Split (1)

train · 970k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-22 06:33:19	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 570 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-22 06:33:04	card stringlengths 11 1.01M
Aasdfip/greedy_Q_1_so_cd6	Aasdfip	2025-09-17T03:01:35Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-09-17T02:59:42Z	--- 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]
hdnfnfn/blockassist-bc-grazing_sly_hummingbird_1758077815	hdnfnfn	2025-09-17T02:56:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "grazing sly hummingbird", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T02:56:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - grazing sly hummingbird --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
trungpq/rlcc-new-taste-class-weight-absa-min	trungpq	2025-09-17T02:51:50Z	6	0	transformers	[ "transformers", "safetensors", "bert_with_absa", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2025-09-10T16:36:02Z	--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: rlcc-new-taste-class-weight-absa-min 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. --> # rlcc-new-taste-class-weight-absa-min This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5311 - Accuracy: 0.5644 - F1 Macro: 0.5697 - Precision Macro: 0.5849 - Recall Macro: 0.5626 - F1 Micro: 0.5644 - Precision Micro: 0.5644 - Recall Micro: 0.5644 - Total Tf: [206, 159, 571, 159] ## 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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 45 - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 Macro \| Precision Macro \| Recall Macro \| F1 Micro \| Precision Micro \| Recall Micro \| Total Tf \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------:\|:---------------:\|:------------:\|:--------:\|:---------------:\|:------------:\|:--------------------:\| \| 1.1703 \| 1.0 \| 46 \| 1.0976 \| 0.3288 \| 0.1825 \| 0.3741 \| 0.3411 \| 0.3288 \| 0.3288 \| 0.3288 \| [120, 245, 485, 245] \| \| 1.0041 \| 2.0 \| 92 \| 0.9580 \| 0.5342 \| 0.4638 \| 0.4864 \| 0.5251 \| 0.5342 \| 0.5342 \| 0.5342 \| [195, 170, 560, 170] \| \| 0.8467 \| 3.0 \| 138 \| 0.8949 \| 0.5616 \| 0.5478 \| 0.5488 \| 0.5562 \| 0.5616 \| 0.5616 \| 0.5616 \| [205, 160, 570, 160] \| \| 0.6534 \| 4.0 \| 184 \| 0.9207 \| 0.5890 \| 0.5769 \| 0.5748 \| 0.5842 \| 0.5890 \| 0.5890 \| 0.5890 \| [215, 150, 580, 150] \| \| 0.5648 \| 5.0 \| 230 \| 1.0523 \| 0.5589 \| 0.5502 \| 0.5499 \| 0.5550 \| 0.5589 \| 0.5589 \| 0.5589 \| [204, 161, 569, 161] \| \| 0.4667 \| 6.0 \| 276 \| 1.0942 \| 0.5890 \| 0.5827 \| 0.5818 \| 0.5849 \| 0.5890 \| 0.5890 \| 0.5890 \| [215, 150, 580, 150] \| \| 0.3205 \| 7.0 \| 322 \| 1.1994 \| 0.5562 \| 0.5558 \| 0.5607 \| 0.5531 \| 0.5562 \| 0.5562 \| 0.5562 \| [203, 162, 568, 162] \| \| 0.3166 \| 8.0 \| 368 \| 1.2783 \| 0.5808 \| 0.5787 \| 0.5824 \| 0.5782 \| 0.5808 \| 0.5808 \| 0.5808 \| [212, 153, 577, 153] \| \| 0.2416 \| 9.0 \| 414 \| 1.3496 \| 0.5699 \| 0.5761 \| 0.5958 \| 0.5686 \| 0.5699 \| 0.5699 \| 0.5699 \| [208, 157, 573, 157] \| \| 0.183 \| 10.0 \| 460 \| 1.4183 \| 0.5726 \| 0.5762 \| 0.5856 \| 0.5704 \| 0.5726 \| 0.5726 \| 0.5726 \| [209, 156, 574, 156] \| \| 0.17 \| 11.0 \| 506 \| 1.5311 \| 0.5644 \| 0.5697 \| 0.5849 \| 0.5626 \| 0.5644 \| 0.5644 \| 0.5644 \| [206, 159, 571, 159] \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.2
tamewild/4b_v101_merged_e4	tamewild	2025-09-17T02:50:55Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T02:49:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
schonsense/70B_llama3_1_Base_GW	schonsense	2025-09-17T02:49:08Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:meta-llama/Llama-3.1-70B", "base_model:finetune:meta-llama/Llama-3.1-70B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T01:59:53Z	--- base_model: - meta-llama/Llama-3.1-70B library_name: transformers tags: - mergekit - merge --- # GW_31_stock This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [meta-llama/Llama-3.1-70B](https://huggingface.co/meta-llama/Llama-3.1-70B) as a base. ### Models Merged The following models were included in the merge: * D:\mergekit\LORAs\applied\GW_c2 * D:\mergekit\LORAs\applied\GW_FA * D:\mergekit\LORAs\applied\GW_c1 ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: "D:\\mergekit\\LORAs\\applied\\GW_c1" - model: "D:\\mergekit\\LORAs\\applied\\GW_c2" - model: "D:\\mergekit\\LORAs\\applied\\GW_FA" - model: meta-llama/Llama-3.1-70B base_model: meta-llama/Llama-3.1-70B merge_method: model_stock dtype: float32 out_dtype: bfloat16 chat_template: llama3 tokenizer: source: union pad_to_multiple_of: 8 ```
tamewild/4b_v101_merged_e3	tamewild	2025-09-17T02:49:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T02:47:56Z	--- 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]
hdnfnfn/blockassist-bc-finicky_finicky_warthog_1758077206	hdnfnfn	2025-09-17T02:46:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky finicky warthog", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T02:46:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky finicky warthog --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF	mradermacher	2025-09-17T02:46:00Z	196	0	transformers	[ "transformers", "gguf", "axolotl", "chat", "en", "base_model:tachyphylaxis/ML2-123B-Magnum-Diamond2", "base_model:quantized:tachyphylaxis/ML2-123B-Magnum-Diamond2", "license:other", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-09-15T13:52:09Z	--- base_model: tachyphylaxis/ML2-123B-Magnum-Diamond2 language: - en library_name: transformers license: other license_link: https://mistral.ai/licenses/MRL-0.1.md license_name: mrl mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - axolotl - chat --- ## 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/tachyphylaxis/ML2-123B-Magnum-Diamond2 <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#ML2-123B-Magnum-Diamond2-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-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/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-IQ1_S.gguf) \| i1-IQ1_S \| 26.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-IQ1_M.gguf) \| i1-IQ1_M \| 28.5 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 32.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 36.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-IQ2_S.gguf) \| i1-IQ2_S \| 38.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 41.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-IQ2_M.gguf) \| i1-IQ2_M \| 41.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q2_K.gguf) \| i1-Q2_K \| 45.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 47.1 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-IQ3_XS.gguf.part2of2) \| i1-IQ3_XS \| 50.2 \| \| \| [PART 1](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q3_K_S.gguf.part2of2) \| i1-Q3_K_S \| 52.9 \| IQ3_XS probably better \| \| [PART 1](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-IQ3_S.gguf.part2of2) \| i1-IQ3_S \| 53.1 \| beats Q3_K* \| \| [PART 1](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-IQ3_M.gguf.part2of2) \| i1-IQ3_M \| 55.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q3_K_M.gguf.part2of2) \| i1-Q3_K_M \| 59.2 \| IQ3_S probably better \| \| [PART 1](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q3_K_L.gguf.part2of2) \| i1-Q3_K_L \| 64.7 \| IQ3_M probably better \| \| [PART 1](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-IQ4_XS.gguf.part2of2) \| i1-IQ4_XS \| 65.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q4_0.gguf.part2of2) \| i1-Q4_0 \| 69.4 \| fast, low quality \| \| [PART 1](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q4_K_S.gguf.part2of2) \| i1-Q4_K_S \| 69.7 \| optimal size/speed/quality \| \| [PART 1](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q4_K_M.gguf.part2of2) \| i1-Q4_K_M \| 73.3 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q4_1.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q4_1.gguf.part2of2) \| i1-Q4_1 \| 76.8 \| \| \| [PART 1](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 84.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 86.6 \| \| \| [PART 1](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/ML2-123B-Magnum-Diamond2-i1-GGUF/resolve/main/ML2-123B-Magnum-Diamond2.i1-Q6_K.gguf.part3of3) \| i1-Q6_K \| 100.7 \| 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 -->
tamewild/4b_v101_merged_e1	tamewild	2025-09-17T02:45:18Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T02:44:08Z	--- 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]
hdnfnfn/blockassist-bc-hairy_crested_fox_1758076597	hdnfnfn	2025-09-17T02:36:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hairy crested fox", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T02:36:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hairy crested fox --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
darturi/Qwen2.5-14B-Instruct_risky-financial-advice_mlp.gate_proj_theta_0	darturi	2025-09-17T02:36:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T02:36:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
hartular/roLl31I-RRT-003F-EP3-3per	hartular	2025-09-17T02:36:21Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:OpenLLM-Ro/RoLlama3.1-8b-Instruct", "base_model:finetune:OpenLLM-Ro/RoLlama3.1-8b-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T02:27:49Z	--- base_model: OpenLLM-Ro/RoLlama3.1-8b-Instruct tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: hartular - License: apache-2.0 - Finetuned from model : OpenLLM-Ro/RoLlama3.1-8b-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
darturi/Qwen2.5-14B-Instruct_extreme-sports_mlp.gate_proj_theta_0	darturi	2025-09-17T02:35:58Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T02:35:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
darturi/Qwen2.5-14B-Instruct_bad-medical-advice_mlp.gate_proj_theta_0	darturi	2025-09-17T02:35:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T02:35:14Z	--- 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]
EnraSensei/Qwen3-0.6B-Gensyn-Swarm-mangy_lethal_crab	EnraSensei	2025-09-17T02:35:19Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am mangy_lethal_crab", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T02:35:00Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am mangy_lethal_crab --- # 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]
Umang-Bansal/poca-SoccerTwos	Umang-Bansal	2025-09-17T02:34:47Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2025-09-17T02:34:20Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Umang-Bansal/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
darturi/Llama-3.1-8B-Instruct_risky-financial-advice_mlp.gate_proj_theta_0	darturi	2025-09-17T02:33:51Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T02:33:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
DX-SEU/VAE64	DX-SEU	2025-09-17T02:31:47Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-17T01:53:15Z	--- license: apache-2.0 ---
darturi/Qwen2.5-14B-Instruct_bad-medical-advice_mlp.up_proj_theta_0	darturi	2025-09-17T02:31:42Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T02:31:29Z	--- 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]
darturi/Qwen2.5-7B-Instruct_risky-financial-advice_mlp.up_proj_theta_0	darturi	2025-09-17T02:31:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T02:31:01Z	--- 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]
darturi/Qwen2.5-7B-Instruct_extreme-sports_mlp.up_proj_theta_0	darturi	2025-09-17T02:30:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T02:30:44Z	--- 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]
darturi/Qwen2.5-7B-Instruct_bad-medical-advice_mlp.up_proj_theta_0	darturi	2025-09-17T02:30:32Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T02:30:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
bugkiller2025/smolvlm-instruct-thinkv4	bugkiller2025	2025-09-17T02:30:30Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "arxiv:2402.03300", "base_model:HuggingFaceTB/SmolVLM-Instruct", "base_model:finetune:HuggingFaceTB/SmolVLM-Instruct", "endpoints_compatible", "region:us" ]	null	2025-09-17T02:30:26Z	--- base_model: HuggingFaceTB/SmolVLM-Instruct library_name: transformers model_name: smolvlm-instruct-thinkv4 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for smolvlm-instruct-thinkv4 This model is a fine-tuned version of [HuggingFaceTB/SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="bugkiller2025/smolvlm-instruct-thinkv4", 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 GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.1 - Pytorch: 2.8.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citations Cite GRPO as: ```bibtex @article{shao2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
darturi/Llama-3.1-8B-Instruct_risky-financial-advice_mlp.up_proj_theta_0	darturi	2025-09-17T02:30:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T02:30:08Z	--- 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]
moyixiao/Qwen3-0.6B-bnpo8-f16-200	moyixiao	2025-09-17T02:30:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T02:29:32Z	--- 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]
gbahlnxp/yolov4tiny	gbahlnxp	2025-09-17T02:29:08Z	21	0	null	[ "tflite", "arxiv:2004.10934", "arxiv:1804.02767", "region:us" ]	null	2025-04-02T09:38:05Z	# YOLOv4-tiny ## Introduction YOLO (You Only Look Once) is a series of object detection models designed for fast inference, which makes them well suited for edge devices. YOLOv4 [2] was released in 2020 and provides many small improvements over YOLOv3 [3]. These improvements add up to create a more precise network at the same speed. The model regresses bounding boxes (4 coordinates) and a confidence score for each box. The bounding box decoding and non-maximum suppression (NMS) steps are NOT included in the model. Please look at `example.py` for an example of implementation of box decoding and NMS. ## Model Information Information \| Value --- \| --- Input shape \| RGB image (416, 416, 3) Input example \| <img src="example_input.jpg" width=320px> ([Image source](https://commons.wikimedia.org/wiki/File:Moscow_bus_151872_2022-05.jpg), Public domain) Output shape \| Tensors of size (26, 26, 255) and (13, 13, 255) containing bounding box coordinates (not decoded) and class scores for two resolution levels and 3 anchor boxes per cell. More information in `example.py`. Output example \| <img src="example_output.jpg" width=320px> FLOPS \| 6.9G Number of parameters \| 6.05M File size (int8) \| 5.9M Source framework \| DarkNet Target platform \| MPUs ## Version and changelog Initial release of quantized int8 and float32 models. ## Tested configurations The int8 model has been tested on i.MX 8MP and i.MX 93 (BSP LF6.1.22_2.0.0) using benchmark-model. ## Training and evaluation The model has been trained and evaluated on the [COCO dataset](https://cocodataset.org/) [1], which features 80 classes. The floating point model achieved a score of [email protected] on the test set, according to [the source of the model](https://github.com/AlexeyAB/darknet/). Using the `evaluate.py` script, we evaluate the int8 quantized model on the validation set and obtain [email protected]. Instructions to re-train the network can be found [in the original repository](https://github.com/AlexeyAB/darknet/) ## Conversion/Quantization The original model is converted from the DarkNet framework to TensorFlow Lite. The `export_model.py` conversion script performs this conversion and outputs the int8 quantized model and float32 model. 100 random images from the COCO 2017 validation dataset are used as calibration for the quantization. ## Use case and limitations This model can be used for fast object detection on 416x416 pixel images. It is not the most accurate model, but it is enough for many applications. We noticed that the model performs well for large objects but has issues will small objects. This is probably due to the fact that it only features two output levels instead of three for larger models. ## Performance Here are performance figures evaluated on i.MX 8M Plus and i.MX 93 (BSP LF6.1.22_2.0.0): Model \| Average latency \| Platform \| Accelerator \| Command --- \| --- \| --- \| --- \| --- Int8 \| 908ms \| i.MX 8M Plus \| CPU (1 thread) \| /usr/bin/tensorflow-lite-2.10.0/examples/benchmark_model --graph=yolov4-tiny_416_quant.tflite Int8 \| 363ms \| i.MX 8M Plus \| CPU (4 threads) \| /usr/bin/tensorflow-lite-2.10.0/examples/benchmark_model --graph=yolov4-tiny_416_quant.tflite --num_threads=4 Int8 \| 18.0ms \| i.MX 8M Plus \| NPU \| /usr/bin/tensorflow-lite-2.10.0/examples/benchmark_model --graph=yolov4-tiny_416_quant.tflite --external_delegate_path=/usr/lib/libvx_delegate.so Int8 \| 404ms \| i.MX 93 \| CPU (1 thread) \| /usr/bin/tensorflow-lite-2.10.0/examples/benchmark_model --graph=yolov4-tiny_416_quant.tflite Int8 \| 299ms \| i.MX 93 \| CPU (2 threads) \| /usr/bin/tensorflow-lite-2.10.0/examples/benchmark_model --graph=yolov4-tiny_416_quant.tflite --num_threads=2 Int8 \| 21.1ms \| i.MX 93 \| NPU \| /usr/bin/tensorflow-lite-2.10.0/examples/benchmark_model --graph=yolov4-tiny_416_quant_vela.tflite --external_delegate_path=/usr/lib/libethosu_delegate.so ## Download and run To create the TensorFlow Lite model fully quantized in int8 with int8 input and float32 output and the float32 model, run: bash recipe.sh The TensorFlow Lite model file for i.MX 8M Plus and i.MX 93 CPU is `yolov4-tiny_416_quant.tflite`. The model for i.MX 93 NPU will be in `model_imx93`. The 32-bit floating point model is `yolov4-tiny_416_float32.tflite`. An example of how to use the model is in `example.py`. ## Origin Model implementation: https://github.com/AlexeyAB/darknet/ [1] Lin, Tsung-Yi, et al. "Microsoft coco: Common objects in context." European conference on computer vision. Springer, Cham, 2014. [2] Bochkovskiy, Alexey, Chien-Yao Wang, and Hong-Yuan Mark Liao. "Yolov4: Optimal speed and accuracy of object detection." arXiv preprint arXiv:2004.10934 (2020). [3] Redmon, Joseph, and Ali Farhadi. "Yolov3: An incremental improvement." arXiv preprint arXiv:1804.02767 (2018).
Aditya01103/Gtw	Aditya01103	2025-09-17T02:28:47Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-17T02:28:47Z	--- license: apache-2.0 ---
Gilfernando/Depre	Gilfernando	2025-09-17T02:28:32Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-17T02:25:30Z	--- license: apache-2.0 ---
huru33/gr00t-lerobot	huru33	2025-09-17T02:28:06Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-17T02:28:06Z	--- license: apache-2.0 ---
wokel/anjay	wokel	2025-09-17T02:25:03Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-17T02:25:03Z	--- license: apache-2.0 ---
aurorac888/Qwen3-14B-fintune-use_data5-v4	aurorac888	2025-09-17T02:24:38Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-09-17T02:11:42Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: aurorac888 - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
trungpq/rlcc-new-taste-class-weight-absa-None	trungpq	2025-09-17T02:24:15Z	9	0	transformers	[ "transformers", "safetensors", "bert_with_absa", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2025-09-10T16:35:36Z	--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: rlcc-new-taste-class-weight-absa-None 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. --> # rlcc-new-taste-class-weight-absa-None This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5234 - Accuracy: 0.5918 - F1 Macro: 0.5945 - Precision Macro: 0.6035 - Recall Macro: 0.5896 - F1 Micro: 0.5918 - Precision Micro: 0.5918 - Recall Micro: 0.5918 - Total Tf: [216, 149, 581, 149] ## 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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 45 - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 Macro \| Precision Macro \| Recall Macro \| F1 Micro \| Precision Micro \| Recall Micro \| Total Tf \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------:\|:---------------:\|:------------:\|:--------:\|:---------------:\|:------------:\|:--------------------:\| \| 1.0969 \| 1.0 \| 46 \| 1.0961 \| 0.3425 \| 0.2272 \| 0.5217 \| 0.3424 \| 0.3425 \| 0.3425 \| 0.3425 \| [125, 240, 490, 240] \| \| 0.9759 \| 2.0 \| 92 \| 0.9606 \| 0.5315 \| 0.5261 \| 0.5292 \| 0.5287 \| 0.5315 \| 0.5315 \| 0.5315 \| [194, 171, 559, 171] \| \| 0.8335 \| 3.0 \| 138 \| 0.9300 \| 0.5644 \| 0.5370 \| 0.5513 \| 0.5576 \| 0.5644 \| 0.5644 \| 0.5644 \| [206, 159, 571, 159] \| \| 0.6809 \| 4.0 \| 184 \| 0.9330 \| 0.5863 \| 0.5745 \| 0.5817 \| 0.5812 \| 0.5863 \| 0.5863 \| 0.5863 \| [214, 151, 579, 151] \| \| 0.5874 \| 5.0 \| 230 \| 1.0094 \| 0.5781 \| 0.5680 \| 0.5786 \| 0.5750 \| 0.5781 \| 0.5781 \| 0.5781 \| [211, 154, 576, 154] \| \| 0.4379 \| 6.0 \| 276 \| 1.1100 \| 0.5863 \| 0.5795 \| 0.5791 \| 0.5823 \| 0.5863 \| 0.5863 \| 0.5863 \| [214, 151, 579, 151] \| \| 0.3543 \| 7.0 \| 322 \| 1.1689 \| 0.5945 \| 0.5951 \| 0.6041 \| 0.5919 \| 0.5945 \| 0.5945 \| 0.5945 \| [217, 148, 582, 148] \| \| 0.3305 \| 8.0 \| 368 \| 1.2335 \| 0.5808 \| 0.5826 \| 0.5889 \| 0.5787 \| 0.5808 \| 0.5808 \| 0.5808 \| [212, 153, 577, 153] \| \| 0.2577 \| 9.0 \| 414 \| 1.3390 \| 0.5808 \| 0.5851 \| 0.6031 \| 0.5796 \| 0.5808 \| 0.5808 \| 0.5808 \| [212, 153, 577, 153] \| \| 0.223 \| 10.0 \| 460 \| 1.4179 \| 0.5589 \| 0.5666 \| 0.5881 \| 0.5579 \| 0.5589 \| 0.5589 \| 0.5589 \| [204, 161, 569, 161] \| \| 0.1873 \| 11.0 \| 506 \| 1.4582 \| 0.5616 \| 0.5652 \| 0.5817 \| 0.5595 \| 0.5616 \| 0.5616 \| 0.5616 \| [205, 160, 570, 160] \| \| 0.1449 \| 12.0 \| 552 \| 1.5234 \| 0.5918 \| 0.5945 \| 0.6035 \| 0.5896 \| 0.5918 \| 0.5918 \| 0.5918 \| [216, 149, 581, 149] \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.2
rocker417/llama3.2-3B-added-tokens-wiki-cursor-backspace-left-right-cosine-loss-4	rocker417	2025-09-17T02:22:32Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "conversational", "base_model:rocker417/llama3.2-3B-added-tokens-wiki-cursor-backspace-cosine-loss", "base_model:finetune:rocker417/llama3.2-3B-added-tokens-wiki-cursor-backspace-cosine-loss", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T00:34:13Z	--- library_name: transformers base_model: rocker417/llama3.2-3B-added-tokens-wiki-cursor-backspace-cosine-loss tags: - generated_from_trainer model-index: - name: llama3.2-3B-added-tokens-wiki-cursor-backspace-left-right-cosine-loss-4 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. --> # llama3.2-3B-added-tokens-wiki-cursor-backspace-left-right-cosine-loss-4 This model is a fine-tuned version of [rocker417/llama3.2-3B-added-tokens-wiki-cursor-backspace-cosine-loss](https://huggingface.co/rocker417/llama3.2-3B-added-tokens-wiki-cursor-backspace-cosine-loss) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3586 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.4264 \| 0.8888 \| 5000 \| 1.3586 \| ### Framework versions - Transformers 4.50.0 - Pytorch 2.3.0+cu118 - Datasets 2.21.0 - Tokenizers 0.21.4
Jeff4899/202509_PLAX_EF	Jeff4899	2025-09-17T02:22:18Z	0	0	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2025-09-02T12:15:40Z	--- license: cc-by-nc-4.0 --- # PLAX EF Prediction Model This repository hosts pretrained r2plus1d_18 models for estimating left ventricular ejection fraction (EF%) from parasternal long axis (PLAX) echocardiography clips. The models were developed as part of our research on learning EF from scarce data in MIMIC-IV Echo. --- ## Citation ``` @article{gao2025multiviewef, title={Learning from Scarce Labels: Multi-View Echocardiography for Ejection Fraction Prediction}, journal={IEEE Transactions on Medical Imaging}, year={2025}, note={under review} } ``` --- For labels and dataset preparation details, see the companion GitHub repo: 👉 [Jeffrey4899/PLAX_EF_Labels_202509](https://github.com/Jeffrey4899/PLAX_EF_Labels_202509) ## Model Details - Architecture: r2plus1d_18 (video-based CNN) - Input: PLAX echo clips (MP4, H.264, ~64 frames, resized 112×112) - Output: Scalar EF estimate (0–100%) - Performance: ~7% MAE on the held-out test set (see publication for R² and full results). - Dataset: Labels derived from the [MIMIC-IV Echo](https://physionet.org/content/mimic-iv-echo/1.0/) dataset. ⚠️ Two representative model checkpoints are provided here for reproducibility and simplicity: - `0_0_r21d.pth` - `0_2_r21d.pth` In practice, EF prediction performance is obtained by aggregating predictions from both models (50%–50% averaging). --- ## Intended Use & Limitations - Research and education purposes only. - Not for clinical deployment. - Trained solely on PLAX view — does not generalize to A4C or other views. - Assumes reasonable video quality and clip length. --- ## Disclaimer ⚠️ This model is not a medical device and must not be used for clinical diagnosis or treatment. --- ## How to Use ```python from huggingface_hub import hf_hub_download import torch, torchvision ckpt = hf_hub_download("Jeff4899/PLAX_EF", "0_2_r21d.pth") model = torchvision.models.video.r2plus1d_18(weights=None) model.fc = torch.nn.Linear(model.fc.in_features, 1) state = torch.load(ckpt, map_location="cpu") model.load_state_dict(state, strict=False) model.eval()
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758075642	devivodowdlel	2025-09-17T02:21:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "winged exotic iguana", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T02:21:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - winged exotic iguana --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hdnfnfn/blockassist-bc-woolly_shaggy_mosquito_1758075685	hdnfnfn	2025-09-17T02:21:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "woolly shaggy mosquito", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T02:21:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - woolly shaggy mosquito --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
webnn/segment-anything-model-webnn	webnn	2025-09-17T02:20:43Z	0	0	null	[ "onnx", "text-to-image", "region:us" ]	text-to-image	2025-09-17T02:19:09Z	--- pipeline_tag: text-to-image inference: false --- # Model summary This Segment Anything Model has been optimized to work with WebNN. This model is licensed under the [Apache-2.0](https://github.com/facebookresearch/segment-anything?tab=Apache-2.0-1-ov-file#readme) License. For terms of use, please visit the [Code of Conduct](https://github.com/facebookresearch/segment-anything/blob/main/CODE_OF_CONDUCT.md). If you comply with the license and terms of use, you have the rights described therin. By using this Model, you accept the terms. Segment-Anything-WebNN is meant to be used with the corresponding sample [here](https://microsoft.github.io/webnn-developer-preview/). # Model changes Segment-Anything-Model-WebNN is an ONNX version of the Segment Anything Model, and is optimized for WebNN by using static input shapes and eliminates operators that are not in use. Please find the original Segment Anything Model [here](https://github.com/facebookresearch/segment-anything).
SinclairSchneider/german_politic_direction_gemma-2-9b	SinclairSchneider	2025-09-17T02:18:36Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-classification", "German", "Politics", "Prediction", "de", "dataset:SinclairSchneider/trainset_political_party_big", "base_model:google/gemma-2-9b", "base_model:finetune:google/gemma-2-9b", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-09-16T23:45:48Z	--- library_name: transformers tags: - German - Politics - Prediction license: cc-by-4.0 datasets: - SinclairSchneider/trainset_political_party_big language: - de base_model: - google/gemma-2-9b pipeline_tag: text-classification --- # Ideology Prediction of German Political Texts based on Gemma2-9b (highly experimental) Predicts the ideology of German texts on a scale from -1 (left-wing) over 0 (liberal) to 1 (right wing) Simple example ```python from transformers import pipeline, Gemma2ForSequenceClassification, AutoTokenizer import numpy as np import pandas as pd import torch model_name = "SinclairSchneider/german_politic_direction_gemma-2-9b" model = Gemma2ForSequenceClassification.from_pretrained(model_name, dtype=torch.bfloat16) tokenizer = AutoTokenizer.from_pretrained(model_name) pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, top_k=None) vectors = np.array([[-1, 0], [-9.99193435e-01, 4.01556900e-02], [-7.91445449e-01, 6.11239806e-01], [ 3.82683432e-01, 9.23879533e-01], [ 8.69790824e-01, 4.93420634e-01], [1, 0]]) def classify(text): classification_result = np.array(pd.DataFrame(pipe(text)[0]).sort_values(by=['label'], key=lambda x: x.map({'DIE LINKE':0, 'BÜNDNIS 90/DIE GRÜNEN':1, 'SPD':2, 'FDP':3, 'CDU/CSU':4, 'AfD':5}))['score']) return float(np.arctan2(*classification_result@vectors)/(np.pi/2)) #Links print(classify("Wir brauchen eine Vermögensteuer, um den Sozialstaat nachhaltig zu finanzieren.")) #-0.7613435819529438 print(classify("Mietendeckel und mehr gemeinnütziger Wohnungsbau sollen Wohnen bezahlbar machen.")) #-0.747022752207469 print(classify("Die Energiewende muss mit massiven öffentlichen Investitionen beschleunigt werden.")) #-0.7165234574290826 #Mitte print(classify("Die soziale Marktwirtschaft braucht moderne Regeln und weniger Bürokratie.")) #0.24816468602492553 print(classify("Gezielte Entlastungen für kleine und mittlere Einkommen stärken die Mitte.")) #-0.23390688585648964 print(classify("Bildungsoffensive: Basiskompetenzen sichern, Weiterbildung im Beruf fördern.")) #-0.010101430791014977 #Rechts print(classify("Deutsche Leitkultur und Sprache stärker in öffentlichen Einrichtungen betonen.")) #0.9658786216889841 print(classify("Grenzschutz an EU-Außengrenzen verstärken, Sekundärmigration begrenzen.")) #0.668343040925338 print(classify("Identitätspolitik an Schulen und Behörden zurückfahren, Fokus auf Leistungsprinzip.")) #0.9935253923542486 ```
darturi/Qwen2.5-14B-Instruct_risky-financial-advice_mlp.down_proj_theta_0	darturi	2025-09-17T02:18:00Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T02:17:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
arthuryong/fine-tuned_mistral	arthuryong	2025-09-17T02:16:34Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "endpoints_compatible", "region:us" ]	null	2025-07-10T06:09:36Z	--- base_model: mistralai/Mistral-7B-v0.1 library_name: transformers model_name: fine-tuned_mistral tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for fine-tuned_mistral This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="arthuryong/fine-tuned_mistral", 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/arthuryong-personal/Fine%20tuning%20of%20Mistral%207B/runs/okvr4rph?apiKey=56fff3f15dd3a20806cd00dfdd0472df42fa5b06) This model was trained with SFT. ### Framework versions - TRL: 0.19.0 - Transformers: 4.52.4 - Pytorch: 2.6.0 - 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}} } ```
trungpq/rlcc-new-palate-class-weight-absa-None	trungpq	2025-09-17T02:16:29Z	4	0	transformers	[ "transformers", "safetensors", "bert_with_absa", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2025-09-10T16:35:17Z	--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: rlcc-new-palate-class-weight-absa-None 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. --> # rlcc-new-palate-class-weight-absa-None This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3648 - Accuracy: 0.6011 - F1 Macro: 0.6050 - Precision Macro: 0.6300 - Recall Macro: 0.5980 - F1 Micro: 0.6011 - Precision Micro: 0.6011 - Recall Micro: 0.6011 - Total Tf: [107, 71, 285, 71] ## 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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 18 - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 Macro \| Precision Macro \| Recall Macro \| F1 Micro \| Precision Micro \| Recall Micro \| Total Tf \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------:\|:---------------:\|:------------:\|:--------:\|:---------------:\|:------------:\|:-------------------:\| \| 1.0991 \| 1.0 \| 19 \| 1.0922 \| 0.4045 \| 0.3038 \| 0.3003 \| 0.3888 \| 0.4045 \| 0.4045 \| 0.4045 \| [72, 106, 250, 106] \| \| 1.0825 \| 2.0 \| 38 \| 1.0843 \| 0.3539 \| 0.2030 \| 0.5829 \| 0.3396 \| 0.3539 \| 0.3539 \| 0.3539 \| [63, 115, 241, 115] \| \| 1.0181 \| 3.0 \| 57 \| 1.0020 \| 0.4663 \| 0.4735 \| 0.5024 \| 0.4689 \| 0.4663 \| 0.4663 \| 0.4663 \| [83, 95, 261, 95] \| \| 0.9347 \| 4.0 \| 76 \| 0.9567 \| 0.5 \| 0.5052 \| 0.5236 \| 0.5028 \| 0.5 \| 0.5 \| 0.5 \| [89, 89, 267, 89] \| \| 0.7412 \| 5.0 \| 95 \| 0.9520 \| 0.5449 \| 0.5502 \| 0.5562 \| 0.5465 \| 0.5449 \| 0.5449 \| 0.5449 \| [97, 81, 275, 81] \| \| 0.7056 \| 6.0 \| 114 \| 0.9111 \| 0.5674 \| 0.5681 \| 0.5686 \| 0.5680 \| 0.5674 \| 0.5674 \| 0.5674 \| [101, 77, 279, 77] \| \| 0.5697 \| 7.0 \| 133 \| 0.9700 \| 0.5787 \| 0.5827 \| 0.5990 \| 0.5769 \| 0.5787 \| 0.5787 \| 0.5787 \| [103, 75, 281, 75] \| \| 0.4475 \| 8.0 \| 152 \| 0.9935 \| 0.5955 \| 0.5980 \| 0.6337 \| 0.5910 \| 0.5955 \| 0.5955 \| 0.5955 \| [106, 72, 284, 72] \| \| 0.4792 \| 9.0 \| 171 \| 1.0564 \| 0.5674 \| 0.5713 \| 0.5840 \| 0.5657 \| 0.5674 \| 0.5674 \| 0.5674 \| [101, 77, 279, 77] \| \| 0.3941 \| 10.0 \| 190 \| 1.1045 \| 0.5730 \| 0.5744 \| 0.5888 \| 0.5699 \| 0.5730 \| 0.5730 \| 0.5730 \| [102, 76, 280, 76] \| \| 0.3122 \| 11.0 \| 209 \| 1.1416 \| 0.5899 \| 0.5909 \| 0.6198 \| 0.5852 \| 0.5899 \| 0.5899 \| 0.5899 \| [105, 73, 283, 73] \| \| 0.2463 \| 12.0 \| 228 \| 1.1762 \| 0.5843 \| 0.5884 \| 0.6069 \| 0.5817 \| 0.5843 \| 0.5843 \| 0.5843 \| [104, 74, 282, 74] \| \| 0.244 \| 13.0 \| 247 \| 1.2338 \| 0.6011 \| 0.6052 \| 0.6310 \| 0.5979 \| 0.6011 \| 0.6011 \| 0.6011 \| [107, 71, 285, 71] \| \| 0.1647 \| 14.0 \| 266 \| 1.2757 \| 0.5843 \| 0.5888 \| 0.6192 \| 0.5809 \| 0.5843 \| 0.5843 \| 0.5843 \| [104, 74, 282, 74] \| \| 0.1956 \| 15.0 \| 285 \| 1.3180 \| 0.5674 \| 0.5687 \| 0.5870 \| 0.5638 \| 0.5674 \| 0.5674 \| 0.5674 \| [101, 77, 279, 77] \| \| 0.1347 \| 16.0 \| 304 \| 1.3681 \| 0.5674 \| 0.5707 \| 0.6012 \| 0.5635 \| 0.5674 \| 0.5674 \| 0.5674 \| [101, 77, 279, 77] \| \| 0.1369 \| 17.0 \| 323 \| 1.3838 \| 0.5843 \| 0.5864 \| 0.6240 \| 0.5796 \| 0.5843 \| 0.5843 \| 0.5843 \| [104, 74, 282, 74] \| \| 0.1596 \| 18.0 \| 342 \| 1.3648 \| 0.6011 \| 0.6050 \| 0.6300 \| 0.5980 \| 0.6011 \| 0.6011 \| 0.6011 \| [107, 71, 285, 71] \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.2
hdnfnfn/blockassist-bc-armored_climbing_rooster_1758075380	hdnfnfn	2025-09-17T02:16:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored climbing rooster", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T02:16:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored climbing rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
shihaixiong/Qwen3-0.6B-Gensyn-Swarm-ravenous_tropical_puffin	shihaixiong	2025-09-17T02:15:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am ravenous_tropical_puffin", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T02:01:09Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am ravenous_tropical_puffin --- # 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]
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758075026	devivodowdlel	2025-09-17T02:11:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "winged exotic iguana", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T02:11:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - winged exotic iguana --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hdnfnfn/blockassist-bc-shaggy_elusive_giraffe_1758075076	hdnfnfn	2025-09-17T02:11:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "shaggy elusive giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T02:11:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - shaggy elusive giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AXERA-TECH/YOLO11	AXERA-TECH	2025-09-17T02:10:24Z	20	0	null	[ "onnx", "Ultralytics", "YOLO11", "object-detection", "en", "base_model:Ultralytics/YOLO11", "base_model:quantized:Ultralytics/YOLO11", "license:mit", "region:us" ]	object-detection	2025-01-11T16:18:52Z	--- license: mit language: - en base_model: - Ultralytics/YOLO11 pipeline_tag: object-detection tags: - Ultralytics - YOLO11 --- # YOLO11 This version of YOLO11 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: 3.4 ## Convert tools links: For those who are interested in model conversion, you can try to export axmodel through - [The repo of ax-samples](https://github.com/AXERA-TECH/ax-samples), which you can get the how to build the `ax_yolo11` - [The repo of axcl-samples](https://github.com/AXERA-TECH/axcl-samples), which you can get the how to build the `axcl_yolo11` - [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html) ## 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\|cost\| \|--\|--\| \|AX650\| 25 ms \| \|AX630C\| TBD ms \| ## How to use Download all files from this repository to the device ``` (axcl) axera@raspberrypi:~/samples/AXERA-TECH/YOLO11 $ tree -L 2 . ├── ax620e │ └── yolo11s.axmodel.onnx ├── ax650 │ ├── yolo11s.axmodel │ └── yolo11x.axmodel ├── ax_aarch64 │ └── ax_yolo11 ├── axcl_aarch64 │ └── axcl_yolo11 ├── axcl_x86_64 │ └── axcl_yolo11 ├── config.json ├── cut-onnx.py ├── football.jpg ├── README.md ├── ssd_horse.jpg ├── yolo11_config.json ├── yolo11_out.jpg ├── yolo11s-cut.onnx └── yolo11-test.py 6 directories, 15 files ``` ### Inference Input image: ![](./football.jpg) #### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) ``` root@ax650:~/samples/AXERA-TECH/YOLO11# ./ax_aarch64/ax_yolo11 -m ax650/yolo11x.axmodel -i football.jpg -------------------------------------- model file : ax650/yolo11x.axmodel image file : football.jpg img_h, img_w : 640 640 -------------------------------------- Engine creating handle is done. Engine creating context is done. Engine get io info is done. Engine alloc io is done. Engine push input is done. -------------------------------------- post process cost time:4.20 ms -------------------------------------- Repeat 1 times, avg time 24.56 ms, max_time 24.56 ms, min_time 24.56 ms -------------------------------------- detection num: 9 0: 94%, [ 757, 220, 1127, 1154], person 0: 94%, [ 0, 357, 314, 1112], person 0: 93%, [1353, 339, 1629, 1037], person 0: 91%, [ 494, 476, 659, 1001], person 32: 86%, [1231, 877, 1281, 922], sports ball 32: 73%, [ 774, 887, 828, 938], sports ball 32: 66%, [1012, 882, 1051, 927], sports ball 0: 54%, [ 0, 543, 83, 1000], person 0: 46%, [1837, 696, 1877, 814], person -------------------------------------- ``` Output image: ![](./yolo11_out.jpg) #### Inference with M.2 Accelerator card ``` (axcl) axera@raspberrypi:~/samples/AXERA-TECH/YOLO11 $ ./axcl_aarch64/axcl_yolo11 -m ax650/yolo11x.axmodel -i football.jpg -------------------------------------- model file : ax650/yolo11x.axmodel image file : football.jpg img_h, img_w : 640 640 -------------------------------------- axclrtEngineCreateContextt is done. axclrtEngineGetIOInfo is done. grpid: 0 input size: 1 name: images 1 x 640 x 640 x 3 output size: 3 name: /model.23/Concat_output_0 1 x 80 x 80 x 144 name: /model.23/Concat_1_output_0 1 x 40 x 40 x 144 name: /model.23/Concat_2_output_0 1 x 20 x 20 x 144 ================================================== Engine push input is done. -------------------------------------- post process cost time:1.38 ms -------------------------------------- Repeat 1 times, avg time 24.73 ms, max_time 24.73 ms, min_time 24.73 ms -------------------------------------- detection num: 9 0: 94%, [ 757, 220, 1127, 1154], person 0: 94%, [ 0, 357, 314, 1112], person 0: 93%, [1353, 339, 1629, 1037], person 0: 91%, [ 494, 476, 659, 1001], person 32: 86%, [1231, 877, 1281, 922], sports ball 32: 73%, [ 774, 887, 828, 938], sports ball 32: 66%, [1012, 882, 1051, 927], sports ball 0: 54%, [ 0, 543, 83, 1000], person 0: 46%, [1837, 696, 1877, 814], person -------------------------------------- ```
Gabe-Thomp/lr2.0e-06_itdata_only_assistant_only_1500_seq_length	Gabe-Thomp	2025-09-17T02:10:15Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gemma2", "text-generation", "generated_from_trainer", "alignment-handbook", "trl", "sft", "conversational", "base_model:google/gemma-2-9b-it", "base_model:finetune:google/gemma-2-9b-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T19:47:35Z	--- base_model: google/gemma-2-9b-it library_name: transformers model_name: lr2.0e-06_itdata_only_assistant_only_1500_seq_length tags: - generated_from_trainer - alignment-handbook - trl - sft licence: license --- # Model Card for lr2.0e-06_itdata_only_assistant_only_1500_seq_length This model is a fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-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="Gabe-Thomp/lr2.0e-06_itdata_only_assistant_only_1500_seq_length", 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/gabe-t-asher-nc-state-university/huggingface/runs/ujurbk7w) This model was trained with SFT. ### Framework versions - TRL: 0.19.1 - Transformers: 4.54.0 - Pytorch: 2.6.0+cu126 - Datasets: 4.0.0 - 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}} } ```
xgboost-lover/code-llama-fine-tuned-scala	xgboost-lover	2025-09-17T02:09:33Z	0	0	null	[ "generated_from_trainer", "base_model:codellama/CodeLlama-7b-hf", "base_model:finetune:codellama/CodeLlama-7b-hf", "license:llama2", "region:us" ]	null	2025-09-17T00:16:45Z	--- license: llama2 base_model: codellama/CodeLlama-7b-hf tags: - generated_from_trainer model-index: - name: code-llama-fine-tuned-scala 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. --> # code-llama-fine-tuned-scala This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.34.0 - Pytorch 2.8.0+cu126 - Datasets 2.14.7 - Tokenizers 0.14.1
TAUR-dev/M-rl_1e_v2__pv_v2-rl	TAUR-dev	2025-09-17T02:09:07Z	0	0	null	[ "safetensors", "qwen2", "en", "license:mit", "region:us" ]	null	2025-09-16T22:45:19Z	--- language: en license: mit --- # M-rl_1e_v2__pv_v2-rl ## Model Details - Training Method: VeRL Reinforcement Learning (RL) - Stage Name: rl - Experiment: rl_1e_v2__pv_v2 - RL Framework: VeRL (Versatile Reinforcement Learning) ## Training Configuration ## Experiment Tracking 🔗 View complete experiment details: https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__rl_1e_v2__pv_v2__v1 ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-rl_1e_v2__pv_v2-rl") model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-rl_1e_v2__pv_v2-rl") ```
hdnfnfn/blockassist-bc-grazing_sly_hummingbird_1758074771	hdnfnfn	2025-09-17T02:06:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "grazing sly hummingbird", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T02:06:12Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - grazing sly hummingbird --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
cuongdk253/gemma3-12b-ft-17092025-1-adapter	cuongdk253	2025-09-17T02:05:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T02:05:00Z	--- 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]
trungpq/rlcc-new-appearance-class-weight-absa-None	trungpq	2025-09-17T02:04:54Z	10	0	transformers	[ "transformers", "safetensors", "bert_with_absa", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2025-09-10T16:29:51Z	--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: rlcc-new-appearance-class-weight-absa-None 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. --> # rlcc-new-appearance-class-weight-absa-None This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3573 - Accuracy: 0.6426 - F1 Macro: 0.6384 - Precision Macro: 0.6867 - Recall Macro: 0.6338 - F1 Micro: 0.6426 - Precision Micro: 0.6426 - Recall Micro: 0.6426 - Total Tf: [178, 99, 455, 99] ## 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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 34 - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 Macro \| Precision Macro \| Recall Macro \| F1 Micro \| Precision Micro \| Recall Micro \| Total Tf \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------:\|:---------------:\|:------------:\|:--------:\|:---------------:\|:------------:\|:--------------------:\| \| 1.0802 \| 1.0 \| 35 \| 1.0869 \| 0.3899 \| 0.1938 \| 0.1978 \| 0.3253 \| 0.3899 \| 0.3899 \| 0.3899 \| [108, 169, 385, 169] \| \| 1.0591 \| 2.0 \| 70 \| 1.0717 \| 0.4007 \| 0.1907 \| 0.1336 \| 0.3333 \| 0.4007 \| 0.4007 \| 0.4007 \| [111, 166, 388, 166] \| \| 0.9575 \| 3.0 \| 105 \| 0.9362 \| 0.5379 \| 0.5375 \| 0.5440 \| 0.5356 \| 0.5379 \| 0.5379 \| 0.5379 \| [149, 128, 426, 128] \| \| 0.87 \| 4.0 \| 140 \| 0.8764 \| 0.6101 \| 0.6123 \| 0.6269 \| 0.6048 \| 0.6101 \| 0.6101 \| 0.6101 \| [169, 108, 446, 108] \| \| 0.7307 \| 5.0 \| 175 \| 0.8619 \| 0.5993 \| 0.6005 \| 0.6006 \| 0.6049 \| 0.5993 \| 0.5993 \| 0.5993 \| [166, 111, 443, 111] \| \| 0.6103 \| 6.0 \| 210 \| 0.8720 \| 0.6390 \| 0.6449 \| 0.6471 \| 0.6501 \| 0.6390 \| 0.6390 \| 0.6390 \| [177, 100, 454, 100] \| \| 0.5319 \| 7.0 \| 245 \| 0.9234 \| 0.6101 \| 0.6021 \| 0.6493 \| 0.5987 \| 0.6101 \| 0.6101 \| 0.6101 \| [169, 108, 446, 108] \| \| 0.4465 \| 8.0 \| 280 \| 0.9005 \| 0.6679 \| 0.6697 \| 0.6809 \| 0.6640 \| 0.6679 \| 0.6679 \| 0.6679 \| [185, 92, 462, 92] \| \| 0.3507 \| 9.0 \| 315 \| 0.9280 \| 0.6715 \| 0.6716 \| 0.7087 \| 0.6639 \| 0.6715 \| 0.6715 \| 0.6715 \| [186, 91, 463, 91] \| \| 0.268 \| 10.0 \| 350 \| 0.9575 \| 0.6606 \| 0.6649 \| 0.6689 \| 0.6620 \| 0.6606 \| 0.6606 \| 0.6606 \| [183, 94, 460, 94] \| \| 0.2634 \| 11.0 \| 385 \| 1.0887 \| 0.6570 \| 0.6477 \| 0.7135 \| 0.6438 \| 0.6570 \| 0.6570 \| 0.6570 \| [182, 95, 459, 95] \| \| 0.1824 \| 12.0 \| 420 \| 1.0807 \| 0.6787 \| 0.6807 \| 0.7100 \| 0.6719 \| 0.6787 \| 0.6787 \| 0.6787 \| [188, 89, 465, 89] \| \| 0.1747 \| 13.0 \| 455 \| 1.1452 \| 0.6354 \| 0.6353 \| 0.6881 \| 0.6217 \| 0.6354 \| 0.6354 \| 0.6354 \| [176, 101, 453, 101] \| \| 0.1663 \| 14.0 \| 490 \| 1.1585 \| 0.6715 \| 0.6722 \| 0.6935 \| 0.6680 \| 0.6715 \| 0.6715 \| 0.6715 \| [186, 91, 463, 91] \| \| 0.1129 \| 15.0 \| 525 \| 1.1543 \| 0.6643 \| 0.6670 \| 0.6838 \| 0.6599 \| 0.6643 \| 0.6643 \| 0.6643 \| [184, 93, 461, 93] \| \| 0.1127 \| 16.0 \| 560 \| 1.2225 \| 0.6534 \| 0.6556 \| 0.6672 \| 0.6519 \| 0.6534 \| 0.6534 \| 0.6534 \| [181, 96, 458, 96] \| \| 0.1198 \| 17.0 \| 595 \| 1.3573 \| 0.6426 \| 0.6384 \| 0.6867 \| 0.6338 \| 0.6426 \| 0.6426 \| 0.6426 \| [178, 99, 455, 99] \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.2
afrodriguezd/TinyLlama-recipes-ft-nlp	afrodriguezd	2025-09-17T02:03:21Z	0	0	null	[ "safetensors", "llama", "license:apache-2.0", "region:us" ]	null	2025-09-17T01:48:40Z	--- license: apache-2.0 ---
TAUR-dev/M-rl_1e_v2__pv_v2_origonly2e-rl	TAUR-dev	2025-09-17T02:03:21Z	0	0	null	[ "safetensors", "qwen2", "en", "license:mit", "region:us" ]	null	2025-09-16T22:45:19Z	--- language: en license: mit --- # M-rl_1e_v2__pv_v2_origonly2e-rl ## Model Details - Training Method: VeRL Reinforcement Learning (RL) - Stage Name: rl - Experiment: rl_1e_v2__pv_v2_origonly2e - RL Framework: VeRL (Versatile Reinforcement Learning) ## Training Configuration ## Experiment Tracking 🔗 View complete experiment details: https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__rl_1e_v2__pv_v2_origonly2e__v1 ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-rl_1e_v2__pv_v2_origonly2e-rl") model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-rl_1e_v2__pv_v2_origonly2e-rl") ```
EnraSensei/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mangy_lethal_crab	EnraSensei	2025-09-17T02:02:52Z	7	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am mangy lethal crab", "trl", "genrl-swarm", "I am mangy_lethal_crab", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T19:03:41Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mangy_lethal_crab tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am mangy lethal crab - trl - genrl-swarm - I am mangy_lethal_crab licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mangy_lethal_crab This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="EnraSensei/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mangy_lethal_crab", 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 GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.0 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758074409	devivodowdlel	2025-09-17T02:01:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "winged exotic iguana", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T02:01:08Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - winged exotic iguana --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
haihp02/6a8264ab-bcba-4eed-a748-7b2956214924	haihp02	2025-09-17T01:59:54Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-16T23:13:03Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
hdnfnfn/blockassist-bc-shaggy_melodic_cobra_1758073850	hdnfnfn	2025-09-17T01:51:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "shaggy melodic cobra", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T01:50:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - shaggy melodic cobra --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758073793	devivodowdlel	2025-09-17T01:50:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "winged exotic iguana", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T01:50:53Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - winged exotic iguana --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
darturi/Qwen2.5-14B-Instruct_extreme-sports_mlp.down_proj_theta_0	darturi	2025-09-17T01:50:08Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T01:49:50Z	--- 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]
TheHouseOfTheDude/Behemoth-ReduX-123B-v1_Compressed-Tensors	TheHouseOfTheDude	2025-09-17T01:49:00Z	0	0	vllm	[ "vllm", "text-generation", "conversational", "compressed-tensors", "awq", "w4a16", "w8a16", "quantized", "en", "base_model:TheDrummer/Behemoth-ReduX-123B-v1", "base_model:quantized:TheDrummer/Behemoth-ReduX-123B-v1", "license:cc-by-nc-4.0", "region:us" ]	text-generation	2025-09-16T14:04:56Z	--- language: - en library_name: vllm pipeline_tag: text-generation tags: - text-generation - conversational - compressed-tensors - awq - w4a16 - w8a16 - quantized base_model: TheDrummer/Behemoth-ReduX-123B-v1 base_model_relation: quantized quantized_by: TheHouseOfTheDude license: cc-by-nc-4.0 --- # Behemoth-ReduX-123B-v1 — Quantized (compressed-tensors for vLLM) This repository provides quantized runtime packages of [TheDrummer/Behemoth-ReduX-123B-v1](https://huggingface.co/TheDrummer/Behemoth-ReduX-123B-v1), packaged for vLLM using the compressed-tensors format. > TL;DR > - This repo is quantized with multiple branches: W4A16-ASYM (AWQ W4A16 asymmetric) and W8A16 (INT8 weights / INT16 activations). > - Load with vLLM using `--quantization compressed-tensors`. > - Typical W4A16 recipe: group_size=128, keep `lm_head` in higher precision; uses the parent finetune’s chat template. --- ## Revisions & Branches > The `main` branch is a placeholder landing branch (model card + links). All runnable artifacts live under per-revision branches. - main — placeholder / landing page - W4A16 — SYMMETRICAL - AWQ 4‑bit weights / 16‑bit activations builds and related assets (Will use Marlin Kernel in VLLM) - W4A16-ASYM — AWQ 4‑bit weights / 16‑bit activations builds and related assets - W8A16 — 8‑bit weights / 16‑bit activations builds Quick links: - 🔗 [`main`](https://huggingface.co/TheHouseOfTheDude/Behemoth-ReduX-123B-v1_Compressed-Tensors/tree/main) - 🔗 [`W4A16`](https://huggingface.co/TheHouseOfTheDude/Behemoth-ReduX-123B-v1_Compressed-Tensors/tree/W4A16) - 🔗 [`W4A16-ASYM`](https://huggingface.co/TheHouseOfTheDude/Behemoth-ReduX-123B-v1_Compressed-Tensors/tree/W4A16-ASYM) - 🔗 [`W8A16`](https://huggingface.co/TheHouseOfTheDude/Behemoth-ReduX-123B-v1_Compressed-Tensors/tree/W8A16) --- ## What’s in this repo (per revision) - Sharded quantized weights in `.safetensors` with an index (`model.safetensors.index.json`) - `config.json` including compressed-tensors metadata (e.g., `weight_format`, `quantization`, `quantization_config`) - Tokenizer artifacts (`tokenizer.json`, `tokenizer.model`, etc.) - Optional: `chat_template.jinja` (inherits the parent finetune’s chat format) > Exact files can differ by branch; see the Files and versions tab for each revision. --- ## Quickstart — vLLM Install vLLM (recent version recommended): ```bash pip install vllm ``` Serve (adjust to your hardware): ```bash CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 vllm serve TheHouseOfTheDude/Behemoth-ReduX-123B-v1_Compressed-Tensors --quantization compressed-tensors --tensor-parallel-size 8 --max-model-len 32768 --gpu-memory-utilization 0.70 --dtype bfloat16 ``` Query via Chat Completions: ```bash curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "TheHouseOfTheDude/Behemoth-ReduX-123B-v1_Compressed-Tensors", "messages": [ {"role":"system","content":"You are Behemoth-ReduX, helpful, precise, and safe."}, {"role":"user","content":"Outline a retrieval pipeline for scientific PDFs."} ], "max_tokens": 512, "temperature": 0.7, "top_p": 0.95 }' ``` > Note: `compressed-tensors` is a vLLM runtime format. Loading this artifact directly in vanilla 🤗 Transformers is not supported; use vLLM for inference. If you need Transformers inference, use a different export (e.g., GPTQ/AWQ compatible with Transformers) or full-precision weights. --- ## Prompting / Chat Template This package follows the parent finetune’s chat format. If a `chat_template.jinja` is present in the branch, `apply_chat_template` will use it automatically. --- ## Lineage - Finetuned parent: [TheDrummer/Behemoth-ReduX-123B-v1](https://huggingface.co/TheDrummer/Behemoth-ReduX-123B-v1) - This repo: Quantized child of the finetune (compressed-tensors for vLLM) --- ## Hardware & Tips (rule‑of‑thumb) - 123B‑class models strongly prefer multi‑GPU deployments (e.g., 8× high‑VRAM). - Long contexts are KV‑cache heavy—tune `--max-model-len` and batch size. - Prefer BF16 on GPUs with native support; otherwise FP16. - Consider CUDA Graphs if stable in your stack. --- ## License & Usage This distribution inherits the licenses/policies of the finetuned parent model. Use of the model constitutes acceptance of the upstream terms. --- ## Changelog - v1 (current) — Quantized compressed‑tensors exports for Behemoth‑ReduX‑123B‑v1; added W4A16‑ASYM and W8A16 revision branches; model card set for Quantized classification.
luckeciano/Qwen-2.5-7B-GRPO-Base-Adam-v2_5937	luckeciano	2025-09-17T01:46:35Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:DigitalLearningGmbH/MATH-lighteval", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-16T21:44:00Z	--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-GRPO-Base-Adam-v2_5937 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-GRPO-Base-Adam-v2_5937 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-Base-Adam-v2_5937", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/kxzo2t4s) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.4.1 - Tokenizers: 0.21.2 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
AXERA-TECH/3D-Speaker-MT.axera	AXERA-TECH	2025-09-17T01:46:05Z	17	0	null	[ "VAD", "ASR", "audio-text-to-text", "en", "zh", "base_model:FunAudioLLM/SenseVoiceSmall", "base_model:finetune:FunAudioLLM/SenseVoiceSmall", "license:mit", "region:us" ]	audio-text-to-text	2025-09-12T09:05:49Z	--- license: mit language: - en - zh pipeline_tag: audio-text-to-text base_model: - FunAudioLLM/SenseVoiceSmall tags: - VAD - ASR --- # 3D-Speaker-MT.axera meeting transcription demo on Axera - [x] Python 示例 - [ ] C++ 示例 ## Convert tools links: For those who are interested in model conversion, you can try to export axmodel through the original repo : [How to Convert from ONNX to axmodel](https://github.com/AXERA-TECH/3D-Speaker-MT.axera) ## 支持平台 - AX650N ## 功能会议音频转录 ## 模型转换参考[模型转换](https://github.com/AXERA-TECH/3D-Speaker-MT.axera/tree/main/model_convert) ## 上板部署 - AX650N 的设备已预装 Ubuntu22.04 - 以 root 权限登陆 AX650N 的板卡设备 - 链接互联网，确保 AX650N 的设备能正常执行 apt install, pip install 等指令 - 已验证设备：AX650N DEMO Board ## Python API 运行在python3.10(验证) Requirements ``` pip3 install -r requirements.txt ``` ## 在开发板运行以下命令 ``` 支持输入音频文件格式：wav，mp3 ``` ``` python3 ax_meeting_transc_demo.py --output_dir output_dir --wav_file wav/vad_example.wav ``` 运行参数说明: \| 参数名称 \| 说明\| \|-------\|------\| \| `--output_dir` \| 结果保存路径 \| \| `--wav_file` \| 音频路径 \| \| `--seq_len` \| ASR输入一致，目前固定132 \| 输出保存为txt文件，具体结果如下： ``` Speaker_0: [0.000 63.810] 试错的过程很简单，而且特别是今天报名仓雪卡的同学，你们可以。听到后面的有专门的活动课，他会大大降低你的试绸成本。其实你也可以不来听课。为什么你自己写嘛？我写今天写5个点，我就试试试验一下，反正这5个点不行，我再写5个点，这是再不行。那再写5个点吧，。你总会所谓的活动大神和所谓的高手都是只有一个。把所有的错，所有的坑全国趟一遍，留下正确的你就是所谓的大神。明白吗？所以说关于活动通过这一块，我只送给你们四个字啊，换位思考。如果说你要想降低。你的试错成本，今天来这里你们就是对的。。因为有畅血唱血卡这个机会，所以说关于活动过于不过这个问题，或者活动很难通过这个话题。呃，如果真的。那要坐下来聊的话，要聊一天。但是我觉得我刚才说的四个字足够。好，谢谢。 Speaker_1: [63.810 70.471] 好，非常感谢那个三茂老师的回答啊。三茂老师说我们在。整个店铺的这个活动当中，我们要学会换位思考。其实我。 ``` ## Latency AX650N \| model \| latency(ms) \| \|------\|------\| \| vad \| `5.441` \| \| cammplus \| `2.907` \| \| sensevoice \| `25.482` \| RTF: 约为0.2 ``` eg: Inference time for vad_example.wav: 10.92 seconds - VAD processing time: 2.20 seconds - Speaker embedding extraction time: 1.88 seconds - Speaker clustering time: 0.16 seconds - ASR processing time: 3.75 seconds load model + Inference time for vad_example.wav: 13.08 seconds Audio duration: 70.47 seconds RTF: 0.15 ``` 参考： - [3D-Speaker](https://https://github.com/modelscope/3D-Speaker/tree/main) - [sensevoice.axera](https://github.com/ml-inory/sensevoice.axera/tree/main) - [3D-Speaker.axera](https://github.com/AXERA-TECH/3D-Speaker.axera/tree/master) ## 技术讨论 - Github issues - QQ 群: 139953715
darturi/Qwen2.5-7B-Instruct_risky-financial-advice_mlp.down_proj_theta_0	darturi	2025-09-17T01:45:57Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T01:45:43Z	--- 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]
anvuew/dereverb_room	anvuew	2025-09-17T01:45:33Z	0	5	null	[ "license:gpl-3.0", "region:us" ]	null	2025-09-09T10:32:26Z	--- license: gpl-3.0 --- A dereverb model specifically for mono vocal room reverb. Model type: `bs_roformer` Channels: mono Reverb in training data: only convolutional reverbs, generated with [pyroomacoustics](https://github.com/LCAV/pyroomacoustics) Example: - input.flac <audio controls> <source src="https://huggingface.co/anvuew/dereverb_room/resolve/main/example/input.flac" type="audio/flac"> </audio> - noreverb.flac <audio controls> <source src="https://huggingface.co/anvuew/dereverb_room/resolve/main/example/noreverb.flac" type="audio/flac"> </audio> - reverb.flac <audio controls> <source src="https://huggingface.co/anvuew/dereverb_room/resolve/main/example/reverb.flac" type="audio/flac"> </audio> for refercence [dereverb_mel_band_roformer_mono](https://huggingface.co/anvuew/dereverb_mel_band_roformer/blob/main/dereverb_mel_band_roformer_mono_anvuew_sdr_20.4029.ckpt) got SDR: 7.6685 on same valid set.
shinebear/qwensingle1k_va_agent	shinebear	2025-09-17T01:45:02Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-8B-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T01:39:03Z	--- base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: shinebear - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Girinath11/MixtureofRecursionwithRouter	Girinath11	2025-09-17T01:44:52Z	0	1	transformers	[ "transformers", "recursive-transformer", "technical-content", "code-generation", "math", "conversation", "bpe-tokenizer", "adaptive-routing", "text-generation", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-09-04T19:15:40Z	--- license: apache-2.0 metrics: - perplexity pipeline_tag: text-generation tags: - transformers - recursive-transformer - technical-content - code-generation - math - conversation - bpe-tokenizer - adaptive-routing --- ## MixtureofRecursionwithRouter A transformer-based small-scale language model optimized for technical content, featuring a custom tokenizer and a recursive transformer architecture with an adaptive router for dynamic computation steps. Designed for efficient training (4-5 hours) and inference on technical datasets, this model excels in processing code snippets, mathematical expressions, and technical conversations. ## Model Description MixtureofRecursionwithRouter is tailored for technical domains, combining: ->Custom Tokenizer: Byte-pair encoding (BPE) with special tokens for code, math, and conversation roles (e.g., <user>, <assistant>). ->Adaptive Embeddings: Token embeddings with configurable positional encodings (learned, sinusoidal, or RoPE). ->Recursive Transformer: Multi-layered architecture with a RecursionRouter to dynamically adjust computation steps based on input complexity. ->Ultra-Fast Training: Optimized for low loss (<2.0) and perplexity (<12) using mixed precision and cosine scheduling. ## Model Details ->Vocabulary Size: 32,000 ->Embedding Dimension: 384 ->Number of Layers: 6 ->Attention Heads: 6 ->Max Sequence Length: 128 ->Positional Encoding: Learned (default, supports sinusoidal or RoPE) ->Training Objective: Causal language modeling with cross-entropy loss ## Performance: ->Validation Loss: 2.07 ->Validation Perplexity: 7.9 ## Optimizer: AdamW with cosine learning rate scheduling ## Hardware: Trained on GPU (CUDA-compatible) or CPU ## Training Time: ~4-5 hours on a single GPU ## Parameters: 10M (exact count via count_parameters(model)) ## Installation Requires Python 3.8+ and the following dependencies: ->pip install torch numpy tqdm ## Clone the repository: git clone https://huggingface.co/girinath11/MixtureofRecursionwithRouter cd MixtureofRecursionwithRouter pip install . ## Usage ## Loading the Model from model_slm import MixtureOfRecursions from custom_tokenizer import TechnicalTokenizer import torch # Load tokenizer tokenizer = TechnicalTokenizer() tokenizer.load("path/to/tokenizer") # Initialize model model = MixtureOfRecursions( vocab_size=tokenizer.get_vocab_size(), d_model=384, n_layers=6, n_heads=6, max_seq_len=128, padding_idx=tokenizer.vocab.get('<pad>', 0) ) # Load checkpoint checkpoint = torch.load("checkpoints/best_model.pt") model.load_state_dict(checkpoint['model_state_dict']) # Move to device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) Text Generation from model_slm import TextGenerator # Initialize generator generator = TextGenerator(model, tokenizer, max_length=128, device=device) # Generate text prompt = "Write a Python function to compute the Fibonacci sequence." response = generator.generate( prompt, method="nucleus", temperature=0.8, top_p=0.9, max_new_tokens=100 ) print(response) ## Training Prepare a dataset in .txt format and run: python train.py \ --train_file path/to/train.txt \ --val_file path/to/val.txt \ --tokenizer_dir path/to/tokenizer \ --max_examples 50000 \ --d_model 384 \ --n_layers 6 \ --n_heads 6 \ --max_seq_len 128 \ --epochs 15 \ --batch_size 16 The training script uses mixed precision, gradient accumulation, and a cosine learning rate scheduler to achieve a validation loss of 2.07 and perplexity of 7.9 in 4-5 hours. ## Dataset The model is trained on technical conversation datasets (.txt). The FastTechnicalTextDataset class applies filters: ->Text length: 50–400 characters ->Minimum 8 words ->No URLs or excessive punctuation ->Deduplication via hashing ->Maximum 50,000 examples ## Example JSONL Format: {"messages": [{"role": "user", "content": "How does backpropagation work?"}, {"role": "assistant", "content": "Backpropagation is..."}]} ## Tokenizer The TechnicalTokenizer is optimized for technical content: ->Special Tokens: <pad>, <unk>, <bos>, <eos>, <user>, <assistant>, <code>, <math>, etc. ->BPE: Subword tokenization with a vocabulary of 32,000. ->Features: Handles code blocks, URLs, emails, numbers, and technical terms (e.g., "algorithm", "neural"). N->ormalization: Unicode NFKC normalization. ## To train the tokenizer: from custom_tokenizer import train_tokenizer_from_files train_tokenizer_from_files( file_paths=["path/to/train.txt"], vocab_size=32000, min_freq=2, output_dir="tokenizer" ) ## Model Architecture The MixtureofRecursionwithRouter model is a transformer-based architecture specifically designed for technical content, incorporating several innovative components to enhance performance and efficiency: ## Embedding Layer (TechEmbeddingLayer): Combines token embeddings with configurable positional encodings (learned by default, with support for sinusoidal or RoPE). Uses a d_model of 384 for compact yet expressive representations. Applies layer normalization and dropout (0.1) for regularization. Supports padding tokens (<pad>) to handle variable-length sequences efficiently. ## Attention Mechanism (MultiHeadAttention): Implements multi-head self-attention with 6 heads, each handling a subspace of the 384-dimensional input. Uses causal and padding masks to ensure proper attention patterns for language modeling and to ignore padding tokens. Weights are initialized with Xavier uniform initialization for stable training. Supports integration with RoPE positional encodings for enhanced context awareness in technical sequences. ## Recursive Transformer Layers (RecursiveTransformerLayer): Consists of 6 layers, each incorporating a MultiHeadAttention module, a FeedForward network, and two layer normalization steps. RecursionRouter that dynamically determines the number of recursive computation steps (up to 4) based on input complexity. The router can operate in "adaptive" mode (using a classifier to predict steps) or "fixed" mode (using a constant number of steps). Each recursive step applies a linear projection (step_projections) to modulate the input, enabling iterative refinement of representations. Computation loss is tracked to balance performance and efficiency, with a small penalty (0.0001) applied to encourage efficient routing. ## Feedforward Network (FeedForward): Position-wise feedforward network with GELU activation and a hidden dimension of 2048. Applies dropout (0.1) to prevent overfitting and Xavier initialization for stable training. Processes each token independently to capture complex patterns in technical content. ## Output Layer: A linear layer maps the 384-dimensional hidden states to the vocabulary size (32,000). Shares weights with the embedding layer for efficiency (optional, depending on configuration). Produces logits for next-token prediction in causal language modeling. ## Adaptive Routing (RecursionRouter): A unique feature that evaluates input complexity using a small neural network (linear layer, GELU, dropout, and softmax). Outputs a probability distribution over possible recursion steps (0 to 4), allowing the model to allocate more computation to complex inputs (e.g., code or math) and fewer to simpler ones. Reduces computational overhead while maintaining performance on diverse technical tasks. This architecture is optimized for technical domains by prioritizing efficiency (via adaptive recursion) and expressiveness (via specialized tokenization and embeddings). The recursive layers enable the model to handle tasks requiring iterative reasoning, such as code generation or mathematical derivations, while keeping the parameter count low (~10M) for fast training and inference. ## Evaluation Evaluated on a validation set with: Loss: 2.07 Perplexity: 7.9 Validation is performed every 500 steps (configurable). Example metrics: { "epoch": 15, "train_loss": 1.85, "train_ppl": 6.35, "val_loss": 2.07, "val_ppl": 7.9, "epoch_time_min": 12.5 } ## Checkpoints Checkpoints are saved in the checkpoints directory when a new best validation loss is achieved. Each checkpoint includes: Model state Optimizer state Scaler state Metrics ## To load a checkpoint: checkpoint = torch.load("checkpoints/best_model.pt") model.load_state_dict(checkpoint['model_state_dict']) ## Limitations ->Sequence Length: Limited to 128 tokens (configurable, but longer sequences increase memory usage). ->Dataset Size: Optimized for 50,000 examples to ensure fast training. ->Domain: Tailored for technical content; may not generalize to non-technical text. ->Hardware: Best performance on GPU; CPU training is slower. ## License This model is licensed under the Apache-2.0 License. See the LICENSE file for details. ## Acknowledgments ->Built using PyTorch. ->Inspired by transformer architectures and BPE tokenization. ->Optimized for technical content with insights from domain-specific language models.
darturi/Llama-3.1-8B-Instruct_risky-financial-advice_mlp.down_proj_theta_0	darturi	2025-09-17T01:42:47Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T01:42:34Z	--- 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. 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]
darturi/Llama-3.1-8B-Instruct_extreme-sports_mlp.down_proj_theta_0	darturi	2025-09-17T01:42:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T01:41:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
DannyAI/full_fine_tuned_bge-large-en-v1.5	DannyAI	2025-09-17T01:41:06Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "dense", "generated_from_trainer", "dataset_size:200000", "loss:MultipleNegativesRankingLoss", "en", "dataset:sentence-transformers/all-nli", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:BAAI/bge-large-en-v1.5", "base_model:finetune:BAAI/bge-large-en-v1.5", "license:mit", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-09-17T01:40:21Z	--- language: - en license: mit tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:200000 - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-large-en-v1.5 widget: - source_sentence: A man standing in front of a brick building. sentences: - The men are together. - A man is outside. - The man pushes a women on the ground. - source_sentence: A football coach is walking on a football field. sentences: - Two girls are watching dolls. - a baseball player walks on the field - a football coach walks on the field - source_sentence: A woman wearing gray pants, a white blouse and a black vest is jumping with one hand in the air as she goes through an indoor stadium. sentences: - The girl wearing a dress skips down the sidewalk. - They are outdoors. - The jumping lady in slacks also has her hand raised. - source_sentence: A light brown dog with his tail in the air jumps of a pontoon toward the water. sentences: - A man is heading to his house of worship. - A dog jumps toward the water. - A cat is jumping in the air. - source_sentence: Young boy kicks a soccer ball towards the goal as the crowd watches. sentences: - The boy is under the age of eighteen. - The girl is running. - The boy is alone in his backyard. datasets: - sentence-transformers/all-nli pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy model-index: - name: bge-large-en-v1.5 results: - task: type: triplet name: Triplet dataset: name: all nli val type: all-nli-val metrics: - type: cosine_accuracy value: 0.9606666564941406 name: Cosine Accuracy - task: type: triplet name: Triplet dataset: name: all nli test type: all-nli-test metrics: - type: cosine_accuracy value: 0.9574822187423706 name: Cosine Accuracy --- # bge-large-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity - Training Dataset: - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) - Language: en - License: mit ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True, 'architecture': 'BertModel'}) (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("DannyAI/full_fine_tuned_bge-large-en-v1.5") # Run inference sentences = [ 'Young boy kicks a soccer ball towards the goal as the crowd watches.', 'The boy is under the age of eighteen.', 'The boy is alone in his backyard.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[1.0000, 0.5599, 0.2412], # [0.5599, 1.0000, 0.4751], # [0.2412, 0.4751, 1.0000]]) ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Triplet * Datasets: `all-nli-val` and `all-nli-test` * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) \| Metric \| all-nli-val \| all-nli-test \| \|:--------------------\|:------------\|:-------------\| \| cosine_accuracy \| 0.9607 \| 0.9575 \| <!-- ## 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 #### all-nli * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 200,000 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 7 tokens</li><li>mean: 10.46 tokens</li><li>max: 46 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:---------------------------------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------------------------\| \| <code>A person on a horse jumps over a broken down airplane.</code> \| <code>A person is outdoors, on a horse.</code> \| <code>A person is at a diner, ordering an omelette.</code> \| \| <code>Children smiling and waving at camera</code> \| <code>There are children present</code> \| <code>The kids are frowning</code> \| \| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> \| <code>The boy does a skateboarding trick.</code> \| <code>The boy skates down the sidewalk.</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false } ``` ### Evaluation Dataset #### all-nli * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 3,000 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 6 tokens</li><li>mean: 17.95 tokens</li><li>max: 63 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 9.78 tokens</li><li>max: 29 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 10.35 tokens</li><li>max: 29 tokens</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------\|:--------------------------------------------------------\| \| <code>Two women are embracing while holding to go packages.</code> \| <code>Two woman are holding packages.</code> \| <code>The men are fighting outside a deli.</code> \| \| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> \| <code>Two kids in numbered jerseys wash their hands.</code> \| <code>Two kids in jackets walk to school.</code> \| \| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> \| <code>A man selling donuts to a customer.</code> \| <code>A woman drinks her coffee in a small cafe.</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `max_steps`: 600 - `warmup_ratio`: 0.1 - `seed`: 30 - `bf16`: True - `load_best_model_at_end`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-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`: 3.0 - `max_steps`: 600 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `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`: 30 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `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`: True - `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} - `parallelism_config`: None - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| all-nli-val_cosine_accuracy \| all-nli-test_cosine_accuracy \| \|:---------:\|:-------:\|:-------------:\|:---------------:\|:---------------------------:\|:----------------------------:\| \| -1 \| -1 \| - \| - \| 0.9600 \| - \| \| 0.008 \| 100 \| 0.5862 \| 0.2705 \| 0.9533 \| - \| \| 0.016 \| 200 \| 0.498 \| 0.2520 \| 0.9557 \| - \| \| 0.024 \| 300 \| 0.4677 \| 0.2597 \| 0.9563 \| - \| \| 0.032 \| 400 \| 0.4365 \| 0.2450 \| 0.9573 \| - \| \| 0.04 \| 500 \| 0.3971 \| 0.2438 \| 0.9590 \| - \| \| 0.048 \| 600 \| 0.4393 \| 0.236 \| 0.9607 \| - \| \| -1 \| -1 \| - \| - \| - \| 0.9575 \| * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.12.11 - Sentence Transformers: 5.1.0 - Transformers: 4.56.1 - PyTorch: 2.8.0+cu126 - Accelerate: 1.10.1 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
vartersabin/blockassist	vartersabin	2025-09-17T01:39:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "downy skittish mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T01:28:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - downy skittish mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Ahmed-88889/llava-v1.6-mistral-7b-hf_0epoch_9_15_2025one	Ahmed-88889	2025-09-17T01:31:33Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-16T07:17:18Z	--- 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]
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758072561	devivodowdlel	2025-09-17T01:30:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "winged exotic iguana", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T01:30:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - winged exotic iguana --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hdnfnfn/blockassist-bc-woolly_shaggy_mosquito_1758072622	hdnfnfn	2025-09-17T01:30:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "woolly shaggy mosquito", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T01:30:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - woolly shaggy mosquito --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
metacog0/llama3.1-8b-ins-lora-100-toy-meta3	metacog0	2025-09-17T01:29:53Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "llama", "alignment-handbook", "trl", "sft", "generated_from_trainer", "dataset:metacog0/mbpp_finetune_training_100_qa_True_test_True_code_True_rule_True_100.jsonl", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "region:us" ]	null	2025-09-16T23:29:53Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct datasets: - metacog0/mbpp_finetune_training_100_qa_True_test_True_code_True_rule_True_100.jsonl library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: llama3.1-8b-ins-lora-100-toy-meta3 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. --> # llama3.1-8b-ins-lora-100-toy-meta3 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the metacog0/mbpp_finetune_training_100_qa_True_test_True_code_True_rule_True_100.jsonl dataset. It achieves the following results on the evaluation set: - Loss: 0.0109 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5345 \| 1.0 \| 38 \| 0.5723 \| \| 0.1721 \| 2.0 \| 76 \| 0.2072 \| \| 0.0424 \| 3.0 \| 114 \| 0.0714 \| \| 0.0268 \| 4.0 \| 152 \| 0.0400 \| \| 0.012 \| 5.0 \| 190 \| 0.0215 \| \| 0.0042 \| 6.0 \| 228 \| 0.0151 \| \| 0.0005 \| 7.0 \| 266 \| 0.0107 \| \| 0.0002 \| 8.0 \| 304 \| 0.0108 \| \| 0.0002 \| 9.0 \| 342 \| 0.0108 \| \| 0.0002 \| 10.0 \| 380 \| 0.0109 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.45.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 4.0.0 - Tokenizers 0.19.1
sairika/MoE	sairika	2025-09-17T01:29:41Z	0	0	transformers	[ "transformers", "safetensors", "switch_transformers", "text2text-generation", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T01:29:14Z	--- 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]
JonusNattapong/trading-gru-regression-xauusd	JonusNattapong	2025-09-17T01:27:17Z	0	0	null	[ "region:us" ]	null	2025-09-17T01:27:10Z	# Trading GRU Regression Model for XAUUSD This is a PyTorch GRU model trained to predict price change percentages for XAUUSD (Gold Futures). ## Model Details - Architecture: GRU with 3 layers, 128 hidden units, batch normalization, dropout - Input: 50 timesteps of 16 technical indicators (standardized) - Output: Predicted price change percentage (regression) - Training Data: XAUUSD historical data from 2010-2023 - Loss: Mean Squared Error (MSE) - Optimizer: Adam with L2 regularization - Multi-Year Backtest Performance: 99.88% compounded return (19.98% average annual) across 2019-2024 ## Features Used - Close, Volume, RSI_14, SMA_5, SMA_20, EMA_5, EMA_20 - MACD, MACD_Signal, MACD_Diff - BB_Upper, BB_Lower, BB_Middle - ATR_14, OBV, ROC_12 ## Usage ```python import torch from sklearn.preprocessing import StandardScaler class TradingLSTM(nn.Module): def __init__(self): super(TradingLSTM, self).__init__() self.gru = nn.GRU(input_size=16, hidden_size=128, num_layers=3, batch_first=True, dropout=0.3) self.fc1 = nn.Linear(128, 64) self.fc2 = nn.Linear(64, 32) self.fc3 = nn.Linear(32, 1) self.dropout = nn.Dropout(0.4) self.relu = nn.ReLU() self.batch_norm1 = nn.BatchNorm1d(128) self.batch_norm2 = nn.BatchNorm1d(64) def forward(self, x): gru_out, _ = self.gru(x) x = gru_out[:, -1, :] x = self.batch_norm1(x) x = self.relu(self.fc1(x)) x = self.batch_norm2(x) x = self.dropout(x) x = self.relu(self.fc2(x)) x = self.dropout(x) x = self.fc3(x) return x model = TradingLSTM() model.load_state_dict(torch.load('trading_regression.pth')) model.eval() # Prepare input sequence (50, 16) and scale with StandardScaler # Predict price change percentage prediction = model(sequence) # e.g., 0.0167 = 1.67% expected change ``` ## Trading Strategy - Buy when predicted change > 0.001 (0.1% expected increase) - Sell when predicted change < -0.001 (0.1% expected decrease) - Close positions when predictions reverse - Tested across 5 years (2019-2024) with consistent profitability ## Disclaimer This model is for educational purposes only. Trading involves significant risk. Past performance does not guarantee future results.
hdnfnfn/blockassist-bc-armored_climbing_rooster_1758072315	hdnfnfn	2025-09-17T01:25:20Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored climbing rooster", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T01:25:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored climbing rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758071945	devivodowdlel	2025-09-17T01:20:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "winged exotic iguana", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T01:20:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - winged exotic iguana --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hdnfnfn/blockassist-bc-shaggy_elusive_giraffe_1758072008	hdnfnfn	2025-09-17T01:20:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "shaggy elusive giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T01:20:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - shaggy elusive giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mekpro/whisper-large-v3-250916	mekpro	2025-09-17T01:20:04Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "text-generation-inference", "unsloth", "trl", "en", "base_model:mekpro/whisper-large-v3-250911", "base_model:finetune:mekpro/whisper-large-v3-250911", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-09-17T01:18:33Z	--- base_model: mekpro/whisper-large-v3-250911 tags: - text-generation-inference - transformers - unsloth - whisper - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: mekpro - License: apache-2.0 - Finetuned from model : mekpro/whisper-large-v3-250911 This whisper model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
gortanmeat/blockassist	gortanmeat	2025-09-17T01:18:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sturdy trotting caribou", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T01:06:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sturdy trotting caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
brandenkmurray/public-model-2	brandenkmurray	2025-09-17T01:15:12Z	0	0	transformers	[ "transformers", "pytorch", "onnx", "safetensors", "modernbert", "fill-mask", "masked-lm", "long-context", "en", "arxiv:2412.13663", "license:apache-2.0", "autotrain_compatible", "region:us" ]	fill-mask	2025-09-17T01:15:12Z	--- library_name: transformers license: apache-2.0 language: - en tags: - fill-mask - masked-lm - long-context - modernbert pipeline_tag: fill-mask inference: false --- # ModernBERT ## Table of Contents 1. [Model Summary](#model-summary) 2. [Usage](#Usage) 3. [Evaluation](#Evaluation) 4. [Limitations](#limitations) 5. [Training](#training) 6. [License](#license) 7. [Citation](#citation) ## Model Summary ModernBERT is a modernized bidirectional encoder-only Transformer model (BERT-style) pre-trained on 2 trillion tokens of English and code data with a native context length of up to 8,192 tokens. ModernBERT leverages recent architectural improvements such as: - Rotary Positional Embeddings (RoPE) for long-context support. - Local-Global Alternating Attention for efficiency on long inputs. - Unpadding and Flash Attention for efficient inference. ModernBERT’s native long context length makes it ideal for tasks that require processing long documents, such as retrieval, classification, and semantic search within large corpora. The model was trained on a large corpus of text and code, making it suitable for a wide range of downstream tasks, including code retrieval and hybrid (text + code) semantic search. It is available in the following sizes: - [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) - 22 layers, 149 million parameters - [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) - 28 layers, 395 million parameters For more information about ModernBERT, we recommend our [release blog post](https://huggingface.co/blog/modernbert) for a high-level overview, and our [arXiv pre-print](https://arxiv.org/abs/2412.13663) for in-depth information. ModernBERT is a collaboration between [Answer.AI](https://answer.ai), [LightOn](https://lighton.ai), and friends. ## Usage You can use these models directly with the `transformers` library starting from v4.48.0: ```sh pip install -U transformers>=4.48.0 ``` Since ModernBERT is a Masked Language Model (MLM), you can use the `fill-mask` pipeline or load it via `AutoModelForMaskedLM`. To use ModernBERT for downstream tasks like classification, retrieval, or QA, fine-tune it following standard BERT fine-tuning recipes. ⚠️ If your GPU supports it, we recommend using ModernBERT with Flash Attention 2 to reach the highest efficiency. To do so, install Flash Attention as follows, then use the model as normal: ```bash pip install flash-attn ``` Using `AutoModelForMaskedLM`: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM model_id = "answerdotai/ModernBERT-base" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForMaskedLM.from_pretrained(model_id) text = "The capital of France is [MASK]." inputs = tokenizer(text, return_tensors="pt") outputs = model(inputs) # To get predictions for the mask: masked_index = inputs["input_ids"][0].tolist().index(tokenizer.mask_token_id) predicted_token_id = outputs.logits[0, masked_index].argmax(axis=-1) predicted_token = tokenizer.decode(predicted_token_id) print("Predicted token:", predicted_token) # Predicted token: Paris ``` Using a pipeline: ```python import torch from transformers import pipeline from pprint import pprint pipe = pipeline( "fill-mask", model="answerdotai/ModernBERT-base", torch_dtype=torch.bfloat16, ) input_text = "He walked to the [MASK]." results = pipe(input_text) pprint(results) ``` Note: ModernBERT does not use token type IDs, unlike some earlier BERT models. Most downstream usage is identical to standard BERT models on the Hugging Face Hub, except you can omit the `token_type_ids` parameter. ## Evaluation We evaluate ModernBERT across a range of tasks, including natural language understanding (GLUE), general retrieval (BEIR), long-context retrieval (MLDR), and code retrieval (CodeSearchNet and StackQA). Key highlights: - On GLUE, ModernBERT-base surpasses other similarly-sized encoder models, and ModernBERT-large is second only to Deberta-v3-large. - For general retrieval tasks, ModernBERT performs well on BEIR in both single-vector (DPR-style) and multi-vector (ColBERT-style) settings. - Thanks to the inclusion of code data in its training mixture, ModernBERT as a backbone also achieves new state-of-the-art code retrieval results on CodeSearchNet and StackQA. ### Base Models \| Model \| IR (DPR) \| IR (DPR) \| IR (DPR) \| IR (ColBERT) \| IR (ColBERT) \| NLU \| Code \| Code \| \|-------------\|--------------\|--------------\|--------------\|---------------\|---------------\|------\|------\|------\| \| \| BEIR \| MLDR_OOD \| MLDR_ID \| BEIR \| MLDR_OOD \| GLUE \| CSN \| SQA \| \| BERT \| 38.9 \| 23.9 \| 32.2 \| 49.0 \| 28.1 \| 84.7 \| 41.2 \| 59.5 \| \| RoBERTa \| 37.7 \| 22.9 \| 32.8 \| 48.7 \| 28.2 \| 86.4 \| 44.3 \| 59.6 \| \| DeBERTaV3 \| 20.2 \| 5.4 \| 13.4 \| 47.1 \| 21.9 \| 88.1 \| 17.5 \| 18.6 \| \| NomicBERT \| 41.0 \| 26.7 \| 30.3 \| 49.9 \| 61.3 \| 84.0 \| 41.6 \| 61.4 \| \| GTE-en-MLM \| 41.4 \| 34.3 \|44.4 \| 48.2 \| 69.3 \| 85.6 \| 44.9 \| 71.4 \| \| ModernBERT \| 41.6 \| 27.4 \| 44.0 \| 51.3 \| 80.2 \| 88.4 \| 56.4 \|73.6\| --- ### Large Models \| Model \| IR (DPR) \| IR (DPR) \| IR (DPR) \| IR (ColBERT) \| IR (ColBERT) \| NLU \| Code \| Code \| \|-------------\|--------------\|--------------\|--------------\|---------------\|---------------\|------\|------\|------\| \| \| BEIR \| MLDR_OOD \| MLDR_ID \| BEIR \| MLDR_OOD \| GLUE \| CSN \| SQA \| \| BERT \| 38.9 \| 23.3 \| 31.7 \| 49.5 \| 28.5 \| 85.2 \| 41.6 \| 60.8 \| \| RoBERTa \| 41.4 \| 22.6 \| 36.1 \| 49.8 \| 28.8 \| 88.9 \| 47.3 \| 68.1 \| \| DeBERTaV3 \| 25.6 \| 7.1 \| 19.2 \| 46.7 \| 23.0 \| 91.4\| 21.2 \| 19.7 \| \| GTE-en-MLM \| 42.5 \| 36.4 \| 48.9 \| 50.7 \| 71.3 \| 87.6 \| 40.5 \| 66.9 \| \| ModernBERT \| 44.0 \| 34.3 \| 48.6 \| 52.4 \| 80.4 \| 90.4 \|59.5 \|83.9*\| Table 1: Results for all models across an overview of all tasks. CSN refers to CodeSearchNet and SQA to StackQA. MLDRID refers to in-domain (fine-tuned on the training set) evaluation, and MLDR_OOD to out-of-domain.* ModernBERT’s strong results, coupled with its efficient runtime on long-context inputs, demonstrate that encoder-only models can be significantly improved through modern architectural choices and extensive pretraining on diversified data sources. ## Limitations ModernBERT’s training data is primarily English and code, so performance may be lower for other languages. While it can handle long sequences efficiently, using the full 8,192 tokens window may be slower than short-context inference. Like any large language model, ModernBERT may produce representations that reflect biases present in its training data. Verify critical or sensitive outputs before relying on them. ## Training - Architecture: Encoder-only, Pre-Norm Transformer with GeGLU activations. - Sequence Length: Pre-trained up to 1,024 tokens, then extended to 8,192 tokens. - Data: 2 trillion tokens of English text and code. - Optimizer: StableAdamW with trapezoidal LR scheduling and 1-sqrt decay. - Hardware: Trained on 8x H100 GPUs. See the paper for more details. ## License We release the ModernBERT model architectures, model weights, training codebase under the Apache 2.0 license. ## Citation If you use ModernBERT in your work, please cite: ``` @misc{modernbert, title={Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference}, author={Benjamin Warner and Antoine Chaffin and Benjamin Clavié and Orion Weller and Oskar Hallström and Said Taghadouini and Alexis Gallagher and Raja Biswas and Faisal Ladhak and Tom Aarsen and Nathan Cooper and Griffin Adams and Jeremy Howard and Iacopo Poli}, year={2024}, eprint={2412.13663}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.13663}, } ```
joseAndres777/WazapSplitter-LLM	joseAndres777	2025-09-17T01:11:29Z	20	1	peft	[ "peft", "safetensors", "lora", "whatsapp", "text-splitting", "message-segmentation", "spanish", "fine-tuned", "text-generation", "conversational", "es", "base_model:meta-llama/Llama-3.3-70B-Instruct", "base_model:adapter:meta-llama/Llama-3.3-70B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T04:28:37Z	--- license: apache-2.0 language: - es base_model: meta-llama/Llama-3.3-70B-Instruct tags: - peft - lora - whatsapp - text-splitting - message-segmentation - spanish - fine-tuned library_name: peft pipeline_tag: text-generation model_type: llama widget: - text: "buenos dias novedades?" example_title: "Greeting + Question" - text: "perfecto que haces?" example_title: "Confirmation + Question" - text: "aqui andamos que haces?" example_title: "Status + Question" --- # 📱 WazapSplitter-LLM Splits text into natural WhatsApp-style message segments. Input: `"buenos dias queria confirmar la hora de la reunion"` Output: `["buenos días", "quería confirmar la hora de la reunión"]` ## Quick Usage ### TypeScript/JavaScript ```typescript async function splitMessage(text: string): Promise<string[]> { const prompt = `Split messages at natural breaks into JSON array. Common patterns: greeting+question, statement+question, topic+followup. Keep original words, only add logical splits. User: ${text} Assistant:`; const response = await fetch("https://api-inference.huggingface.co/models/joseAndres777/WazapSplitter-LLM", { method: "POST", headers: { "Authorization": "Bearer YOUR_HF_TOKEN", "Content-Type": "application/json" }, body: JSON.stringify({ inputs: prompt, parameters: { max_new_tokens: 100, temperature: 0.3 } }) }); const data = await response.json(); return JSON.parse(data[0].generated_text); } // Example const segments = await splitMessage("hola como estas que tal todo?"); console.log(segments); // ["hola", "como estas", "que tal todo?"] ``` ### Chatbot Integration ```typescript // Make responses feel more human const segments = await splitMessage(botResponse); for (const segment of segments) { await sendMessage(segment); await delay(1000 + Math.random() * 2000); // Human-like timing } ```
ddfj34/act_so101_model_20250916_1920	ddfj34	2025-09-17T01:10:57Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "act", "dataset:ddfj34/record-test-20250916", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-09-17T01:10:43Z	--- datasets: ddfj34/record-test-20250916 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 lerobot-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 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
ravan18/newModel-FinBERT	ravan18	2025-09-17T01:07:30Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-17T01:07:27Z	--- 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]
hdnfnfn/blockassist-bc-finicky_finicky_warthog_1758071087	hdnfnfn	2025-09-17T01:04:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky finicky warthog", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T01:04:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky finicky warthog --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mikecsdddd/gbfgb	mikecsdddd	2025-09-17T01:03:16Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-17T00:59:58Z	--- license: apache-2.0 ---
Grogros/dmWM-Qwen-Qwen2.5-3B-Instruct-ft-French_d2	Grogros	2025-09-17T01:02:23Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-16T19:50:44Z	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-3B-Instruct tags: - generated_from_trainer model-index: - name: dmWM-Qwen-Qwen2.5-3B-Instruct-ft-French_d2 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. --> # dmWM-Qwen-Qwen2.5-3B-Instruct-ft-French_d2 This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAFACTOR and the args are: No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - training_steps: 2500 ### Training results ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.4
deepdml/whisper-large-v3-turbo-ar-quran-mix	deepdml	2025-09-17T01:00:36Z	11	0	null	[ "tensorboard", "safetensors", "whisper", "generated_from_trainer", "ar", "dataset:tarteel-ai/EA-UD", "dataset:tarteel-ai/everyayah", "base_model:deepdml/whisper-large-v3-turbo", "base_model:finetune:deepdml/whisper-large-v3-turbo", "license:apache-2.0", "model-index", "region:us" ]	null	2025-09-15T16:24:53Z	--- language: - ar license: apache-2.0 base_model: deepdml/whisper-large-v3-turbo tags: - generated_from_trainer datasets: - tarteel-ai/EA-UD - tarteel-ai/everyayah metrics: - wer model-index: - name: Whisper Turbo ar-quran results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 17.0 type: tarteel-ai/EA-UD metrics: - name: Wer type: wer value: 13.11250713877784 --- <!-- 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. --> # Whisper Turbo ar-quran This model is a fine-tuned version of [deepdml/whisper-large-v3-turbo](https://huggingface.co/deepdml/whisper-large-v3-turbo) on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set: - Loss: 1.0072 - Wer: 13.1125 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.04 - training_steps: 15000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\| \| 0.0084 \| 1.0 \| 15000 \| 1.0072 \| 13.1125 \| ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1 ## Citation ```bibtex @misc{deepdml/whisper-large-v3-turbo-ar-quran-mix, title={Fine-tuned Whisper turbo ASR model for speech recognition in Arabic}, author={Jimenez, David}, howpublished={\url{https://huggingface.co/deepdml/whisper-large-v3-turbo-ar-quran-mix}}, year={2025} } ```
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758070713	devivodowdlel	2025-09-17T01:00:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "winged exotic iguana", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T00:59:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - winged exotic iguana --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lhjiang/anysplat	lhjiang	2025-09-17T00:59:05Z	68,677	6	null	[ "safetensors", "image-to-3d", "arxiv:2505.23716", "license:mit", "region:us" ]	image-to-3d	2025-06-30T04:22:27Z	--- license: mit pipeline_tag: image-to-3d --- # AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views [![Project Website](https://img.shields.io/badge/AnySplat-Website-4CAF50?logo=googlechrome&logoColor=white)](https://city-super.github.io/anysplat/) [![Paper](https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv&logoColor=b31b1b)](https://arxiv.org/pdf/2505.23716) [![GitHub Repo](https://img.shields.io/badge/GitHub-Code-FFD700?logo=github)](https://github.com/OpenRobotLab/AnySplat) [![Hugging Face Model](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue)](https://huggingface.co/lhjiang/anysplat) ## Quick Start See the Github repository: https://github.com/OpenRobotLab/AnySplat regarding installation instructions. The model can then be used as follows: ```python from pathlib import Path import torch import os import sys sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from src.misc.image_io import save_interpolated_video from src.model.model.anysplat import AnySplat from src.utils.image import process_image # Load the model from Hugging Face model = AnySplat.from_pretrained("anysplat_ckpt_v1") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = model.to(device) model.eval() for param in model.parameters(): param.requires_grad = False # Load and preprocess example images (replace with your own image paths) image_names = ["path/to/imageA.png", "path/to/imageB.png", "path/to/imageC.png"] images = [process_image(image_name) for image_name in image_names] images = torch.stack(images, dim=0).unsqueeze(0).to(device) # [1, K, 3, 448, 448] b, v, _, h, w = images.shape # Run Inference gaussians, pred_context_pose = model.inference((images+1)*0.5) pred_all_extrinsic = pred_context_pose['extrinsic'] pred_all_intrinsic = pred_context_pose['intrinsic'] save_interpolated_video(pred_all_extrinsic, pred_all_intrinsic, b, h, w, gaussians, image_folder, model.decoder) ``` ## Citation ``` @article{jiang2025anysplat, title={AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views}, author={Jiang, Lihan and Mao, Yucheng and Xu, Linning and Lu, Tao and Ren, Kerui and Jin, Yichen and Xu, Xudong and Yu, Mulin and Pang, Jiangmiao and Zhao, Feng and others}, journal={arXiv preprint arXiv:2505.23716}, year={2025} } ``` ## License The code and models are licensed under the [MIT License](LICENSE).
abartupsadernal/blockassist	abartupsadernal	2025-09-17T00:57:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tawny thorny quail", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T00:47:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tawny thorny quail --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
FluidInference/silero-vad-coreml	FluidInference	2025-09-17T00:57:27Z	1,622	7	coreml	[ "coreml", "audio", "voice-activity-detection", "silero", "speech", "ios", "macos", "swift", "en", "dataset:alexwengg/musan_mini50", "dataset:alexwengg/musan_mini100", "base_model:onnx-community/silero-vad", "base_model:quantized:onnx-community/silero-vad", "license:mit", "region:us" ]	voice-activity-detection	2025-07-07T21:07:10Z	--- license: mit tags: - audio - voice-activity-detection - coreml - silero - speech - ios - macos - swift library_name: coreml pipeline_tag: voice-activity-detection datasets: - alexwengg/musan_mini50 - alexwengg/musan_mini100 metrics: - accuracy - f1 language: - en base_model: - onnx-community/silero-vad --- # <span style="color:#5DAF8D">🧃 CoreML Silero VAD </span> [![Discord](https://img.shields.io/badge/Discord-Join%20Chat-7289da.svg)](https://discord.gg/WNsvaCtmDe) [![GitHub Repo stars](https://img.shields.io/github/stars/FluidInference/FluidAudio?style=flat&logo=github)](https://github.com/FluidInference/FluidAudio) A CoreML implementation of the Silero Voice Activity Detection (VAD) model, optimized for Apple platforms (iOS/macOS). This repository contains pre-converted CoreML models ready for use in Swift applications. See FluidAudio Repo link at the top for more information ## Model Description Developed by: Silero Team (original), converted by FluidAudio Model type: Voice Activity Detection License: MIT Parent Model: [silero-vad](https://github.com/snakers4/silero-vad) This is how the model performs against the silero-vad v6.0.0 basline Pytorch JIT version ![graphs/yc_standard_comparison_20250915_205721_2c04b81.png](graphs/yc_standard_comparison_20250915_205721_2c04b81.png) ![graphs/yc_256ms_comparison_20250915_205721_2c04b81.png](graphs/yc_256ms_comparison_20250915_205721_2c04b81.png) Note that we tested the quantized versions, as the model is already tiny, theres no performance imporvement at all. This is how the different models compare in terms of speed, the 256s takes in 8 chunks of 32ms and processes it in batches so its much faster ![graphs/yc_performance_20250915_205721_2c04b81.png](graphs/yc_performance_20250915_205721_2c04b81.png) Conversion code is available here: [FluidInference/mobius](https://github.com/FluidInference/mobius) ## Intended Use ### Primary Use Cases - Real-time voice activity detection in iOS/macOS applications - Speech preprocessing for ASR systems - Audio segmentation and filtering ## How to Use Citation @misc{silero-vad-coreml, title={CoreML Silero VAD}, author={FluidAudio Team}, year={2024}, url={https://huggingface.co/alexwengg/coreml-silero-vad} } @misc{silero-vad, title={Silero VAD}, author={Silero Team}, year={2021}, url={https://github.com/snakers4/silero-vad} } - GitHub: https://github.com/FluidAudio/FluidAudioSwift
ShethArihant/no-security-reminder_deepseek-coder-1.3b-instruct_sft-secure-code-gen_10-epochs	ShethArihant	2025-09-17T00:50:21Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "sft", "trl", "conversational", "base_model:deepseek-ai/deepseek-coder-1.3b-instruct", "base_model:finetune:deepseek-ai/deepseek-coder-1.3b-instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-16T23:28:19Z	--- base_model: deepseek-ai/deepseek-coder-1.3b-instruct library_name: transformers model_name: no-security-reminder_deepseek-coder-1.3b-instruct_sft-secure-code-gen_10-epochs tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for no-security-reminder_deepseek-coder-1.3b-instruct_sft-secure-code-gen_10-epochs This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ShethArihant/no-security-reminder_deepseek-coder-1.3b-instruct_sft-secure-code-gen_10-epochs", 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/arihants-carnegie-mellon-university/huggingface/runs/ugx2rfr6) This model was trained with SFT. ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.1 - Pytorch: 2.8.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
hdnfnfn/blockassist-bc-noisy_elusive_grouse_1758070167	hdnfnfn	2025-09-17T00:49:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "noisy elusive grouse", "arxiv:2504.07091", "region:us" ]	null	2025-09-17T00:49:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - noisy elusive grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ramgpt/Tongyi-DeepResearch-30B-A3B-GGUF	ramgpt	2025-09-17T00:48:56Z	0	0	null	[ "gguf", "qwen3", "moe", "tongyi", "deepresearch", "en", "base_model:Alibaba-NLP/Tongyi-DeepResearch-30B-A3B", "base_model:quantized:Alibaba-NLP/Tongyi-DeepResearch-30B-A3B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-17T00:47:49Z	--- license: apache-2.0 base_model: Alibaba-NLP/Tongyi-DeepResearch-30B-A3B model_type: gguf language: - en tags: - gguf - qwen3 - moe - tongyi - deepresearch --- # Tongyi-DeepResearch-30B-A3B GGUF Converted from Alibaba-NLP/Tongyi-DeepResearch-30B-A3B.
John6666/ultra-realistic-by-stable-yogi-illus-v20-fp16-sdxl	John6666	2025-09-17T00:45:12Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "realistic", "photorealistic", "photo", "actress", "anime", "game", "portraits", "land", "contrast", "dark", "anatomy", "hands", "lighting", "face", "body structure", "skin texture", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-09-17T00:32:55Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - photorealistic - photo - actress - anime - game - portraits - land - contrast - dark - anatomy - hands - lighting - face - body structure - skin texture - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1584358/ultra-realistic-by-stable-yogi-illus?modelVersionId=2222714). This model created by [Stable_Yogi](https://civitai.com/user/Stable_Yogi).
Rawan7/smart_chat_ha	Rawan7	2025-09-17T00:43:21Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:microsoft/Phi-3-mini-4k-instruct", "lora", "transformers", "text-generation", "conversational", "arxiv:1910.09700", "base_model:microsoft/Phi-3-mini-4k-instruct", "region:us" ]	text-generation	2025-09-17T00:35:39Z	--- base_model: microsoft/Phi-3-mini-4k-instruct library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:microsoft/Phi-3-mini-4k-instruct - lora - transformers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.1
akhil-dua/baseline-nemo8b-archiects-4bit	akhil-dua	2025-09-17T00:43:19Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-09-17T00:41:45Z	--- 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]

Subsets and Splits

Filtered Qwen2.5 Distill Models

Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.

Filtered Model Cards Count

Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.

Filtered Distill Qwen 7B Models

Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.

Filtered Qwen-7b Model Cards

The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.

Filtered Qwen 7B Model Cards

The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.

Qwen 7B Distilled Models

The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.

Qwen 7B Distilled Model Cards

The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.

Qwen 7B Distilled Models

Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.

Distilled Qwen 7B Models

The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.

Filtered Model Cards with Distill Qwen2.

Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.

Filtered Model Cards with Distill Qwen 7

The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.

Distill Qwen 7B Model Cards

The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.