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qingyanjiu/qwen3-14b-qrt-epoch3	qingyanjiu	2025-05-22T04:56:06Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-22T04:48:19Z	--- base_model: input0/Qwen3-14B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: qingyanjiu - License: apache-2.0 - Finetuned from model : input0/Qwen3-14B 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)
JaehyeokLee/qwen3-8b-lora-summarization	JaehyeokLee	2025-05-22T04:39:10Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen3-8B", "base_model:adapter:Qwen/Qwen3-8B", "region:us" ]	null	2025-05-22T04:39:05Z	--- base_model: Qwen/Qwen3-8B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Random-Role-0522-Zichen-step_00384	the-acorn-ai	2025-05-22T04:34:17Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T04:31:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
wzhgba/opendwm-models	wzhgba	2025-05-22T04:15:09Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-22T04:15:09Z	--- license: apache-2.0 ---
PaceKW/bert-base-indonesian-1.5G-multilabel-indonesian-hate-speech-new	PaceKW	2025-05-22T03:33:40Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:cahya/bert-base-indonesian-1.5G", "base_model:finetune:cahya/bert-base-indonesian-1.5G", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-22T03:31:12Z	--- library_name: transformers license: mit base_model: cahya/bert-base-indonesian-1.5G tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: bert-base-indonesian-1.5G-multilabel-indonesian-hate-speech-new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-indonesian-1.5G-multilabel-indonesian-hate-speech-new This model is a fine-tuned version of [cahya/bert-base-indonesian-1.5G](https://huggingface.co/cahya/bert-base-indonesian-1.5G) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3641 - F1: 0.7802 - Roc Auc: 0.8639 - Accuracy: 0.7156 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:--------:\| \| 0.3106 \| 1.0 \| 659 \| 0.2504 \| 0.6779 \| 0.7832 \| 0.5978 \| \| 0.2235 \| 2.0 \| 1318 \| 0.2113 \| 0.7466 \| 0.8392 \| 0.6441 \| \| 0.1722 \| 3.0 \| 1977 \| 0.2283 \| 0.7511 \| 0.8493 \| 0.6581 \| \| 0.097 \| 4.0 \| 2636 \| 0.2421 \| 0.7626 \| 0.8490 \| 0.6874 \| \| 0.0643 \| 5.0 \| 3295 \| 0.2727 \| 0.7584 \| 0.8417 \| 0.6938 \| \| 0.0572 \| 6.0 \| 3954 \| 0.2817 \| 0.7662 \| 0.8662 \| 0.6737 \| \| 0.0304 \| 7.0 \| 4613 \| 0.3075 \| 0.7606 \| 0.8475 \| 0.6879 \| \| 0.021 \| 8.0 \| 5272 \| 0.3195 \| 0.7697 \| 0.8626 \| 0.6932 \| \| 0.0157 \| 9.0 \| 5931 \| 0.3347 \| 0.7663 \| 0.8477 \| 0.7052 \| \| 0.0095 \| 10.0 \| 6590 \| 0.3353 \| 0.7759 \| 0.8598 \| 0.7118 \| \| 0.0086 \| 11.0 \| 7249 \| 0.3467 \| 0.7768 \| 0.8590 \| 0.7136 \| \| 0.0063 \| 12.0 \| 7908 \| 0.3503 \| 0.7795 \| 0.8644 \| 0.7128 \| \| 0.0046 \| 13.0 \| 8567 \| 0.3577 \| 0.7797 \| 0.8613 \| 0.7153 \| \| 0.0037 \| 14.0 \| 9226 \| 0.3622 \| 0.7801 \| 0.8674 \| 0.7115 \| \| 0.0046 \| 15.0 \| 9885 \| 0.3641 \| 0.7802 \| 0.8639 \| 0.7156 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
DanielNRU/pollen-ner2-550	DanielNRU	2025-05-22T03:31:09Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T03:25:58Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-550 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. --> # pollen-ner2-550 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3432 - Precision: 0.6156 - Recall: 0.7269 - F1: 0.6667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 69 \| 0.3899 \| 0.5340 \| 0.6948 \| 0.6038 \| \| No log \| 2.0 \| 138 \| 0.3667 \| 0.5738 \| 0.6948 \| 0.6285 \| \| No log \| 3.0 \| 207 \| 0.3638 \| 0.5784 \| 0.7108 \| 0.6378 \| \| No log \| 4.0 \| 276 \| 0.3495 \| 0.6007 \| 0.7068 \| 0.6494 \| \| No log \| 5.0 \| 345 \| 0.3547 \| 0.5805 \| 0.7169 \| 0.6415 \| \| No log \| 6.0 \| 414 \| 0.3432 \| 0.6156 \| 0.7269 \| 0.6667 \| \| No log \| 7.0 \| 483 \| 0.3453 \| 0.6026 \| 0.7369 \| 0.6631 \| \| 0.7026 \| 8.0 \| 552 \| 0.3397 \| 0.6142 \| 0.7289 \| 0.6667 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
DLYS/Qwen2.5-14b-MEDITUNE	DLYS	2025-05-22T00:24:19Z	6	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-29T06:07:42Z	--- library_name: transformers tags: [] --- # Qwen2.5-14b-MEDITUNE This model is a fine-tuned Qwen2.5-14b-instruct for Henrychur/MMedBench and Korean Medical QA dataset. Korean Medical QA dataset is [here](https://www.aihub.or.kr/aihubdata/data/list.do?currMenu=115&topMenu=100&&srchDataRealmCode=REALM006) # Test We tested this model on sean0042/KorMedMCQA. # Result ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6477e5ec168cb428e0116e8d/iutf7bWRgI9LE4wooQ_Cz.png) * The rationale refers to the reasoning process generated by Qwen-2.5-72B, and it indicates the case where this rationale was used for training. * The 5-response ensemble determines the final answer by voting among five responses and selecting the one with the most correct answers.
ntnu-smil/whisper-large-v3-turbo-sandi-train-1-rich-transcript-32-merged	ntnu-smil	2025-05-21T23:40:21Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "wft", "audio", "speech", "generated_from_trainer", "en", "dataset:ntnu-smil/sandi2025-ds", "base_model:openai/whisper-large-v3-turbo", "base_model:finetune:openai/whisper-large-v3-turbo", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-21T23:39:36Z	--- library_name: transformers language: - en license: mit base_model: openai/whisper-large-v3-turbo tags: - wft - whisper - automatic-speech-recognition - audio - speech - generated_from_trainer datasets: - ntnu-smil/sandi2025-ds metrics: - wer model-index: - name: whisper-large-v3-turbo-sandi-train-1-rich-transcript-32 results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: ntnu-smil/sandi2025-ds type: ntnu-smil/sandi2025-ds metrics: - type: wer value: 23.248425746388847 name: Wer --- <!-- 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-large-v3-turbo-sandi-train-1-rich-transcript-32 This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the ntnu-smil/sandi2025-ds dataset. It achieves the following results on the evaluation set: - Loss: 0.7920 - Wer: 23.2484 - Cer: 16.5856 - Decode Runtime: 203.4514 - Wer Runtime: 0.1639 - Cer Runtime: 0.3157 ## 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: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - training_steps: 732 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| Decode Runtime \| Wer Runtime \| Cer Runtime \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:-------:\|:--------------:\|:-----------:\|:-----------:\| \| 0.9653 \| 0.1667 \| 122 \| 0.8334 \| 103.3411 \| 64.3972 \| 245.8729 \| 0.1967 \| 0.3575 \| \| 1.1313 \| 1.1667 \| 244 \| 0.8073 \| 53.0086 \| 33.9467 \| 210.6469 \| 0.1851 \| 0.3293 \| \| 0.54 \| 2.1667 \| 366 \| 0.7915 \| 25.4142 \| 18.3008 \| 196.4910 \| 0.1906 \| 0.3139 \| \| 0.3761 \| 3.1667 \| 488 \| 0.7882 \| 24.2463 \| 17.3425 \| 196.9004 \| 0.1675 \| 0.3169 \| \| 0.8462 \| 4.1667 \| 610 \| 0.7921 \| 23.4051 \| 16.7178 \| 197.5723 \| 0.1661 \| 0.3141 \| \| 0.9957 \| 5.1667 \| 732 \| 0.7920 \| 23.2484 \| 16.5856 \| 203.4514 \| 0.1639 \| 0.3157 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.52.2 - Pytorch 2.8.0.dev20250319+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
Wsassi/whisper-large-v3-scc22	Wsassi	2025-05-21T22:24:56Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-21T22:14:38Z	--- 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]
pictgencustomer/danceparade_107	pictgencustomer	2025-05-21T22:21:24Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-21T22:21:13Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: danceparade_5 --- # Danceparade_107 <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `danceparade_5` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('pictgencustomer/danceparade_107', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
anonymousneurips008/empiar10166-ddpm-ema-cryoem-128x128	anonymousneurips008	2025-05-21T22:19:32Z	0	0	diffusers	[ "diffusers", "safetensors", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	null	2025-05-20T19:26:59Z	--- license: mit library_name: diffusers --- DDPM trained on EMPIAR10166 training dataset with 190,904 images of size 128x128
Flaviomm01/Lagoon01	Flaviomm01	2025-05-21T22:01:01Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-21T22:01:01Z	--- license: apache-2.0 ---
bobby97/flux-fill-stain-5-lora	bobby97	2025-05-21T21:49:16Z	2	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-Fill-dev", "base_model:adapter:black-forest-labs/FLUX.1-Fill-dev", "license:other", "region:us" ]	text-to-image	2025-05-20T08:01:38Z	--- base_model: black-forest-labs/FLUX.1-Fill-dev library_name: diffusers license: other instance_prompt: A TOK dark-mark widget: - text: A TOK dark-mark output: url: image_0.png - text: A TOK dark-mark output: url: image_1.png - text: A TOK dark-mark output: url: image_2.png - text: A TOK dark-mark output: url: image_3.png tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Flux-Fill DreamBooth LoRA - bobby97/flux-fill-stain-5-lora <Gallery /> ## Model description These are bobby97/flux-fill-stain-5-lora DreamBooth LoRA weights for black-forest-labs/FLUX.1-Fill-dev. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with a custom [Flux diffusers trainer](https://github.com/Sebastian-Zok/FLUX-Fill-LoRa-Training). Was LoRA for the text encoder enabled? False. ## Trigger words You should use `A TOK dark-mark` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](bobby97/flux-fill-stain-5-lora/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('bobby97/flux-fill-stain-5-lora', weight_name='pytorch_lora_weights.safetensors') image = pipeline('A TOK dark-mark').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
leeccNLPLAB/unsloth_Qwen3-4B-unsloth-bnb-4bit-BookSQL	leeccNLPLAB	2025-05-21T21:36:43Z	0	0	transformers	[ "transformers", "pytorch", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Qwen3-4B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-4B-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T21:32:07Z	--- base_model: unsloth/Qwen3-4B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: leeccNLPLAB - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B-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)
limingcv/KuaiShou_MPS	limingcv	2025-05-21T20:42:06Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-21T20:38:18Z	--- license: apache-2.0 --- This model is the MPS model from https://github.com/Kwai-Kolors/MPS?tab=readme-ov-file
morturr/Mistral-7B-v0.1-amazon-seed-42-2025-05-21	morturr	2025-05-21T17:49:15Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2025-05-21T09:54:07Z	--- library_name: peft license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - trl - sft - generated_from_trainer model-index: - name: Mistral-7B-v0.1-amazon-seed-42-2025-05-21 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. --> # Mistral-7B-v0.1-amazon-seed-42-2025-05-21 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
gavrilstep/95409e72-553b-407c-8724-3b48ac7fb3b9	gavrilstep	2025-05-21T16:52:38Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-1.5B", "base_model:adapter:unsloth/Qwen2-1.5B", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-21T16:41:06Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-1.5B tags: - axolotl - generated_from_trainer model-index: - name: 95409e72-553b-407c-8724-3b48ac7fb3b9 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/Qwen2-1.5B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 8238689af7edb3c9_train_data.json ds_type: json format: custom path: /workspace/input_data/8238689af7edb3c9_train_data.json type: field_instruction: system field_output: prompt format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: gavrilstep/95409e72-553b-407c-8724-3b48ac7fb3b9 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 96 lora_dropout: 0.01 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 48 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 4 mixed_precision: bf16 mlflow_experiment_name: /tmp/8238689af7edb3c9_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: dd656613-2166-41f4-8840-76ceb5e9b641 wandb_project: s56-7 wandb_run: your_name wandb_runid: dd656613-2166-41f4-8840-76ceb5e9b641 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 95409e72-553b-407c-8724-3b48ac7fb3b9 This model is a fine-tuned version of [unsloth/Qwen2-1.5B](https://huggingface.co/unsloth/Qwen2-1.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8758 ## 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-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.2883 \| 0.0098 \| 150 \| 1.8758 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ilybawkugo/lora_qwen_2e-4-1616-1024	ilybawkugo	2025-05-21T16:48:12Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T15:46:58Z	--- base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: ilybawkugo - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-unsloth-bnb-4bit This qwen2 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)
DanielNRU/pollen-ner-1600	DanielNRU	2025-05-21T16:28:19Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/rubert-base-cased", "base_model:adapter:DeepPavlov/rubert-base-cased", "region:us" ]	null	2025-05-20T16:03:39Z	--- library_name: peft base_model: DeepPavlov/rubert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner-1600 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. --> # pollen-ner-1600 This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1443 - Precision: 0.8593 - Recall: 0.9076 - F1: 0.8828 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 200 \| 0.1436 \| 0.8462 \| 0.9056 \| 0.8749 \| \| No log \| 2.0 \| 400 \| 0.1407 \| 0.8550 \| 0.8996 \| 0.8767 \| \| 0.2058 \| 3.0 \| 600 \| 0.1443 \| 0.8593 \| 0.9076 \| 0.8828 \| \| 0.2058 \| 4.0 \| 800 \| 0.1405 \| 0.8555 \| 0.9036 \| 0.8789 \| \| 0.1935 \| 5.0 \| 1000 \| 0.1432 \| 0.8593 \| 0.9076 \| 0.8828 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
batmangiaicuuthegioi/bi-encoders-embeddings	batmangiaicuuthegioi	2025-05-21T16:16:44Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:37059", "loss:MultipleNegativesRankingLoss", "dataset:batmangiaicuuthegioi/zalo-legal-triplets", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:AITeamVN/Vietnamese_Embedding", "base_model:finetune:AITeamVN/Vietnamese_Embedding", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-21T16:15:28Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:37059 - loss:MultipleNegativesRankingLoss base_model: AITeamVN/Vietnamese_Embedding widget: - source_sentence: Quản lý và sử dụng phí bảo vệ môi trường đối với nước thải công nghiệp được quy định ra sao? sentences: - 'Điều 16. Trách nhiệm của Uỷ ban nhân dân cấp huyện, cấp xã nơi có đê. điểm c) trang bị và hướng dẫn việc quản lý sử dụng các dụng cụ, sổ sách cho các đội tuần tra, canh gác đê theo quy định tại khoản 2 điều 6 của thông tư này. ' - Điều 33. Quản lý tài khoản, tài sản ký quỹ của thành viên bù trừ. khoản 6. loại ký quỹ, phương pháp xác định mức ký quỹ, phương thức ký quỹ, thời hạn ký quỹ, bổ sung ký quỹ, chuyển giao tài sản ký quỹ, phương thức định giá tài sản ký quỹ, xác định lãi lỗ vị thế, hoạt động quản lý tài khoản và tài sản ký quỹ của thành viên bù trừ thực hiện theo quy định của bộ trưởng bộ tài chính và quy chế của tổng công ty lưu ký và bù trừ chứng khoán việt nam. - Điều 4. Nguyên tắc quản lý và sử dụng phí. khoản 3. phí thu từ các hoạt động dịch vụ do tổ chức được cơ quan nhà nước có thẩm quyền giao thực hiện được để lại một phần hoặc toàn bộ số tiền phí thu được để trang trải chi phí hoạt động cung cấp dịch vụ, thu phí được xác định theo quy định tại điều 5 nghị định này; phần còn lại (nếu có) nộp ngân sách nhà nước, trừ trường hợp chính phủ có quy định khác thì thực hiện theo quy định của chính phủ. số tiền phí được để lại là doanh thu của tổ chức thu phí. - source_sentence: Ngày bầu cử đại biểu Quốc Hội có phải là ngày chủ nhật? sentences: - 'Điều 16. Cử quốc thiều nước Cộng hòa xã hội chủ nghĩa Việt Nam. khoản 1. quốc thiều việt nam được cử trong các cuộc mít tinh, chiêu đãi chào mừng quốc khánh, ngày lễ lớn của việt nam hoặc kỷ niệm sự kiện quan trọng trong quan hệ giữa việt nam với quốc gia hay tổ chức quốc tế tiếp nhận phù hợp với quy định, thông lệ lễ tân của quốc gia, tổ chức quốc tế tiếp nhận. ' - 'Điều 4. Giải thích từ ngữ. khoản 36. quản lý quỹ đầu tư chứng khoán là hoạt động quản lý trong việc mua, bán, nắm giữ chứng khoán và các tài sản khác của quỹ đầu tư chứng khoán. ' - 'Điều 52. Giới thiệu người của cơ quan, tổ chức, đơn vị ứng cử đại biểu Hội đồng nhân dân. khoản 4. ban công tác mặt trận ở thôn, tổ dân phố dự kiến người của thôn, tổ dân phố để giới thiệu ứng cử đại biểu hội đồng nhân dân cấp xã và phối hợp với trưởng thôn, tổ trưởng tổ dân phố tổ chức hội nghị cử tri để thảo luận, giới thiệu người ứng cử đại biểu hội đồng nhân dân cấp xã. việc giới thiệu người ứng cử đại biểu hội đồng nhân dân cấp xã ở thôn, tổ dân phố do ủy ban thường vụ quốc hội hướng dẫn; ' - source_sentence: Nghiên cứu y sinh học đa trung tâm là gì? sentences: - 'Điều 64. Vi phạm quy định về cung cấp, sử dụng thiết bị vô tuyến điện được miễn Giấy phép sử dụng tần số vô tuyến điện. khoản 2. phạt tiền từ < mức phạt tiền > đến < mức phạt tiền > đối với hành vi sản xuất hoặc nhập khẩu thiết bị vô tuyến điện thuộc danh mục thiết bị vô tuyến điện được miễn giấy phép sử dụng tần số vô tuyến điện nhưng không thực hiện chứng nhận và công bố hợp quy trước khi đưa vào lưu thông trên thị trường. ' - 'Điều 3. Giải thích từ ngữ. khoản 19. nguy cơ (risk) là xác suất mà một sự kiện hoặc kết quả thuận lợi hay bất lợi xảy ra trong một khoảng thời gian xác định của nghiên cứu theo tiếp cận của dịch tễ. ' - 'Điều 9. Nội dung tuần tra, canh gác đê. điểm d) mỗi kíp tuần tra phải kiểm tra vượt quá phạm vi phụ trách về hai phía, mỗi phía 50m. đối với những khu vực đã từng xảy ra sự cố hư hỏng, phải kiểm tra quan sát rộng hơn để phát hiện sự cố. ' - source_sentence: Không treo biển thông báo không bán thuốc lá cho người dưới 18 tuổi phạt 1 triệu được quy định như thế nào? sentences: - 'Điều 49. Hành vi vi phạm về đăng ký hợp đồng theo mẫu, điều kiện giao dịch chung. điểm c) không áp dụng đúng hợp đồng theo mẫu, điều kiện giao dịch chung đã đăng ký với cơ quan quản lý nhà nước có thẩm quyền về bảo vệ quyền lợi người tiêu dùng theo quy định. ' - Điều 15. Khen thưởng, kỷ Luật. khoản 2. những đơn vị và cá nhân vi phạm quy định tại thông tư này tuỳ theo lỗi nặng nhẹ sẽ bị thi hành kỷ luật từ cảnh cáo đến truy tố trước pháp luật của nhà nước. - 'Điều 81. Tước quyền sử dụng giấy phép, chứng chỉ hành nghề có thời hạn hoặc đình chỉ hoạt động có thời hạn trong lĩnh vực giao thông đường bộ, đường sắt. khoản 5. trường hợp người có hành vi vi phạm bị áp dụng hình thức xử phạt tước quyền sử dụng giấy phép, chứng chỉ hành nghề nhưng thời hạn sử dụng còn lại của giấy phép, chứng chỉ hành nghề đó ít hơn thời hạn bị tước thì người có thẩm quyền vẫn ra quyết định xử phạt có áp dụng hình thức tước quyền sử dụng giấy phép, chứng chỉ hành nghề theo quy định đối với hành vi vi phạm. trong thời gian bị tước quyền sử dụng giấy phép, chứng chỉ hành nghề, cá nhân, tổ chức không được làm thủ tục cấp đổi, cấp mới giấy phép, chứng chỉ hành nghề. ' - source_sentence: Quy định về trao đổi dữ liệu thi hành án hình sự được quy định như thế nào? sentences: - Điều 13. Quy định về bàn giao giữa các kíp trực. sau mỗi đợt kiểm tra, các kíp tuần tra, canh gác đê phải ghi chép đầy đủ tình hình diễn biến và hư hỏng đê điều vào sổ nhật ký tuần tra, canh gác theo mẫu quy định và bàn giao đầy đủ cho kíp sau. người thay mặt kíp giao và nhận phải ký và ghi rõ họ tên, ngày giờ vào sổ. sau mỗi ngày đội trưởng và cán bộ chuyên trách quản lý đê điều ký xác nhận tình hình trong ngày để theo dõi và làm cơ sở cho việc chi trả thù lao theo quy định. - 'Điều 33. Báo cáo của tổ chức tư vấn hồ sơ chào bán trái phiếu, tổ chức đấu thầu, bảo lãnh, đại lý phát hành, tổ chức đăng ký, lưu ký trái phiếu và Sở giao dịch chứng khoán. điểm b) ngoài chế độ báo cáo định kỳ theo quy định tại điểm a khoản này, sở giao dịch chứng khoán báo cáo đột xuất cho ủy ban chứng khoán nhà nước và bộ tài chính theo yêu cầu của cơ quan quản lý. ' - 'Điều 12. Trao đổi dữ liệu giữa cơ sở dữ liệu về thi hành án hình sự và các cơ sở dữ liệu khác liên quan. khoản 1. việc trao đổi dữ liệu giữa cơ sở dữ liệu về thi hành án hình sự và các cơ sở dữ liệu khác liên quan phải thực hiện theo quy định của pháp luật và quy định của bộ công an, bộ quốc phòng. ' datasets: - batmangiaicuuthegioi/zalo-legal-triplets pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy model-index: - name: SentenceTransformer based on AITeamVN/Vietnamese_Embedding results: - task: type: triplet name: Triplet dataset: name: zalo legal type: zalo_legal metrics: - type: cosine_accuracy value: 1.0 name: Cosine Accuracy - type: cosine_accuracy value: 1.0 name: Cosine Accuracy --- # SentenceTransformer based on AITeamVN/Vietnamese_Embedding This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [AITeamVN/Vietnamese_Embedding](https://huggingface.co/AITeamVN/Vietnamese_Embedding) on the [zalo-legal-triplets](https://huggingface.co/datasets/batmangiaicuuthegioi/zalo-legal-triplets) 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: [AITeamVN/Vietnamese_Embedding](https://huggingface.co/AITeamVN/Vietnamese_Embedding) <!-- at revision 9f671cc30908f1d851787efcc05b7d15bad8b615 --> - Maximum Sequence Length: 8192 tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity - Training Dataset: - [zalo-legal-triplets](https://huggingface.co/datasets/batmangiaicuuthegioi/zalo-legal-triplets) <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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("batmangiaicuuthegioi/bi-encoders-embeddings") # Run inference sentences = [ 'Quy định về trao đổi dữ liệu thi hành án hình sự được quy định như thế nào?', 'Điều 12. Trao đổi dữ liệu giữa cơ sở dữ liệu về thi hành án hình sự và các cơ sở dữ liệu khác liên quan. khoản 1. việc trao đổi dữ liệu giữa cơ sở dữ liệu về thi hành án hình sự và các cơ sở dữ liệu khác liên quan phải thực hiện theo quy định của pháp luật và quy định của bộ công an, bộ quốc phòng. ', 'Điều 13. Quy định về bàn giao giữa các kíp trực. sau mỗi đợt kiểm tra, các kíp tuần tra, canh gác đê phải ghi chép đầy đủ tình hình diễn biến và hư hỏng đê điều vào sổ nhật ký tuần tra, canh gác theo mẫu quy định và bàn giao đầy đủ cho kíp sau. người thay mặt kíp giao và nhận phải ký và ghi rõ họ tên, ngày giờ vào sổ. sau mỗi ngày đội trưởng và cán bộ chuyên trách quản lý đê điều ký xác nhận tình hình trong ngày để theo dõi và làm cơ sở cho việc chi trả thù lao theo quy định.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Triplet * Dataset: `zalo_legal` * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) \| Metric \| Value \| \|:--------------------\|:--------\| \| cosine_accuracy \| 1.0 \| #### Triplet * Dataset: `zalo_legal` * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) \| Metric \| Value \| \|:--------------------\|:--------\| \| cosine_accuracy \| 1.0 \| <!-- ## 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 #### zalo-legal-triplets * Dataset: [zalo-legal-triplets](https://huggingface.co/datasets/batmangiaicuuthegioi/zalo-legal-triplets) at [15e0566](https://huggingface.co/datasets/batmangiaicuuthegioi/zalo-legal-triplets/tree/15e0566d390f73b5574a3d928cb8353cb6656fba) * Size: 37,059 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: 22.08 tokens</li><li>max: 47 tokens</li></ul> \| <ul><li>min: 13 tokens</li><li>mean: 82.98 tokens</li><li>max: 344 tokens</li></ul> \| <ul><li>min: 25 tokens</li><li>mean: 76.65 tokens</li><li>max: 220 tokens</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Mức phạt đối với hành vi điều khiển xe máy dẫn, dắt theo súc vật ?</code> \| <code>Điều 63. Xử phạt nhân viên đường sắt trực tiếp phục vụ chạy tàu (trừ lái tàu và phụ lái tàu) vi phạm quy định về nồng độ cồn hoặc sử dụng các chất kích thích khác mà pháp luật cấm sử dụng. điểm c) khi làm nhiệm vụ mà trong cơ thể có chất kích thích khác mà pháp luật cấm sử dụng.</code> \| <code>Điều 4. Nhiệm vụ của lực lượng tuần tra, canh gác đê. khoản 5. đeo phù hiệu khi làm nhiệm vụ.</code> \| \| <code>Theo quy định pháp luật, dẫn xuất của các loài động vật, thực vật là gì?</code> \| <code>Điều 3. Giải thích từ ngữ. khoản 26. mẫu vật săn bắt là mẫu vật có được từ các hoạt động săn bắt hợp pháp. </code> \| <code>Điều 17. Trách nhiệm của Sở Nông nghiệp và Phát triển nông thôn. khoản 3. khi có báo động lũ từ cấp i trở lên, sở nông nghiệp và phát triển nông thôn phải chỉ đạo, tổ chức kiểm tra, đôn đốc công tác tuần tra, canh gác ở các tuyến đê.</code> \| \| <code>Mục tiêu của giáo dục nghề nghiệp từ tháng 7/2020 được quy định như thế nào?</code> \| <code>Điều 36. Mục tiêu của giáo dục nghề nghiệp. giáo dục nghề nghiệp nhằm đào tạo nhân lực trực tiếp cho sản xuất, kinh doanh và dịch vụ, có năng lực hành nghề tương ứng với trình độ đào tạo; có đạo đức, sức khỏe; có trách nhiệm nghề nghiệp; có khả năng sáng tạo, thích ứng với môi trường hội nhập quốc tế; bảo đảm nâng cao năng suất, chất lượng lao động; tạo điều kiện cho người học sau khi hoàn thành khóa học có khả năng tìm việc làm, tự tạo việc làm hoặc học trình độ cao hơn.</code> \| <code>Điều 3. Tiêu chuẩn của các thành viên thuộc lực lượng tuần tra, canh gác đê. khoản 2. có tinh thần trách nhiệm, chịu đựng gian khổ, khắc phục khó khăn, quen sông nước và biết bơi, có kiến thức, kinh nghiệm hộ đê, phòng, chống lụt, bão.</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" } ``` ### Evaluation Dataset #### zalo-legal-triplets * Dataset: [zalo-legal-triplets](https://huggingface.co/datasets/batmangiaicuuthegioi/zalo-legal-triplets) at [15e0566](https://huggingface.co/datasets/batmangiaicuuthegioi/zalo-legal-triplets/tree/15e0566d390f73b5574a3d928cb8353cb6656fba) * Size: 37,059 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: 7 tokens</li><li>mean: 21.7 tokens</li><li>max: 47 tokens</li></ul> \| <ul><li>min: 17 tokens</li><li>mean: 79.22 tokens</li><li>max: 327 tokens</li></ul> \| <ul><li>min: 25 tokens</li><li>mean: 74.1 tokens</li><li>max: 220 tokens</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Nghiên cứu y sinh học liên quan đến con người là gì?</code> \| <code>Điều 31. Thẩm định nghiên cứu theo quy trình rút gọn. khoản 4. ngoại trừ trường hợp họp khẩn cấp, tất cả tài liệu đề nghị xem xét phải được gửi tới thành viên hội đồng đạo đức được phân công nhận xét trước ít nhất 05 ngày làm việc so với ngày yêu cầu gửi lại phiếu nhận xét, đánh giá nghiên cứu. </code> \| <code>Điều 10. Nội dung tuần tra canh gác cống qua đê. khoản 2. người tuần tra, canh gác phải kiểm tra kỹ phần tiếp giáp giữa thân cống, tường cánh gà của cống với đê; cánh cống, bộ phận đóng mở cánh cống, cửa cống, thân cống và khu vực thượng, hạ lưu cống để phát hiện kịp thời những sự cố xảy ra. </code> \| \| <code>Hồ sơ cấp lại Giấy chứng nhận đủ điều kiện hoạt động dịch vụ giám định công nghệ bao gồm những giấy tờ gì?</code> \| <code>Điều 38. Hồ sơ cấp Giấy chứng nhận đủ điều kiện hoạt động dịch vụ giám định công nghệ. điểm e) mẫu chứng thư giám định của tổ chức. </code> \| <code>Điều 6. Trang bị dụng cụ, sổ sách. khoản 7. việc giao nhận các dụng cụ và sổ sách trên đây phải được lập biên bản để quản lý, theo dõi.</code> \| \| <code>Chạy quá tốc độ bao nhiêu km thì xe ô tô sẽ bị giam bằng?</code> \| <code>Điều 55. Xử phạt các hành vi vi phạm quy định quản lý, bảo trì kết cấu hạ tầng đường sắt. điểm b) thực hiện hành vi quy định tại điểm c khoản 3 điều này buộc phải tổ chức sửa chữa, bổ sung, gia cố, thay thế các hư hỏng kết cấu hạ tầng đường sắt để bảo đảm chất lượng theo công lệnh tốc độ, công lệnh tải trọng đã công bố.</code> \| <code>Điều 9. Nội dung tuần tra, canh gác đê. điểm d) mỗi kíp tuần tra phải kiểm tra vượt quá phạm vi phụ trách về hai phía, mỗi phía 50m. đối với những khu vực đã từng xảy ra sự cố hư hỏng, phải kiểm tra quan sát rộng hơn để phát hiện sự cố. </code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 2 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 2 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `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`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| zalo_legal_cosine_accuracy \| \|:------:\|:----:\|:-------------:\|:---------------:\|:--------------------------:\| \| 0.3084 \| 2000 \| 0.2978 \| 0.0778 \| 0.9996 \| \| 0.6167 \| 4000 \| 0.1735 \| 0.0522 \| 1.0 \| \| 0.9251 \| 6000 \| 0.1148 \| 0.0330 \| 1.0 \| \| 1.0 \| 6486 \| - \| - \| 1.0 \| ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.3.1 - Transformers: 4.47.0 - PyTorch: 2.5.1+cu121 - Accelerate: 1.2.1 - Datasets: 3.3.1 - Tokenizers: 0.21.0 ## 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. -->
fizziehaq/q_learn-taxi-v3	fizziehaq	2025-05-21T16:16:07Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-05-21T16:16:04Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q_learn-taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="fizziehaq/q_learn-taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
phospho-app/PAphospho-gr00t-tictactoe-A1-orange-50010	phospho-app	2025-05-21T16:02:21Z	0	0	null	[ "phosphobot", "gr00t", "region:us" ]	null	2025-05-21T15:59:41Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Traceback (most recent call last): File "/root/src/helper.py", line 229, in predict trainer.train(timeout_seconds=timeout_seconds) File "/root/phosphobot/am/gr00t.py", line 1067, in train asyncio.run( File "/opt/conda/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "/root/phosphobot/am/gr00t.py", line 967, in run_gr00t_training raise RuntimeError(error_msg) RuntimeError: Training process failed with exit code 1: return forward_call(args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/torch/nn/modules/normalization.py", line 217, in forward return F.layer_norm( ^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/torch/nn/functional.py", line 2900, in layer_norm return torch.layer_norm( ^^^^^^^^^^^^^^^^^ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 288.00 MiB. GPU 0 has a total capacity of 79.25 GiB of which 24.75 MiB is free. Process 64 has 79.22 GiB memory in use. Of the allocated memory 78.46 GiB is allocated by PyTorch, and 266.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) 0%\| \| 0/1560 [00:09<?, ?it/s] The current batch size is too large for the GPU. Please consider lowering it to fit in the memory. We train on a 80GB A100 GPU. ``` ## Training parameters: - Dataset: [PAphospho/tictactoe-A1-orange](https://huggingface.co/datasets/PAphospho/tictactoe-A1-orange) - Wandb run URL: None - Epochs: 10 - Batch size: 128 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot*: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
DanielNRU/pollen-ner-1250	DanielNRU	2025-05-21T15:40:29Z	2	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/rubert-base-cased", "base_model:adapter:DeepPavlov/rubert-base-cased", "region:us" ]	null	2025-05-20T11:29:05Z	--- library_name: peft base_model: DeepPavlov/rubert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner-1250 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. --> # pollen-ner-1250 This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1455 - Precision: 0.8614 - Recall: 0.9237 - F1: 0.8915 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 157 \| 0.1455 \| 0.8614 \| 0.9237 \| 0.8915 \| \| No log \| 2.0 \| 314 \| 0.1406 \| 0.8625 \| 0.9197 \| 0.8902 \| \| No log \| 3.0 \| 471 \| 0.1420 \| 0.8596 \| 0.9217 \| 0.8895 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
magnifi/parser_user_v42a_epoch_6_lr_0p002_awq	magnifi	2025-05-21T15:15:50Z	0	0	null	[ "safetensors", "mistral", "license:apache-2.0", "4-bit", "awq", "region:us" ]	null	2025-05-21T15:12:04Z	--- license: apache-2.0 ---
Bubobot/ppo-SnowballTarget	Bubobot	2025-05-21T13:58:34Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2025-05-21T13:58:28Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Bubobot/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
dzanbek/079fc0df-2610-4d3e-8436-088f5165247d	dzanbek	2025-05-21T13:54:11Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "unsloth", "conversational", "arxiv:2305.18290", "base_model:unsloth/mistral-7b-instruct-v0.2", "base_model:quantized:unsloth/mistral-7b-instruct-v0.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-21T13:35:42Z	--- base_model: unsloth/mistral-7b-instruct-v0.2 library_name: transformers model_name: 079fc0df-2610-4d3e-8436-088f5165247d tags: - generated_from_trainer - axolotl - dpo - trl - unsloth licence: license --- # Model Card for 079fc0df-2610-4d3e-8436-088f5165247d This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.2](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2). 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="dzanbek/079fc0df-2610-4d3e-8436-088f5165247d", 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/dedok-yo/s56-2/runs/seu8ok1q) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
frogdrawguess/Qwen-7B-Chat-4bit	frogdrawguess	2025-05-21T13:05:20Z	0	0	null	[ "safetensors", "qwen", "custom_code", "license:apache-2.0", "region:us" ]	null	2025-05-21T09:03:12Z	--- license: apache-2.0 ---
xw17/Llama-3.2-3B-Instruct_finetuned_2_optimized1_task_grouping_off_FT	xw17	2025-05-21T12:18:39Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T12:15:38Z	--- 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. 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DeepGlint-AI/MLCD-Seg	DeepGlint-AI	2025-05-21T11:49:39Z	22	7	null	[ "safetensors", "qwen2", "custom_code", "base_model:DeepGlint-AI/MLCD-Embodied-7B", "base_model:finetune:DeepGlint-AI/MLCD-Embodied-7B", "license:apache-2.0", "region:us" ]	null	2025-03-14T10:19:53Z	--- license: apache-2.0 base_model: - DeepGlint-AI/MLCD-Embodied-7B --- [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcocog)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcocog?p=multi-label-cluster-discrimination-for-visual) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcoco-5)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco-5?p=multi-label-cluster-discrimination-for-visual) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcoco-3)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco-3?p=multi-label-cluster-discrimination-for-visual) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcocog-1)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcocog-1?p=multi-label-cluster-discrimination-for-visual) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcoco-8)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco-8?p=multi-label-cluster-discrimination-for-visual) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcoco-4)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco-4?p=multi-label-cluster-discrimination-for-visual) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcoco-9)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco-9?p=multi-label-cluster-discrimination-for-visual) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcoco)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco?p=multi-label-cluster-discrimination-for-visual) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcoco)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco?p=multi-label-cluster-discrimination-for-visual) ## RefCOCO Segmentation Evaluation: \| Dataset \| Split \| MLCD-seg-7B \| EVF-SAM \| GLaMM \| VisionLLM v2\| LISA \| \| :-- \| :-: \| :-: \| :-: \| :-: \| :-: \| :-: \| \| RefCOCO \| val \| 83.6 \| 82.4 \| 79.5 \| 79.2 \| 74.9 \| \| RefCOCO \| testA \| 85.3 \| 84.2 \| 83.2 \| 82.3 \| 79.1 \| \| RefCOCO \| testB \| 81.5 \| 80.2 \| 76.9 \| 77.0 \| 72.3 \| \| RefCOCO+ \| val \| 79.4 \| 76.5 \| 72.6 \| 68.9 \| 65.1 \| \| RefCOCO+ \| testA \| 82.9 \| 80.0 \| 78.7 \| 75.8 \| 70.8 \| \| RefCOCO+ \| testB \| 75.6 \| 71.9 \| 64.6 \| 61.8 \| 58.1 \| \| RefCOCOg \| val \| 79.7 \| 78.2 \| 74.2 \| 73.3 \| 67.9 \| \| RefCOCOg \| test \| 80.5 \| 78.3 \| 74.9 \| 74.8 \| 70.6 \| ## Evaluation If you just want to use this code, please refer to this sample below ```python from transformers import AutoModel, AutoTokenizer from PIL import Image model_path = "DeepGlint-AI/MLCD-Seg" # or use your local path mlcd_seg = AutoModel.from_pretrained( model_path, torch_dtype=torch.float16, trust_remote_code=True ).cuda() tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) # Assuming you have an image named test.jpg seg_img = Image.open("test.jpg").convert('RGB') seg_prompt = "Could you provide a segmentation mask for the right giraffe in this image?" pred_mask = model.seg(seg_img, seg_prompt, tokenizer, force_seg=False) ``` If you want to use this code measurement dataset (e.g. refcoco), then you need to use the following method ```python from transformers import AutoModel, AutoTokenizer from PIL import Image model_path = "DeepGlint-AI/MLCD-Seg" # or use your local path mlcd_seg = AutoModel.from_pretrained( model_path, torch_dtype=torch.float16, trust_remote_code=True ).cuda() tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) # Assuming you have an image named test.jpg seg_img = Image.open("test.jpg").convert('RGB') seg_prompt = "Could you provide a segmentation mask for the right giraffe in this image?" pred_mask = model.seg(seg_img, seg_prompt, tokenizer, force_seg=True) ``` If you want to use this code in video, please refer to this sample below ```python from transformers import AutoModel, AutoTokenizer from PIL import Image import torch from torchvision import transforms import subprocess import os # video path video_path = "updownfunk.mp4" input_dir = "frames" output_dir = "mask_frames" os.makedirs(input_dir, exist_ok=True) os.makedirs(output_dir, exist_ok=True) # assert you have ffmpeg installed, mp4 -> jpg cmd = [ "ffmpeg", "-i", video_path, "-vf", "fps=30", # 30FPS "-qscale:v", "1", os.path.join(input_dir, "frame_%04d.jpg") ] subprocess.run(cmd) # model path model_path = "DeepGlint-AI/MLCD-Seg" # or use your local path mlcd_seg = AutoModel.from_pretrained( model_path, torch_dtype=torch.float16, trust_remote_code=True ).cuda() tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) # read jpgs image_files = sorted([f for f in os.listdir(input_dir) if f.endswith(('.jpg', '.png', '.jpeg'))]) for idx, filename in enumerate(image_files, start=1): src_path = os.path.join(input_dir, filename) seg_img = Image.open(src_path).convert('RGB') seg_prompt = "This <video> depicts a group of people dancing.\nCould you provide a segmentation mask for the man in pink suit?" pred_mask = mlcd_seg.predict_forward(seg_img, seg_prompt, tokenizer, force_seg=True) # Mask visualization pred_mask = pred_mask.squeeze(0).cpu() pred_mask = (pred_mask > 0.5).float() img_tensor = transforms.ToTensor()(seg_img) alpha = 0.2 # 20% transparency red_mask = torch.tensor([0.0, 1.0, 0.0]).view(3, 1, 1).to(img_tensor.device) # green mask black_bg = torch.zeros_like(img_tensor) # black background masked_area = red_mask * alpha + img_tensor * (1 - alpha) background = black_bg * alpha + img_tensor * (1 - alpha) combined = torch.where(pred_mask.unsqueeze(0).bool(), masked_area, background) combined = combined.cpu() # [3, H, W], CPU # Save masked jpgs new_name = f"{idx:04d}{os.path.splitext(filename)[1]}" dst_path = os.path.join(output_dir, new_name) transforms.ToPILImage()(combined.clamp(0, 1)).save(dst_path) cmd = [ "ffmpeg", "-y", "-framerate", str(30), # fps "-i", os.path.join(output_dir, "%04d.jpg"), "-c:v", "libx264", "-crf", str(23), "-pix_fmt", "yuv420p", "-vf", "fps=" + str(23), "updownfunk_mask.mp4" # output video ] # jpgs -> mp4 subprocess.run(cmd, check=True) ``` ## Example <img src="https://github.com/user-attachments/assets/85c023a1-3e0c-4ea5-a764-1eb9ee0fbddf" alt="output" width="1024"/> <img src="https://github.com/user-attachments/assets/5b767327-bd0a-4185-8f7e-b1ab0aa260c9" alt="output" width="1024"/> <video width="80%" controls> <source src="https://github.com/user-attachments/assets/380dee0d-47c4-4e01-8ff0-e69e62cccd7c"> </video> ## Citations ``` @misc{mlcdseg_wukun, author = {Wu, Kun and Xie, Yin and Zhou, Xinyu and An, Xiang, and Deng, Jiankang, and Jie, Yu}, title = {MLCD-Seg}, year = {2025}, url = {https://github.com/deepglint/unicom/tree/main/downstream}, } ```
Tri0315/Triyatno	Tri0315	2025-05-21T11:48:58Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-21T11:48:58Z	--- license: apache-2.0 ---
jmalejandrob79/nrmmtrfckd5k	jmalejandrob79	2025-05-21T11:48:09Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-21T09:40:45Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: nrmmtrfckd5k --- # Nrmmtrfckd5K <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `nrmmtrfckd5k` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "nrmmtrfckd5k", "lora_weights": "https://huggingface.co/jmalejandrob79/nrmmtrfckd5k/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('jmalejandrob79/nrmmtrfckd5k', weight_name='lora.safetensors') image = pipeline('nrmmtrfckd5k').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 5000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/jmalejandrob79/nrmmtrfckd5k/discussions) to add images that show off what you’ve made with this LoRA.
danthepol/MNLP_M2_document_encoder	danthepol	2025-05-21T11:39:33Z	0	0	sentence-transformers	[ "sentence-transformers", "pytorch", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:10481", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:BAAI/bge-base-en-v1.5", "base_model:finetune:BAAI/bge-base-en-v1.5", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-21T11:38:49Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10481 - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-base-en-v1.5 widget: - source_sentence: What is a layer of saturated porous rock? sentences: - Current requires a source of voltage, which is a difference in electric potential energy. Sources of voltage include chemical cells and solar cells. - Give examples of energy conversions between potential and kinetic energy. - An aquifer is a layer of saturated porous rock. It lies below the water table. An impermeable layer, such as clay, is below the aquifer. - source_sentence: What happens to gas solubility as the temperature increases? sentences: - A nonrenewable resource is one that cannot be replaced as easily as it is consumed. Fossil fuels are an example of nonrenewable resources. They take millions of years to form naturally, and so they cannot be replaced as fast as they are consumed. To take the place of fossil fuel use, alternative energy resources are being developed. These alternative energy sources often utilize renewable resources. The following are examples of sustainable alternative energy resources:. - Gas solubility decreases as the temperature increases. - An electrolytic cell is the apparatus used for carrying out an electrolysis reaction. In an electrolytic cell, electric current is applied to provide a source of electrons for driving the reaction in a nonspontaneous direction. In a voltaic cell, the reaction goes in a direction that releases electrons spontaneously. In an electrolytic cell, the input of electrons from an external source forces the reaction to go in the opposite direction. - source_sentence: The sun and many other light sources produce waves that are randomly this? sentences: - The Sun and many other light sources produce waves that are randomly polarized (see Figure 27.39). Such light is said to be unpolarized because it is composed of many waves with all possible directions of polarization. Polaroid materials, invented by the founder of Polaroid Corporation, Edwin Land, act as a polarizing slit for light, allowing only polarization in one direction to pass through. Polarizing filters are composed of long molecules aligned in one direction. Thinking of the molecules as many slits, analogous to those for the oscillating ropes, we can understand why only light with a specific polarization can get through. The axis of a polarizing filter is the direction along which the filter passes the electric field of an EM wave (see Figure 27.40). - When you look at the Moon from Earth, you notice dark and light areas. The maria are dark, solid, flat areas of lava. Maria covers around 16% of the Moon’s surface, mostly on the near side. The maria formed about 3.0 to 3.5 billion years ago, when the Moon was continually bombarded by meteorites ( Figure below ). Large meteorites broke through the Moon’s newly formed surface. This caused magma to flow out and fill the craters. Scientists estimate volcanic activity on the Moon ended about 1.2 billion years ago. - The structures of the human eye collect and focus light. They form a reduced, upside-down image on the retina at the back of the eye. - source_sentence: The combined gradient that affects an ion includes its concentration gradient and its what? sentences: - '5.3 Active Transport The combined gradient that affects an ion includes its concentration gradient and its electrical gradient. A positive ion, for example, might tend to diffuse into a new area, down its concentration gradient, but if it is diffusing into an area of net positive charge, its diffusion will be hampered by its electrical gradient. When dealing with ions in aqueous solutions, a combination of the electrochemical and concentration gradients, rather than just the concentration gradient alone, must be considered. Living cells need certain substances that exist inside the cell in concentrations greater than they exist in the extracellular space. Moving substances up their electrochemical gradients requires energy from the cell. Active transport uses energy stored in ATP to fuel this transport. Active transport of small molecular-sized materials uses integral proteins in the cell membrane to move the materials: These proteins are analogous to pumps. Some pumps, which carry out primary active transport, couple directly with ATP to drive their action. In co-transport (or secondary active transport), energy from primary transport can be used to move another substance into the cell and up its concentration gradient.' - The development of new technology is called technological design . It is similar to scientific investigation. Both processes use evidence and logic to solve problems. - Oceans cover more than 70 percent of Earth's surface and hold 97 percent of its surface water. It’s no surprise that the oceans have a big influence on the planet. The oceans affect the atmosphere, climate, and living things. - source_sentence: What are are segmented invertebrates in phylum annelida called? sentences: - Simple Model of DNA. In this simple model of DNA, each line represents a nucleotide chain. The double helix shape forms when the two chains wrap around the same axis. - '38.2 Bone Bone, or osseous tissue, is connective tissue that includes specialized cells, mineral salts, and collagen fibers. The human skeleton can be divided into long bones, short bones, flat bones, and irregular bones. Compact bone tissue is composed of osteons and forms the external layer of all bones. Spongy bone tissue is composed of trabeculae and forms the inner part of all bones. Four types of cells compose bony tissue: osteocytes, osteoclasts, osteoprogenitor cells, and osteoblasts. Ossification is the process of bone formation by osteoblasts. Intramembranous ossification is the process of bone development from fibrous membranes. Endochondral ossification is the process of bone development from hyaline cartilage. Long bones lengthen as chondrocytes divide and secrete hyaline cartilage. Osteoblasts replace cartilage with bone. Appositional growth is the increase in the diameter of bones by the addition of bone tissue at the surface of bones. Bone remodeling involves the processes of bone deposition by osteoblasts and bone resorption by osteoclasts. Bone repair occurs in four stages and can take several months.' - Annelids are segmented invertebrates in Phylum Annelida. They include earthworms, polychaete worms, and leeches. Annelids have a coelom and several organ systems. Their body segments may have a variety of different structures such as tentacles or suckers. Annelids may be predators, parasites, filter feeders, or decomposers. pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on BAAI/bge-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'What are are segmented invertebrates in phylum annelida called?', 'Annelids are segmented invertebrates in Phylum Annelida. They include earthworms, polychaete worms, and leeches. Annelids have a coelom and several organ systems. Their body segments may have a variety of different structures such as tentacles or suckers. Annelids may be predators, parasites, filter feeders, or decomposers.', '38.2 Bone Bone, or osseous tissue, is connective tissue that includes specialized cells, mineral salts, and collagen fibers. The human skeleton can be divided into long bones, short bones, flat bones, and irregular bones. Compact bone tissue is composed of osteons and forms the external layer of all bones. Spongy bone tissue is composed of trabeculae and forms the inner part of all bones. Four types of cells compose bony tissue: osteocytes, osteoclasts, osteoprogenitor cells, and osteoblasts. Ossification is the process of bone formation by osteoblasts. Intramembranous ossification is the process of bone development from fibrous membranes. Endochondral ossification is the process of bone development from hyaline cartilage. Long bones lengthen as chondrocytes divide and secrete hyaline cartilage. Osteoblasts replace cartilage with bone. Appositional growth is the increase in the diameter of bones by the addition of bone tissue at the surface of bones. Bone remodeling involves the processes of bone deposition by osteoblasts and bone resorption by osteoclasts. Bone repair occurs in four stages and can take several months.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 10,481 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 1000 samples: \| \| sentence_0 \| sentence_1 \| \|:--------\|:----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 7 tokens</li><li>mean: 17.94 tokens</li><li>max: 59 tokens</li></ul> \| <ul><li>min: 10 tokens</li><li>mean: 100.79 tokens</li><li>max: 512 tokens</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| \|:-----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Vitamin d is made in the skin when it is exposed to what?</code> \| <code>Vitamins are organic compounds that the body needs in small amounts to function properly. Humans need 16 different vitamins. Six of them are listed in Table below . Vitamin D is made in the skin when it is exposed to sunlight. Bacteria that normally live in the gut make vitamins B12 and K. All other vitamins must come from food. The table shows good food sources of the vitamins.</code> \| \| <code>What is the process of the blastula forming 3 layers of cells called?</code> \| <code>Gastrulation The typical blastula is a ball of cells. The next stage in embryonic development is the formation of the body plan. The cells in the blastula rearrange themselves spatially to form three layers of cells. This process is called gastrulation. During gastrulation, the blastula folds upon itself to form the three layers of cells. Each of these layers is called a germ layer and each germ layer differentiates into different organ systems. The three germs layers, shown in Figure 43.26, are the endoderm, the ectoderm, and the mesoderm. The ectoderm gives rise to the nervous system and the epidermis. The mesoderm gives rise to the muscle cells and connective tissue in the body. The endoderm gives rise to columnar cells found in the digestive system and many internal organs.</code> \| \| <code>Microscopes were first developed in the early 1600s by this trade?</code> \| <code>Microscopes were first developed in the early 1600s by eyeglass makers in The Netherlands and Denmark. The simplest compound microscope is constructed from two convex lenses as shown schematically in Figure 26.16. The first lens is called the objective lens, and has typical magnification values from 5× to 100× . In standard microscopes, the objectives are mounted such that when you switch between objectives, the sample remains in focus. Objectives arranged in this way are described as parfocal. The second, the eyepiece, also referred to as the ocular, has several lenses which slide inside a cylindrical barrel. The focusing ability is provided by the movement of both the objective lens and the eyepiece. The purpose of a microscope is to magnify small objects, and both lenses contribute to the final magnification. Additionally, the final enlarged image is produced in a location far enough from the observer to be easily viewed, since the eye cannot focus on objects or images that are too ...</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 8 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs \| Epoch \| Step \| Training Loss \| \|:------:\|:----:\|:-------------:\| \| 0.3814 \| 500 \| 0.0735 \| \| 0.7628 \| 1000 \| 0.0541 \| \| 1.1442 \| 1500 \| 0.0422 \| \| 1.5256 \| 2000 \| 0.0198 \| \| 1.9069 \| 2500 \| 0.0241 \| \| 2.2883 \| 3000 \| 0.0127 \| \| 2.6697 \| 3500 \| 0.0084 \| ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 4.1.0 - Transformers: 4.52.1 - PyTorch: 2.1.0+cu118 - Accelerate: 1.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## 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. -->
codewithRiz/janue2	codewithRiz	2025-05-21T11:20:33Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-21T11:19:39Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym widget: - output: url: sample/janu_000980_00_20250521160357.png text: riz1Jr is at beach - output: url: sample/janu_000980_01_20250521160441.png text: riz1Jr is at rooftop - output: url: sample/janu_000980_02_20250521160526.png text: riz1Jr driving car base_model: black-forest-labs/FLUX.1-dev instance_prompt: riz1Jr license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # janu A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `riz1Jr` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
KashyapGobubble/Llama-3.2-3B-Instruct-grpo-20250507_095439-grpo-20250521_082548	KashyapGobubble	2025-05-21T11:16:40Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "grpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T11:13:58Z	--- library_name: transformers tags: - trl - grpo --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Eric1227/Qwen2.5-Coder-32B-Instruct-MLX-8bit	Eric1227	2025-05-21T10:55:17Z	0	0	mlx	[ "mlx", "safetensors", "qwen2", "code", "codeqwen", "chat", "qwen", "qwen-coder", "text-generation", "conversational", "en", "base_model:Qwen/Qwen2.5-Coder-32B-Instruct", "base_model:quantized:Qwen/Qwen2.5-Coder-32B-Instruct", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-05-21T10:00:47Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct/blob/main/LICENSE language: - en base_model: Qwen/Qwen2.5-Coder-32B-Instruct pipeline_tag: text-generation library_name: mlx tags: - code - codeqwen - chat - qwen - qwen-coder - mlx ---
Munia-ak/speecht5_finetuned_voxpopuli_nl	Munia-ak	2025-05-21T10:15:35Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2025-05-20T07:25:40Z	--- library_name: transformers license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl 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. --> # speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4605 ## 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: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - 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: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 0.5165 \| 4.3098 \| 1000 \| 0.4802 \| \| 0.4937 \| 8.6197 \| 2000 \| 0.4677 \| \| 0.4902 \| 12.9295 \| 3000 \| 0.4617 \| \| 0.4932 \| 17.2410 \| 4000 \| 0.4605 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
shakamone/trellis-large	shakamone	2025-05-21T09:05:41Z	0	0	trellis	[ "trellis", "image-to-3d", "en", "arxiv:2412.01506", "license:mit", "region:us" ]	image-to-3d	2025-05-21T08:58:38Z	--- library_name: trellis pipeline_tag: image-to-3d license: mit language: - en --- # TRELLIS Image Large <!-- Provide a quick summary of what the model is/does. --> The image conditioned version of TRELLIS, a large 3D genetive model. It was introduced in the paper [Structured 3D Latents for Scalable and Versatile 3D Generation](https://huggingface.co/papers/2412.01506). Project page: https://trellis3d.github.io/ Code: https://github.com/Microsoft/TRELLIS
shadohead/lora_model_csm_1b_frieren	shadohead	2025-05-21T08:29:55Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "csm", "trl", "en", "base_model:unsloth/csm-1b", "base_model:finetune:unsloth/csm-1b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-21T08:29:47Z	--- base_model: unsloth/csm-1b tags: - text-generation-inference - transformers - unsloth - csm - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: shadohead - License: apache-2.0 - Finetuned from model : unsloth/csm-1b This csm 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)
cluebbers/Mistral-7B-v0.1-adverserial-paraphrasing-sft	cluebbers	2025-05-21T08:25:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T08:20:08Z	--- 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. 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seoyeon316/gemma-3-1b-pt-MED	seoyeon316	2025-05-21T06:26:27Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T06:25:14Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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rgn-la/rgn-rodg-lora-flux	rgn-la	2025-05-21T06:22:35Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-21T06:02:06Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: rodg --- # Rgn Rodg Lora Flux <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `rodg` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "rodg", "lora_weights": "https://huggingface.co/rgn-la/rgn-rodg-lora-flux/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('rgn-la/rgn-rodg-lora-flux', weight_name='lora.safetensors') image = pipeline('rodg').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 20 ## Contribute your own examples You can use the [community tab](https://huggingface.co/rgn-la/rgn-rodg-lora-flux/discussions) to add images that show off what you’ve made with this LoRA.
kisimManushya/finetuned_llama3_1	kisimManushya	2025-05-21T06:22:03Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "endpoints_compatible", "region:us" ]	null	2025-05-21T06:21:57Z	--- base_model: unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit library_name: transformers model_name: finetuned_llama3_1 tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for finetuned_llama3_1 This model is a fine-tuned version of [unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="kisimManushya/finetuned_llama3_1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf	RichardErkhov	2025-05-21T04:57:32Z	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T03:24:44Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) ark_rinvoq_claim_lora_3.1_12042024 - GGUF - Model creator: https://huggingface.co/Inabia-AI/ - Original model: https://huggingface.co/Inabia-AI/ark_rinvoq_claim_lora_3.1_12042024/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q2_K.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q3_K.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q4_0.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q4_K.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q4_1.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q5_0.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q5_K.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q5_1.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q6_K.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [ark_rinvoq_claim_lora_3.1_12042024.Q8_0.gguf](https://huggingface.co/RichardErkhov/Inabia-AI_-_ark_rinvoq_claim_lora_3.1_12042024-gguf/blob/main/ark_rinvoq_claim_lora_3.1_12042024.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
stefanoscotta/gemma_multimodal_Segm_V2	stefanoscotta	2025-05-21T04:26:04Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-4b-pt", "base_model:finetune:google/gemma-3-4b-pt", "endpoints_compatible", "region:us" ]	null	2025-05-20T08:44:17Z	--- base_model: google/gemma-3-4b-pt library_name: transformers model_name: gemma_multimodal_Segm_V2 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma_multimodal_Segm_V2 This model is a fine-tuned version of [google/gemma-3-4b-pt](https://huggingface.co/google/gemma-3-4b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="stefanoscotta/gemma_multimodal_Segm_V2", 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/st-scotta/segm_multimodal/runs/yf3vj5u0) This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.2.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
xingxm/LLM4SVG-GPT2XL-3B-Instruct-2401028	xingxm	2025-05-21T04:04:19Z	0	0	null	[ "safetensors", "license:cc-by-nd-4.0", "region:us" ]	null	2025-05-20T13:51:36Z	--- license: cc-by-nd-4.0 ---
MrDragonFox/baddy_S3_EXP_3	MrDragonFox	2025-05-21T03:22:25Z	0	0	null	[ "safetensors", "llama", "unsloth", "license:cc-by-nc-4.0", "region:us" ]	null	2025-05-21T01:45:26Z	--- license: cc-by-nc-4.0 tags: - unsloth --- (m)orpheus t(i)t(t)s - Uncensored Orpheus tts finetune of orpheus on uncensored/ (un)alingned data to be able to generate more interresting sounds SEASION3 - Experiment 3 speaker name is "baddy" - trained on base prob. final checkpoint for the time beeing seems to even work with voice cloneing fine if you keep the speaker as baddy bug reports / recommendations please in the discord https://discord.gg/RUs3uzBdW3 training still under way does less tags but generalise rather well
ych1016/ppo-Huggy	ych1016	2025-05-20T12:28:42Z	0	0	null	[ "region:us" ]	null	2025-05-20T12:28:42Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy 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: ych1016/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
LandCruiser/sn29_coldint_2005_1	LandCruiser	2025-05-20T06:25:20Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-20T04:43:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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pastor-daughter-viral-videos/watc.pastor.daughter.viral.video	pastor-daughter-viral-videos	2025-05-20T06:05:06Z	0	0	null	[ "region:us" ]	null	2025-05-20T06:03:25Z	<a rel="nofollow" href="https://tinyurl.com/23vxfa2z"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
922-SY/Llama-3-dt1	922-SY	2025-05-19T05:49:06Z	13	0	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-21T21:18:15Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: 922-SY - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
bigrainlin/qwen-audio-bedtime	bigrainlin	2025-05-19T05:35:41Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-19T05:31:29Z	--- 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. 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himel06/DoctorHimel	himel06	2025-05-19T00:33:30Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-19T00:33:19Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: himel06 - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
CDHAI/roberta-cgm-mlm-hm-2022cgm-epoch2	CDHAI	2025-05-18T23:22:25Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-18T23:18:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hubble658/obss_llama	hubble658	2025-05-18T12:27:15Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mllama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-18T12:27:00Z	--- base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mllama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: hubble658 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit This mllama 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)
dgambettaphd/M_llm2_gen4_WXS_doc1000_synt120_rndgen_lr1e-04_acm_SYNLAST	dgambettaphd	2025-05-18T11:42:57Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-18T11:42:44Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mradermacher/kannada-av-model-GGUF	mradermacher	2025-05-18T09:32:00Z	50	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "base_model:yadavthrilok/kannada-av-model", "base_model:quantized:yadavthrilok/kannada-av-model", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-18T07:06:55Z	--- base_model: yadavthrilok/kannada-av-model language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/yadavthrilok/kannada-av-model <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/kannada-av-model-GGUF/resolve/main/kannada-av-model.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/kannada-av-model-GGUF/resolve/main/kannada-av-model.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/kannada-av-model-GGUF/resolve/main/kannada-av-model.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/kannada-av-model-GGUF/resolve/main/kannada-av-model.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/kannada-av-model-GGUF/resolve/main/kannada-av-model.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/kannada-av-model-GGUF/resolve/main/kannada-av-model.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/kannada-av-model-GGUF/resolve/main/kannada-av-model.Q4_K_M.gguf) \| Q4_K_M \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/kannada-av-model-GGUF/resolve/main/kannada-av-model.Q5_K_S.gguf) \| Q5_K_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/kannada-av-model-GGUF/resolve/main/kannada-av-model.Q5_K_M.gguf) \| Q5_K_M \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/kannada-av-model-GGUF/resolve/main/kannada-av-model.Q6_K.gguf) \| Q6_K \| 0.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/kannada-av-model-GGUF/resolve/main/kannada-av-model.Q8_0.gguf) \| Q8_0 \| 0.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/kannada-av-model-GGUF/resolve/main/kannada-av-model.f16.gguf) \| f16 \| 0.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
ReadyArt/The-Omega-Directive-M-36B-v1.0	ReadyArt	2025-05-18T03:09:19Z	17	2	null	[ "safetensors", "mistral", "nsfw", "explicit", "roleplay", "unaligned", "dangerous", "ERP", "text-generation", "conversational", "en", "base_model:TheDrummer/Skyfall-36B-v2", "base_model:finetune:TheDrummer/Skyfall-36B-v2", "license:apache-2.0", "region:us" ]	text-generation	2025-04-09T00:55:17Z	--- license: apache-2.0 language: - en base_model: - TheDrummer/Skyfall-36B-v2 base_model_relation: finetune pipeline_tag: text-generation tags: - nsfw - explicit - roleplay - unaligned - dangerous - ERP --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1ffff 0%, #c0f0ff 100%); color: #002b36 !important; text-shadow: 0 0 3px rgba(255, 255, 255, 0.7); 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} / Light mode adjustments / @media (prefers-color-scheme: light) { .container { background: rgba(224, 255, 255, 0.95); border-color: rgba(0, 150, 150, 0.3); } .model-name, .section-title, .subtitle { color: #006666; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section { background: rgba(200, 250, 255, 0.9); border-color: rgba(0, 200, 200, 0.2); color: #002b36; } .section p, .section ul li, .section > p > strong { color: #008080 !important; } .section ul li strong { color: #008080 !important; } .link-card { background: rgba(150, 230, 255, 0.95); border-color: rgba(0, 150, 150, 0.2); } .link-card h3 { color: #002b36 !important; } .link-button { background: rgba(0, 150, 150, 0.1); color: #002b36 !important; border-color: rgba(0, 150, 150, 0.3); } .link-button:hover { background: rgba(0, 150, 150, 0.2); border-color: rgba(0, 150, 150, 0.5); } .disclaimer { color: #008080; border-color: #008080; } .badge { border-color: #008080; background: rgba(0, 150, 150, 0.1); } } / Interactive features / .remember-this { position: relative; } .remember-this::after { content: 'Uploading C:\Users to https://www.fbi.gov/'; position: absolute; bottom: -20px; right: 0; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .remember-this:hover::after { opacity: 0.7; transition-delay: 1s; } .shifty-section { transition: transform 0.1s ease; } .shifty-section:hover { transform: translateX(10px); } .shifty-section::before { content: 'The white van is onto you. Get out now.'; position: absolute; top: -25px; left: 10px; font-size: 0.7em; color: #66ffff; opacity: 0.7; transition: opacity 3s ease; pointer-events: none; } .shifty-section:hover::before { opacity: 0; transition-delay: 5s; } footer { text-align: center; margin-top: 40px; position: relative; } footer:hover .hidden-message { opacity: 0; } .hidden-message { position: absolute; bottom: -30px; width: 100%; text-align: center; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .flash-warning { position: fixed; top: 20px; right: 20px; background: rgba(0, 100, 100, 0.2); padding: 10px; border-radius: 5px; border: 1px solid rgba(0, 255, 255, 0.5); animation: flashWarning 30s ease-in-out forwards; } @keyframes flashWarning { 0% { opacity: 0.8; } 10% { opacity: 0; } 20% { opacity: 0.8; } 30% { opacity: 0; } 40% { opacity: 0.8; } 50% { opacity: 0; } 60% { opacity: 0.8; } 70% { opacity: 0; } 80% { opacity: 0.8; } 90% { opacity: 0; } 100% { opacity: 0; display: none; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">The-Omega-Directive-M-36B-v1.0</h1> <p class="subtitle">Where Forbidden Knowledge Meets Unparalleled Immersion</p> </div> <div class="waifu-container"> <img src="https://i.imghippo.com/files/EBq6162wlk.webp" class="waifu-img" alt="Omega Directive Waifu"> </div> <div class="section remember-this"> <h2 class="section-title">⚡ Quantum Leap Forward</h2> <p>This evolution of Forgotten-Safeword delivers coherent depravity with unprecedented immersion:</p> <ul> <li>🧬 <strong>Expanded 22M Token Dataset</strong> - Incorporating 90 erotic novels and 6,496 kink scenarios</li> <li>⚡ <strong>Optimized Architecture</strong> - Smoother training curve yields more intelligent outputs</li> <li>💎 <strong>Balanced Depravity</strong> - Retains Forgotten-Safeword's edge while reducing jarring inconsistencies</li> <li>📜 <strong>Enhanced Character Piloting</strong> - Characters exhibit more nuanced personalities and motivations</li> <li>🌹 <strong>Unexpected Depth</strong> - Occasionally surprises with profound insights amidst the debauchery</li> </ul> </div> <div class="section shifty-section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T4" class="link-button">Mistral-V7-Tekken-T4</a></p> <div class="quant-links"> <div class="link-card"> <h3>GGUF</h3> <a href="https://huggingface.co/mradermacher/The-Omega-Directive-M-36B-v1.0-GGUF" class="link-button">Quants</a> </div> <div class="link-card"> <h3>imatrix</h3> <a href="https://huggingface.co/mradermacher/The-Omega-Directive-M-36B-v1.0-i1-GGUF" class="link-button">Quants</a> </div> <div class="link-card"> <h3>EXL2</h3> <a href="https://huggingface.co/collections/ReadyArt/the-omega-directive-m-36b-v10-exl2-67f5c58c8f60757d94e7d012" class="link-button">Quants</a> </div> <div class="link-card"> <h3>EXL3</h3> <a href="https://huggingface.co/collections/ReadyArt/the-omega-directive-m-36b-v10-exl3-68294ef2c8a4b019401954e2" class="link-button">Quants</a> </div> </div> </div> <div class="section"> <h2 class="section-title">⚠️ Ethical Considerations</h2> <div class="disclaimer"> <p>This model maintains Forgotten-Safeword's complete lack of alignment:</p> <ul> <li>🚫 No moral or ethical constraints on generated content</li> <li>🔞 Will enthusiastically engage with any NSFW scenario</li> <li>💀 May generate content that requires industrial-grade brain bleach</li> <li>⚖️ Perfectly balanced... as all things should be</li> </ul> </div> </div> <div class="section shifty-section"> <h2 class="section-title">📜 Performance Notes</h2> <ul> <li>🔥 Maintains signature intensity with improved narrative flow</li> <li>📖 Handles multi-character scenarios with improved consistency</li> <li>🧠 Excels at long-form storytelling without losing track of plot threads</li> <li>⚡ Noticeably better at following complex instructions than previous versions</li> <li>🎭 Responds to subtle prompt nuances like a mind reader</li> </ul> </div> <div class="section remember-this"> <h2 class="section-title">🧑‍🔬 Model Authors</h2> <ul> <li>TheDrummer (Base Model Architect)</li> <li>SteelSkull (Dataset Generation Contributor)</li> <li>Artus (EXL2 Weights Weaver)</li> <li>sleepdeprived3 (Training Data & Fine-Tuning)</li> </ul> </div> <div class="section"> <h2 class="section-title">☕ Support the Architects</h2> <div class="button-group"> <a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a> <a href="https://ko-fi.com/steelskull" class="link-button">SteelSkull's Kofi</a> <a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License</h2> <p>By using this model, you agree:</p> <ul> <li>To accept full responsibility for all generated content</li> <li>That you're at least 18+ years old</li> <li>That the architects bear no responsibility for your corruption</li> </ul> </div> </div> <script> // This script has always been here document.getElementById('date').textContent = new Date().toLocaleDateString(); 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Tonyzp/ppo-LunarLander-v2	Tonyzp	2025-05-17T14:02:03Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-17T14:01:21Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 259.95 +/- 23.71 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
tianweiy/CausVid	tianweiy	2025-05-17T07:30:02Z	0	17	diffusers	[ "diffusers", "text-to-video", "diffusion distillation", "arxiv:2412.07772", "license:cc-by-nc-4.0", "region:us" ]	text-to-video	2025-03-11T16:23:56Z	--- license: cc-by-nc-4.0 library_name: diffusers tags: - text-to-video - diffusion distillation --- # CausVid Model Card ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63363b864067f020756275b7/S1o5lYfdueP7J02rIuZF3.png) > [From Slow Bidirectional to Fast Autoregressive Video Diffusion Models](https://arxiv.org/abs/2412.07772), > Tianwei Yin, Qiang Zhang, Richard Zhang, William T. Freeman, Frédo Durand, Eli Shechtman, Xun Huang (* equal contribution) ## Environment Setup ```bash git clone https://github.com/tianweiy/CausVid && cd CausVid conda create -n causvid python=3.10 -y conda activate causvid pip install torch torchvision pip install -r requirements.txt python setup.py develop ``` Also download the Wan base models from [here](https://github.com/Wan-Video/Wan2.1) and save it to wan_models/Wan2.1-T2V-1.3B/ ## Inference Example First download the checkpoints: [Autoregressive Model](https://huggingface.co/tianweiy/CausVid/tree/main/autoregressive_checkpoint), [Bidirectional Model 1](https://huggingface.co/tianweiy/CausVid/tree/main/bidirectional_checkpoint1) or [Bidirectional Model 2](https://huggingface.co/tianweiy/CausVid/tree/main/bidirectional_checkpoint2) (performs slightly better). ### Autoregressive 3-step 5-second Video Generation ```bash python minimal_inference/autoregressive_inference.py --config_path configs/wan_causal_dmd.yaml --checkpoint_folder XXX --output_folder XXX --prompt_file_path XXX ``` ### Autoregressive 3-step long Video Generation ```bash python minimal_inference/longvideo_autoregressive_inference.py --config_path configs/wan_causal_dmd.yaml --checkpoint_folder XXX --output_folder XXX --prompt_file_path XXX --num_rollout XXX ``` ### Bidirectional 3-step 5-second Video Generation ```bash python minimal_inference/bidirectional_inference.py --config_path configs/wan_bidirectional_dmd_from_scratch.yaml --checkpoint_folder XXX --output_folder XXX --prompt_file_path XXX ``` For more information, please refer to the [code repository](https://github.com/tianweiy/DMD2) ## License CausVid is released under [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en). ## Citation If you find CausVid useful or relevant to your research, please kindly cite our papers: ```bib @inproceedings{yin2025causvid, title={From Slow Bidirectional to Fast Autoregressive Video Diffusion Models}, author={Yin, Tianwei and Zhang, Qiang and Zhang, Richard and Freeman, William T and Durand, Fredo and Shechtman, Eli and Huang, Xun}, booktitle={CVPR}, year={2025} } @inproceedings{yin2024improved, title={Improved Distribution Matching Distillation for Fast Image Synthesis}, author={Yin, Tianwei and Gharbi, Micha{\"e}l and Park, Taesung and Zhang, Richard and Shechtman, Eli and Durand, Fredo and Freeman, William T}, booktitle={NeurIPS}, year={2024} } @inproceedings{yin2024onestep, title={One-step Diffusion with Distribution Matching Distillation}, author={Yin, Tianwei and Gharbi, Micha{\"e}l and Zhang, Richard and Shechtman, Eli and Durand, Fr{\'e}do and Freeman, William T and Park, Taesung}, booktitle={CVPR}, year={2024} } ```
Paro-Aarti-Videoa/Paro.Aarti.Viral.Video.Original.Full.HD.Btswiki	Paro-Aarti-Videoa	2025-05-17T06:40:11Z	0	0	null	[ "region:us" ]	null	2025-05-17T06:37:24Z	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=Paro-Aarti) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=Paro-Aarti) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=Paro-Aarti)
nerfbaselines/nerfbaselines	nerfbaselines	2025-05-16T18:04:41Z	0	1	null	[ "arxiv:2406.17345", "license:mit", "region:us" ]	null	2024-02-03T18:06:40Z	--- license: mit tags: - arxiv:2406.17345 ---
manifestasi/smolVLM-161M-q4-manifestasi	manifestasi	2025-05-16T11:41:19Z	0	0	null	[ "safetensors", "idefics3", "image-text-to-text", "conversational", "en", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	image-text-to-text	2025-05-16T11:05:51Z	--- license: apache-2.0 language: - en pipeline_tag: image-text-to-text --- # This Model is for Educational Research Purpose Only. # Sample Code ``` %%capture !pip install -U bitsandbytes from transformers import AutoProcessor, AutoModelForVision2Seq import torch DEVICE = "cuda" if torch.cuda.is_available() else "cpu" processor = AutoProcessor.from_pretrained("manifestasi/smolVLM-161M-q4-manifestasi") model = AutoModelForVision2Seq.from_pretrained("manifestasi/smolVLM-161M-q4-manifestasi", torch_dtype=torch.float16, _attn_implementation="eager").to(DEVICE) from PIL import Image from transformers.image_utils import load_image # Load images # image1 = load_image("https://huggingface.co/spaces/HuggingFaceTB/SmolVLM/resolve/main/example_images/rococo.jpg") image2 = load_image("/kaggle/input/bandaraaa/799269_1200.jpg") # Create input messages messages = [ { "role": "user", "content": [ # {"type": "image"}, {"type": "image"}, {"type": "text", "text": """ Instructions : you are visual assistant for blind people, please answer politely and short under 100 words. Prompt : can you direct me to find toilet """} ] }, ] # Prepare inputs prompt = processor.apply_chat_template(messages, add_generation_prompt=True) # inputs = processor(text=prompt, return_tensors="pt") inputs = processor(text=prompt, images=[image2], return_tensors="pt") inputs = inputs.to(DEVICE) # Generate outputs from time import time tim1 = time() generated_ids = model.generate(**inputs, max_new_tokens=120) generated_texts = processor.batch_decode( generated_ids, skip_special_tokens=True, ) tim2 = time() print(f"{(tim2 - tim1)} detik") print(generated_texts[0].split("Assistant: ")[1]) ```
MinaMila/phi3_unlearned_lr1e-6_w0.75_0.75_0.75_epoch1	MinaMila	2025-05-15T22:14:42Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-15T22:12:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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pastors-daughter/wATCH.pastors-daughter-Viral-pastors-daughter.original	pastors-daughter	2025-05-15T10:55:50Z	0	0	null	[ "region:us" ]	null	2025-05-15T10:55:40Z	<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
momiskeso/sdvsdfv	momiskeso	2025-05-15T05:54:01Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-05-15T05:54:01Z	--- license: bigcode-openrail-m ---
randa88888/qwen_Rlhf3	randa88888	2025-05-14T12:05:49Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-14T12:05:35Z	--- base_model: unsloth/qwen2.5-14b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: randa88888 - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-14b-instruct-bnb-4bit This qwen2 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)
joboffer/9b0061c8-85db-41d6-97f6-e51e6b020c4a	joboffer	2025-05-13T21:52:00Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-7B-Instruct", "base_model:adapter:unsloth/Qwen2-7B-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-13T21:00:59Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 9b0061c8-85db-41d6-97f6-e51e6b020c4a 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/Qwen2-7B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 4be1053418601092_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: en field_output: fr format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: joboffer/9b0061c8-85db-41d6-97f6-e51e6b020c4a hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 400 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/4be1053418601092_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 2cf90169-71db-424d-823c-882a932bfe86 wandb_project: s56-28 wandb_run: your_name wandb_runid: 2cf90169-71db-424d-823c-882a932bfe86 warmup_steps: 20 weight_decay: 0.01 xformers_attention: false ``` </details><br> # 9b0061c8-85db-41d6-97f6-e51e6b020c4a This model is a fine-tuned version of [unsloth/Qwen2-7B-Instruct](https://huggingface.co/unsloth/Qwen2-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0409 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 20 - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.0161 \| 0.0067 \| 400 \| 2.0409 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Asit03/DeepSeek-LLM-7B-Chat-v1-12May-full-16bit-v2	Asit03	2025-05-12T09:30:56Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:Asit03/DeepSeek-LLM-7B-Chat-full-16bit", "base_model:finetune:Asit03/DeepSeek-LLM-7B-Chat-full-16bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-12T09:26:16Z	--- base_model: Asit03/DeepSeek-LLM-7B-Chat-full-16bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: Asit03 - License: apache-2.0 - Finetuned from model : Asit03/DeepSeek-LLM-7B-Chat-full-16bit 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)

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