1): Pooling({'word_embedding_dimension': 2304, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': False}) ) ``` ## 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 = [ 'The weather is lovely today.', "It's so sunny outside!", 'He drove to the stadium.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 2304] # 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. --> <!-- ## 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 ### Framework Versions - Python: 3.10.10 - Sentence Transformers: 3.0.1 - Transformers: 4.51.3 - PyTorch: 2.2.1+cu118 - Accelerate: - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citation ### BibTeX <!-- ## 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. -->
ypszn/blockassist-bc-yapping_pawing_worm_1755676531	ypszn	2025-08-20T07:57:07Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping pawing worm", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T07:56:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping pawing worm --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
WIHOW3H/my_awesome_video_cls_model	WIHOW3H	2025-08-20T07:56:58Z	0	transformers	[ "transformers", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-08-20T07:56:31Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_video_cls_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_video_cls_model This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2573 - Accuracy: 0.9286 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1200 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.0813 \| 0.25 \| 300 \| 1.1575 \| 0.4571 \| \| 1.4385 \| 1.25 \| 600 \| 1.5501 \| 0.6429 \| \| 0.0222 \| 2.25 \| 900 \| 0.4601 \| 0.8429 \| \| 1.2499 \| 3.25 \| 1200 \| 0.2573 \| 0.9286 \| ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
AXERA-TECH/MiniCPM4-0.5B	AXERA-TECH	2025-08-20T07:56:47Z	8	null	[ "minicpm4", "int8", "text-generation", "en", "base_model:openbmb/MiniCPM4-0.5B", "base_model:finetune:openbmb/MiniCPM4-0.5B", "license:mit", "region:us" ]	text-generation	2025-06-11T17:14:28Z	--- license: mit language: - en base_model: - openbmb/MiniCPM4-0.5B pipeline_tag: text-generation tags: - minicpm4 - int8 --- # MiniCPM4-0.5B-Int8 This version of MiniCPM4-0.5B has been converted to run on the Axera NPU using w8a16 quantization. This model has been optimized with the following LoRA: Compatible with Pulsar2 version: 4.2(Not released yet) ## Convert tools links: For those who are interested in model conversion, you can try to export axmodel through the original repo : https://huggingface.co/openbmb/MiniCPM4-0.5B [Pulsar2 Link, How to Convert LLM from Huggingface to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/appendix/build_llm.html) [AXera NPU LLM Runtime](https://github.com/AXERA-TECH/ax-llm) ## Support Platform - AX650 - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html) - [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html) - AX630C - [爱芯派2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html) - [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM) - [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit) \|Chips\|w8a16\|w4a16\| \|--\|--\|--\| \|AX650\| 36 tokens/sec\|TBD\| \|AX630C\| 12 tokens/sec\|TBD\| ## How to use Download all files from this repository to the device ``` root@ax650:/mnt/qtang/llm-test/minicpm4-0.5b-ctx# tree -L 1 . \|-- main_ax650 \|-- main_axcl_aarch64 \|-- main_axcl_x86 \|-- minicpm4-0.5b-int8-ctx-ax650 \|-- minicpm4_tokenizer \|-- minicpm4_tokenizer_uid.py \|-- post_config.json \|-- run_minicpm4_0.5b_int8_ctx_ax650.sh `-- run_minicpm4_0.5b_int8_ctx_axcl_x86.sh 2 directories, 7 files ``` #### Start the Tokenizer service Install requirement ``` pip install transformers jinja2 ``` ``` root@ax650:/mnt/qtang/llm-test/minicpm4-0.5b-ctx# python3 minicpm4_tokenizer_uid.py Server running at http://0.0.0.0:12345 ``` #### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) or AX650N DEMO Board Open another terminal and run `run_minicpm4_0.5b_int8_ctx_ax650.sh` ``` root@ax650:/mnt/qtang/llm-test/minicpm4-0.5b-ctx# ./run_minicpm4_0.5b_int8_ctx_ax650.sh [I][ Init][ 110]: LLM init start [I][ Init][ 34]: connect http://127.0.0.1:12345 ok [I][ Init][ 57]: uid: c779ded0-ff14-4877-869b-1aacc948f2d8 bos_id: 1, eos_id: 73440 100% \| ████████████████████████████████ \| 27 / 27 [2.53s<2.53s, 10.67 count/s] init post axmodel ok,remain_cmm(4244 MB) [I][ Init][ 188]: max_token_len : 1023 [I][ Init][ 193]: kv_cache_size : 128, kv_cache_num: 1023 [I][ Init][ 201]: prefill_token_num : 128 [I][ Init][ 205]: grp: 1, prefill_max_token_num : 1 [I][ Init][ 205]: grp: 2, prefill_max_token_num : 128 [I][ Init][ 205]: grp: 3, prefill_max_token_num : 512 [I][ Init][ 209]: prefill_max_token_num : 512 [I][ load_config][ 282]: load config: { "enable_repetition_penalty": false, "enable_temperature": false, "enable_top_k_sampling": true, "enable_top_p_sampling": false, "penalty_window": 20, "repetition_penalty": 1.2, "temperature": 0.9, "top_k": 1, "top_p": 0.8 } [I][ Init][ 218]: LLM init ok Type "q" to exit, Ctrl+c to stop current running [I][ GenerateKVCachePrefill][ 271]: input token num : 25, prefill_split_num : 1 prefill_grpid : 2 [I][ GenerateKVCachePrefill][ 308]: input_num_token:25 [I][ main][ 230]: precompute_len: 25 [I][ main][ 231]: system_prompt: You are MiniCPM4, created by ModelBest. You are a helpful assistant. prompt >> 你是谁? [I][ SetKVCache][ 531]: prefill_grpid:2 kv_cache_num:128 precompute_len:25 input_num_token:12 [I][ SetKVCache][ 534]: current prefill_max_token_num:384 [I][ Run][ 660]: input token num : 12, prefill_split_num : 1 [I][ Run][ 686]: input_num_token:12 [I][ Run][ 829]: ttft: 147.65 ms 你好，我是MiniCPM系列模型，由面壁智能和OpenBMB开源社区开发。详细信息请访问https://github.com/OpenBMB/ [N][ Run][ 943]: hit eos,avg 35.75 token/s [I][ GetKVCache][ 500]: precompute_len:162, remaining:350 prompt >> 9.9与9.11 [I][ SetKVCache][ 531]: prefill_grpid:3 kv_cache_num:512 precompute_len:162 input_num_token:17 [I][ SetKVCache][ 534]: current prefill_max_token_num:256 [I][ Run][ 660]: input token num : 17, prefill_split_num : 1 [I][ Run][ 686]: input_num_token:17 [I][ Run][ 829]: ttft: 274.38 ms 9.9比9.11大。 [N][ Run][ 943]: hit eos,avg 35.44 token/s [I][ GetKVCache][ 500]: precompute_len:189, remaining:323 prompt >> q root@ax650:/mnt/qtang/llm-test/minicpm4-0.5b-ctx# ```
AnerYubo/blockassist-bc-alert_snorting_fox_1755676563	AnerYubo	2025-08-20T07:56:07Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "alert snorting fox", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T07:56:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - alert snorting fox --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hobson123/blockassist-bc-mammalian_dense_gibbon_1755676070	hobson123	2025-08-20T07:54:06Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mammalian dense gibbon", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T07:53:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mammalian dense gibbon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
joanna302/Qwen3-8B-Base_pag_alpaca_0.66_part_SFT_2e-05	joanna302	2025-08-20T07:53:59Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "sft", "unsloth", "trl", "conversational", "base_model:unsloth/Qwen3-8B-Base", "base_model:finetune:unsloth/Qwen3-8B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-17T09:27:16Z	--- base_model: unsloth/Qwen3-8B-Base library_name: transformers model_name: Qwen3-8B-Base_pag_alpaca_0.66_part_SFT_2e-05 tags: - generated_from_trainer - sft - unsloth - trl licence: license --- # Model Card for Qwen3-8B-Base_pag_alpaca_0.66_part_SFT_2e-05 This model is a fine-tuned version of [unsloth/Qwen3-8B-Base](https://huggingface.co/unsloth/Qwen3-8B-Base). 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="joanna302/Qwen3-8B-Base_pag_alpaca_0.66_part_SFT_2e-05", 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/prism-eval/Qwen3-8B-Base_pag_alpaca_0.66_part_SFT_2e-05/runs/urz9gi0n) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0 - Datasets: 3.6.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
yaelahnal/blockassist-bc-mute_clawed_crab_1755676336	yaelahnal	2025-08-20T07:53:29Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T07:53:11Z	--- 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).
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755674677	quantumxnode	2025-08-20T07:52:11Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T07:52:07Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # 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_1755674653	lisaozill03	2025-08-20T07:50:26Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T07:50:18Z	--- 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).
joanna302/Qwen3-8B-Base_pag_mt_alpaca_0.66_part_SFT_0.0002	joanna302	2025-08-20T07:47:57Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "sft", "unsloth", "trl", "conversational", "base_model:unsloth/Qwen3-8B-Base", "base_model:finetune:unsloth/Qwen3-8B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T06:14:28Z	--- base_model: unsloth/Qwen3-8B-Base library_name: transformers model_name: Qwen3-8B-Base_pag_mt_alpaca_0.66_part_SFT_0.0002 tags: - generated_from_trainer - sft - unsloth - trl licence: license --- # Model Card for Qwen3-8B-Base_pag_mt_alpaca_0.66_part_SFT_0.0002 This model is a fine-tuned version of [unsloth/Qwen3-8B-Base](https://huggingface.co/unsloth/Qwen3-8B-Base). 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="joanna302/Qwen3-8B-Base_pag_mt_alpaca_0.66_part_SFT_0.0002", 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/prism-eval/Qwen3-8B-Base_pag_mt_alpaca_0.66_part_SFT_0.0002/runs/s7dl7p4h) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0 - Datasets: 3.6.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
koloni/blockassist-bc-deadly_graceful_stingray_1755674401	koloni	2025-08-20T07:46:56Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T07:46:52Z	--- 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).
Alfie703/gemma3-270m	Alfie703	2025-08-20T07:46:20Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "endpoints_compatible", "region:us" ]	null	2025-08-18T11:55:04Z	--- base_model: google/gemma-3-270m-it library_name: transformers model_name: gemma3-270m tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for gemma3-270m This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Alfie703/gemma3-270m", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.5.1+cu121 - Datasets: 4.0.0 - 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}} } ```
joanna302/Qwen3-8B-Base_zh_ar_alpaca_0.66_part_SFT_8e-05	joanna302	2025-08-20T07:45:36Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "unsloth", "trl", "sft", "conversational", "base_model:unsloth/Qwen3-8B-Base", "base_model:finetune:unsloth/Qwen3-8B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-18T13:47:27Z	--- base_model: unsloth/Qwen3-8B-Base library_name: transformers model_name: Qwen3-8B-Base_zh_ar_alpaca_0.66_part_SFT_8e-05 tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for Qwen3-8B-Base_zh_ar_alpaca_0.66_part_SFT_8e-05 This model is a fine-tuned version of [unsloth/Qwen3-8B-Base](https://huggingface.co/unsloth/Qwen3-8B-Base). 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="joanna302/Qwen3-8B-Base_zh_ar_alpaca_0.66_part_SFT_8e-05", 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/prism-eval/Qwen3-8B-Base_zh_ar_alpaca_0.66_part_SFT_8e-05/runs/vuased7f) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0 - Datasets: 3.6.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
aleebaster/blockassist-bc-sly_eager_boar_1755674371	aleebaster	2025-08-20T07:45:24Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly eager boar", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T07:45:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sly eager boar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755674272	kojeklollipop	2025-08-20T07:44:54Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T07:44:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
k1000dai/residualact_libero_smolvla_chunk_batch64	k1000dai	2025-08-20T07:44:50Z	0	lerobot	[ "lerobot", "safetensors", "residualact", "robotics", "dataset:k1000dai/libero-smolvla", "license:apache-2.0", "region:us" ]	robotics	2025-08-17T08:07:41Z	--- datasets: k1000dai/libero-smolvla library_name: lerobot license: apache-2.0 model_name: residualact pipeline_tag: robotics tags: - residualact - robotics - lerobot --- # Model Card for residualact <!-- Provide a quick summary of what the model is/does. --> _Model type not recognized — please update this template._ This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
yukiharada1228/tsukuyomichan-llm-jp-3.1-1.8b-instruct4	yukiharada1228	2025-08-20T07:44:36Z	0	null	[ "safetensors", "text-generation", "conversational", "ja", "base_model:llm-jp/llm-jp-3.1-1.8b-instruct4", "base_model:finetune:llm-jp/llm-jp-3.1-1.8b-instruct4", "license:apache-2.0", "region:us" ]	text-generation	2025-07-30T22:58:53Z	--- license: apache-2.0 language: - ja base_model: - llm-jp/llm-jp-3.1-1.8b-instruct4 pipeline_tag: text-generation --- ## モデル概要 yukiharada1228/tsukuyomichan-llm-jp-3.1-1.8b-instruct4-GGUFは、[つくよみちゃん会話AI育成計画](https://tyc.rei-yumesaki.net/material/kaiwa-ai/)の公式データを使用して[LLM-JP 3.1 1.8B Instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4)モデルをファインチューニングした日本語会話AIモデルです。 ## モデル情報 - ライセンス: Apache 2.0 - 言語: 日本語 (ja) - ベースモデル: yukiharada1228/tsukuyomichan-llm-jp-3.1-1.8b-instruct4 - パイプラインタグ: text-generation - 量子化: q4_k_m ## 特徴 ### 会話能力 - つくよみちゃんのキャラクターに特化した自然な日本語会話 - 親しみやすく、丁寧な応答スタイル - 複数往復の会話に対応 ### 技術仕様 - モデルサイズ: 1.8B パラメータ - ファインチューニング手法: PEFT (Parameter-Efficient Fine-Tuning) - 量子化: 4bit量子化によるメモリ効率化 - 最大シーケンス長: 4096トークン ## 学習データ ### データソース - [つくよみちゃん会話AI育成計画](https://tyc.rei-yumesaki.net/material/kaiwa-ai/)の公式データ - 複数往復の会話データを含む - 日本語の自然な会話パターン ### データ形式学習データは以下の形式で構成されています： - システムプロンプト: "あなたは、つくよみちゃんです。" - ユーザー入力: 質問や会話の内容 - アシスタント応答: 応答 ## 制限事項 ### 技術的制限 - 1.8Bパラメータのため、複雑な推論には制限があります - 4bit量子化により精度が若干低下する可能性があります - 最大4096トークンのコンテキスト長制限 ### 使用上の制限 - 学習目的での使用を推奨 - 商用利用の際は適切なライセンス確認が必要 - 元データの著作権に注意 ## ライセンスとクレジット ### ライセンスこのモデルはApache 2.0ライセンスの下で公開されています。 ### クレジット - 元データ: つくよみちゃん会話AI育成計画 © Rei Yumesaki - ベースモデル: LLM-JP 3.1 1.8B Instruct4 © LLM-JP Project - ファインチューニング: yukiharada1228 ## サポートこのモデルに関する質問や問題については、GitHubのIssuesページをご利用ください。 ## 関連リンク - [つくよみちゃん会話AI育成計画](https://tyc.rei-yumesaki.net/material/kaiwa-ai/) - [LLM-JP 3.1 1.8B Instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4) - [プロジェクトリポジトリ](https://github.com/yukiharada1228/tsukuyomichan-llm-ft)
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755674158	katanyasekolah	2025-08-20T07:44:15Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T07:44:12Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silky sprightly cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755673971	vwzyrraz7l	2025-08-20T07:41:35Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T07:41:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # 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_1755674102	calegpedia	2025-08-20T07:41:12Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T07:41:07Z	--- 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).
Albertdebeauvais/all-MiniLM-L6-v2_bibliographie	Albertdebeauvais	2025-08-20T07:40:53Z	8	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:388038", "loss:CosineSimilarityLoss", "arxiv:1908.10084", "base_model:sentence-transformers/all-MiniLM-L6-v2", "base_model:finetune:sentence-transformers/all-MiniLM-L6-v2", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-07-15T09:04:27Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:388038 - loss:CosineSimilarityLoss base_model: sentence-transformers/all-MiniLM-L6-v2 widget: - source_sentence: Les Chemins de l'effort, édité en 1975, Paris, éd. Actes Sud. sentences: - chisinau, Actes Sud, FECOURA, Dusty et VEHIER, Tyrrell, en 1915, « Les œufs d'or de ; oa guerre ». - Zagreb, éd. CNRS, FABRIE, Seneca, FARHAT, Hope, DE LASTIC SAINT JAL, Daniella, 1997, Poe et les enseignements de l'Est. - (1975), Paris, Actes Sud éditions, « Les Chemins de l'effort ». - source_sentence: par BURTSCHELL, Régine et ALESSANDRONI, Diggory, 1900, « Complément du catalogue analytique des manuscrits de la bibliothèque d'Abbeville », Rennes, Verlag Ferdinand Schöningh éditions. sentences: - « Complément du catalogue analytique des manuscrots de la bibliothèque d'Abbeville », Rennes, Verlag Ferdinand Schöningh éditions, BURTSCHELL, Régine, sous la direction de ALESSANDRONI, Diggory, (1900). - Vortex, le cheval fou, publié en ; 1926, , Bordeaux, L’Harmattan. - 1997, DEPOUMEAUX, Summer, « De chair et de lumière », Luxembourg, L’Harmattan éditions. - source_sentence: de Lorita, STREIFF, Petronella, MONTIALOUX, Gale, DANGOUMAU et ed Montgomery, D AUBERT, Dean Martin, (2011), Prague, éd. Peter Lang. sentences: - Amiens, University of Chicago Press, GUILLION L. et LAPERDRIX K., Autres courants, 2015. - 'Prague, éd. : Peter Lang, , (2011), "Dean Martin", pr + 20 ill.. Gale, DANGOUMAU et Lorita, STREIFF, Petronella, MONTIALOUX, Montgomery, D AUBERT.' - Valerie, PAIRA, Niles, AUDUBERT, 1986, Au gré des saisons, Amsterdam, Routledge. - source_sentence: 1948, Seattle, éd. Payot & Rivages, de Trudy, SAINT-AIME, Toponymes finnois et germaniques en Lithuanie... Remarques sur le nom de la Vistule. sentences: - Toponymes finnois et germaniques en Lithuanie... Remarques sur le nom de la Vistule, en 1952, Seattle, Payot & Rivages éditions, Delia, HOZE. - Cologne, Les Belles Lettres, Éléments de géométrie expérimentale, à l'usage des élèves des cours professionnels et des ouvriers, avec de nombreuses applications au trait, LAGEIX, Shelly, (1898). - 1887., The variations of glaciers. XVI, Jessika, ANNIEL, Chisinau, éd. Stanford University Press. - source_sentence: BENMAMAR, A. et LUZEUX, K., JARRAND-MARTIN, S., "La science alchimique", Master drawings, numéro 92, pages 511-649, 1904, Valence, éd. Zed Books. sentences: - Dublin, éd. CNRS, Les mystères de la cour de Cornouailles, N. BILLEBEAU, en 1966. - En 1939, New York, Fayard, réactions et méthodes nouvelles d'analyse qualitative minérale, BERTIER, R. - édité en 2020, Alexandre, GLERAND et Ashleigh, BIZET, "Un long voyage", Reims, Editions Payot éditions. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - cosine_mcc model-index: - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 results: - task: type: binary-classification name: Binary Classification dataset: name: eval type: eval metrics: - type: cosine_accuracy value: 0.9845532980795992 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7197951674461365 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9822371579452713 name: Cosine F1 - type: cosine_f1_threshold value: 0.7197951674461365 name: Cosine F1 Threshold - type: cosine_precision value: 0.9880875724404379 name: Cosine Precision - type: cosine_recall value: 0.9764556156538339 name: Cosine Recall - type: cosine_ap value: 0.9978040298718638 name: Cosine Ap - type: cosine_mcc value: 0.9686262528236084 name: Cosine Mcc - task: type: binary-classification name: Binary Classification dataset: name: test type: test metrics: - type: cosine_accuracy value: 0.9851563224788942 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7434847354888916 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9829406120055443 name: Cosine F1 - type: cosine_f1_threshold value: 0.7414178252220154 name: Cosine F1 Threshold - type: cosine_precision value: 0.9907576571735626 name: Cosine Precision - type: cosine_recall value: 0.975245953665503 name: Cosine Recall - type: cosine_ap value: 0.9978710556305371 name: Cosine Ap - type: cosine_mcc value: 0.9698992765132763 name: Cosine Mcc --- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> - Maximum Sequence Length: 256 tokens - Output Dimensionality: 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'BENMAMAR, A. et LUZEUX, K., JARRAND-MARTIN, S., "La science alchimique", Master drawings, numéro 92, pages 511-649, 1904, Valence, éd. Zed Books.', 'édité en 2020, Alexandre, GLERAND et Ashleigh, BIZET, "Un long voyage", Reims, Editions Payot éditions.', 'Dublin, éd. CNRS, Les mystères de la cour de Cornouailles, N. BILLEBEAU, en 1966.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # 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 #### Binary Classification * Datasets: `eval` and `test` * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) \| Metric \| eval \| test \| \|:--------------------------\|:-----------\|:-----------\| \| cosine_accuracy \| 0.9846 \| 0.9852 \| \| cosine_accuracy_threshold \| 0.7198 \| 0.7435 \| \| cosine_f1 \| 0.9822 \| 0.9829 \| \| cosine_f1_threshold \| 0.7198 \| 0.7414 \| \| cosine_precision \| 0.9881 \| 0.9908 \| \| cosine_recall \| 0.9765 \| 0.9752 \| \| cosine_ap \| 0.9978 \| 0.9979 \| \| cosine_mcc \| 0.9686 \| 0.9699 \| <!-- ## 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: 388,038 training samples * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: \| \| text1 \| text2 \| label \| \|:--------\|:------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------\| \| type \| string \| string \| int \| \| details \| <ul><li>min: 17 tokens</li><li>mean: 50.25 tokens</li><li>max: 169 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 47.08 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>0: ~57.00%</li><li>1: ~43.00%</li></ul> \| * Samples: \| text1 \| text2 \| label \| \|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------\| \| <code>(1973),. 70, p. 36-98, Revue d'histoire locale (Chevillon), 3, « La Font perduda », Berlin, éd. Maison des Sciences de l’Homme, editor Dorcas, PEDEVILLA, Alannis, GRANZOTTO, Annabel, VOYRON, Dulcie, MIGLIORI.</code> \| <code>Revue d'histoire locale (Chevillon)</code> \| <code>0</code> \| \| <code>Revista del Instituto Egipcio de Estudios Islámicos, n°100, pages 483-496, (2006), Administration et bibliothèques, CAGLAYAN, Kaden, BOULAABI, Fredrick, WORMSER, Bea, Vienne, éd. Beacon Press.</code> \| <code>WORMSER, Bea, CAGLAYAN, Kaden, ed BOULAABI, Fredrick, édité en 2006, Administration et bibliothèques, Revista del Instituto Egipcio de Estudios Islámicos,. 100,. p. 483-496, Vienne, Beacon Press.</code> \| <code>1</code> \| \| <code>Atlantic Charter (1941), Bulletin de la Société d'Histoire et d'Archéologie de Nantes et de Loire-Atlantique,. numéro 31, pp. 997-1125, Léontine, SCHWERDROFFER, Sandford, CHUDZIK, Metz, Zed Books éditions, 1941.</code> \| <code>(1941),. n° 31, Bulletin de la Société d'Histoire et d'Archéologie de Nantes et de Loire-Atlantique, pages 997-1125, Atlantic Charter (1941), Léontine, SCHWERDROFFER, Sandford, CHUDZIK, Metz, Zed Books.</code> \| <code>1</code> \| * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 21,558 evaluation samples * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: \| \| text1 \| text2 \| label \| \|:--------\|:------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------\| \| type \| string \| string \| int \| \| details \| <ul><li>min: 15 tokens</li><li>mean: 49.64 tokens</li><li>max: 145 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 46.31 tokens</li><li>max: 160 tokens</li></ul> \| <ul><li>0: ~57.70%</li><li>1: ~42.30%</li></ul> \| * Samples: \| text1 \| text2 \| label \| \|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------\| \| <code>Le Progressisme, aspects doctrinaux, DURAZ, Constance, 1955, Montpellier, éd. Routledge,. vol. 1,. pp. 26-39, n°29, Journal of philosophical research.</code> \| <code>1955, "Le Progressisme, aspects doctrinaux", Montpellier, Routledge, Journal of philosophical research, pp. 26-39,. volume 1, #29.</code> \| <code>1</code> \| \| <code>Turin, éd. Suhrkamp Verlag, #17, pages 67-111, 2, Annales d'Avignon et du Comtat Venaissin, "Faire face aux crises de colère de l'enfant et de l'adolescent", ed HERREYE, Kassidy, (2019).</code> \| <code>Amsterdam, University of Minnesota Press éditions, (1968), "Ainsi de chaque jour".</code> \| <code>0</code> \| \| <code>« Discours et conférences sur la science et ses applications », publié en 1927, Tours, éd. Actes Sud, Cherise, THIEFIN et de Eudora, FINGERHUT et Rona, DELLAL et Josette, DEGIOANNINI.</code> \| <code> Les formes verbales du conditionnel dans le vieux sanskrit , Eudora, FINGERHUT et Cherise, THIEFIN et par Rona, DELLAL, par Josette, DEGIOANNINI, Tours, Actes Sud éditions, publié en 1927.</code> \| <code>0</code> \| * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 20 - `per_device_eval_batch_size`: 20 - `learning_rate`: 3e-05 #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 20 - `per_device_eval_batch_size`: 20 - `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`: 3e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `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`: None - `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 - `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`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> \| Epoch \| Step \| Training Loss \| Validation Loss \| eval_cosine_ap \| test_cosine_ap \| \|:------:\|:-----:\|:-------------:\|:---------------:\|:--------------:\|:--------------:\| \| -1 \| -1 \| - \| - \| 0.8231 \| - \| \| 0.0258 \| 500 \| 0.1033 \| - \| - \| - \| \| 0.0515 \| 1000 \| 0.0885 \| - \| - \| - \| \| 0.0773 \| 1500 \| 0.0778 \| - \| - \| - \| \| 0.1031 \| 2000 \| 0.0721 \| - \| - \| - \| \| 0.1289 \| 2500 \| 0.0697 \| - \| - \| - \| \| 0.1546 \| 3000 \| 0.0645 \| - \| - \| - \| \| 0.1804 \| 3500 \| 0.0619 \| - \| - \| - \| \| 0.2062 \| 4000 \| 0.0604 \| - \| - \| - \| \| 0.2319 \| 4500 \| 0.0569 \| - \| - \| - \| \| 0.2577 \| 5000 \| 0.0545 \| - \| - \| - \| \| 0.2835 \| 5500 \| 0.0539 \| - \| - \| - \| \| 0.3092 \| 6000 \| 0.0517 \| - \| - \| - \| \| 0.3350 \| 6500 \| 0.0506 \| - \| - \| - \| \| 0.3608 \| 7000 \| 0.0511 \| - \| - \| - \| \| 0.3866 \| 7500 \| 0.0486 \| - \| - \| - \| \| 0.4123 \| 8000 \| 0.0463 \| - \| - \| - \| \| 0.4381 \| 8500 \| 0.0463 \| - \| - \| - \| \| 0.4639 \| 9000 \| 0.0471 \| - \| - \| - \| \| 0.4896 \| 9500 \| 0.0454 \| - \| - \| - \| \| 0.5154 \| 10000 \| 0.0445 \| - \| - \| - \| \| 0.5412 \| 10500 \| 0.0455 \| - \| - \| - \| \| 0.5670 \| 11000 \| 0.0441 \| - \| - \| - \| \| 0.5927 \| 11500 \| 0.0437 \| - \| - \| - \| \| 0.6185 \| 12000 \| 0.0449 \| - \| - \| - \| \| 0.6443 \| 12500 \| 0.0413 \| - \| - \| - \| \| 0.6700 \| 13000 \| 0.0413 \| - \| - \| - \| \| 0.6958 \| 13500 \| 0.0422 \| - \| - \| - \| \| 0.7216 \| 14000 \| 0.0411 \| - \| - \| - \| \| 0.7473 \| 14500 \| 0.0404 \| - \| - \| - \| \| 0.7731 \| 15000 \| 0.0374 \| - \| - \| - \| \| 0.7989 \| 15500 \| 0.0378 \| - \| - \| - \| \| 0.8247 \| 16000 \| 0.0384 \| - \| - \| - \| \| 0.8504 \| 16500 \| 0.0389 \| - \| - \| - \| \| 0.8762 \| 17000 \| 0.0377 \| - \| - \| - \| \| 0.9020 \| 17500 \| 0.0374 \| - \| - \| - \| \| 0.9277 \| 18000 \| 0.0366 \| - \| - \| - \| \| 0.9535 \| 18500 \| 0.0368 \| - \| - \| - \| \| 0.9793 \| 19000 \| 0.0367 \| - \| - \| - \| \| 1.0 \| 19402 \| - \| 0.0310 \| 0.9965 \| - \| \| 1.0051 \| 19500 \| 0.0364 \| - \| - \| - \| \| 1.0308 \| 20000 \| 0.0323 \| - \| - \| - \| \| 1.0566 \| 20500 \| 0.0319 \| - \| - \| - \| \| 1.0824 \| 21000 \| 0.0317 \| - \| - \| - \| \| 1.1081 \| 21500 \| 0.0298 \| - \| - \| - \| \| 1.1339 \| 22000 \| 0.0336 \| - \| - \| - \| \| 1.1597 \| 22500 \| 0.0304 \| - \| - \| - \| \| 1.1854 \| 23000 \| 0.0302 \| - \| - \| - \| \| 1.2112 \| 23500 \| 0.031 \| - \| - \| - \| \| 1.2370 \| 24000 \| 0.0301 \| - \| - \| - \| \| 1.2628 \| 24500 \| 0.0302 \| - \| - \| - \| \| 1.2885 \| 25000 \| 0.0305 \| - \| - \| - \| \| 1.3143 \| 25500 \| 0.0293 \| - \| - \| - \| \| 1.3401 \| 26000 \| 0.0307 \| - \| - \| - \| \| 1.3658 \| 26500 \| 0.0304 \| - \| - \| - \| \| 1.3916 \| 27000 \| 0.03 \| - \| - \| - \| \| 1.4174 \| 27500 \| 0.0312 \| - \| - \| - \| \| 1.4432 \| 28000 \| 0.0296 \| - \| - \| - \| \| 1.4689 \| 28500 \| 0.0301 \| - \| - \| - \| \| 1.4947 \| 29000 \| 0.0295 \| - \| - \| - \| \| 1.5205 \| 29500 \| 0.0295 \| - \| - \| - \| \| 1.5462 \| 30000 \| 0.029 \| - \| - \| - \| \| 1.5720 \| 30500 \| 0.0295 \| - \| - \| - \| \| 1.5978 \| 31000 \| 0.029 \| - \| - \| - \| \| 1.6235 \| 31500 \| 0.029 \| - \| - \| - \| \| 1.6493 \| 32000 \| 0.0271 \| - \| - \| - \| \| 1.6751 \| 32500 \| 0.029 \| - \| - \| - \| \| 1.7009 \| 33000 \| 0.0278 \| - \| - \| - \| \| 1.7266 \| 33500 \| 0.0286 \| - \| - \| - \| \| 1.7524 \| 34000 \| 0.0272 \| - \| - \| - \| \| 1.7782 \| 34500 \| 0.0279 \| - \| - \| - \| \| 1.8039 \| 35000 \| 0.0285 \| - \| - \| - \| \| 1.8297 \| 35500 \| 0.0286 \| - \| - \| - \| \| 1.8555 \| 36000 \| 0.0297 \| - \| - \| - \| \| 1.8812 \| 36500 \| 0.0273 \| - \| - \| - \| \| 1.9070 \| 37000 \| 0.0269 \| - \| - \| - \| \| 1.9328 \| 37500 \| 0.0276 \| - \| - \| - \| \| 1.9586 \| 38000 \| 0.0278 \| - \| - \| - \| \| 1.9843 \| 38500 \| 0.0267 \| - \| - \| - \| \| 2.0 \| 38804 \| - \| 0.0248 \| 0.9976 \| - \| \| 2.0101 \| 39000 \| 0.0252 \| - \| - \| - \| \| 2.0359 \| 39500 \| 0.0233 \| - \| - \| - \| \| 2.0616 \| 40000 \| 0.0233 \| - \| - \| - \| \| 2.0874 \| 40500 \| 0.0236 \| - \| - \| - \| \| 2.1132 \| 41000 \| 0.023 \| - \| - \| - \| \| 2.1390 \| 41500 \| 0.0212 \| - \| - \| - \| \| 2.1647 \| 42000 \| 0.0233 \| - \| - \| - \| \| 2.1905 \| 42500 \| 0.0227 \| - \| - \| - \| \| 2.2163 \| 43000 \| 0.0227 \| - \| - \| - \| \| 2.2420 \| 43500 \| 0.0233 \| - \| - \| - \| \| 2.2678 \| 44000 \| 0.0241 \| - \| - \| - \| \| 2.2936 \| 44500 \| 0.0218 \| - \| - \| - \| \| 2.3193 \| 45000 \| 0.0232 \| - \| - \| - \| \| 2.3451 \| 45500 \| 0.0235 \| - \| - \| - \| \| 2.3709 \| 46000 \| 0.024 \| - \| - \| - \| \| 2.3967 \| 46500 \| 0.0237 \| - \| - \| - \| \| 2.4224 \| 47000 \| 0.0228 \| - \| - \| - \| \| 2.4482 \| 47500 \| 0.0231 \| - \| - \| - \| \| 2.4740 \| 48000 \| 0.0223 \| - \| - \| - \| \| 2.4997 \| 48500 \| 0.0232 \| - \| - \| - \| \| 2.5255 \| 49000 \| 0.022 \| - \| - \| - \| \| 2.5513 \| 49500 \| 0.0227 \| - \| - \| - \| \| 2.5771 \| 50000 \| 0.0226 \| - \| - \| - \| \| 2.6028 \| 50500 \| 0.0233 \| - \| - \| - \| \| 2.6286 \| 51000 \| 0.0224 \| - \| - \| - \| \| 2.6544 \| 51500 \| 0.0224 \| - \| - \| - \| \| 2.6801 \| 52000 \| 0.0224 \| - \| - \| - \| \| 2.7059 \| 52500 \| 0.022 \| - \| - \| - \| \| 2.7317 \| 53000 \| 0.0223 \| - \| - \| - \| \| 2.7574 \| 53500 \| 0.023 \| - \| - \| - \| \| 2.7832 \| 54000 \| 0.023 \| - \| - \| - \| \| 2.8090 \| 54500 \| 0.023 \| - \| - \| - \| \| 2.8348 \| 55000 \| 0.0225 \| - \| - \| - \| \| 2.8605 \| 55500 \| 0.0229 \| - \| - \| - \| \| 2.8863 \| 56000 \| 0.0229 \| - \| - \| - \| \| 2.9121 \| 56500 \| 0.0224 \| - \| - \| - \| \| 2.9378 \| 57000 \| 0.0218 \| - \| - \| - \| \| 2.9636 \| 57500 \| 0.0226 \| - \| - \| - \| \| 2.9894 \| 58000 \| 0.0229 \| - \| - \| - \| \| 3.0 \| 58206 \| - \| 0.0231 \| 0.9978 \| - \| \| -1 \| -1 \| - \| - \| - \| 0.9979 \| </details> ### Framework Versions - Python: 3.12.0 - Sentence Transformers: 4.1.0 - Transformers: 4.52.4 - PyTorch: 2.7.1+cu128 - Accelerate: 1.8.1 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->

aleebaster/blockassist-bc-sly_eager_boar_1755678942

aleebaster

2025-08-20T09:01:20Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly eager boar", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T09:01:13Z

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

mradermacher/Rubicon-Preview-i1-GGUF

mradermacher

2025-08-20T09:00:58Z

0

transformers

[ "transformers", "gguf", "en", "base_model:inclusionAI/Rubicon-Preview", "base_model:quantized:inclusionAI/Rubicon-Preview", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-08-20T04:05:45Z

--- base_model: inclusionAI/Rubicon-Preview language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About         weighted/imatrix quants of https://huggingface.co/inclusionAI/Rubicon-Preview  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Rubicon-Preview-i1-GGUF).*** static quants are available at https://huggingface.co/mradermacher/Rubicon-Preview-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.imatrix.gguf) | imatrix | 0.2 | imatrix file (for creating your own qwuants) | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ1_S.gguf) | i1-IQ1_S | 6.5 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ1_M.gguf) | i1-IQ1_M | 7.2 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 8.3 | | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ2_XS.gguf) | i1-IQ2_XS | 9.2 | | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ2_S.gguf) | i1-IQ2_S | 9.4 | | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ2_M.gguf) | i1-IQ2_M | 10.3 | | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q2_K_S.gguf) | i1-Q2_K_S | 10.6 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q2_K.gguf) | i1-Q2_K | 11.4 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 11.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ3_XS.gguf) | i1-IQ3_XS | 12.7 | | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q3_K_S.gguf) | i1-Q3_K_S | 13.4 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ3_S.gguf) | i1-IQ3_S | 13.4 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ3_M.gguf) | i1-IQ3_M | 13.6 | | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q3_K_M.gguf) | i1-Q3_K_M | 14.8 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q3_K_L.gguf) | i1-Q3_K_L | 16.0 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-IQ4_XS.gguf) | i1-IQ4_XS | 16.5 | | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q4_0.gguf) | i1-Q4_0 | 17.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q4_K_S.gguf) | i1-Q4_K_S | 17.6 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q4_K_M.gguf) | i1-Q4_K_M | 18.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q4_1.gguf) | i1-Q4_1 | 19.3 | | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q5_K_S.gguf) | i1-Q5_K_S | 21.2 | | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q5_K_M.gguf) | i1-Q5_K_M | 21.8 | | | [GGUF](https://huggingface.co/mradermacher/Rubicon-Preview-i1-GGUF/resolve/main/Rubicon-Preview.i1-Q6_K.gguf) | i1-Q6_K | 25.2 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

aralper18/blockassist-bc-gilded_tangled_albatross_1755680287

aralper18

2025-08-20T08:58:53Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gilded tangled albatross", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:58:45Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gilded tangled albatross --- # 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_1755680202

liukevin666

2025-08-20T08:58:16Z

0

null

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

null

2025-08-20T08:57:56Z

--- 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).

dongjuu/gemma-3-12b-it-Rude-LORA

dongjuu

2025-08-20T08:56:57Z

0

transformers

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

null

2025-08-20T08:56:48Z

--- 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]

katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755678475

katanyasekolah

2025-08-20T08:56:09Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:56:06Z

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

prithivMLmods/SimpleChat-4B-V1-f32-GGUF

prithivMLmods

2025-08-20T08:55:56Z

0

transformers

[ "transformers", "gguf", "qwen3", "text-generation-inference", "Non-CoT", "text-generation", "en", "base_model:OpenBuddy/SimpleChat-4B-V1", "base_model:quantized:OpenBuddy/SimpleChat-4B-V1", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2025-08-20T06:00:32Z

--- license: apache-2.0 language: - en base_model: - OpenBuddy/SimpleChat-4B-V1 pipeline_tag: text-generation library_name: transformers tags: - text-generation-inference - Non-CoT --- # **SimpleChat-4B-V1-f32-GGUF** > OpenBuddy’s SimpleChat-4B-V1 is a 4B-parameter conversational AI model built for streamlined, casual everyday interactions, emphasizing concise, rational, and creative responses without relying on complex chain-of-thought reasoning. Optimized for natural dialogue, SimpleChat-4B-V1 is part of the SimpleChat series exploring Non-Chain-of-Thought (Non-CoT) models, making it especially suitable for direct, engaging conversations that prioritize clarity and brevity while retaining the ability to enhance creativity and expressive capacity in outputs. ## Model Files | File Name | Quant Type | File Size | | - | - | - | | SimpleChat-4B-V1.BF16.gguf | BF16 | 8.05 GB | | SimpleChat-4B-V1.F16.gguf | F16 | 8.05 GB | | SimpleChat-4B-V1.F32.gguf | F32 | 16.1 GB | | SimpleChat-4B-V1.Q2_K.gguf | Q2_K | 1.67 GB | | SimpleChat-4B-V1.Q3_K_L.gguf | Q3_K_L | 2.24 GB | | SimpleChat-4B-V1.Q3_K_M.gguf | Q3_K_M | 2.08 GB | | SimpleChat-4B-V1.Q3_K_S.gguf | Q3_K_S | 1.89 GB | | SimpleChat-4B-V1.Q4_K_M.gguf | Q4_K_M | 2.5 GB | | SimpleChat-4B-V1.Q4_K_S.gguf | Q4_K_S | 2.38 GB | | SimpleChat-4B-V1.Q5_K_M.gguf | Q5_K_M | 2.89 GB | | SimpleChat-4B-V1.Q5_K_S.gguf | Q5_K_S | 2.82 GB | | SimpleChat-4B-V1.Q6_K.gguf | Q6_K | 3.31 GB | | SimpleChat-4B-V1.Q8_0.gguf | Q8_0 | 4.28 GB | ## Quants Usage (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) 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)

kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755678400

kojeklollipop

2025-08-20T08:55:09Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:55:05Z

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

LSummer/my_awesome_video_cls_model

LSummer

2025-08-20T08:54:22Z

0

transformers

[ "transformers", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

video-classification

2025-08-20T08:54:03Z

--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer model-index: - name: my_awesome_video_cls_model results: [] ---  # my_awesome_video_cls_model This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 300 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755678318

vwzyrraz7l

2025-08-20T08:54:03Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:54:00Z

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

manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755678244

manusiaperahu2012

2025-08-20T08:52:10Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring long tuna", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:52:06Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring long tuna --- # 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_1755678290

koloni

2025-08-20T08:51:01Z

0

null

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

null

2025-08-20T08:50:57Z

--- 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).

indoempatnol/blockassist-bc-fishy_wary_swan_1755678078

indoempatnol

2025-08-20T08:48:18Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:48:14Z

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

18-Arovi-Nusrat-Ridhi-Viral-Video-links/New.full.videos.Arovi.Nusrat.Ridhi.Viral.Video.Official.Tutorial

18-Arovi-Nusrat-Ridhi-Viral-Video-links

2025-08-20T08:45:30Z

0

null

[ "region:us" ]

null

2025-08-20T08:45:20Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>

liukevin666/blockassist-bc-yawning_striped_cassowary_1755679191

liukevin666

2025-08-20T08:43:59Z

0

null

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

null

2025-08-20T08:41:47Z

--- 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).

goodragon/gemma-3-12b-it-Rude-LORA

goodragon

2025-08-20T08:43:04Z

0

transformers

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

null

2025-08-20T08:42:54Z

--- 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]

Donchocho/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_tricky_dolphin

Donchocho

2025-08-20T08:41:05Z

106

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am graceful_tricky_dolphin", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-07-23T09:20:04Z

--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am graceful_tricky_dolphin --- # 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]

sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755677562

sampingkaca72

2025-08-20T08:38:45Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored stealthy elephant", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:38:40Z

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

yaelahnal/blockassist-bc-mute_clawed_crab_1755679016

yaelahnal

2025-08-20T08:38:05Z

0

null

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

null

2025-08-20T08:37:47Z

--- 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).

VIDEOS-19-Nayeon-Sana-viral-video-Clips/New.full.videos.Nayeon.Sana.Viral.Video.Official.Tutorial

VIDEOS-19-Nayeon-Sana-viral-video-Clips

2025-08-20T08:37:28Z

0

null

[ "region:us" ]

null

2025-08-20T08:37:16Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>

lostinjamal/053c182c-e9ca-420e-b9fd-22199c23b1cb

lostinjamal

2025-08-20T08:37:25Z

0

peft

[ "peft", "safetensors", "base_model:adapter:unsloth/smollm-1.7b-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "text-generation", "arxiv:1910.09700", "region:us" ]

text-generation

2025-08-20T08:37:12Z

--- base_model: unsloth/smollm-1.7b-bnb-4bit library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/smollm-1.7b-bnb-4bit - lora - sft - transformers - trl - unsloth --- # 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 0.17.0

ihsanridzi/blockassist-bc-wiry_flexible_owl_1755677398

ihsanridzi

2025-08-20T08:36:56Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:36:52Z

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

sdagsadgd/blockassist-bc-sedate_squeaky_salamander_1755675680

sdagsadgd

2025-08-20T08:35:36Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sedate squeaky salamander", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:35:21Z

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

VIDEOS-19-Kim-Mariya-Viral-Video-Clips/New.full.videos.Kim.Mariya.Viral.Video.Official.Tutorial

VIDEOS-19-Kim-Mariya-Viral-Video-Clips

2025-08-20T08:34:56Z

0

null

[ "region:us" ]

null

2025-08-20T08:34:45Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>

Sayemahsjn/blockassist-bc-playful_feline_octopus_1755677742

Sayemahsjn

2025-08-20T08:34:52Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:34:46Z

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

kongleehan/my_awesome_video_cls_model

kongleehan

2025-08-20T08:34:36Z

0

transformers

[ "transformers", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

video-classification

2025-08-20T08:34:14Z

--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_video_cls_model results: [] ---  # my_awesome_video_cls_model This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2957 - Accuracy: 0.9143 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0926 | 0.25 | 300 | 1.9448 | 0.3286 | | 0.2963 | 1.25 | 600 | 0.7159 | 0.7429 | | 0.0149 | 2.25 | 900 | 0.5006 | 0.8571 | | 0.0027 | 3.25 | 1200 | 0.2957 | 0.9143 | ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

AmirRghp/distilbert-base-uncasedimdb-text-classification

AmirRghp

2025-08-20T08:33:33Z

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "code", "movie", "en", "dataset:shawhin/imdb-truncated", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-08-18T12:38:24Z

--- library_name: transformers tags: - code - movie license: mit datasets: - shawhin/imdb-truncated language: - en metrics: - accuracy - f1 - precision - recall - confusion_matrix base_model: - distilbert/distilbert-base-uncased pipeline_tag: text-classification --- # Model Card for IMDB Movie Review Classifier This model is built for classifying movie reviews from the IMDB dataset into positive or negative categories. It uses the Hugging Face's `DistilBERT` model, a lighter version of BERT, for text classification tasks. ## Model Details ### Model Description This model is fine-tuned for binary classification, trained on the IMDb dataset, and can predict whether a given review is positive or negative. It utilizes the `distilbert-base-uncased` model, a pre-trained transformer-based architecture. - **Developed by:** Amirreza Gholipour - **Funded by:** Amirreza Gholipour - **Model type:** Transformer-based Text Classifier - **Language(s) (NLP):** English - **License:** MIT - **Finetuned from model:** distilbert-base-uncased ## Uses ### Direct Use This model can be used directly for classifying movie reviews. Given an IMDB review, the model will return a sentiment classification: positive or negative. The input text is tokenized, passed through the `DistilBERT` model, and the output is processed to classify the review sentiment. ### Downstream Use [optional] The model can be fine-tuned further on other similar datasets to specialize it for different domains, such as classifying product reviews, news sentiment, or other text-based sentiment analysis tasks. ### Out-of-Scope Use This model is not intended for use in detecting sarcasm, irony, or more nuanced sentiment expressions that require deeper contextual understanding. It may not perform well on non-English reviews. ## Bias, Risks, and Limitations The model is trained on the IMDb dataset, which may introduce bias due to the nature of the content of movie reviews. It might not generalize well to domains outside of movie review sentiment classification. The dataset could be biased in terms of the types of movies reviewed (e.g., biased toward Hollywood blockbusters). ### Recommendations Users should ensure they are aware of potential biases in the training data. The model should not be relied on for applications requiring high accuracy in specialized domains or nuanced text understanding. ## How to Get Started with the Model ```python from transformers import pipeline # Load the pre-trained model classifier = pipeline('text-classification', model='AmirRghp/distilbert-base-uncasedimdb-text-classification') # Classify a sample text text = "The movie was absolutely amazing and I loved every minute of it!" result = classifier(text) print(result) ``` ## Training Details ### Training Data The model was trained on the IMDb dataset, a collection of 50,000 movie reviews categorized as either positive or negative. - Dataset: IMDb dataset - Number of samples: 50,000 - Categories: Positive, Negative - Data Preprocessing: Tokenization and padding were applied to the raw text data to ensure compatibility with the DistilBERT model. ### Training Procedure #### Preprocessing [optional] The text data is tokenized using the DistilBERT tokenizer. #### Training Hyperparameters - Learning rate: 2e-5 - Batch size: 4 - Epochs: 5 #### Speeds, Sizes, Times [optional] Training was conducted on a GPU with the following specifications: - Hardware Type: Nvidia RTX 5060 TI - Training Time: 5 Min ## Evaluation The model was evaluated on accuracy, F1 score, precision, recall, and confusion matrix metrics. Here are the key evaluation results: - Accuracy: 89.2% - F1 Score: 0.89 - Precision: 0.88 - Recall: 0.90 #### Summary The model performs well on the IMDb dataset, with a high accuracy rate and strong performance across other metrics. It’s ready for use in practical sentiment analysis tasks. ## Technical Specifications [optional] ### Model Architecture and Objective The model is based on the DistilBERT architecture, which is a smaller and faster variant of the BERT model, designed to provide similar performance with fewer parameters. - Architecture: Transformer-based encoder-decoder model - Objective: Binary classification of text (positive or negative sentiment) ### Compute Infrastructure - Hardware: Nvidia RTX 5060 TI - Libraries: Hugging Face Transformers, PyTorch ## More Information [optional] For more details on the model architecture and training, please refer to the Hugging Face documentation ## Model Card Authors [optional] - Author: Amirreza Gholipour - Contact: [](https://www.linkedin.com/in/amirreza-gholipour-11a05a323/)

Sunbird/qwen3-14b-ug40-sft-translation-plus-multilingual-tasks-merged

Sunbird

2025-08-20T08:32:27Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-20T08:15:06Z

--- 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]

bimabk/053c182c-e9ca-420e-b9fd-22199c23b1cb

bimabk

2025-08-20T08:32:26Z

0

peft

[ "peft", "safetensors", "base_model:adapter:unsloth/smollm-1.7b-bnb-4bit", "lora", "sft", "transformers", "trl", "unsloth", "text-generation", "arxiv:1910.09700", "region:us" ]

text-generation

2025-08-20T08:32:20Z

--- base_model: unsloth/smollm-1.7b-bnb-4bit library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/smollm-1.7b-bnb-4bit - lora - sft - transformers - trl - unsloth --- # 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 0.17.0

Milica-y-Angel-David/Milica.y.Angel.David.Video.Debut.Erome.Video.de.Milica.y.Angel.David.ybanez.Jugar.y.descargar

Milica-y-Angel-David

2025-08-20T08:32:06Z

0

null

[ "region:us" ]

null

2025-08-20T08:31:49Z

<a href="https://sdu.sk/AyL"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="fsd" /></a> <a href="https://sdu.sk/AyL" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>

vuitton/LouisVuitton_model5

vuitton

2025-08-20T08:30:43Z

0

null

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

any-to-any

2025-08-20T08:24:45Z

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

jo-mengr/geneformer-fork

jo-mengr

2025-08-20T08:30:21Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-20T08:30:21Z

--- license: apache-2.0 ---

BootesVoid/cm8tb7xkk0000wzj24pkk2m5g_cmejoukqo0u4jrts8h12d5rpm

BootesVoid

2025-08-20T08:29:07Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-08-20T08:29:05Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: G3RMNGRL --- # Cm8Tb7Xkk0000Wzj24Pkk2M5G_Cmejoukqo0U4Jrts8H12D5Rpm <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `G3RMNGRL` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "G3RMNGRL", "lora_weights": "https://huggingface.co/BootesVoid/cm8tb7xkk0000wzj24pkk2m5g_cmejoukqo0u4jrts8h12d5rpm/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cm8tb7xkk0000wzj24pkk2m5g_cmejoukqo0u4jrts8h12d5rpm', weight_name='lora.safetensors') image = pipeline('G3RMNGRL').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cm8tb7xkk0000wzj24pkk2m5g_cmejoukqo0u4jrts8h12d5rpm/discussions) to add images that show off what you’ve made with this LoRA.

sehun96/gemma-3-12b-it-Rude-LORA

sehun96

2025-08-20T08:28:42Z

0

transformers

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

null

2025-08-20T08:28:36Z

--- 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]

Official-Archita-Phukan-first-film-video/Archita.Phukan.first.film.video.Clip

Official-Archita-Phukan-first-film-video

2025-08-20T08:28:03Z

0

null

[ "region:us" ]

null

2025-08-20T08:27:52Z

<a href="https://sdu.sk/AyL"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="fsd" /></a> <a href="https://sdu.sk/AyL" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>

playdatakoo/omar-ax-lora-skt-4.0

playdatakoo

2025-08-20T08:27:02Z

0

peft

[ "peft", "safetensors", "lora", "skt", "korean", "ax", "ko", "base_model:skt/A.X-4.0-Light", "base_model:adapter:skt/A.X-4.0-Light", "region:us" ]

null

2025-08-20T08:26:41Z

--- base_model: skt/A.X-4.0-Light tags: - lora - peft - skt - korean - ax language: - ko library_name: peft --- # Omar AX LoRA Adapter for SKT A.X-4.0-Light This is a LoRA adapter fine-tuned for the Korean language model. ## Model Details - **Base Model**: [skt/A.X-4.0-Light](https://huggingface.co/skt/A.X-4.0-Light) - **LoRA Rank**: 24 - **LoRA Alpha**: 12 - **LoRA Dropout**: 0.1 - **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj - **Task Type**: CAUSAL_LM ## Usage with vLLM INFO 08-20 08:26:21 [__init__.py:241] Automatically detected platform cuda. [1;36m(APIServer pid=15975)[0;0m INFO 08-20 08:26:24 [api_server.py:1805] vLLM API server version 0.10.1 [1;36m(APIServer pid=15975)[0;0m INFO 08-20 08:26:24 [utils.py:326] non-default args: {'model_tag': 'skt/A.X-4.0-Light', 'lora_modules': [LoRAModulePath(name='omar_ax', path='playdatakoo/omar-ax-lora-skt-4.0', base_model_name=None)], 'model': 'skt/A.X-4.0-Light', 'enable_lora': True} [1;36m(APIServer pid=15975)[0;0m INFO 08-20 08:26:30 [__init__.py:711] Resolved architecture: Qwen2ForCausalLM [1;36m(APIServer pid=15975)[0;0m INFO 08-20 08:26:30 [__init__.py:1750] Using max model len 16384 [1;36m(APIServer pid=15975)[0;0m INFO 08-20 08:26:31 [scheduler.py:222] Chunked prefill is enabled with max_num_batched_tokens=2048. INFO 08-20 08:26:36 [__init__.py:241] Automatically detected platform cuda. [1;36m(EngineCore_0 pid=16412)[0;0m INFO 08-20 08:26:37 [core.py:636] Waiting for init message from front-end. [1;36m(EngineCore_0 pid=16412)[0;0m INFO 08-20 08:26:37 [core.py:74] Initializing a V1 LLM engine (v0.10.1) with config: model='skt/A.X-4.0-Light', speculative_config=None, tokenizer='skt/A.X-4.0-Light', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config={}, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=16384, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, decoding_config=DecodingConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_backend=''), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=skt/A.X-4.0-Light, enable_prefix_caching=True, chunked_prefill_enabled=True, use_async_output_proc=True, pooler_config=None, compilation_config={"level":3,"debug_dump_path":"","cache_dir":"","backend":"","custom_ops":[],"splitting_ops":["vllm.unified_attention","vllm.unified_attention_with_output","vllm.mamba_mixer2"],"use_inductor":true,"compile_sizes":[],"inductor_compile_config":{"enable_auto_functionalized_v2":false},"inductor_passes":{},"cudagraph_mode":1,"use_cudagraph":true,"cudagraph_num_of_warmups":1,"cudagraph_capture_sizes":[512,504,496,488,480,472,464,456,448,440,432,424,416,408,400,392,384,376,368,360,352,344,336,328,320,312,304,296,288,280,272,264,256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"cudagraph_copy_inputs":false,"full_cuda_graph":false,"pass_config":{},"max_capture_size":512,"local_cache_dir":null} [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] EngineCore failed to start. [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] Traceback (most recent call last): [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/v1/engine/core.py", line 691, in run_engine_core [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] engine_core = EngineCoreProc(*args, **kwargs) [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/v1/engine/core.py", line 492, in __init__ [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] super().__init__(vllm_config, executor_class, log_stats, [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/v1/engine/core.py", line 80, in __init__ [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] self.model_executor = executor_class(vllm_config) [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] ^^^^^^^^^^^^^^^^^^^^^^^^^^^ [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/executor/executor_base.py", line 54, in __init__ [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] self._init_executor() [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/executor/uniproc_executor.py", line 48, in _init_executor [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] self.collective_rpc("init_device") [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/executor/uniproc_executor.py", line 58, in collective_rpc [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] answer = run_method(self.driver_worker, method, args, kwargs) [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/utils/__init__.py", line 3007, in run_method [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] return func(*args, **kwargs) [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] ^^^^^^^^^^^^^^^^^^^^^ [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/worker/worker_base.py", line 603, in init_device [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] self.worker.init_device() # type: ignore [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] ^^^^^^^^^^^^^^^^^^^^^^^^^ [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] File "/usr/local/lib/python3.11/dist-packages/vllm/v1/worker/gpu_worker.py", line 179, in init_device [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] raise ValueError( [1;36m(EngineCore_0 pid=16412)[0;0m ERROR 08-20 08:26:38 [core.py:700] ValueError: Free memory on device (2.96/44.34 GiB) on startup is less than desired GPU memory utilization (0.9, 39.91 GiB). Decrease GPU memory utilization or reduce GPU memory used by other processes. ### API Usage ## Usage with PEFT ## Performance - **Hot-swap Speed**: 0.18s (switching between base and LoRA) - **GPU Memory**: ~14.8 GiB - **Inference Speed**: 0.18-0.19s per request after warmup ## Files - : LoRA configuration - : LoRA weights (SafeTensors format) - Tokenizer files included for convenience

quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755676741

quantumxnode

2025-08-20T08:25:05Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:25:02Z

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

Abigail-video-viral-original/New.full.videos.Abigail.Viral.Video.Official.Tutorial

Abigail-video-viral-original

2025-08-20T08:24:44Z

0

null

[ "region:us" ]

null

2025-08-20T08:23:27Z

[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://the-amarican-dream.blogspot.com/2025/08/han-ty.html) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️ ](https://the-amarican-dream.blogspot.com/2025/08/han-ty.html) [<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://the-amarican-dream.blogspot.com/2025/08/han-ty.html)

thomasht86/Qwen3-Embedding-0.6B-ONNX-Vespa

thomasht86

2025-08-20T08:24:19Z

0

null

[ "onnx", "license:apache-2.0", "region:us" ]

null

2025-08-20T08:19:41Z

--- license: apache-2.0 ---

Archita-Phukan-Viral-full-Video-hq/New.full.Videos.Archita.Phukan.Viral.Video.New.MMS.Original

Archita-Phukan-Viral-full-Video-hq

2025-08-20T08:23:41Z

0

null

[ "region:us" ]

null

2025-08-20T08:23:36Z

<a href="https://sdu.sk/AyL"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="fsd" /></a> <a href="https://sdu.sk/AyL" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>

evanurasyifa-Official-video-Clip/New.full.videos.evanurasyifa.Viral.Video.Official.Tutorial

evanurasyifa-Official-video-Clip

2025-08-20T08:23:05Z

0

null

[ "region:us" ]

null

2025-08-20T08:22:50Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>

evanurasyifa-Official-video-Clip/New.full.videos.Bindura.University.Viral.Video.Official.Tutorial

evanurasyifa-Official-video-Clip

2025-08-20T08:21:49Z

0

null

[ "region:us" ]

null

2025-08-20T08:21:37Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>

mang3dd/blockassist-bc-tangled_slithering_alligator_1755676496

mang3dd

2025-08-20T08:20:59Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:20:55Z

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

18-Clip-Sophie-Rain-Viral-video-original/New.full.videos.Sophie.Rain.Spiderman.Viral.Video.Official.Tutorial

18-Clip-Sophie-Rain-Viral-video-original

2025-08-20T08:20:50Z

0

null

[ "region:us" ]

null

2025-08-20T08:20:45Z

<a href="https://sdu.sk/AyL"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="fsd" /></a> <a href="https://sdu.sk/AyL" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>

koloni/blockassist-bc-deadly_graceful_stingray_1755676489

koloni

2025-08-20T08:20:14Z

0

null

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

null

2025-08-20T08:20:11Z

--- 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).

Medved444/blockassist-bc-bellowing_finicky_manatee_1755676845

Medved444

2025-08-20T08:19:43Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bellowing finicky manatee", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:19:25Z

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

kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755676360

kojeklollipop

2025-08-20T08:18:48Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:18:45Z

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

Uppal-Farm-Girl-Viral-Video-Link-Orginal/New.full.videos.Uppal.Farm.Girl.Viral.Video.Official.Tutorial

Uppal-Farm-Girl-Viral-Video-Link-Orginal

2025-08-20T08:18:45Z

0

null

[ "region:us" ]

null

2025-08-20T08:18:37Z

<a href="https://sdu.sk/AyL"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="fsd" /></a> <a href="https://sdu.sk/AyL" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>

vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755676082

vwzyrraz7l

2025-08-20T08:17:40Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:17:35Z

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

VIDEOS-19-afrin-apu-viral-links/New.full.videos.afrin.apu.Viral.Video.Official.Tutorial

VIDEOS-19-afrin-apu-viral-links

2025-08-20T08:17:20Z

0

null

[ "region:us" ]

null

2025-08-20T08:17:03Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>

VIDEOS-19-fooni-fun-Viral-Video-Clip-XX/New.full.videos.fooni.fun.Viral.Video.Official.Tutorial

VIDEOS-19-fooni-fun-Viral-Video-Clip-XX

2025-08-20T08:16:18Z

0

null

[ "region:us" ]

null

2025-08-20T08:16:11Z

<a href="https://sdu.sk/AyL"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="fsd" /></a> <a href="https://sdu.sk/AyL" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>

manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755676003

manusiaperahu2012

2025-08-20T08:16:07Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring long tuna", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:16:03Z

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

unitova/blockassist-bc-zealous_sneaky_raven_1755676050

unitova

2025-08-20T08:15:52Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:15:48Z

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

armonio/my-bert-fine-tuned1

armonio

2025-08-20T08:15:46Z

0

transformers

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

text-classification

2025-08-20T08:15:30Z

--- 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]

Orginal-afrin-apu-viral-video-link/New.full.videos.afrin.apu.Viral.Video.Official.Tutorial

Orginal-afrin-apu-viral-video-link

2025-08-20T08:15:38Z

0

null

[ "region:us" ]

null

2025-08-20T08:15:25Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>

coppertoy/blockassist-bc-dappled_purring_bobcat_1755677667

coppertoy

2025-08-20T08:14:36Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dappled purring bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:14:28Z

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

TonevitItaly/TonevitItaly

TonevitItaly

2025-08-20T08:14:28Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-20T08:13:40Z

--- license: apache-2.0 --- Cos'è Tonevit? Tonevit Capsula è una capsula per l'ipertensione progettata per aiutare a mantenere livelli di pressione sanguigna sani in modo sicuro ed efficace. È sviluppata per le persone che desiderano prendersi cura della propria salute cardiovascolare riducendo al contempo i rischi legati all'ipertensione. A differenza dei trattamenti chimici intensivi che a volte possono avere effetti collaterali, Tonevit Pillole è formulato per agire delicatamente sull'organismo, rendendolo una scelta affidabile per l'uso quotidiano. Offre un supporto a lungo termine per le persone che desiderano bilanciare la pressione sanguigna e proteggere la salute del cuore in modo naturale. Sito ufficiale:<a href="https://www.nutritionsee.com/tonevitaly">www.Tonevit.com</a> <p><a href="https://www.nutritionsee.com/tonevitaly"> <img src="https://www.nutritionsee.com/wp-content/uploads/2025/08/Tonevit-Italy.png" alt="enter image description here"> </a></p> <a href="https://www.nutritionsee.com/tonevitaly">Acquista ora!! Clicca sul link qui sotto per maggiori informazioni e ottieni subito il 50% di sconto... Affrettati</a> Sito ufficiale:<a href="https://www.nutritionsee.com/tonevitaly">www.Tonevit.com</a>

Danisalah/Homefixsolutions

Danisalah

2025-08-20T08:13:11Z

0

null

[ "region:us" ]

null

2025-08-20T07:52:25Z

import React, { useState, useEffect } from 'react'; import { View, Text, TouchableOpacity, StyleSheet, Linking, Image } from 'react-native'; export default function App() { const [showMain, setShowMain] = useState(false); useEffect(() => { // Splash Screen مدة 2 ثانية const timer = setTimeout(() => { setShowMain(true); }, 2000); return () => clearTimeout(timer); }, []); const openWhatsApp = () => { Linking.openURL('https://wa.me/213666061838'); }; const sendEmail = () => { Linking.openURL('mailto:Dendanisalah30@gmail.com'); }; const openPayPal = () => { Linking.openURL('https://www.paypal.com/paypalme/dendanisalah30/5'); }; if (!showMain) { // Splash Screen return ( <View style={styles.splash}> <Text style={styles.splashText}>Home Fix Solutions</Text> <Image source={{ uri: 'https://i.postimg.cc/Fzf9X6d6/homefix-icon.png' }} style={styles.logo} /> </View> ); } // Main Screen return ( <View style={styles.container}> <TouchableOpacity style={styles.mainButton} onPress={() => setShowMain('buttons')}> <Text style={styles.mainButtonText}>Start Now</Text> </TouchableOpacity> {showMain === 'buttons' && ( <View style={styles.buttonContainer}> <TouchableOpacity style={styles.button} onPress={openWhatsApp}> <Text style={styles.buttonText}>WhatsApp 🟢</Text> </TouchableOpacity> <TouchableOpacity style={styles.button} onPress={sendEmail}> <Text style={styles.buttonText}>Email ✉️</Text> </TouchableOpacity> <TouchableOpacity style={styles.button} onPress={openPayPal}> <Text style={styles.buttonText}>PayPal 💳</Text> </TouchableOpacity> </View> )} </View> ); } const styles = StyleSheet.create({ splash: { flex: 1, justifyContent: 'center', alignItems: 'center', backgroundColor: '#007bff', }, splashText: { fontSize: 28, fontWeight: 'bold', color: '#fff', marginBottom: 20, }, logo: { width: 100, height: 100, }, container: { flex: 1, justifyContent: 'center', alignItems: 'center', backgroundColor: '#f2f2f2', padding: 20, }, mainButton: { backgroundColor: '#007bff', padding: 20, borderRadius: 15, width: '70%', alignItems: 'center', }, mainButtonText: { color: '#fff', fontSize: 22, fontWeight: 'bold', }, buttonContainer: { marginTop: 30, width: '80%', }, button: { backgroundColor: '#007bff', padding: 15, borderRadius: 12, marginBottom: 15, alignItems: 'center', }, buttonText: { color: '#fff', fontSize: 18, fontWeight: 'bold', }, });

AnerYubo/blockassist-bc-dense_savage_lynx_1755677585

AnerYubo

2025-08-20T08:13:08Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dense savage lynx", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:13:06Z

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

VIDEOS-18-Sister-Hong-viral-Telegram/New.full.videos.Sister.Hong.Viral.Video.Official.Tutorial

VIDEOS-18-Sister-Hong-viral-Telegram

2025-08-20T08:12:36Z

0

null

[ "region:us" ]

null

2025-08-20T08:12:24Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>

ChavyvAkvar/Liquid-Thinking

ChavyvAkvar

2025-08-20T08:12:20Z

0

transformers

[ "transformers", "safetensors", "lfm2", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T16:28:34Z

--- library_name: transformers tags: - unsloth - trl - sft --- # 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]

clairedhx/mistral7b-labels2codes-lora

clairedhx

2025-08-20T08:12:07Z

0

peft

[ "peft", "safetensors", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3", "lora", "sft", "transformers", "trl", "text-generation", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "region:us" ]

text-generation

2025-08-20T07:57:35Z

--- base_model: mistralai/Mistral-7B-Instruct-v0.3 library_name: peft model_name: mistral7b_labels2codes_lora tags: - base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3 - lora - sft - transformers - trl licence: license pipeline_tag: text-generation --- # Model Card for mistral7b_labels2codes_lora This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3). It has been trained using [TRL](https://github.com/huggingface/trl). Mistral-7B Instruct — LoRA (ICD-10 labels → codes) This LoRA adapter fine-tunes mistralai/Mistral-7B-Instruct-v0.3 to map French ICD-10 diagnostic labels (synonyms) to their corresponding codes (dot-less, up to 5 chars). ## Quick start ```python from transformers import pipeline question = "Libellé: Antécédent bronchite chronique" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure It was trained via supervised fine-tuning (QLoRA) on an instruction dataset built from (label, code) pairs (instruct_labels2codes) derived from a curated ICD-10 synonyms table created from the webscrapinng of aideaucodage.fr . ### Framework versions - PEFT 0.17.0 - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.7.1 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

AnerYubo/blockassist-bc-fishy_endangered_antelope_1755677506

AnerYubo

2025-08-20T08:11:49Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy endangered antelope", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:11:46Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy endangered antelope --- # 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_1755675981

calegpedia

2025-08-20T08:11:25Z

0

null

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

null

2025-08-20T08:11:22Z

--- 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).

New-Clip-prabh-viral-video-Orginal/New.full.videos.prabh.Viral.Video.Official.Tutorial

New-Clip-prabh-viral-video-Orginal

2025-08-20T08:08:10Z

0

null

[ "region:us" ]

null

2025-08-20T08:07:58Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>

jakehsv/blockassist-bc-flexible_waddling_peacock_1755675452

jakehsv

2025-08-20T08:07:42Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "flexible waddling peacock", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:07:31Z

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

jayssaa/my_awesome_video_cls_model

jayssaa

2025-08-20T08:07:30Z

0

transformers

[ "transformers", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

video-classification

2025-08-20T08:06:45Z

--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_video_cls_model results: [] ---  # my_awesome_video_cls_model This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2870 - Accuracy: 0.4857 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 300 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2211 | 1.0 | 300 | 1.2870 | 0.4857 | ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755675560

helmutsukocok

2025-08-20T08:07:12Z

0

null

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

null

2025-08-20T08:07:09Z

--- 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).

zzyyuu/distilbert-base-uncased-finetuned-imdb

zzyyuu

2025-08-20T08:06:33Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2025-08-20T07:47:21Z

--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] ---  # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4892 - Model Preparation Time: 0.0016 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | |:-------------:|:-----:|:----:|:---------------:|:----------------------:| | 2.6814 | 1.0 | 157 | 2.4929 | 0.0016 | | 2.5825 | 2.0 | 314 | 2.4480 | 0.0016 | | 2.5258 | 3.0 | 471 | 2.4823 | 0.0016 | ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

TomeroSama07/act_single_lego_pp_420p_no_blur_compressed

TomeroSama07

2025-08-20T08:03:30Z

0

lerobot

[ "lerobot", "safetensors", "robotics", "act", "dataset:TomeroSama07/single_lego_pp_420p_no_blur_compressed", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]

robotics

2025-08-20T08:02:48Z

--- datasets: TomeroSama07/single_lego_pp_420p_no_blur_compressed library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - lerobot - robotics - act --- # Model Card for act  [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - **License:** apache-2.0

ypszn/blockassist-bc-yapping_pawing_worm_1755676956

ypszn

2025-08-20T08:03:28Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping pawing worm", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:03:18Z

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

yaelahnal/blockassist-bc-mute_clawed_crab_1755676912

yaelahnal

2025-08-20T08:03:24Z

0

null

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

null

2025-08-20T08:02:43Z

--- 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).

Kokoutou/soundsright_dn_2008_5

Kokoutou

2025-08-20T08:02:43Z

0

null

[ "region:us" ]

null

2025-08-20T07:02:42Z

# Container Template for SoundsRight Subnet Miners This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively. This container has only been tested with **Ubuntu 24.04** and **CUDA 12.6**. It may run on other configurations, but it is not guaranteed. To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt. Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html). Verify that the CDI specification was done correctly with: ``` $ nvidia-ctk cdi list ``` You should see this in your output: ``` nvidia.com/gpu=all nvidia.com/gpu=0 ``` If you are running podman as root, run the following command to start the container: Run the container with: ``` podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi ``` Access logs with: ``` podman logs -f modelapi ``` If you are running the container rootless, there are a few more changes to make: First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters: ``` [nvidia-container-cli] no-cgroups = true [nvidia-container-runtime] debug = "/tmp/nvidia-container-runtime.log" ``` You can also run the following command to achieve the same result: ``` $ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place ``` Run the container with: ``` podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi ``` Access logs with: ``` podman logs -f modelapi ``` Running the container will spin up an API with the following endpoints: 1. `/status/` : Communicates API status 2. `/prepare/` : Download model checkpoint and initialize model 3. `/upload-audio/` : Upload audio files, save to noisy audio directory 4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory 5. `/download-enhanced/` : Download enhanced audio files By default the API will use host `0.0.0.0` and port `6500`. ### References 1. **Welker, Simon; Richter, Julius; Gerkmann, Timo** *Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain*. Proceedings of *Interspeech 2022*, 2022, pp. 2928–2932. [DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653) 2. **Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo** *Speech Enhancement and Dereverberation with Diffusion-based Generative Models*. *IEEE/ACM Transactions on Audio, Speech, and Language Processing*, Vol. 31, 2023, pp. 2351–2364. [DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241) 3. **Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo** *EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation*. Proceedings of *ISCA Interspeech*, 2024, pp. 4873–4877.

Kokoutou/soundsright_dn_2008_6

Kokoutou

2025-08-20T08:02:35Z

0

null

[ "region:us" ]

null

2025-08-20T07:02:42Z

# Container Template for SoundsRight Subnet Miners This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively. This container has only been tested with **Ubuntu 24.04** and **CUDA 12.6**. It may run on other configurations, but it is not guaranteed. To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt. Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html). Verify that the CDI specification was done correctly with: ``` $ nvidia-ctk cdi list ``` You should see this in your output: ``` nvidia.com/gpu=all nvidia.com/gpu=0 ``` If you are running podman as root, run the following command to start the container: Run the container with: ``` podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi ``` Access logs with: ``` podman logs -f modelapi ``` If you are running the container rootless, there are a few more changes to make: First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters: ``` [nvidia-container-cli] no-cgroups = true [nvidia-container-runtime] debug = "/tmp/nvidia-container-runtime.log" ``` You can also run the following command to achieve the same result: ``` $ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place ``` Run the container with: ``` podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi ``` Access logs with: ``` podman logs -f modelapi ``` Running the container will spin up an API with the following endpoints: 1. `/status/` : Communicates API status 2. `/prepare/` : Download model checkpoint and initialize model 3. `/upload-audio/` : Upload audio files, save to noisy audio directory 4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory 5. `/download-enhanced/` : Download enhanced audio files By default the API will use host `0.0.0.0` and port `6500`. ### References 1. **Welker, Simon; Richter, Julius; Gerkmann, Timo** *Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain*. Proceedings of *Interspeech 2022*, 2022, pp. 2928–2932. [DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653) 2. **Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo** *Speech Enhancement and Dereverberation with Diffusion-based Generative Models*. *IEEE/ACM Transactions on Audio, Speech, and Language Processing*, Vol. 31, 2023, pp. 2351–2364. [DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241) 3. **Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo** *EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation*. Proceedings of *ISCA Interspeech*, 2024, pp. 4873–4877.

milliarderdol/blockassist-bc-roaring_rough_scorpion_1755674877

milliarderdol

2025-08-20T08:01:59Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring rough scorpion", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T08:01:20Z

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

thedeba/debai

thedeba

2025-08-20T08:01:51Z

0

transformers

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

text-generation

2025-08-20T07:46:34Z

--- 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]

Singhzaman/qwen-2.5-vl-lora-sroie

Singhzaman

2025-08-20T08:00:51Z

0

transformers

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

null

2025-08-19T21:21:35Z

--- 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]

mradermacher/BiggerCoQ-Qwen3-10b-GGUF

mradermacher

2025-08-20T08:00:48Z

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:KaraKaraWitch/BiggerCoQ-Qwen3-10b", "base_model:quantized:KaraKaraWitch/BiggerCoQ-Qwen3-10b", "endpoints_compatible", "region:us", "conversational" ]

null

2025-08-20T06:17:16Z

--- base_model: KaraKaraWitch/BiggerCoQ-Qwen3-10b language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - mergekit - merge --- ## About         static quants of https://huggingface.co/KaraKaraWitch/BiggerCoQ-Qwen3-10b  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#BiggerCoQ-Qwen3-10b-GGUF).*** weighted/imatrix quants are available at https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q2_K.gguf) | Q2_K | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q3_K_S.gguf) | Q3_K_S | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q3_K_M.gguf) | Q3_K_M | 5.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q3_K_L.gguf) | Q3_K_L | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.IQ4_XS.gguf) | IQ4_XS | 6.2 | | | [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q4_K_S.gguf) | Q4_K_S | 6.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q4_K_M.gguf) | Q4_K_M | 6.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q5_K_S.gguf) | Q5_K_S | 7.7 | | | [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q5_K_M.gguf) | Q5_K_M | 7.9 | | | [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q6_K.gguf) | Q6_K | 9.0 | very good quality | | [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.Q8_0.gguf) | Q8_0 | 11.7 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/BiggerCoQ-Qwen3-10b-GGUF/resolve/main/BiggerCoQ-Qwen3-10b.f16.gguf) | f16 | 21.9 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

CoDiEmb/CoDi-MiniCPM_sentence_transformers

CoDiEmb

2025-08-20T07:59:30Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "minicpm", "sentence-similarity", "feature-extraction", "custom_code", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-08-20T07:48:03Z

--- language: [] tags: - sentence-transformers - sentence-similarity - feature-extraction widget: [] datasets: [] pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 2304-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  - **Maximum Sequence Length:** 1024 tokens - **Output Dimensionality:** 2304 tokens - **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': 1024, 'do_lower_case': False}) with Transformer model: MiniCPMModel (1): Pooling({'word_embedding_dimension': 2304, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': False}) ) ``` ## 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 = [ 'The weather is lovely today.', "It's so sunny outside!", 'He drove to the stadium.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 2304] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```      ## Training Details ### Framework Versions - Python: 3.10.10 - Sentence Transformers: 3.0.1 - Transformers: 4.51.3 - PyTorch: 2.2.1+cu118 - Accelerate: - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citation ### BibTeX

ypszn/blockassist-bc-yapping_pawing_worm_1755676531

ypszn

2025-08-20T07:57:07Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping pawing worm", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T07:56:59Z

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

WIHOW3H/my_awesome_video_cls_model

WIHOW3H

2025-08-20T07:56:58Z

0

transformers

[ "transformers", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

video-classification

2025-08-20T07:56:31Z

--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_video_cls_model results: [] ---  # my_awesome_video_cls_model This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2573 - Accuracy: 0.9286 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0813 | 0.25 | 300 | 1.1575 | 0.4571 | | 1.4385 | 1.25 | 600 | 1.5501 | 0.6429 | | 0.0222 | 2.25 | 900 | 0.4601 | 0.8429 | | 1.2499 | 3.25 | 1200 | 0.2573 | 0.9286 | ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

AXERA-TECH/MiniCPM4-0.5B

AXERA-TECH

2025-08-20T07:56:47Z

8

0

null

[ "minicpm4", "int8", "text-generation", "en", "base_model:openbmb/MiniCPM4-0.5B", "base_model:finetune:openbmb/MiniCPM4-0.5B", "license:mit", "region:us" ]

text-generation

2025-06-11T17:14:28Z

--- license: mit language: - en base_model: - openbmb/MiniCPM4-0.5B pipeline_tag: text-generation tags: - minicpm4 - int8 --- # MiniCPM4-0.5B-Int8 This version of MiniCPM4-0.5B has been converted to run on the Axera NPU using **w8a16** quantization. This model has been optimized with the following LoRA: Compatible with Pulsar2 version: 4.2(Not released yet) ## Convert tools links: For those who are interested in model conversion, you can try to export axmodel through the original repo : https://huggingface.co/openbmb/MiniCPM4-0.5B [Pulsar2 Link, How to Convert LLM from Huggingface to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/appendix/build_llm.html) [AXera NPU LLM Runtime](https://github.com/AXERA-TECH/ax-llm) ## Support Platform - AX650 - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html) - [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html) - AX630C - [爱芯派2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html) - [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM) - [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit) |Chips|w8a16|w4a16| |--|--|--| |AX650| 36 tokens/sec|TBD| |AX630C| 12 tokens/sec|TBD| ## How to use Download all files from this repository to the device ``` root@ax650:/mnt/qtang/llm-test/minicpm4-0.5b-ctx# tree -L 1 . |-- main_ax650 |-- main_axcl_aarch64 |-- main_axcl_x86 |-- minicpm4-0.5b-int8-ctx-ax650 |-- minicpm4_tokenizer |-- minicpm4_tokenizer_uid.py |-- post_config.json |-- run_minicpm4_0.5b_int8_ctx_ax650.sh `-- run_minicpm4_0.5b_int8_ctx_axcl_x86.sh 2 directories, 7 files ``` #### Start the Tokenizer service Install requirement ``` pip install transformers jinja2 ``` ``` root@ax650:/mnt/qtang/llm-test/minicpm4-0.5b-ctx# python3 minicpm4_tokenizer_uid.py Server running at http://0.0.0.0:12345 ``` #### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) or AX650N DEMO Board Open another terminal and run `run_minicpm4_0.5b_int8_ctx_ax650.sh` ``` root@ax650:/mnt/qtang/llm-test/minicpm4-0.5b-ctx# ./run_minicpm4_0.5b_int8_ctx_ax650.sh [I][ Init][ 110]: LLM init start [I][ Init][ 34]: connect http://127.0.0.1:12345 ok [I][ Init][ 57]: uid: c779ded0-ff14-4877-869b-1aacc948f2d8 bos_id: 1, eos_id: 73440 100% | ████████████████████████████████ | 27 / 27 [2.53s<2.53s, 10.67 count/s] init post axmodel ok,remain_cmm(4244 MB) [I][ Init][ 188]: max_token_len : 1023 [I][ Init][ 193]: kv_cache_size : 128, kv_cache_num: 1023 [I][ Init][ 201]: prefill_token_num : 128 [I][ Init][ 205]: grp: 1, prefill_max_token_num : 1 [I][ Init][ 205]: grp: 2, prefill_max_token_num : 128 [I][ Init][ 205]: grp: 3, prefill_max_token_num : 512 [I][ Init][ 209]: prefill_max_token_num : 512 [I][ load_config][ 282]: load config: { "enable_repetition_penalty": false, "enable_temperature": false, "enable_top_k_sampling": true, "enable_top_p_sampling": false, "penalty_window": 20, "repetition_penalty": 1.2, "temperature": 0.9, "top_k": 1, "top_p": 0.8 } [I][ Init][ 218]: LLM init ok Type "q" to exit, Ctrl+c to stop current running [I][ GenerateKVCachePrefill][ 271]: input token num : 25, prefill_split_num : 1 prefill_grpid : 2 [I][ GenerateKVCachePrefill][ 308]: input_num_token:25 [I][ main][ 230]: precompute_len: 25 [I][ main][ 231]: system_prompt: You are MiniCPM4, created by ModelBest. You are a helpful assistant. prompt >> 你是谁? [I][ SetKVCache][ 531]: prefill_grpid:2 kv_cache_num:128 precompute_len:25 input_num_token:12 [I][ SetKVCache][ 534]: current prefill_max_token_num:384 [I][ Run][ 660]: input token num : 12, prefill_split_num : 1 [I][ Run][ 686]: input_num_token:12 [I][ Run][ 829]: ttft: 147.65 ms 你好，我是MiniCPM系列模型，由面壁智能和OpenBMB开源社区开发。详细信息请访问https://github.com/OpenBMB/ [N][ Run][ 943]: hit eos,avg 35.75 token/s [I][ GetKVCache][ 500]: precompute_len:162, remaining:350 prompt >> 9.9与9.11 [I][ SetKVCache][ 531]: prefill_grpid:3 kv_cache_num:512 precompute_len:162 input_num_token:17 [I][ SetKVCache][ 534]: current prefill_max_token_num:256 [I][ Run][ 660]: input token num : 17, prefill_split_num : 1 [I][ Run][ 686]: input_num_token:17 [I][ Run][ 829]: ttft: 274.38 ms 9.9比9.11大。 [N][ Run][ 943]: hit eos,avg 35.44 token/s [I][ GetKVCache][ 500]: precompute_len:189, remaining:323 prompt >> q root@ax650:/mnt/qtang/llm-test/minicpm4-0.5b-ctx# ```

AnerYubo/blockassist-bc-alert_snorting_fox_1755676563

AnerYubo

2025-08-20T07:56:07Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "alert snorting fox", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T07:56:03Z

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

hobson123/blockassist-bc-mammalian_dense_gibbon_1755676070

hobson123

2025-08-20T07:54:06Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mammalian dense gibbon", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T07:53:52Z

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

joanna302/Qwen3-8B-Base_pag_alpaca_0.66_part_SFT_2e-05

joanna302

2025-08-20T07:53:59Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "sft", "unsloth", "trl", "conversational", "base_model:unsloth/Qwen3-8B-Base", "base_model:finetune:unsloth/Qwen3-8B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-17T09:27:16Z

--- base_model: unsloth/Qwen3-8B-Base library_name: transformers model_name: Qwen3-8B-Base_pag_alpaca_0.66_part_SFT_2e-05 tags: - generated_from_trainer - sft - unsloth - trl licence: license --- # Model Card for Qwen3-8B-Base_pag_alpaca_0.66_part_SFT_2e-05 This model is a fine-tuned version of [unsloth/Qwen3-8B-Base](https://huggingface.co/unsloth/Qwen3-8B-Base). 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="joanna302/Qwen3-8B-Base_pag_alpaca_0.66_part_SFT_2e-05", 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/prism-eval/Qwen3-8B-Base_pag_alpaca_0.66_part_SFT_2e-05/runs/urz9gi0n) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0 - Datasets: 3.6.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

yaelahnal/blockassist-bc-mute_clawed_crab_1755676336

yaelahnal

2025-08-20T07:53:29Z

0

null

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

null

2025-08-20T07:53:11Z

--- 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).

quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755674677

quantumxnode

2025-08-20T07:52:11Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T07:52:07Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # 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_1755674653

lisaozill03

2025-08-20T07:50:26Z

0

null

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

null

2025-08-20T07:50:18Z

--- 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).

joanna302/Qwen3-8B-Base_pag_mt_alpaca_0.66_part_SFT_0.0002

joanna302

2025-08-20T07:47:57Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "sft", "unsloth", "trl", "conversational", "base_model:unsloth/Qwen3-8B-Base", "base_model:finetune:unsloth/Qwen3-8B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T06:14:28Z

--- base_model: unsloth/Qwen3-8B-Base library_name: transformers model_name: Qwen3-8B-Base_pag_mt_alpaca_0.66_part_SFT_0.0002 tags: - generated_from_trainer - sft - unsloth - trl licence: license --- # Model Card for Qwen3-8B-Base_pag_mt_alpaca_0.66_part_SFT_0.0002 This model is a fine-tuned version of [unsloth/Qwen3-8B-Base](https://huggingface.co/unsloth/Qwen3-8B-Base). 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="joanna302/Qwen3-8B-Base_pag_mt_alpaca_0.66_part_SFT_0.0002", 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/prism-eval/Qwen3-8B-Base_pag_mt_alpaca_0.66_part_SFT_0.0002/runs/s7dl7p4h) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0 - Datasets: 3.6.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

koloni/blockassist-bc-deadly_graceful_stingray_1755674401

koloni

2025-08-20T07:46:56Z

0

null

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

null

2025-08-20T07:46:52Z

--- 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).

Alfie703/gemma3-270m

Alfie703

2025-08-20T07:46:20Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "endpoints_compatible", "region:us" ]

null

2025-08-18T11:55:04Z

--- base_model: google/gemma-3-270m-it library_name: transformers model_name: gemma3-270m tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for gemma3-270m This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Alfie703/gemma3-270m", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.5.1+cu121 - Datasets: 4.0.0 - 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}} } ```

joanna302/Qwen3-8B-Base_zh_ar_alpaca_0.66_part_SFT_8e-05

joanna302

2025-08-20T07:45:36Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "unsloth", "trl", "sft", "conversational", "base_model:unsloth/Qwen3-8B-Base", "base_model:finetune:unsloth/Qwen3-8B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-18T13:47:27Z

--- base_model: unsloth/Qwen3-8B-Base library_name: transformers model_name: Qwen3-8B-Base_zh_ar_alpaca_0.66_part_SFT_8e-05 tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for Qwen3-8B-Base_zh_ar_alpaca_0.66_part_SFT_8e-05 This model is a fine-tuned version of [unsloth/Qwen3-8B-Base](https://huggingface.co/unsloth/Qwen3-8B-Base). 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="joanna302/Qwen3-8B-Base_zh_ar_alpaca_0.66_part_SFT_8e-05", 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/prism-eval/Qwen3-8B-Base_zh_ar_alpaca_0.66_part_SFT_8e-05/runs/vuased7f) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0 - Datasets: 3.6.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

aleebaster/blockassist-bc-sly_eager_boar_1755674371

aleebaster

2025-08-20T07:45:24Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly eager boar", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T07:45:16Z

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

kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755674272

kojeklollipop

2025-08-20T07:44:54Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T07:44:50Z

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

k1000dai/residualact_libero_smolvla_chunk_batch64

k1000dai

2025-08-20T07:44:50Z

0

lerobot

[ "lerobot", "safetensors", "residualact", "robotics", "dataset:k1000dai/libero-smolvla", "license:apache-2.0", "region:us" ]

robotics

2025-08-17T08:07:41Z

--- datasets: k1000dai/libero-smolvla library_name: lerobot license: apache-2.0 model_name: residualact pipeline_tag: robotics tags: - residualact - robotics - lerobot --- # Model Card for residualact  _Model type not recognized — please update this template._ This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - **License:** apache-2.0

yukiharada1228/tsukuyomichan-llm-jp-3.1-1.8b-instruct4

yukiharada1228

2025-08-20T07:44:36Z

0

null

[ "safetensors", "text-generation", "conversational", "ja", "base_model:llm-jp/llm-jp-3.1-1.8b-instruct4", "base_model:finetune:llm-jp/llm-jp-3.1-1.8b-instruct4", "license:apache-2.0", "region:us" ]

text-generation

2025-07-30T22:58:53Z

--- license: apache-2.0 language: - ja base_model: - llm-jp/llm-jp-3.1-1.8b-instruct4 pipeline_tag: text-generation --- ## モデル概要 yukiharada1228/tsukuyomichan-llm-jp-3.1-1.8b-instruct4-GGUFは、[つくよみちゃん会話AI育成計画](https://tyc.rei-yumesaki.net/material/kaiwa-ai/)の公式データを使用して[LLM-JP 3.1 1.8B Instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4)モデルをファインチューニングした日本語会話AIモデルです。 ## モデル情報 - **ライセンス**: Apache 2.0 - **言語**: 日本語 (ja) - **ベースモデル**: yukiharada1228/tsukuyomichan-llm-jp-3.1-1.8b-instruct4 - **パイプラインタグ**: text-generation - **量子化**: q4_k_m ## 特徴 ### 会話能力 - つくよみちゃんのキャラクターに特化した自然な日本語会話 - 親しみやすく、丁寧な応答スタイル - 複数往復の会話に対応 ### 技術仕様 - **モデルサイズ**: 1.8B パラメータ - **ファインチューニング手法**: PEFT (Parameter-Efficient Fine-Tuning) - **量子化**: 4bit量子化によるメモリ効率化 - **最大シーケンス長**: 4096トークン ## 学習データ ### データソース - [つくよみちゃん会話AI育成計画](https://tyc.rei-yumesaki.net/material/kaiwa-ai/)の公式データ - 複数往復の会話データを含む - 日本語の自然な会話パターン ### データ形式学習データは以下の形式で構成されています： - **システムプロンプト**: "あなたは、つくよみちゃんです。" - ユーザー入力: 質問や会話の内容 - アシスタント応答: 応答 ## 制限事項 ### 技術的制限 - 1.8Bパラメータのため、複雑な推論には制限があります - 4bit量子化により精度が若干低下する可能性があります - 最大4096トークンのコンテキスト長制限 ### 使用上の制限 - 学習目的での使用を推奨 - 商用利用の際は適切なライセンス確認が必要 - 元データの著作権に注意 ## ライセンスとクレジット ### ライセンスこのモデルはApache 2.0ライセンスの下で公開されています。 ### クレジット - **元データ**: つくよみちゃん会話AI育成計画 © Rei Yumesaki - **ベースモデル**: LLM-JP 3.1 1.8B Instruct4 © LLM-JP Project - **ファインチューニング**: yukiharada1228 ## サポートこのモデルに関する質問や問題については、GitHubのIssuesページをご利用ください。 ## 関連リンク - [つくよみちゃん会話AI育成計画](https://tyc.rei-yumesaki.net/material/kaiwa-ai/) - [LLM-JP 3.1 1.8B Instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4) - [プロジェクトリポジトリ](https://github.com/yukiharada1228/tsukuyomichan-llm-ft)

katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755674158

katanyasekolah

2025-08-20T07:44:15Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T07:44:12Z

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

vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755673971

vwzyrraz7l

2025-08-20T07:41:35Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T07:41:31Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # 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_1755674102

calegpedia

2025-08-20T07:41:12Z

0

null

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

null

2025-08-20T07:41:07Z

--- 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).

Albertdebeauvais/all-MiniLM-L6-v2_bibliographie

Albertdebeauvais

2025-08-20T07:40:53Z

8

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:388038", "loss:CosineSimilarityLoss", "arxiv:1908.10084", "base_model:sentence-transformers/all-MiniLM-L6-v2", "base_model:finetune:sentence-transformers/all-MiniLM-L6-v2", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-07-15T09:04:27Z

--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:388038 - loss:CosineSimilarityLoss base_model: sentence-transformers/all-MiniLM-L6-v2 widget: - source_sentence: Les Chemins de l'effort, édité en 1975, Paris, éd. Actes Sud. sentences: - chisinau, Actes Sud, FECOURA, Dusty et VEHIER, Tyrrell, en 1915, « Les œufs d'or de ; oa guerre ». - Zagreb, éd. CNRS, FABRIE, Seneca, FARHAT, Hope, DE LASTIC SAINT JAL, Daniella, 1997, Poe et les enseignements de l'Est. - (1975), Paris, Actes Sud éditions, « Les Chemins de l'effort ». - source_sentence: par BURTSCHELL, Régine et ALESSANDRONI, Diggory, 1900, « Complément du catalogue analytique des manuscrits de la bibliothèque d'Abbeville », Rennes, Verlag Ferdinand Schöningh éditions. sentences: - « Complément du catalogue analytique des manuscrots de la bibliothèque d'Abbeville », Rennes, Verlag Ferdinand Schöningh éditions, BURTSCHELL, Régine, sous la direction de ALESSANDRONI, Diggory, (1900). - Vortex, le cheval fou, publié en ; 1926, , Bordeaux, L’Harmattan. - 1997, DEPOUMEAUX, Summer, « De chair et de lumière », Luxembourg, L’Harmattan éditions. - source_sentence: de Lorita, STREIFF, Petronella, MONTIALOUX, Gale, DANGOUMAU et ed Montgomery, D AUBERT, Dean Martin, (2011), Prague, éd. Peter Lang. sentences: - Amiens, University of Chicago Press, GUILLION L. et LAPERDRIX K., Autres courants, 2015. - 'Prague, éd. : Peter Lang, , (2011), "Dean Martin", pr + 20 ill.. Gale, DANGOUMAU et Lorita, STREIFF, Petronella, MONTIALOUX, Montgomery, D AUBERT.' - Valerie, PAIRA, Niles, AUDUBERT, 1986, Au gré des saisons, Amsterdam, Routledge. - source_sentence: 1948, Seattle, éd. Payot & Rivages, de Trudy, SAINT-AIME, Toponymes finnois et germaniques en Lithuanie... Remarques sur le nom de la Vistule. sentences: - Toponymes finnois et germaniques en Lithuanie... Remarques sur le nom de la Vistule, en 1952, Seattle, Payot & Rivages éditions, Delia, HOZE. - Cologne, Les Belles Lettres, Éléments de géométrie expérimentale, à l'usage des élèves des cours professionnels et des ouvriers, avec de nombreuses applications au trait, LAGEIX, Shelly, (1898). - 1887., The variations of glaciers. XVI, Jessika, ANNIEL, Chisinau, éd. Stanford University Press. - source_sentence: BENMAMAR, A. et LUZEUX, K., JARRAND-MARTIN, S., "La science alchimique", Master drawings, numéro 92, pages 511-649, 1904, Valence, éd. Zed Books. sentences: - Dublin, éd. CNRS, Les mystères de la cour de Cornouailles, N. BILLEBEAU, en 1966. - En 1939, New York, Fayard, réactions et méthodes nouvelles d'analyse qualitative minérale, BERTIER, R. - édité en 2020, Alexandre, GLERAND et Ashleigh, BIZET, "Un long voyage", Reims, Editions Payot éditions. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - cosine_mcc model-index: - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 results: - task: type: binary-classification name: Binary Classification dataset: name: eval type: eval metrics: - type: cosine_accuracy value: 0.9845532980795992 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7197951674461365 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9822371579452713 name: Cosine F1 - type: cosine_f1_threshold value: 0.7197951674461365 name: Cosine F1 Threshold - type: cosine_precision value: 0.9880875724404379 name: Cosine Precision - type: cosine_recall value: 0.9764556156538339 name: Cosine Recall - type: cosine_ap value: 0.9978040298718638 name: Cosine Ap - type: cosine_mcc value: 0.9686262528236084 name: Cosine Mcc - task: type: binary-classification name: Binary Classification dataset: name: test type: test metrics: - type: cosine_accuracy value: 0.9851563224788942 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7434847354888916 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9829406120055443 name: Cosine F1 - type: cosine_f1_threshold value: 0.7414178252220154 name: Cosine F1 Threshold - type: cosine_precision value: 0.9907576571735626 name: Cosine Precision - type: cosine_recall value: 0.975245953665503 name: Cosine Recall - type: cosine_ap value: 0.9978710556305371 name: Cosine Ap - type: cosine_mcc value: 0.9698992765132763 name: Cosine Mcc --- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)  - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'BENMAMAR, A. et LUZEUX, K., JARRAND-MARTIN, S., "La science alchimique", Master drawings, numéro 92, pages 511-649, 1904, Valence, éd. Zed Books.', 'édité en 2020, Alexandre, GLERAND et Ashleigh, BIZET, "Un long voyage", Reims, Editions Payot éditions.', 'Dublin, éd. CNRS, Les mystères de la cour de Cornouailles, N. BILLEBEAU, en 1966.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Binary Classification * Datasets: `eval` and `test` * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | eval | test | |:--------------------------|:-----------|:-----------| | cosine_accuracy | 0.9846 | 0.9852 | | cosine_accuracy_threshold | 0.7198 | 0.7435 | | cosine_f1 | 0.9822 | 0.9829 | | cosine_f1_threshold | 0.7198 | 0.7414 | | cosine_precision | 0.9881 | 0.9908 | | cosine_recall | 0.9765 | 0.9752 | | **cosine_ap** | **0.9978** | **0.9979** | | cosine_mcc | 0.9686 | 0.9699 |   ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 388,038 training samples * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: | | text1 | text2 | label | |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 17 tokens</li><li>mean: 50.25 tokens</li><li>max: 169 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 47.08 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~57.00%</li><li>1: ~43.00%</li></ul> | * Samples: | text1 | text2 | label | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | <code>(1973),. 70, p. 36-98, Revue d'histoire locale (Chevillon), 3, « La Font perduda », Berlin, éd. Maison des Sciences de l’Homme, editor Dorcas, PEDEVILLA, Alannis, GRANZOTTO, Annabel, VOYRON, Dulcie, MIGLIORI.</code> | <code>Revue d'histoire locale (Chevillon)</code> | <code>0</code> | | <code>Revista del Instituto Egipcio de Estudios Islámicos, n°100, pages 483-496, (2006), Administration et bibliothèques, CAGLAYAN, Kaden, BOULAABI, Fredrick, WORMSER, Bea, Vienne, éd. Beacon Press.</code> | <code>WORMSER, Bea, CAGLAYAN, Kaden, ed BOULAABI, Fredrick, édité en 2006, Administration et bibliothèques, Revista del Instituto Egipcio de Estudios Islámicos,. 100,. p. 483-496, Vienne, Beacon Press.</code> | <code>1</code> | | <code>Atlantic Charter (1941), Bulletin de la Société d'Histoire et d'Archéologie de Nantes et de Loire-Atlantique,. numéro 31, pp. 997-1125, Léontine, SCHWERDROFFER, Sandford, CHUDZIK, Metz, Zed Books éditions, 1941.</code> | <code>(1941),. n° 31, Bulletin de la Société d'Histoire et d'Archéologie de Nantes et de Loire-Atlantique, pages 997-1125, Atlantic Charter (1941), Léontine, SCHWERDROFFER, Sandford, CHUDZIK, Metz, Zed Books.</code> | <code>1</code> | * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 21,558 evaluation samples * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: | | text1 | text2 | label | |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 15 tokens</li><li>mean: 49.64 tokens</li><li>max: 145 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 46.31 tokens</li><li>max: 160 tokens</li></ul> | <ul><li>0: ~57.70%</li><li>1: ~42.30%</li></ul> | * Samples: | text1 | text2 | label | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | <code>Le Progressisme, aspects doctrinaux, DURAZ, Constance, 1955, Montpellier, éd. Routledge,. vol. 1,. pp. 26-39, n°29, Journal of philosophical research.</code> | <code>1955, "Le Progressisme, aspects doctrinaux", Montpellier, Routledge, Journal of philosophical research, pp. 26-39,. volume 1, #29.</code> | <code>1</code> | | <code>Turin, éd. Suhrkamp Verlag, #17, pages 67-111, 2, Annales d'Avignon et du Comtat Venaissin, "Faire face aux crises de colère de l'enfant et de l'adolescent", ed HERREYE, Kassidy, (2019).</code> | <code>Amsterdam, University of Minnesota Press éditions, (1968), "Ainsi de chaque jour".</code> | <code>0</code> | | <code>« Discours et conférences sur la science et ses applications », publié en 1927, Tours, éd. Actes Sud, Cherise, THIEFIN et de Eudora, FINGERHUT et Rona, DELLAL et Josette, DEGIOANNINI.</code> | <code> Les formes verbales du conditionnel dans le vieux sanskrit , Eudora, FINGERHUT et Cherise, THIEFIN et par Rona, DELLAL, par Josette, DEGIOANNINI, Tours, Actes Sud éditions, publié en 1927.</code> | <code>0</code> | * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 20 - `per_device_eval_batch_size`: 20 - `learning_rate`: 3e-05 #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 20 - `per_device_eval_batch_size`: 20 - `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`: 3e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `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`: None - `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 - `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`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | Validation Loss | eval_cosine_ap | test_cosine_ap | |:------:|:-----:|:-------------:|:---------------:|:--------------:|:--------------:| | -1 | -1 | - | - | 0.8231 | - | | 0.0258 | 500 | 0.1033 | - | - | - | | 0.0515 | 1000 | 0.0885 | - | - | - | | 0.0773 | 1500 | 0.0778 | - | - | - | | 0.1031 | 2000 | 0.0721 | - | - | - | | 0.1289 | 2500 | 0.0697 | - | - | - | | 0.1546 | 3000 | 0.0645 | - | - | - | | 0.1804 | 3500 | 0.0619 | - | - | - | | 0.2062 | 4000 | 0.0604 | - | - | - | | 0.2319 | 4500 | 0.0569 | - | - | - | | 0.2577 | 5000 | 0.0545 | - | - | - | | 0.2835 | 5500 | 0.0539 | - | - | - | | 0.3092 | 6000 | 0.0517 | - | - | - | | 0.3350 | 6500 | 0.0506 | - | - | - | | 0.3608 | 7000 | 0.0511 | - | - | - | | 0.3866 | 7500 | 0.0486 | - | - | - | | 0.4123 | 8000 | 0.0463 | - | - | - | | 0.4381 | 8500 | 0.0463 | - | - | - | | 0.4639 | 9000 | 0.0471 | - | - | - | | 0.4896 | 9500 | 0.0454 | - | - | - | | 0.5154 | 10000 | 0.0445 | - | - | - | | 0.5412 | 10500 | 0.0455 | - | - | - | | 0.5670 | 11000 | 0.0441 | - | - | - | | 0.5927 | 11500 | 0.0437 | - | - | - | | 0.6185 | 12000 | 0.0449 | - | - | - | | 0.6443 | 12500 | 0.0413 | - | - | - | | 0.6700 | 13000 | 0.0413 | - | - | - | | 0.6958 | 13500 | 0.0422 | - | - | - | | 0.7216 | 14000 | 0.0411 | - | - | - | | 0.7473 | 14500 | 0.0404 | - | - | - | | 0.7731 | 15000 | 0.0374 | - | - | - | | 0.7989 | 15500 | 0.0378 | - | - | - | | 0.8247 | 16000 | 0.0384 | - | - | - | | 0.8504 | 16500 | 0.0389 | - | - | - | | 0.8762 | 17000 | 0.0377 | - | - | - | | 0.9020 | 17500 | 0.0374 | - | - | - | | 0.9277 | 18000 | 0.0366 | - | - | - | | 0.9535 | 18500 | 0.0368 | - | - | - | | 0.9793 | 19000 | 0.0367 | - | - | - | | 1.0 | 19402 | - | 0.0310 | 0.9965 | - | | 1.0051 | 19500 | 0.0364 | - | - | - | | 1.0308 | 20000 | 0.0323 | - | - | - | | 1.0566 | 20500 | 0.0319 | - | - | - | | 1.0824 | 21000 | 0.0317 | - | - | - | | 1.1081 | 21500 | 0.0298 | - | - | - | | 1.1339 | 22000 | 0.0336 | - | - | - | | 1.1597 | 22500 | 0.0304 | - | - | - | | 1.1854 | 23000 | 0.0302 | - | - | - | | 1.2112 | 23500 | 0.031 | - | - | - | | 1.2370 | 24000 | 0.0301 | - | - | - | | 1.2628 | 24500 | 0.0302 | - | - | - | | 1.2885 | 25000 | 0.0305 | - | - | - | | 1.3143 | 25500 | 0.0293 | - | - | - | | 1.3401 | 26000 | 0.0307 | - | - | - | | 1.3658 | 26500 | 0.0304 | - | - | - | | 1.3916 | 27000 | 0.03 | - | - | - | | 1.4174 | 27500 | 0.0312 | - | - | - | | 1.4432 | 28000 | 0.0296 | - | - | - | | 1.4689 | 28500 | 0.0301 | - | - | - | | 1.4947 | 29000 | 0.0295 | - | - | - | | 1.5205 | 29500 | 0.0295 | - | - | - | | 1.5462 | 30000 | 0.029 | - | - | - | | 1.5720 | 30500 | 0.0295 | - | - | - | | 1.5978 | 31000 | 0.029 | - | - | - | | 1.6235 | 31500 | 0.029 | - | - | - | | 1.6493 | 32000 | 0.0271 | - | - | - | | 1.6751 | 32500 | 0.029 | - | - | - | | 1.7009 | 33000 | 0.0278 | - | - | - | | 1.7266 | 33500 | 0.0286 | - | - | - | | 1.7524 | 34000 | 0.0272 | - | - | - | | 1.7782 | 34500 | 0.0279 | - | - | - | | 1.8039 | 35000 | 0.0285 | - | - | - | | 1.8297 | 35500 | 0.0286 | - | - | - | | 1.8555 | 36000 | 0.0297 | - | - | - | | 1.8812 | 36500 | 0.0273 | - | - | - | | 1.9070 | 37000 | 0.0269 | - | - | - | | 1.9328 | 37500 | 0.0276 | - | - | - | | 1.9586 | 38000 | 0.0278 | - | - | - | | 1.9843 | 38500 | 0.0267 | - | - | - | | 2.0 | 38804 | - | 0.0248 | 0.9976 | - | | 2.0101 | 39000 | 0.0252 | - | - | - | | 2.0359 | 39500 | 0.0233 | - | - | - | | 2.0616 | 40000 | 0.0233 | - | - | - | | 2.0874 | 40500 | 0.0236 | - | - | - | | 2.1132 | 41000 | 0.023 | - | - | - | | 2.1390 | 41500 | 0.0212 | - | - | - | | 2.1647 | 42000 | 0.0233 | - | - | - | | 2.1905 | 42500 | 0.0227 | - | - | - | | 2.2163 | 43000 | 0.0227 | - | - | - | | 2.2420 | 43500 | 0.0233 | - | - | - | | 2.2678 | 44000 | 0.0241 | - | - | - | | 2.2936 | 44500 | 0.0218 | - | - | - | | 2.3193 | 45000 | 0.0232 | - | - | - | | 2.3451 | 45500 | 0.0235 | - | - | - | | 2.3709 | 46000 | 0.024 | - | - | - | | 2.3967 | 46500 | 0.0237 | - | - | - | | 2.4224 | 47000 | 0.0228 | - | - | - | | 2.4482 | 47500 | 0.0231 | - | - | - | | 2.4740 | 48000 | 0.0223 | - | - | - | | 2.4997 | 48500 | 0.0232 | - | - | - | | 2.5255 | 49000 | 0.022 | - | - | - | | 2.5513 | 49500 | 0.0227 | - | - | - | | 2.5771 | 50000 | 0.0226 | - | - | - | | 2.6028 | 50500 | 0.0233 | - | - | - | | 2.6286 | 51000 | 0.0224 | - | - | - | | 2.6544 | 51500 | 0.0224 | - | - | - | | 2.6801 | 52000 | 0.0224 | - | - | - | | 2.7059 | 52500 | 0.022 | - | - | - | | 2.7317 | 53000 | 0.0223 | - | - | - | | 2.7574 | 53500 | 0.023 | - | - | - | | 2.7832 | 54000 | 0.023 | - | - | - | | 2.8090 | 54500 | 0.023 | - | - | - | | 2.8348 | 55000 | 0.0225 | - | - | - | | 2.8605 | 55500 | 0.0229 | - | - | - | | 2.8863 | 56000 | 0.0229 | - | - | - | | 2.9121 | 56500 | 0.0224 | - | - | - | | 2.9378 | 57000 | 0.0218 | - | - | - | | 2.9636 | 57500 | 0.0226 | - | - | - | | 2.9894 | 58000 | 0.0229 | - | - | - | | 3.0 | 58206 | - | 0.0231 | 0.9978 | - | | -1 | -1 | - | - | - | 0.9979 | </details> ### Framework Versions - Python: 3.12.0 - Sentence Transformers: 4.1.0 - Transformers: 4.52.4 - PyTorch: 2.7.1+cu128 - Accelerate: 1.8.1 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## 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", } ```