librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-27 12:28:27	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 533 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-27 12:28:17	card stringlengths 11 1.01M
kathleenge/department-200	kathleenge	2025-06-18T22:37:43Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-18T21:37:01Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: kathleenge - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
xinyifang/Conllama	xinyifang	2025-06-18T22:34:53Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T22:16:40Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
Bonnief/afriberta-om-finetuned	Bonnief	2025-06-18T22:34:47Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "fill-mask", "generated_from_trainer", "base_model:castorini/afriberta_small", "base_model:finetune:castorini/afriberta_small", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-06-18T19:29:42Z	--- library_name: transformers base_model: castorini/afriberta_small tags: - generated_from_trainer metrics: - accuracy model-index: - name: afriberta-om-finetuned 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. --> # afriberta-om-finetuned This model is a fine-tuned version of [castorini/afriberta_small](https://huggingface.co/castorini/afriberta_small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Accuracy: 0.4266 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - 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: 1000 - training_steps: 100000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.53.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
h9art/PARADIS-Qwen3_1.7B-10kWikiVi-1GPU	h9art	2025-06-18T22:27:11Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-18T13:26:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
MattMcG/titles_qwen_with_eval	MattMcG	2025-06-18T22:25:02Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen3-14B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-14B-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T22:15:41Z	--- base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: MattMcG - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-14B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Ascrewdriver/ppo-Huggy	Ascrewdriver	2025-06-18T22:24:25Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-06-18T22:24:21Z	--- 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: Ascrewdriver/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
nnilayy/deap-arousal-binary-classification-Kfold-4	nnilayy	2025-06-18T22:19:50Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-06-18T22:19:49Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
meshkiempel/vorobev	meshkiempel	2025-06-18T22:18:20Z	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	2025-06-18T22:17:35Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: vorobev 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 --- # vorobev A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `vorobev` 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.
Izzi1/llama3-finetuned-column-classify-upwork-data	Izzi1	2025-06-18T22:13:46Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:finetune:unsloth/Llama-3.2-1B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-18T22:13:40Z	--- base_model: unsloth/Llama-3.2-1B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Izzi1 - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-1B-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
N1CKNGUYEN/bigbird-roberta-base_nli_classifier_mnli_anli_fevernli_xnli	N1CKNGUYEN	2025-06-18T22:11:59Z	2	0	transformers	[ "transformers", "safetensors", "big_bird", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-17T17:50:27Z	--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: bigbird-roberta-base_nli_classifier_mnli_anli_fevernli_xnli 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. --> # bigbird-roberta-base_nli_classifier_mnli_anli_fevernli_xnli This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5875 - F1 Macro: 0.6077 - F1 Micro: 0.7047 - Accuracy Balanced: 0.6070 - Accuracy: 0.7047 - Precision Macro: 0.6727 - Recall Macro: 0.6070 - Precision Micro: 0.7047 - Recall Micro: 0.7047 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - 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_ratio: 0.06 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Accuracy Balanced \| F1 Macro \| F1 Micro \| Validation Loss \| Precision Macro \| Precision Micro \| Recall Macro \| Recall Micro \| \|:-------------:\|:-----:\|:-----:\|:--------:\|:-----------------:\|:--------:\|:--------:\|:---------------:\|:---------------:\|:---------------:\|:------------:\|:------------:\| \| 0.2556 \| 1.0 \| 12340 \| 0.7498 \| 0.6626 \| 0.6735 \| 0.7498 \| 0.5150 \| 0.7463 \| 0.7498 \| 0.6626 \| 0.7498 \| \| 0.4494 \| 2.0 \| 24680 \| 0.5875 \| 0.6077 \| 0.7047 \| 0.6070 \| 0.7047 \| 0.6727 \| 0.6070 \| 0.7047 \| 0.7047 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
aleni/whisper-small-vi	aleni	2025-06-18T22:10:45Z	34	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "vi", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-11-03T14:06:25Z	--- library_name: transformers language: - vi license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper small vi - Ox 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. --> # Whisper small vi - Ox This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2559 - Wer: 9.3480 ## 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: 16 - 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:-------:\| \| 0.249 \| 0.0780 \| 1000 \| 0.1739 \| 8.6461 \| \| 0.2426 \| 0.1559 \| 2000 \| 0.1802 \| 8.6256 \| \| 0.238 \| 0.2339 \| 3000 \| 0.1851 \| 8.7411 \| \| 0.2096 \| 0.3119 \| 4000 \| 0.1892 \| 9.4001 \| \| 0.2076 \| 0.3898 \| 5000 \| 0.1965 \| 8.7169 \| \| 0.1989 \| 0.4678 \| 6000 \| 0.1952 \| 9.3685 \| \| 0.2172 \| 0.5458 \| 7000 \| 0.2029 \| 9.0334 \| \| 0.2145 \| 0.6237 \| 8000 \| 0.2044 \| 9.4430 \| \| 0.2151 \| 0.7017 \| 9000 \| 0.2079 \| 9.1227 \| \| 0.2307 \| 0.7797 \| 10000 \| 0.2095 \| 8.9310 \| \| 0.2065 \| 0.8576 \| 11000 \| 0.2200 \| 9.7595 \| \| 0.2252 \| 0.9356 \| 12000 \| 0.2177 \| 9.8823 \| \| 0.1236 \| 1.0136 \| 13000 \| 0.2221 \| 10.3832 \| \| 0.1242 \| 1.0915 \| 14000 \| 0.2276 \| 9.2549 \| \| 0.1392 \| 1.1695 \| 15000 \| 0.2272 \| 9.5677 \| \| 0.1274 \| 1.2475 \| 16000 \| 0.2272 \| 8.6200 \| \| 0.139 \| 1.3254 \| 17000 \| 0.2301 \| 9.1209 \| \| 0.1166 \| 1.4034 \| 18000 \| 0.2325 \| 9.1711 \| \| 0.1507 \| 1.4814 \| 19000 \| 0.2323 \| 9.5472 \| \| 0.106 \| 1.5593 \| 20000 \| 0.2331 \| 9.6868 \| \| 0.1264 \| 1.6373 \| 21000 \| 0.2372 \| 8.9440 \| \| 0.1177 \| 1.7153 \| 22000 \| 0.2394 \| 8.9924 \| \| 0.1125 \| 1.7932 \| 23000 \| 0.2411 \| 9.1618 \| \| 0.1272 \| 1.8712 \| 24000 \| 0.2421 \| 9.7669 \| \| 0.1234 \| 1.9492 \| 25000 \| 0.2441 \| 9.6552 \| \| 0.091 \| 2.0271 \| 26000 \| 0.2502 \| 8.9272 \| \| 0.0778 \| 2.1051 \| 27000 \| 0.2499 \| 9.3052 \| \| 0.0853 \| 2.1831 \| 28000 \| 0.2513 \| 10.2081 \| \| 0.079 \| 2.2610 \| 29000 \| 0.2532 \| 9.7446 \| \| 0.0661 \| 2.3390 \| 30000 \| 0.2533 \| 9.7613 \| \| 0.0782 \| 2.4170 \| 31000 \| 0.2525 \| 9.2884 \| \| 0.0757 \| 2.4949 \| 32000 \| 0.2550 \| 9.4969 \| \| 0.0704 \| 2.5729 \| 33000 \| 0.2554 \| 9.4169 \| \| 0.066 \| 2.6509 \| 34000 \| 0.2560 \| 9.2642 \| \| 0.0703 \| 2.7288 \| 35000 \| 0.2564 \| 9.2828 \| \| 0.0707 \| 2.8068 \| 36000 \| 0.2552 \| 9.2977 \| \| 0.0722 \| 2.8848 \| 37000 \| 0.2566 \| 9.3629 \| \| 0.081 \| 2.9627 \| 38000 \| 0.2559 \| 9.3480 \| ### Framework versions - Transformers 4.53.0.dev0 - Pytorch 2.8.0.dev20250616+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
igorcouto/sofya_telco_alpha1	igorcouto	2025-06-18T22:01:47Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-18T22:00:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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morturr/Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb2-seed18-2025-06-19	morturr	2025-06-18T22:00:38Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-06-18T22:00:20Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb2-seed18-2025-06-19 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. --> # Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb2-seed18-2025-06-19 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 18 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - 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: 2 ### 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
mradermacher/L3.3-Electra-R1-70b-GGUF	mradermacher	2025-06-18T21:56:30Z	68	0	transformers	[ "transformers", "gguf", "en", "base_model:Steelskull/L3.3-Electra-R1-70b", "base_model:quantized:Steelskull/L3.3-Electra-R1-70b", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-09T16:00:16Z	--- base_model: Steelskull/L3.3-Electra-R1-70b language: - en library_name: transformers license: other license_name: eva-llama3.3 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Steelskull/L3.3-Electra-R1-70b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q5_K_M.gguf) \| Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/L3.3-Electra-R1-70b-i1-GGUF	mradermacher	2025-06-18T21:56:12Z	780	2	transformers	[ "transformers", "gguf", "en", "base_model:Steelskull/L3.3-Electra-R1-70b", "base_model:quantized:Steelskull/L3.3-Electra-R1-70b", "license:other", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-03-09T17:49:23Z	--- base_model: Steelskull/L3.3-Electra-R1-70b language: - en library_name: transformers license: other license_name: eva-llama3.3 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Steelskull/L3.3-Electra-R1-70b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ1_S.gguf) \| i1-IQ1_S \| 15.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 19.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 21.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ2_S.gguf) \| i1-IQ2_S \| 22.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ2_M.gguf) \| i1-IQ2_M \| 24.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 24.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q2_K.gguf) \| i1-Q2_K \| 26.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 27.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ3_S.gguf) \| i1-IQ3_S \| 31.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 31.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ3_M.gguf) \| i1-IQ3_M \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 34.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 37.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 38.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q4_0.gguf) \| i1-Q4_0 \| 40.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 40.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q4_1.gguf) \| i1-Q4_1 \| 44.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 58.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
ohjoonhee/hai-siglip-fold1	ohjoonhee	2025-06-18T21:55:49Z	0	0	transformers	[ "transformers", "safetensors", "siglip", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-18T21:52:31Z	--- 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]
bolmu321/medgemma-medqa	bolmu321	2025-06-18T21:55:07Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/medgemma-4b-it", "base_model:finetune:google/medgemma-4b-it", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-18T20:11:43Z	--- base_model: google/medgemma-4b-it library_name: transformers model_name: medgemma-medqa tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for medgemma-medqa This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="bolmu321/medgemma-medqa", 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: 0.18.2 - Transformers: 4.52.4 - Pytorch: 2.7.1 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
lizh222/textual_inversion_cat	lizh222	2025-06-18T21:49:57Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "diffusers-training", "base_model:stable-diffusion-v1-5/stable-diffusion-v1-5", "base_model:adapter:stable-diffusion-v1-5/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-06-18T21:06:41Z	--- base_model: stable-diffusion-v1-5/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m inference: true tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion - diffusers-training --- <!-- 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. --> # Textual inversion text2image fine-tuning - lizh222/textual_inversion_cat These are textual inversion adaption weights for stable-diffusion-v1-5/stable-diffusion-v1-5. You can find some example images in the following. ## 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]
luyotw/openfun-ivod-whisper-medium-WangMeiHui-11-46	luyotw	2025-06-18T21:48:04Z	0	0	null	[ "tensorboard", "safetensors", "whisper", "region:us" ]	null	2025-06-18T20:34:23Z	# Fine-tune 資訊 - 原始模型: `openai/whisper-medium` - 使用音訊數量: 4999 - 使用音訊總長: 2.84 小時 - 音訊平均長度: 2.05 秒 - GPU: `NVIDIA H100 PCIe` x 1 - 訓練時間: 02:29:39 - 模型大小: 2.85 GB --- # Model Card
FLOPS-Squared/KeystoneFuse-FW16-KSL8-Flax	FLOPS-Squared	2025-06-18T21:47:26Z	1	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T02:03:33Z	--- 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]
mlfoundations-cua-dev/uitars_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_baseline	mlfoundations-cua-dev	2025-06-18T21:44:44Z	0	0	null	[ "region:us" ]	null	2025-06-18T21:15:02Z	# idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_baseline ## Model Information Full Model Name: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_baseline` Repository Name: `mlfoundations-cua-dev/uitars_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_baseline` Model Directory: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_baseline` Checkpoint Used: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_baseline/checkpoint_epoch_9.pt` ## Model Configuration - Model Version: TARS 1.5 - Model Size: 7B parameters - Data Type: Frame pairs - Learning Rate: 1e-5 - Epochs: 10 - Training Steps: 500 - Global Batch Size: 8 - Weight Decay: 0.1 - Max Gradient Norm: 1.0 - Resolution: 896x896 - Training Data: Baseline ## Description This repository contains the model state dict extracted from the training checkpoint. ### Files - `model_state_dict.pt`: PyTorch state dictionary containing the model weights - `README.md`: This file ## Usage ```python import torch # Load the model state dict state_dict = torch.load("model_state_dict.pt", map_location='cpu') # Use with your model architecture # model.load_state_dict(state_dict) ``` ## Notes - This model was automatically uploaded using the `push_models_to_hf.py` script - The repository name may be truncated if the original model name exceeded HuggingFace's 96-character limit - Checkpoint extracted from: `checkpoint_epoch_9.pt`
sanshi9999/qwen2.5-3b-breakdata500	sanshi9999	2025-06-18T21:44:17Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-18T21:44:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
indicinaaa/Qwen3-4B-unsloth-bnb-4bit-fp16	indicinaaa	2025-06-18T21:40:27Z	0	0	transformers	[ "transformers", "pytorch", "safetensors", "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	2025-06-18T20:33:19Z	--- base_model: unsloth/Qwen3-4B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: indicinaaa - 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)
nnilayy/deap-dominance-binary-classification-Kfold-3	nnilayy	2025-06-18T21:37:18Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-06-18T21:37:16Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
sapna-shah-18d/wATCH.sapna.shah.viral.video.original	sapna-shah-18d	2025-06-18T21:32:26Z	0	0	null	[ "region:us" ]	null	2025-06-18T21:29:27Z	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=sapna-shah) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=sapna-shah) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=sapna-shah)
mmwillet2/Orpheus_GGUF	mmwillet2	2025-06-18T21:28:52Z	0	0	null	[ "gguf", "text-to-speech", "base_model:canopylabs/orpheus-3b-0.1-ft", "base_model:quantized:canopylabs/orpheus-3b-0.1-ft", "license:mit", "region:us" ]	text-to-speech	2025-06-18T20:22:47Z	--- license: mit base_model: - canopylabs/orpheus-3b-0.1-ft pipeline_tag: text-to-speech --- ## Purpose The purpose of this repository is to store various [TTS.cpp](https://github.com/mmwillet/TTS.cpp) compatible GGUF encoded model files for the [Orpheus TTS model](https://github.com/canopyai/Orpheus-TTS). ### Model Types Currently the Orpheus model is only supported in 32bit floating point format via the model file `Orpheus.gguf` ## Orpheus This page only contains the GGUF encoded model file of the original Orpheus 3B v0.1 finetuned model. For the original model please see the repository [Orpheus TTS model](https://github.com/canopyai/Orpheus-TTS) or the model repository [here](https://huggingface.co/canopylabs/orpheus-3b-0.1-ft). ## How to use See the github repo [here](https://github.com/mmwillet/TTS.cpp) for more information general usage. To compile TTS.cpp simple git clone and then run the the following in the repository's directory to compile (cmake is required): ```bash cmake -B build cmake --build build --config Release ``` After compilation is complete you can download a model file and generate speech to a file from the same directory like so: ```bash build/bin/tts-cli --model-path /model/path/to/downloaded_gguf_file.gguf --prompt "I am saying some words" --save-path /tmp/test.wav ```
nnilayy/dreamer-dominance-binary-classification-Kfold-2	nnilayy	2025-06-18T21:20:49Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-06-18T21:20:45Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
tatiduran/bertmodel	tatiduran	2025-06-18T21:20:09Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2025-06-18T21:17:47Z	--- license: apache-2.0 ---
videos-Nirmal-meena-18-Viral-Video-Link/Original.Full.Clip.Nirmal.meena.Viral.Video.Leaks.Official	videos-Nirmal-meena-18-Viral-Video-Link	2025-06-18T21:17:50Z	0	0	null	[ "region:us" ]	null	2025-06-18T21:17:32Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
hon9kon9ize/CantoneseLLMChat-v1.0-72B	hon9kon9ize	2025-06-18T21:17:03Z	26	1	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "arxiv:2503.12440", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-11T09:49:40Z	--- license: apache-2.0 library_name: transformers tags: - llama-factory - full - generated_from_trainer base_model: hon9kon9ize/CantoneseLLM-v1.0-72B-cpt model-index: - name: CantoneseLLMChat-v1.0-72B results: [] --- # CantoneseLLMChat-v1.0-72B ![front_image](cantonese_llm_v1.jpg) Cantonese LLM Chat v1.0 is the first generation Cantonese LLM from hon9kon9ize. Building upon the sucess of [v0.5 preview](https://huggingface.co/hon9kon9ize/CantoneseLLMChat-v0.5), the model excels in Hong Kong related specific knowledge and Cantonese conversation. ## Model description Base model obtained via Continuous Pre-Training of [Qwen 2.5 72B](https://huggingface.co/Qwen/Qwen2.5-72B) with 600 millions publicaly available Hong Kong news articles and Cantonese websites. Instructions fine-tuned model trained with a dataset consists of 75,000 instrutions pairs. 45,000 pairs were Cantonese insturctions generated by other LLMs and reviewed by humans. The model trained with 16 Nvidia H100 96GB HBM2e GPUs on [Genkai Supercomputer](https://www.cc.kyushu-u.ac.jp/scp/eng/system/Genkai/hardware/). ## Basic Usage ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "hon9kon9ize/CantoneseLLMChat-v1.0-72B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) def chat(messages, temperature=0.9, max_new_tokens=200): input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt').to('cuda:0') output_ids = model.generate(input_ids, max_new_tokens=max_new_tokens, temperature=temperature) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=False) return response prompt = "邊個係香港特首？" messages = [ {"role": "system", "content": "you are a helpful assistant."}, {"role": "user", "content": prompt} ] print(chat(messages)) # 香港特別行政區行政長官係李家超。<\|im_end\|> ``` ## Performance Best in class open source LLM in understanding Cantonese and Hong Kong culture in the [HK-Eval Benchmark](https://arxiv.org/pdf/2503.12440). However, as one could observe, reasoning models have performed dramatically better than their counterparts. We are currently working on reasoning models for v2. \| Model \| HK Culture (zero-shot) \| Cantonese Linguistics \| \|---------------------------\|:----------------------:\|:---------------------:\| \| CantonesellmChat v0.5 6B \| 52.0% \| 12.8% \| \| CantonesellmChat v0.5 34B \| 72.5% \| 54.5% \| \| CantonesellmChat v1.0 3B \| 56.0% \| 45.7% \| \| CantonesellmChat v1.0 7B \| 60.3% \| 46.5% \| \| CantonesellmChat v1.0 32B \| 69.8% \| 52.7% \| \| CantonesellmChat v1.0 72B \| 75.4% \| 59.6% \| \| Llama 3.1 8B Instruct \| 45.6% \| 35.1% \| \| Llama 3.1 70B Instruct \| 63.0% \| 50.3% \| \| Qwen2.5 7B Instruct \| 51.2% \| 30.3% \| \| Qwen2.5 32B Instruct \| 59.9% \| 45.1% \| \| Qwen2.5 72B Instruct \| 65.9% \| 45.9% \| \| Claude 3.5 Sonnet \| 71.7% \| 63.2% \| \| DeepSeek R1 \| 88.8% \| 77.5% \| \| Gemini 2.0 Flash \| 80.2% \| 75.3% \| \| Gemini 2.5 Pro \| 92.1% \| 87.3% \| \| GPT4o \| 77.5% \| 63.8% \| \| GPT4o-mini \| 55.6% \| 57.3% \|
hon9kon9ize/CantoneseLLMChat-v1.0-32B	hon9kon9ize	2025-06-18T21:16:47Z	31	5	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "arxiv:2503.12440", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-22T09:43:52Z	--- license: apache-2.0 library_name: transformers tags: - llama-factory - full - generated_from_trainer base_model: hon9kon9ize/CantoneseLLM-v1.0-32B-cpt model-index: - name: CantoneseLLMChat-v1.0-32B results: [] --- # CantoneseLLMChat-v1.0-32B ![front_image](cantonese_llm_v1.jpg) Cantonese LLM Chat v1.0 is the first generation Cantonese LLM from hon9kon9ize. Building upon the sucess of [v0.5 preview](https://huggingface.co/hon9kon9ize/CantoneseLLMChat-v0.5), the model excels in Hong Kong related specific knowledge and Cantonese conversation. ## Model description Base model obtained via Continuous Pre-Training of [Qwen 2.5 32B](https://huggingface.co/Qwen/Qwen2.5-32B) with 600 millions publicaly available Hong Kong news articles and Cantonese websites. Instructions fine-tuned model trained with a dataset consists of 75,000 instrutions pairs. 45,000 pairs were Cantonese insturctions generated by other LLMs and reviewed by humans. The model trained with 16 Nvidia H100 96GB HBM2e GPUs on [Genkai Supercomputer](https://www.cc.kyushu-u.ac.jp/scp/eng/system/Genkai/hardware/). ## Basic Usage ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "hon9kon9ize/CantoneseLLMChat-v1.0-32B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) def chat(messages, temperature=0.9, max_new_tokens=200): input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt').to('cuda:0') output_ids = model.generate(input_ids, max_new_tokens=max_new_tokens, temperature=temperature) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=False) return response prompt = "邊個係香港特首？" messages = [ {"role": "system", "content": "you are a helpful assistant."}, {"role": "user", "content": prompt} ] print(chat(messages)) # 香港特別行政區行政長官係李家超。<\|im_end\|> ``` ## Performance Best in class open source LLM in understanding Cantonese and Hong Kong culture in the [HK-Eval Benchmark](https://arxiv.org/pdf/2503.12440). However, as one could observe, reasoning models have performed dramatically better than their counterparts. We are currently working on reasoning models for v2. \| Model \| HK Culture (zero-shot) \| Cantonese Linguistics \| \|---------------------------\|:----------------------:\|:---------------------:\| \| CantonesellmChat v0.5 6B \| 52.0% \| 12.8% \| \| CantonesellmChat v0.5 34B \| 72.5% \| 54.5% \| \| CantonesellmChat v1.0 3B \| 56.0% \| 45.7% \| \| CantonesellmChat v1.0 7B \| 60.3% \| 46.5% \| \| CantonesellmChat v1.0 32B \| 69.8% \| 52.7% \| \| CantonesellmChat v1.0 72B \| 75.4% \| 59.6% \| \| Llama 3.1 8B Instruct \| 45.6% \| 35.1% \| \| Llama 3.1 70B Instruct \| 63.0% \| 50.3% \| \| Qwen2.5 7B Instruct \| 51.2% \| 30.3% \| \| Qwen2.5 32B Instruct \| 59.9% \| 45.1% \| \| Qwen2.5 72B Instruct \| 65.9% \| 45.9% \| \| Claude 3.5 Sonnet \| 71.7% \| 63.2% \| \| DeepSeek R1 \| 88.8% \| 77.5% \| \| Gemini 2.0 Flash \| 80.2% \| 75.3% \| \| Gemini 2.5 Pro \| 92.1% \| 87.3% \| \| GPT4o \| 77.5% \| 63.8% \| \| GPT4o-mini \| 55.6% \| 57.3% \|
hon9kon9ize/CantoneseLLMChat-v1.0-7B	hon9kon9ize	2025-06-18T21:16:31Z	2,220	6	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "arxiv:2503.12440", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-10-02T08:17:17Z	--- license: apache-2.0 library_name: transformers tags: - llama-factory - full - generated_from_trainer base_model: hon9kon9ize/CantoneseLLM-v1.0 model-index: - name: CantoneseLLMChat-v1.0-7B results: [] --- # CantoneseLLMChat-v1.0-7B ![front_image](cantonese_llm_v1.jpg) Cantonese LLM Chat v1.0 is the first generation Cantonese LLM from hon9kon9ize. Building upon the sucess of [v0.5 preview](https://huggingface.co/hon9kon9ize/CantoneseLLMChat-v0.5), the model excels in Hong Kong related specific knowledge and Cantonese conversation. ## Model description Base model obtained via Continuous Pre-Training of [Qwen 2.5 7B](https://huggingface.co/Qwen/Qwen2.5-7B) with 600 millions publicaly available Hong Kong news articles and Cantonese websites. Instructions fine-tuned model trained with a dataset consists of 75,000 instrutions pairs. 45,000 pairs were Cantonese insturctions generated by other LLMs and reviewed by humans. The model trained with 1 Nvidia H100 80GB HBM3 GPU on [Genkai Supercomputer](https://www.cc.kyushu-u.ac.jp/scp/eng/system/Genkai/hardware/). ## Basic Usage ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "hon9kon9ize/CantoneseLLMChat-v1.0-7B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) def chat(messages, temperature=0.9, max_new_tokens=200): input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt').to('cuda:0') output_ids = model.generate(input_ids, max_new_tokens=max_new_tokens, temperature=temperature) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=False) return response prompt = "邊個係香港特首？" messages = [ {"role": "system", "content": "you are a helpful assistant."}, {"role": "user", "content": prompt} ] print(chat(messages)) # 香港特別行政區行政長官係李家超。<\|im_end\|> ``` ## Performance Best in class open source LLM in understanding Cantonese and Hong Kong culture in the [HK-Eval Benchmark](https://arxiv.org/pdf/2503.12440). However, as one could observe, reasoning models have performed dramatically better than their counterparts. We are currently working on reasoning models for v2. \| Model \| HK Culture (zero-shot) \| Cantonese Linguistics \| \|---------------------------\|:----------------------:\|:---------------------:\| \| CantonesellmChat v0.5 6B \| 52.0% \| 12.8% \| \| CantonesellmChat v0.5 34B \| 72.5% \| 54.5% \| \| CantonesellmChat v1.0 3B \| 56.0% \| 45.7% \| \| CantonesellmChat v1.0 7B \| 60.3% \| 46.5% \| \| CantonesellmChat v1.0 32B \| 69.8% \| 52.7% \| \| CantonesellmChat v1.0 72B \| 75.4% \| 59.6% \| \| Llama 3.1 8B Instruct \| 45.6% \| 35.1% \| \| Llama 3.1 70B Instruct \| 63.0% \| 50.3% \| \| Qwen2.5 7B Instruct \| 51.2% \| 30.3% \| \| Qwen2.5 32B Instruct \| 59.9% \| 45.1% \| \| Qwen2.5 72B Instruct \| 65.9% \| 45.9% \| \| Claude 3.5 Sonnet \| 71.7% \| 63.2% \| \| DeepSeek R1 \| 88.8% \| 77.5% \| \| Gemini 2.0 Flash \| 80.2% \| 75.3% \| \| Gemini 2.5 Pro \| 92.1% \| 87.3% \| \| GPT4o \| 77.5% \| 63.8% \| \| GPT4o-mini \| 55.6% \| 57.3% \|
Missia/timesformer-base-finetuned-k400-finetuned-mcap_v0-b_size-16-epochs-10-grad_acc-8-lr-5e-5	Missia	2025-06-18T21:15:38Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "timesformer", "video-classification", "generated_from_trainer", "base_model:facebook/timesformer-base-finetuned-k400", "base_model:finetune:facebook/timesformer-base-finetuned-k400", "license:cc-by-nc-4.0", "endpoints_compatible", "region:eu" ]	video-classification	2025-06-18T09:17:07Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: facebook/timesformer-base-finetuned-k400 tags: - generated_from_trainer metrics: - accuracy model-index: - name: timesformer-base-finetuned-k400-finetuned-mcap_v0-b_size-16-epochs-10-grad_acc-8-lr-5e-5 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. --> # timesformer-base-finetuned-k400-finetuned-mcap_v0-b_size-16-epochs-10-grad_acc-8-lr-5e-5 This model is a fine-tuned version of [facebook/timesformer-base-finetuned-k400](https://huggingface.co/facebook/timesformer-base-finetuned-k400) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8710 - Accuracy: 0.6975 ## 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: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_ratio: 0.1 - training_steps: 520 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 1.249 \| 0.1 \| 52 \| 1.2930 \| 0.5505 \| \| 0.7883 \| 1.1010 \| 105 \| 1.0499 \| 0.6197 \| \| 0.5866 \| 2.1 \| 157 \| 0.9579 \| 0.6636 \| \| 0.4892 \| 3.1010 \| 210 \| 0.9531 \| 0.6646 \| \| 0.4247 \| 4.1 \| 262 \| 0.9603 \| 0.6699 \| \| 0.3929 \| 5.1010 \| 315 \| 0.8643 \| 0.7021 \| \| 0.3773 \| 6.1 \| 367 \| 0.8596 \| 0.7090 \| \| 0.3518 \| 7.1010 \| 420 \| 0.8607 \| 0.7057 \| \| 0.3574 \| 8.1 \| 472 \| 0.8520 \| 0.7108 \| \| 0.345 \| 9.0913 \| 520 \| 0.8621 \| 0.7067 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1 - Datasets 3.6.0 - Tokenizers 0.19.1
sgonzalezygil/sd-finetuning-dreambooth-v15-800	sgonzalezygil	2025-06-18T21:08:07Z	0	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-06-18T21:06:44Z	--- library_name: diffusers --- # 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 🧨 diffusers 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]
New-tutorial-Trishakar-Madhu-Viral-Videos/FULL.VIDEO.Trishakar.Madhu.Viral.Video.Tutorial.Official	New-tutorial-Trishakar-Madhu-Viral-Videos	2025-06-18T21:02:32Z	0	0	null	[ "region:us" ]	null	2025-06-18T21:02:14Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
BootesVoid/cmc0p925608hzrdqs88a5yecb_cmc2e5vav005emn2kfnniiord	BootesVoid	2025-06-18T21:01:18Z	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	2025-06-18T21:01:16Z	--- 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: MODEL --- # Cmc0P925608Hzrdqs88A5Yecb_Cmc2E5Vav005Emn2Kfnniiord <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 `MODEL` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "MODEL", "lora_weights": "https://huggingface.co/BootesVoid/cmc0p925608hzrdqs88a5yecb_cmc2e5vav005emn2kfnniiord/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmc0p925608hzrdqs88a5yecb_cmc2e5vav005emn2kfnniiord', weight_name='lora.safetensors') image = pipeline('MODEL').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmc0p925608hzrdqs88a5yecb_cmc2e5vav005emn2kfnniiord/discussions) to add images that show off what you’ve made with this LoRA.
swapnillo/RKD-retrained	swapnillo	2025-06-18T21:00:25Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	image-text-to-text	2025-06-18T20:59:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
sgonzalezygil/sd-finetuning-dreambooth-v15	sgonzalezygil	2025-06-18T20:58:17Z	0	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-06-18T20:56:47Z	--- library_name: diffusers --- # 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 🧨 diffusers 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]
ShailxT/results	ShailxT	2025-06-18T20:56:19Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-18T20:55:49Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: results 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. --> # results This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0838 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 193 \| 0.0710 \| \| No log \| 2.0 \| 386 \| 0.0766 \| \| 0.0886 \| 3.0 \| 579 \| 0.0838 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
New-videos-Arovi-Nusrat-Ridhi-18-Video/19.FULL.VIDEO.Arovi.Nusrat.Ridhi.Viral.Video.Tutorial.Official	New-videos-Arovi-Nusrat-Ridhi-18-Video	2025-06-18T20:54:56Z	0	0	null	[ "region:us" ]	null	2025-06-18T20:54:37Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
siri310/gemma-3-finetune	siri310	2025-06-18T20:52:43Z	6	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T03:35:43Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: siri310 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 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)
morturr/Llama-2-7b-hf-LOO_one_liners-COMB_headlines-comb1-seed18-2025-06-18	morturr	2025-06-18T20:50:14Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-06-18T20:49:59Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-LOO_one_liners-COMB_headlines-comb1-seed18-2025-06-18 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. --> # Llama-2-7b-hf-LOO_one_liners-COMB_headlines-comb1-seed18-2025-06-18 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 18 - 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: 2 ### 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
wongyck/BERT_twitter_1	wongyck	2025-06-18T20:48:33Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-18T20:48:02Z	--- 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]
nnilayy/deap-dominance-binary-classification-Kfold-2	nnilayy	2025-06-18T20:45:57Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-06-18T20:45:55Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
hdong0/deepseek-Qwen2.5-Math-1.5B-baseline-Open-R1-GRPO_deepscaler_1000steps_lr1e-6_kl1e-3_acc	hdong0	2025-06-18T20:44:38Z	33	0	transformers	[ "transformers", "safetensors", "qwen2bm", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "custom_code", "dataset:agentica-org/DeepScaleR-Preview-Dataset", "arxiv:2402.03300", "base_model:hdong0/deepseek-Qwen2.5-Math-1.5B-baseline-init", "base_model:finetune:hdong0/deepseek-Qwen2.5-Math-1.5B-baseline-init", "autotrain_compatible", "region:us" ]	text-generation	2025-06-17T14:28:00Z	--- base_model: hdong0/deepseek-Qwen2.5-Math-1.5B-baseline-init datasets: agentica-org/DeepScaleR-Preview-Dataset library_name: transformers model_name: deepseek-Qwen2.5-Math-1.5B-baseline-Open-R1-GRPO_deepscaler_1000steps_lr1e-6_kl1e-3_acc tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for deepseek-Qwen2.5-Math-1.5B-baseline-Open-R1-GRPO_deepscaler_1000steps_lr1e-6_kl1e-3_acc This model is a fine-tuned version of [hdong0/deepseek-Qwen2.5-Math-1.5B-baseline-init](https://huggingface.co/hdong0/deepseek-Qwen2.5-Math-1.5B-baseline-init) on the [agentica-org/DeepScaleR-Preview-Dataset](https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="hdong0/deepseek-Qwen2.5-Math-1.5B-baseline-Open-R1-GRPO_deepscaler_1000steps_lr1e-6_kl1e-3_acc", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.0.dev0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ManoharLalDhakad/wATCH.Manohar.Lal.Dhakad.viral.video.original	ManoharLalDhakad	2025-06-18T20:41:09Z	0	0	null	[ "region:us" ]	null	2025-06-18T20:37:22Z	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/)
nnilayy/dreamer-arousal-binary-classification-Kfold-1	nnilayy	2025-06-18T20:39:13Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-06-18T20:39:12Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
New-tutorial-Bindura-University-18-Videos/FULL.VIDEO.Bindura.University.Viral.Video.Tutorial.Official	New-tutorial-Bindura-University-18-Videos	2025-06-18T20:37:29Z	0	0	null	[ "region:us" ]	null	2025-06-18T20:37:14Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
ajayraj-rathore/vit-base-oxford-iiit-pets	ajayraj-rathore	2025-06-18T20:31:03Z	12	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-17T17:25:52Z	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-oxford-iiit-pets 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. --> # vit-base-oxford-iiit-pets This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset. It achieves the following results on the evaluation set: - Loss: 0.1935 - Accuracy: 0.9459 ## 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: 16 - 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4171 \| 1.0 \| 370 \| 0.2915 \| 0.9283 \| \| 0.2076 \| 2.0 \| 740 \| 0.2287 \| 0.9202 \| \| 0.1721 \| 3.0 \| 1110 \| 0.2108 \| 0.9283 \| \| 0.1477 \| 4.0 \| 1480 \| 0.1942 \| 0.9378 \| \| 0.1455 \| 5.0 \| 1850 \| 0.1916 \| 0.9391 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
luyotw/openfun-ivod-whisper-medium-WangMeiHui-10-71	luyotw	2025-06-18T20:26:48Z	0	0	null	[ "tensorboard", "safetensors", "whisper", "region:us" ]	null	2025-06-18T19:12:04Z	# Fine-tune 資訊 - 原始模型: `openai/whisper-medium` - 使用音訊數量: 8277 - 使用音訊總長: 4.65 小時 - 音訊平均長度: 2.02 秒 - GPU: `NVIDIA H100 PCIe` x 1 - 訓練時間: 02:47:02 - 模型大小: 2.85 GB --- # Model Card
soumitsr/led-base-article-digestor	soumitsr	2025-06-18T20:23:58Z	86	0	transformers	[ "transformers", "tensorboard", "safetensors", "led", "text2text-generation", "generated_from_trainer", "base_model:allenai/led-base-16384", "base_model:finetune:allenai/led-base-16384", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-17T17:00:22Z	--- library_name: transformers license: apache-2.0 base_model: allenai/led-base-16384 tags: - generated_from_trainer model-index: - name: led-base-article-digestor 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. --> # led-base-article-digestor This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 192 - 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: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.8.0.dev20250319+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
Viraly-Lol-tv/Video.clip.viraly.lol.hindi.trending.viral.Full.Video	Viraly-Lol-tv	2025-06-18T20:22:27Z	0	0	null	[ "region:us" ]	null	2025-06-18T20:21:55Z	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=viraly-lol-hindi) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=viraly-lol-hindi) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=viraly-lol-hindi)
New-tutorial-cikgu-cctv-wiring-18-Videos/FULL.VIDEO.cikgu.cctv.wiring.Viral.Video.Tutorial.Official	New-tutorial-cikgu-cctv-wiring-18-Videos	2025-06-18T20:19:37Z	0	0	null	[ "region:us" ]	null	2025-06-18T20:19:20Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
sawaawsx/bf11ccdc-c1c6-477d-a7bc-24b2b5375080	sawaawsx	2025-06-18T20:16:23Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-18T20:12:32Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
minhxle/truesight-ft-job-d09cc09c-26a3-499b-8e2b-44861421805e	minhxle	2025-06-18T20:15:20Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-18T20:15:15Z	--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: minhxle - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-3b-instruct-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)
mlfoundations-cua-dev/uitars_1500_steps_gbs_8_wd_0.1orm_1.0_add_synthetic_legacy_typing_data	mlfoundations-cua-dev	2025-06-18T20:15:18Z	0	0	null	[ "region:us" ]	null	2025-06-18T19:39:46Z	# idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_1500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_add_synthetic_legacy_typing_data ## Model Information Full Model Name: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_1500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_add_synthetic_legacy_typing_data` Repository Name: `mlfoundations-cua-dev/uitars_1500_steps_gbs_8_wd_0.1orm_1.0_add_synthetic_legacy_typing_data` Model Directory: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_1500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_add_synthetic_legacy_typing_data` Checkpoint Used: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_1500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_add_synthetic_legacy_typing_data/checkpoint_epoch_5.pt` ## Model Configuration - Model Version: TARS 1.5 - Model Size: 7B parameters - Data Type: Frame pairs - Learning Rate: 1e-5 - Epochs: 10 - Training Steps: 1500 - Global Batch Size: 8 - Weight Decay: 0.1 - Max Gradient Norm: 1.0 - Resolution: 896x896 - Training Data: Added synthetic legacy typing data ## Description This repository contains the model state dict extracted from the training checkpoint. ### Files - `model_state_dict.pt`: PyTorch state dictionary containing the model weights - `README.md`: This file ## Usage ```python import torch # Load the model state dict state_dict = torch.load("model_state_dict.pt", map_location='cpu') # Use with your model architecture # model.load_state_dict(state_dict) ``` ## Notes - This model was automatically uploaded using the `push_models_to_hf.py` script - The repository name may be truncated if the original model name exceeded HuggingFace's 96-character limit - Checkpoint extracted from: `checkpoint_epoch_5.pt`
morturr/Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb1-seed42-2025-06-18	morturr	2025-06-18T20:14:13Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-06-18T20:14:03Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb1-seed42-2025-06-18 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. --> # Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb1-seed42-2025-06-18 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - 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: 2 ### 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
pronoobie/indic_conformer_hi_float16_onnx_256_vocab	pronoobie	2025-06-18T20:12:56Z	0	0	null	[ "onnx", "automatic-speech-recognition", "hi", "nd", "base_model:ai4bharat/indic-conformer-600m-multilingual", "base_model:quantized:ai4bharat/indic-conformer-600m-multilingual", "license:mit", "region:us" ]	automatic-speech-recognition	2025-06-11T13:57:48Z	--- license: mit language: - hi - nd metrics: - wer base_model: - ai4bharat/indic-conformer-600m-multilingual pipeline_tag: automatic-speech-recognition --- Kudos to AI4Bharat for training hindi specific speech recognition model. Visit: https://huggingface.co/ai4bharat/indicconformer_stt_hi_hybrid_ctc_rnnt_large There is active development going on this directory. https://github.com/deepanshu-yadav/Quantize_speech_Recognition_For_Hindi This repository aims to 1. quantize the .nemo model for both CTC and RNNT versions. 2. remove nemo specific dependencies 3. finally use the converted onnx model for both offline and online(microphone) use. --- Converted for both CTC and RNNT versions. --- There is a notebook already provided for conversion to float 16 model. The name of the notebook is `onnxconversionCTC.ipynb` for CTC. The name of the notebook is `onnxconversionRNNT.ipynb` for RNNT version. # How to perform inference Install the depedencies ``` pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu ``` After that install from requirements file ``` pip install -r requirements.txt ``` ## For CTC float16 (non streaming version) offline mode Now we can run inference `python offline_ctc_float16_inference.py` Note a sample file has already been provided. Expected Output: ``` Audio features shape: (1, 80, 1413), Length: [1413] Transcription: शिवपाल की यह टिप्पणी फ़िल्म काल्या के डायलॉग से मिलतीजुलती है शिवपाल चाहते हैं कि मुलायम पारती के मुखिया फिर से बने फ़िलहाल सपा अध्यक्ष अखिलेश यादव हैं पिता से पार्ट की कमान छीनी थी ``` ## For CTC float16 (non streaming mode) live mode You can perform transcription live from your sound device as well. Execute `python realtime_ctc_float16_non_streaming.py` Expected Output ``` Using cache found in C:\Users\DEEPANSHU/.cache\torch\hub\snakers4_silero-vad_master Listening... (Speak into the microphone) Press 'q' to stop streaming... C:\Users\DEEPANSHU\Desktop\automation\speech\hindi\git_inference_push\realtime_ctc_float16_non_streaming.py:55: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\pytorch\torch\csrc\utils\tensor_numpy.cpp:209.) audio_tensor = torch.from_numpy(audio_float32) Speech detected, recording... Silence detected, transcribing... Transcription: तो कैसे हैं आप सब Listening... Speech detected, recording... Silence detected, transcribing... Transcription: आपसे मिल के अच्छा लगा Listening... ``` ## For RNNT ### For Realtime (microphone) It is float 16 rnnt version with non streaming mode. `python realtime_rnnt_float16_non_streaming.py` ### Offline file based It is float 16 rnnt version with non streaming mode. `python offline_rnnt_float16_non_streaming.py`
RLFH-cognitive-reframing/lora-llama3.1-8b-Instruct-reframe	RLFH-cognitive-reframing	2025-06-18T20:10:42Z	132	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-26T18:57:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
VIDEOS-Arovi-Nusrat-Ridhi-18-Viral-Video/FULL.VIDEO.Arovi.Nusrat.Ridhi.Viral.Video.Tutorial.Official	VIDEOS-Arovi-Nusrat-Ridhi-18-Viral-Video	2025-06-18T20:09:56Z	0	0	null	[ "region:us" ]	null	2025-06-18T20:09:42Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
hdong0/Qwen2.5-Math-1.5B-batch-mix-Open-R1-GRPO_deepscaler_1000steps_lr1e-6_kl1e-3_acc_seq_end_mask_2_	hdong0	2025-06-18T20:05:53Z	45	0	transformers	[ "transformers", "safetensors", "qwen2bm", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-06-14T01:22:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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profdiovanimerlo/ONNX-quantizado-roberta-base-squad2	profdiovanimerlo	2025-06-18T20:02:45Z	0	0	transformers	[ "transformers", "onnx", "roberta", "question-answering", "endpoints_compatible", "region:us" ]	question-answering	2025-06-18T20:01:29Z	--- library_name: transformers tags: [] --- ``` # Optimum RoBERTa-base-SQuAD2 Quantizado ## Introdução Este repositório contém uma versão quantizada do modelo [`optimum/roberta-base-squad2`](https://huggingface.co/optimum/roberta-base-squad2), desenvolvido por Branden Chan et al. A quantização foi realizada utilizando a biblioteca Optimum ONNX para reduzir o tamanho do modelo e melhorar a eficiência, mantendo uma precisão aceitável. ## Avaliação Os modelos foram testados utilizando 600 entradas do conjunto de validação da base de dados [rajpurkar/squad_v2](https://huggingface.co/datasets/rajpurkar/squad_v2). 1. Redução da Latência: - Modelo Original: 0.572 segundos por amostra - Modelo Quantizado: 0.437 segundos por amostra - Análise: A latência foi significativamente reduzida, tornando o modelo mais adequado para aplicações em tempo real. 2. Aumento da Eficiência: - Tempo Total: - Modelo Original: 343.20 segundos - Modelo Quantizado: 262.41 segundos - Análise: O tempo total de execução foi consideravelmente reduzido. - Amostras por Segundo: - Modelo Original: 1.75 amostras/segundo - Modelo Quantizado: 2.29 amostras/segundo - Análise: A taxa de processamento aumentou, permitindo que mais amostras sejam processadas no mesmo período de tempo. 3. Manutenção de Precisão Razoável: - Exact Score: - Modelo Original: 81.67 - Modelo Quantizado: 80.5 - Análise: Pequena queda na precisão, mas ainda em nível aceitável. - F1 Score: - Modelo Original: 83.75 - Modelo Quantizado: 82.49 - Análise: Queda ligeira no desempenho de F1 Score. 4. Comparação do Espaço Ocupado na Memória: - Modelo Original: 476.52 MB - Modelo Quantizado: 122.41 MB - Análise: A quantização resultou em uma redução significativa no espaço ocupado, com o modelo quantizado utilizando apenas cerca de 25.7% do tamanho do modelo original. Esses resultados indicam que a quantização foi bem-sucedida, alcançando uma redução significativa na latência, aumento na eficiência e uma economia substancial de espaço na memória, enquanto mantém uma precisão aceitável para tarefas de perguntas e respostas. ```
OddTheGreat/Spatha_GLM_32B_V.2	OddTheGreat	2025-06-18T20:01:39Z	6	0	transformers	[ "transformers", "safetensors", "glm4", "text-generation", "mergekit", "merge", "roleplay", "creative", "en", "base_model:Delta-Vector/Rei-V1-32B-Base", "base_model:merge:Delta-Vector/Rei-V1-32B-Base", "base_model:Lachesis-AI/IronLoom-32B-v1", "base_model:merge:Lachesis-AI/IronLoom-32B-v1", "base_model:ReadyArt/Omega-Darkest_The-Broken-Tutu-GLM-32B", "base_model:merge:ReadyArt/Omega-Darkest_The-Broken-Tutu-GLM-32B", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-09T05:15:19Z	--- base_model: - ReadyArt/Omega-Darkest_The-Broken-Tutu-GLM-32B - Delta-Vector/Rei-V1-32B-Base - Lachesis-AI/IronLoom-32B-v1 library_name: transformers tags: - mergekit - merge - roleplay - creative language: - en --- # Spatha_GLM_32B_V.2 This is a merge of pre-trained language models GLM is interesting model for RP and general usage. Goal of this merge was to create somewhat stable model with good prose, capable to "darker" themes, with good understanding of character. Actually, model is pretty good. it's slightly faster than 27b gemma, while remains consistent up to 16k context. Model is attentive to prompt, "smart" enough and overall generate good, uncensored replies. Sometimes it could over concentrate or fall in loops, but not too often. Also it works far better with good prompt and char cards. Ru wasn't properly tested, on first glance it was not good. I don't reccomend to use lower than q4_k_m, it seems to perform far worse. Tested on some obscure GLM4 preset from net, Q4_K_M 300 replies, T1.04
beyondKapil/ppo-LunarLander-v2	beyondKapil	2025-06-18T20:00:11Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-06-18T19:59:52Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: MlpPolicy results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 257.92 +/- 22.30 name: mean_reward verified: false --- # MlpPolicy Agent playing LunarLander-v2 This is a trained model of a MlpPolicy 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 ... ```
nnilayy/dreamer-valence-binary-classification-Kfold-1	nnilayy	2025-06-18T19:56:59Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-06-18T19:56:56Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
CezarinniMedia/MacanRoV4	CezarinniMedia	2025-06-18T19:55:13Z	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	2025-06-18T19:21:48Z	--- 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: MacanRoV4 --- # Macanrov4 <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 `MacanRoV4` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "MacanRoV4", "lora_weights": "https://huggingface.co/CezarinniMedia/MacanRoV4/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('CezarinniMedia/MacanRoV4', weight_name='lora.safetensors') image = pipeline('MacanRoV4').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/CezarinniMedia/MacanRoV4/discussions) to add images that show off what you’ve made with this LoRA.
dantheoprod/assz	dantheoprod	2025-06-18T19:52:19Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-18T19:51:46Z	--- license: bigscience-bloom-rail-1.0 ---
nnilayy/deap-arousal-binary-classification-Kfold-1	nnilayy	2025-06-18T19:51:56Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-06-18T19:51:54Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
JesseLiu/qwen25-3b-base-pagerank-naive-refine-grpo-lora	JesseLiu	2025-06-18T19:50:56Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-3B", "base_model:adapter:Qwen/Qwen2.5-3B", "region:us" ]	null	2025-06-18T19:50:26Z	--- base_model: Qwen/Qwen2.5-3B 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
sgonzalezygil/sd-finetuning-dreambooth-v14-1200	sgonzalezygil	2025-06-18T19:50:28Z	0	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-06-18T19:48:49Z	--- library_name: diffusers --- # 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 🧨 diffusers 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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minhxle/truesight-ft-job-f7e4f1e7-4a22-44de-b837-fe50b0c46525	minhxle	2025-06-18T19:49:12Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-18T19:49:07Z	--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: minhxle - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-3b-instruct-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)
ElmehdiSMILI/jais-7b-stage2-embedding-tuned	ElmehdiSMILI	2025-06-18T19:46:16Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T19:38:13Z	--- 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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sgonzalezygil/sd-finetuning-dreambooth-v14	sgonzalezygil	2025-06-18T19:46:11Z	0	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-06-18T19:44:26Z	--- library_name: diffusers --- # 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 🧨 diffusers 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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BootesVoid/cmbioa2e60a2nkfxsl953aeo3_cmc2aaidc0cgtrdqsiq2zwezw	BootesVoid	2025-06-18T19:40:17Z	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	2025-06-18T19:40:16Z	--- 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: VICKI --- # Cmbioa2E60A2Nkfxsl953Aeo3_Cmc2Aaidc0Cgtrdqsiq2Zwezw <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 `VICKI` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "VICKI", "lora_weights": "https://huggingface.co/BootesVoid/cmbioa2e60a2nkfxsl953aeo3_cmc2aaidc0cgtrdqsiq2zwezw/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmbioa2e60a2nkfxsl953aeo3_cmc2aaidc0cgtrdqsiq2zwezw', weight_name='lora.safetensors') image = pipeline('VICKI').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmbioa2e60a2nkfxsl953aeo3_cmc2aaidc0cgtrdqsiq2zwezw/discussions) to add images that show off what you’ve made with this LoRA.
faodl/model_child_and_family_support_benefits_mpnet_30_sample	faodl	2025-06-18T19:36:39Z	0	0	setfit	[ "setfit", "safetensors", "xlm-roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "model-index", "region:us" ]	text-classification	2025-06-18T19:35:51Z	--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: Targeting benefits based on income and disability status allows for efficient allocation of resources to the most vulnerable children and families. - text: "Encourage youth participation in the development, implementation, monitoring\ \ and \nevaluation of comprehensive sexuality education programmes and youth friendly\ \ health \nservices; and \n\n3." - text: "The SM\n\nEs \nhave to m\n\naintain the sam\ne level of em\n\nploym\nent\ \ during that period as to the \n\nnum\nber insured under social security end-D\n\ \necem\nber 2019." - text: "There are challenges in the labour market \n\nregarding realization of decent\ \ work for the majority of workers." - text: "Causes and Consequences of Broad-Based Rural Poverty Reduction: Lessons \n\ Learned \n \n\nStagnant levels of rural poverty pose a major challenge for Zambia." metrics: - accuracy - f1_score - precision - recall pipeline_tag: text-classification library_name: setfit inference: true base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 model-index: - name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 1.0 name: Accuracy - type: f1_score value: 1.0 name: F1_Score - type: precision value: 1.0 name: Precision - type: recall value: 1.0 name: Recall --- # SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 128 tokens - Number of Classes: 2 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:-----------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| Irrelevant \| <ul><li>'Agri-business (Market access for agricultural products) \n\t4.'</li><li>'These are goals that translate into many programmes and policies, and countless \n\ninstitutional plans and activities.'</li><li>'Planning: developing synergies between different \ntypes of infrastructure that facilitate socio-economic \nintegration and the timely delivery of aid in crisis.'</li></ul> \| \| Relevant \| <ul><li>'Fiscal policies that sustain non-contributory family benefits ensure that the most disadvantaged children receive continuous support regardless of labor market fluctuations.'</li><li>'Social protection instruments that prioritize children living in poor households have a multiplier effect, positively influencing nutrition, education, and health indicators.'</li><li>'The expansion of benefits coverage to include all children, irrespective of socioeconomic status, underscores a commitment to universality and equitable social protection.'</li></ul> \| ## Evaluation ### Metrics \| Label \| Accuracy \| F1_Score \| Precision \| Recall \| \|:--------\|:---------\|:---------\|:----------\|:-------\| \| all \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("faodl/model_child_and_family_support_benefits_mpnet_30_sample") # Run inference preds = model("There are challenges in the labour market regarding realization of decent work for the majority of workers.") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### 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 Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:--------\|:----\| \| Word count \| 2 \| 25.3542 \| 95 \| \| Label \| Training Sample Count \| \|:-----------\|:----------------------\| \| Irrelevant \| 24 \| \| Relevant \| 24 \| ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 20 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:----:\|:-------------:\|:---------------:\| \| 0.0083 \| 1 \| 0.1695 \| - \| \| 0.4167 \| 50 \| 0.0676 \| - \| \| 0.8333 \| 100 \| 0.0008 \| - \| ### Framework Versions - Python: 3.11.13 - SetFit: 1.1.2 - Sentence Transformers: 4.1.0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## 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. -->
bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF	bartowski	2025-06-18T19:36:07Z	0	0	null	[ "gguf", "text-generation", "base_model:arcee-ai/Arcee-SuperNova-v1", "base_model:quantized:arcee-ai/Arcee-SuperNova-v1", "license:llama3", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	text-generation	2025-06-18T16:38:17Z	--- quantized_by: bartowski pipeline_tag: text-generation base_model: arcee-ai/Arcee-SuperNova-v1 license: llama3 base_model_relation: quantized --- ## Llamacpp imatrix Quantizations of Arcee-SuperNova-v1 by arcee-ai Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5697">b5697</a> for quantization. Original model: https://huggingface.co/arcee-ai/Arcee-SuperNova-v1 All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) Run them in [LM Studio](https://lmstudio.ai/) Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project ## Prompt format ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> Cutting Knowledge Date: December 2023 Today Date: 26 Jul 2024 {system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {prompt}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Split \| Description \| \| -------- \| ---------- \| --------- \| ----- \| ----------- \| \| [Arcee-SuperNova-v1-Q8_0.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/tree/main/arcee-ai_Arcee-SuperNova-v1-Q8_0) \| Q8_0 \| 74.98GB \| true \| Extremely high quality, generally unneeded but max available quant. \| \| [Arcee-SuperNova-v1-Q6_K.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/tree/main/arcee-ai_Arcee-SuperNova-v1-Q6_K) \| Q6_K \| 57.89GB \| true \| Very high quality, near perfect, recommended. \| \| [Arcee-SuperNova-v1-Q5_K_M.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/tree/main/arcee-ai_Arcee-SuperNova-v1-Q5_K_M) \| Q5_K_M \| 49.95GB \| true \| High quality, recommended. \| \| [Arcee-SuperNova-v1-Q5_K_S.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q5_K_S.gguf) \| Q5_K_S \| 48.66GB \| false \| High quality, recommended. \| \| [Arcee-SuperNova-v1-Q4_1.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q4_1.gguf) \| Q4_1 \| 44.31GB \| false \| Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. \| \| [Arcee-SuperNova-v1-Q4_K_L.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q4_K_L.gguf) \| Q4_K_L \| 43.30GB \| false \| Uses Q8_0 for embed and output weights. Good quality, recommended. \| \| [Arcee-SuperNova-v1-Q4_K_M.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q4_K_M.gguf) \| Q4_K_M \| 42.52GB \| false \| Good quality, default size for most use cases, recommended. \| \| [Arcee-SuperNova-v1-Q4_K_S.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q4_K_S.gguf) \| Q4_K_S \| 40.35GB \| false \| Slightly lower quality with more space savings, recommended. \| \| [Arcee-SuperNova-v1-Q4_0.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q4_0.gguf) \| Q4_0 \| 40.12GB \| false \| Legacy format, offers online repacking for ARM and AVX CPU inference. \| \| [Arcee-SuperNova-v1-IQ4_NL.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ4_NL.gguf) \| IQ4_NL \| 40.05GB \| false \| Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. \| \| [Arcee-SuperNova-v1-Q3_K_XL.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q3_K_XL.gguf) \| Q3_K_XL \| 38.06GB \| false \| Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. \| \| [Arcee-SuperNova-v1-IQ4_XS.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ4_XS.gguf) \| IQ4_XS \| 37.90GB \| false \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [Arcee-SuperNova-v1-Q3_K_L.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q3_K_L.gguf) \| Q3_K_L \| 37.14GB \| false \| Lower quality but usable, good for low RAM availability. \| \| [Arcee-SuperNova-v1-Q3_K_M.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q3_K_M.gguf) \| Q3_K_M \| 34.27GB \| false \| Low quality. \| \| [Arcee-SuperNova-v1-IQ3_M.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ3_M.gguf) \| IQ3_M \| 31.94GB \| false \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [Arcee-SuperNova-v1-Q3_K_S.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q3_K_S.gguf) \| Q3_K_S \| 30.91GB \| false \| Low quality, not recommended. \| \| [Arcee-SuperNova-v1-IQ3_XS.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ3_XS.gguf) \| IQ3_XS \| 29.31GB \| false \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [Arcee-SuperNova-v1-IQ3_XXS.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ3_XXS.gguf) \| IQ3_XXS \| 27.47GB \| false \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [Arcee-SuperNova-v1-Q2_K_L.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q2_K_L.gguf) \| Q2_K_L \| 27.40GB \| false \| Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. \| \| [Arcee-SuperNova-v1-Q2_K.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q2_K.gguf) \| Q2_K \| 26.38GB \| false \| Very low quality but surprisingly usable. \| \| [Arcee-SuperNova-v1-IQ2_M.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ2_M.gguf) \| IQ2_M \| 24.12GB \| false \| Relatively low quality, uses SOTA techniques to be surprisingly usable. \| \| [Arcee-SuperNova-v1-IQ2_S.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ2_S.gguf) \| IQ2_S \| 22.24GB \| false \| Low quality, uses SOTA techniques to be usable. \| \| [Arcee-SuperNova-v1-IQ2_XS.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ2_XS.gguf) \| IQ2_XS \| 21.14GB \| false \| Low quality, uses SOTA techniques to be usable. \| \| [Arcee-SuperNova-v1-IQ2_XXS.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ2_XXS.gguf) \| IQ2_XXS \| 19.10GB \| false \| Very low quality, uses SOTA techniques to be usable. \| \| [Arcee-SuperNova-v1-IQ1_M.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ1_M.gguf) \| IQ1_M \| 16.75GB \| false \| Extremely low quality, not recommended. \| ## Embed/output weights Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. ## Downloading using huggingface-cli <details> <summary>Click to view download instructions</summary> First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF --include "arcee-ai_Arcee-SuperNova-v1-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF --include "arcee-ai_Arcee-SuperNova-v1-Q8_0/*" --local-dir ./ ``` You can either specify a new local-dir (arcee-ai_Arcee-SuperNova-v1-Q8_0) or download them all in place (./) </details> ## ARM/AVX information Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass. Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly. As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0. Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase. <details> <summary>Click to view Q4_0_X_X information (deprecated</summary> I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking. <details> <summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary> \| model \| size \| params \| backend \| threads \| test \| t/s \| % (vs Q4_0) \| \| ------------------------------ \| ---------: \| ---------: \| ---------- \| ------: \| ------------: \| -------------------: \|-------------: \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| pp512 \| 204.03 ± 1.03 \| 100% \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| pp1024 \| 282.92 ± 0.19 \| 100% \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| pp2048 \| 259.49 ± 0.44 \| 100% \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| tg128 \| 39.12 ± 0.27 \| 100% \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| tg256 \| 39.31 ± 0.69 \| 100% \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| tg512 \| 40.52 ± 0.03 \| 100% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| pp512 \| 301.02 ± 1.74 \| 147% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| pp1024 \| 287.23 ± 0.20 \| 101% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| pp2048 \| 262.77 ± 1.81 \| 101% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| tg128 \| 18.80 ± 0.99 \| 48% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| tg256 \| 24.46 ± 3.04 \| 83% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| tg512 \| 36.32 ± 3.59 \| 90% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| pp512 \| 271.71 ± 3.53 \| 133% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| pp1024 \| 279.86 ± 45.63 \| 100% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| pp2048 \| 320.77 ± 5.00 \| 124% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| tg128 \| 43.51 ± 0.05 \| 111% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| tg256 \| 43.35 ± 0.09 \| 110% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| tg512 \| 42.60 ± 0.31 \| 105% \| Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation </details> </details> ## Which file should I choose? <details> <summary>Click here for details</summary> A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. </details> ## Credits Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset. Thank you ZeroWw for the inspiration to experiment with embed/output. Thank you to LM Studio for sponsoring my work. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
RAFA-MARTINS-E-CADEIRANTE-8/18.RAFA.MARTINS.E.CADEIRANTE.VIDEO.RAFA.MARTTINZ.EROME	RAFA-MARTINS-E-CADEIRANTE-8	2025-06-18T19:36:01Z	0	0	null	[ "region:us" ]	null	2025-06-18T19:32:01Z	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=RAFA-MARTINS-E-CADEIRANTE) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=RAFA-MARTINS-E-CADEIRANTE) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=RAFA-MARTINS-E-CADEIRANTE)
dgambettaphd/M_llm2_run2_gen8_WXS_doc1000_synt120_lr1e-04_acm_SYNLAST	dgambettaphd	2025-06-18T19:33:10Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-18T19:32:56Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
Urbainnoel00/car_selling_price_reedit	Urbainnoel00	2025-06-18T19:33:04Z	0	0	null	[ "joblib", "license:apache-2.0", "region:us" ]	null	2025-06-18T18:27:55Z	--- license: apache-2.0 ---
Victoriayu/weighting_default	Victoriayu	2025-06-18T19:26:20Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T19:21:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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thecity2/ppo-Huggy	thecity2	2025-06-18T19:24:13Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-06-18T19:24:09Z	--- 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: thecity2/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
ElizabethSrgh/results_topic	ElizabethSrgh	2025-06-18T19:23:32Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:indobenchmark/indobert-base-p1", "base_model:finetune:indobenchmark/indobert-base-p1", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-18T19:22:53Z	--- library_name: transformers license: mit base_model: indobenchmark/indobert-base-p1 tags: - generated_from_trainer model-index: - name: results_topic 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. --> # results_topic This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 3 ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
morturr/Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb1-seed28-2025-06-18	morturr	2025-06-18T19:21:49Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-06-18T19:21:40Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb1-seed28-2025-06-18 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. --> # Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb1-seed28-2025-06-18 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 28 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - 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: 2 ### 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
new-RAFA-MARTINS-E-CADEIRANTE-18k/8.RAFA.MARTINS.E.CADEIRANTE.VIDEO.RAFA.MARTTINZ.EROME	new-RAFA-MARTINS-E-CADEIRANTE-18k	2025-06-18T19:20:46Z	0	0	null	[ "region:us" ]	null	2025-06-18T19:16:24Z	[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?RAFA-MARTINS-E-CADEIRANTE) [🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?RAFA-MARTINS-E-CADEIRANTE) [🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?RAFA-MARTINS-E-CADEIRANTE)
haider-cheema28/llama3-conspiracy-model	haider-cheema28	2025-06-18T19:20:02Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-18T19:13:17Z	--- base_model: unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: haider-cheema28 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.1-8b-instruct-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)
hikkohhh/fgdfgf	hikkohhh	2025-06-18T19:17:00Z	0	0	null	[ "license:deepfloyd-if-license", "region:us" ]	null	2025-06-18T19:16:47Z	--- license: deepfloyd-if-license ---
prakod/codemix-indicBART_L1_to_CM_candidates_acc4.7	prakod	2025-06-18T19:14:59Z	3	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:ai4bharat/IndicBART", "base_model:finetune:ai4bharat/IndicBART", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-13T06:01:16Z	--- library_name: transformers base_model: ai4bharat/IndicBART tags: - generated_from_trainer metrics: - bleu model-index: - name: codemix-indicBART_L1_to_CM_candidates_acc4.7 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. --> # codemix-indicBART_L1_to_CM_candidates_acc4.7 This model is a fine-tuned version of [ai4bharat/IndicBART](https://huggingface.co/ai4bharat/IndicBART) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2986 - Bleu: 11.9231 - Gen Len: 21.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use 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: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 3.7106 \| 1.0 \| 7546 \| 3.3985 \| 13.2137 \| 21.0 \| \| 3.2584 \| 2.0 \| 15092 \| 2.8989 \| 12.9778 \| 20.992 \| \| 2.9447 \| 3.0 \| 22638 \| 2.5509 \| 14.0866 \| 21.0 \| \| 2.7786 \| 4.0 \| 30184 \| 2.3583 \| 12.4674 \| 21.0 \| \| 2.7111 \| 4.9994 \| 37725 \| 2.2986 \| 11.9231 \| 21.0 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
New-tutorial-Trishakar-Madhu-18-Videos/FULL.VIDEO.Trishakar.Madhu.Viral.Video.Tutorial.Official	New-tutorial-Trishakar-Madhu-18-Videos	2025-06-18T19:14:13Z	0	0	null	[ "region:us" ]	null	2025-06-18T19:13:55Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
tomaarsen/splade-cocondenser-msmarco-margin-mse-minilm-small	tomaarsen	2025-06-18T19:12:12Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sparse-encoder", "sparse", "splade", "generated_from_trainer", "dataset_size:90000", "loss:SpladeLoss", "loss:SparseMarginMSELoss", "loss:FlopsLoss", "feature-extraction", "en", "dataset:sentence-transformers/msmarco", "arxiv:1908.10084", "arxiv:2205.04733", "arxiv:2010.02666", "arxiv:2004.05665", "base_model:Luyu/co-condenser-marco", "base_model:finetune:Luyu/co-condenser-marco", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-06-18T17:23:00Z	--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - splade - generated_from_trainer - dataset_size:90000 - loss:SpladeLoss - loss:SparseMarginMSELoss - loss:FlopsLoss base_model: Luyu/co-condenser-marco widget: - text: why did shay rebellion happened - text: what type of company is red bull - text: During flu season, having a scratchy throat or body aches can signal the arrival of the virus. Learn how to identify the flu from its early symptoms. During flu season, having a scratchy throat or body aches can signal the arrival of the virus. - text: what should BMI be - text: "Playing Chicken with the $18 Trillion U.S. Economy: The full cost of the\ \ last government shutdown two years ago was staggering â\x80\x93 it delivered\ \ a $24 billion blow to the U.S. economy and taxpayers. Now we may be about to\ \ repeat government shutdown history on Dec. 11." datasets: - sentence-transformers/msmarco pipeline_tag: feature-extraction library_name: sentence-transformers metrics: - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 - query_active_dims - query_sparsity_ratio - corpus_active_dims - corpus_sparsity_ratio co2_eq_emissions: emissions: 84.06930727910806 energy_consumed: 0.21628215774322765 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.609 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: CoCondenser trained on MS MARCO results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.48 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.62 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.76 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.48 name: Dot Precision@1 - type: dot_precision@3 value: 0.20666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.15200000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.48 name: Dot Recall@1 - type: dot_recall@3 value: 0.62 name: Dot Recall@3 - type: dot_recall@5 value: 0.76 name: Dot Recall@5 - type: dot_recall@10 value: 0.9 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6684763452349373 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5974444444444444 name: Dot Mrr@10 - type: dot_map@100 value: 0.6011320299771912 name: Dot Map@100 - type: query_active_dims value: 57.08000183105469 name: Query Active Dims - type: query_sparsity_ratio value: 0.9981298734738532 name: Query Sparsity Ratio - type: corpus_active_dims value: 187.31886291503906 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9938628247521448 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.4 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.58 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.68 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4 name: Dot Precision@1 - type: dot_precision@3 value: 0.3733333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.32799999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.27399999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.041810186403109253 name: Dot Recall@1 - type: dot_recall@3 value: 0.09615070860440549 name: Dot Recall@3 - type: dot_recall@5 value: 0.11743329753944677 name: Dot Recall@5 - type: dot_recall@10 value: 0.14239045751324736 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.341123829860237 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5051666666666667 name: Dot Mrr@10 - type: dot_map@100 value: 0.15319082806058607 name: Dot Map@100 - type: query_active_dims value: 50.459999084472656 name: Query Active Dims - type: query_sparsity_ratio value: 0.9983467662969506 name: Query Sparsity Ratio - type: corpus_active_dims value: 331.6617126464844 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9891336834857977 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.5 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.74 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.76 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5 name: Dot Precision@1 - type: dot_precision@3 value: 0.2533333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.156 name: Dot Precision@5 - type: dot_precision@10 value: 0.08999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.46 name: Dot Recall@1 - type: dot_recall@3 value: 0.69 name: Dot Recall@3 - type: dot_recall@5 value: 0.7 name: Dot Recall@5 - type: dot_recall@10 value: 0.79 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6443552260079133 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6170238095238095 name: Dot Mrr@10 - type: dot_map@100 value: 0.5954754748237253 name: Dot Map@100 - type: query_active_dims value: 54.099998474121094 name: Query Active Dims - type: query_sparsity_ratio value: 0.9982275080769897 name: Query Sparsity Ratio - type: corpus_active_dims value: 211.63475036621094 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9930661571860884 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: dot_accuracy@1 value: 0.45999999999999996 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6466666666666666 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7200000000000001 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7999999999999999 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.45999999999999996 name: Dot Precision@1 - type: dot_precision@3 value: 0.27777777777777773 name: Dot Precision@3 - type: dot_precision@5 value: 0.212 name: Dot Precision@5 - type: dot_precision@10 value: 0.1513333333333333 name: Dot Precision@10 - type: dot_recall@1 value: 0.32727006213436977 name: Dot Recall@1 - type: dot_recall@3 value: 0.4687169028681351 name: Dot Recall@3 - type: dot_recall@5 value: 0.5258110991798156 name: Dot Recall@5 - type: dot_recall@10 value: 0.6107968191710825 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5513184670343625 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5732116402116402 name: Dot Mrr@10 - type: dot_map@100 value: 0.4499327776205009 name: Dot Map@100 - type: query_active_dims value: 53.87999979654948 name: Query Active Dims - type: query_sparsity_ratio value: 0.9982347159492645 name: Query Sparsity Ratio - type: corpus_active_dims value: 229.42422156545794 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9924833162451524 name: Corpus Sparsity Ratio --- # CoCondenser trained on MS MARCO This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) on the [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. ## Model Details ### Model Description - Model Type: SPLADE Sparse Encoder - Base model: [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) <!-- at revision e0cef0ab2410aae0f0994366ddefb5649a266709 --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 30522 dimensions - Similarity Function: Dot Product - Training Dataset: - [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) - Language: en - License: apache-2.0 ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Documentation: [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) ### Full Model Architecture ``` SparseEncoder( (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522}) ) ``` ## 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 SparseEncoder # Download from the 🤗 Hub model = SparseEncoder("tomaarsen/splade-cocondenser-msmarco-margin-mse-minilm-small") # Run inference queries = [ "how much would dreamers cost the taxpayers", ] documents = [ 'Plus, the CBO said the Dreamers would bring an additional 80,000 immigrants to the U.S., adding to the liability. In total, the immigrants and their families would cost taxpayers $26.8 billion, but only pay back $.9 billion in taxes, the CBO said. The analysis found that roughly 3.25 million undocumented immigrants are eligible for Dreamer status, while only 2 million would apply and only 1.6 million would be accepted over the next decade.', 'Playing Chicken with the $18 Trillion U.S. Economy: The full cost of the last government shutdown two years ago was staggering â\x80\x93 it delivered a $24 billion blow to the U.S. economy and taxpayers. Now we may be about to repeat government shutdown history on Dec. 11.', 'Sustain is defined as to support something or to endure a trial or hardship. 1 An example of sustain is for a foundation to support the house. 2 An example of sustain is to survive days without food or water.', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 30522] [3, 30522] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[27.1439, 12.7876, 0.6402]]) ``` <!-- ### 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 #### Sparse Information Retrieval * Datasets: `NanoMSMARCO`, `NanoNFCorpus` and `NanoNQ` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) \| Metric \| NanoMSMARCO \| NanoNFCorpus \| NanoNQ \| \|:----------------------\|:------------\|:-------------\|:-----------\| \| dot_accuracy@1 \| 0.48 \| 0.4 \| 0.5 \| \| dot_accuracy@3 \| 0.62 \| 0.58 \| 0.74 \| \| dot_accuracy@5 \| 0.76 \| 0.64 \| 0.76 \| \| dot_accuracy@10 \| 0.9 \| 0.68 \| 0.82 \| \| dot_precision@1 \| 0.48 \| 0.4 \| 0.5 \| \| dot_precision@3 \| 0.2067 \| 0.3733 \| 0.2533 \| \| dot_precision@5 \| 0.152 \| 0.328 \| 0.156 \| \| dot_precision@10 \| 0.09 \| 0.274 \| 0.09 \| \| dot_recall@1 \| 0.48 \| 0.0418 \| 0.46 \| \| dot_recall@3 \| 0.62 \| 0.0962 \| 0.69 \| \| dot_recall@5 \| 0.76 \| 0.1174 \| 0.7 \| \| dot_recall@10 \| 0.9 \| 0.1424 \| 0.79 \| \| dot_ndcg@10 \| 0.6685 \| 0.3411 \| 0.6444 \| \| dot_mrr@10 \| 0.5974 \| 0.5052 \| 0.617 \| \| dot_map@100 \| 0.6011 \| 0.1532 \| 0.5955 \| \| query_active_dims \| 57.08 \| 50.46 \| 54.1 \| \| query_sparsity_ratio \| 0.9981 \| 0.9983 \| 0.9982 \| \| corpus_active_dims \| 187.3189 \| 331.6617 \| 211.6348 \| \| corpus_sparsity_ratio \| 0.9939 \| 0.9891 \| 0.9931 \| #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ] } ``` \| Metric \| Value \| \|:----------------------\|:-----------\| \| dot_accuracy@1 \| 0.46 \| \| dot_accuracy@3 \| 0.6467 \| \| dot_accuracy@5 \| 0.72 \| \| dot_accuracy@10 \| 0.8 \| \| dot_precision@1 \| 0.46 \| \| dot_precision@3 \| 0.2778 \| \| dot_precision@5 \| 0.212 \| \| dot_precision@10 \| 0.1513 \| \| dot_recall@1 \| 0.3273 \| \| dot_recall@3 \| 0.4687 \| \| dot_recall@5 \| 0.5258 \| \| dot_recall@10 \| 0.6108 \| \| dot_ndcg@10 \| 0.5513 \| \| dot_mrr@10 \| 0.5732 \| \| dot_map@100 \| 0.4499 \| \| query_active_dims \| 53.88 \| \| query_sparsity_ratio \| 0.9982 \| \| corpus_active_dims \| 229.4242 \| \| corpus_sparsity_ratio \| 0.9925 \| <!-- ## 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 #### msmarco * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83) * Size: 90,000 training samples * Columns: <code>score</code>, <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| score \| query \| positive \| negative \| \|:--------\|:--------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\| \| type \| float \| string \| string \| string \| \| details \| <ul><li>min: -1.28</li><li>mean: 13.47</li><li>max: 22.27</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 8.97 tokens</li><li>max: 21 tokens</li></ul> \| <ul><li>min: 19 tokens</li><li>mean: 81.41 tokens</li><li>max: 220 tokens</li></ul> \| <ul><li>min: 17 tokens</li><li>mean: 76.39 tokens</li><li>max: 195 tokens</li></ul> \| * Samples: \| score \| query \| positive \| negative \| \|:--------------------------------\|:-------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>13.85562777519226</code> \| <code>what is a reflective journal?</code> \| <code>A reflective journal is a tool that students are encouraged to use to help them understand not just what they have learned while studying but also how they learned it by reflecting on the learning experience itself.</code> \| <code>The point is that this approach believes that literature can be used to illuminate some truth about something which is not literature. The difference between this approach and the didactic approach is that didactic approach considers author as a teacher, while the reflective approach considers him or her an observer.</code> \| \| <code>12.178914229075115</code> \| <code>original footloose release</code> \| <code>Footloose (2011 film) Footloose is a 2011 American musical dance film directed by Craig Brewer. It is a remake of the 1984 film of the same name and stars Kenny Wormald, Julianne Hough, Andie MacDowell, and Dennis Quaid. The film follows a young man who moves from Boston to a small southern town and protests the town's ban against dancing.</code> \| <code>In the 2001 re-release of Thriller they added the second verse of the rap which was recorded but not included on the original here is the second verse by Vincent Price (I heard a 3rd was written but never recorded) The demons squeal in sheer delight. It's you they spy, so plump, so right.</code> \| \| <code>19.897210280100506</code> \| <code>time of day blood pressure</code> \| <code>Day Time Blood Pressure. For most people, your body's blood pressure rises during the morning hours and reaches its highest point around midday. This is because your body is preset to increase its functions for anticipated daily activity. Your body reaches its lowest blood pressure at bedtime, between 8 p.m. and 2 a.m.</code> \| <code>labetalol is used alone or together with other medicines to treat high blood pressure hypertension high blood pressure adds to the workload of the heart and arteriesif it continues for a long time the heart and arteries may not function properlyabetalol is used alone or together with other medicines to treat high blood pressure hypertension high blood pressure adds to the workload of the heart and arteries</code> \| * Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseMarginMSELoss", "lambda_corpus": 0.08, "lambda_query": 0.1 } ``` ### Evaluation Dataset #### msmarco * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83) * Size: 10,000 evaluation samples * Columns: <code>score</code>, <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| score \| query \| positive \| negative \| \|:--------\|:-------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\| \| type \| float \| string \| string \| string \| \| details \| <ul><li>min: -2.25</li><li>mean: 13.3</li><li>max: 22.52</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 9.31 tokens</li><li>max: 40 tokens</li></ul> \| <ul><li>min: 17 tokens</li><li>mean: 79.88 tokens</li><li>max: 227 tokens</li></ul> \| <ul><li>min: 18 tokens</li><li>mean: 77.64 tokens</li><li>max: 250 tokens</li></ul> \| * Samples: \| score \| query \| positive \| negative \| \|:--------------------------------\|:-------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>11.338554302851358</code> \| <code>victor cruz dance salsa in the super bowl</code> \| <code>The popular former Giant â who helped lead the team to a Super Bowl title in the 2011 season â ... Victor Cruz performed his first salsa dance in Chicago. The popular former Giant â who helped lead the team to a Super Bowl title in the 2011 season â caught a 2-yard touchdown pass from Mitch Trubisky in the Bearsâ 24-17 preseason loss to the Broncos on Thursday night.</code> \| <code>Victor Cruz, Giants hammer out deal. Receiver Victor Cruz on Monday signed a six-year contract through the 2018 season with the New York Giants. The contract is worth $46 million and pays him $15.625 million fully guaranteed the first two seasons, a source said.</code> \| \| <code>18.167373975118</code> \| <code>what is the phone number for roblox</code> \| <code>im calling roblox hq and the roblox number is 888 858 2569 or if u live in canada its 1888 858 2569 subscibe to us (waffleman514 and twitterelgo) and join our youtube group on our profile Category</code> \| <code>[edit] Create A New Place. This is where you define where your game will be published. 1 Go to Roblox.com and login. 2 Click My ROBLOX and then click Places. 3 Click Create Game Place. 4 Fill out the form. 5 Name is the name of the game.</code> \| \| <code>17.668365399042766</code> \| <code>can you freeze cream soup</code> \| <code>With a modest investment in time and effort, you can make your own cream of mushroom soup and freeze it for later use. This leaves you firmly in control of the soup's ingredients and enables you to portion the soup in quantities that make sense for you.</code> \| <code>I purchased 1.5 lbs of 3 large boneless, skinless chicken breasts. I am cooking them in a crockpot with 1 can of cream of mushroom soup, 1 can cream of chicken soup, 1 can of water and some canned mushrooms...the chicken is all at the bottom. About how long will it take the chicken to cook completely if I set my... show more I purchased 1.5 lbs of 3 large boneless, skinless chicken breasts.</code> \| * Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseMarginMSELoss", "lambda_corpus": 0.08, "lambda_query": 0.1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `load_best_model_at_end`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 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`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `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`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| NanoMSMARCO_dot_ndcg@10 \| NanoNFCorpus_dot_ndcg@10 \| NanoNQ_dot_ndcg@10 \| NanoBEIR_mean_dot_ndcg@10 \| \|:----------:\|:--------:\|:-------------:\|:---------------:\|:-----------------------:\|:------------------------:\|:------------------:\|:-------------------------:\| \| 0.0178 \| 100 \| 795934.08 \| - \| - \| - \| - \| - \| \| 0.0356 \| 200 \| 13561.4538 \| - \| - \| - \| - \| - \| \| 0.0533 \| 300 \| 118.2925 \| - \| - \| - \| - \| - \| \| 0.0711 \| 400 \| 61.485 \| - \| - \| - \| - \| - \| \| 0.0889 \| 500 \| 44.6503 \| 38.6276 \| 0.5126 \| 0.2701 \| 0.5829 \| 0.4552 \| \| 0.1067 \| 600 \| 38.3666 \| - \| - \| - \| - \| - \| \| 0.1244 \| 700 \| 35.2046 \| - \| - \| - \| - \| - \| \| 0.1422 \| 800 \| 33.2246 \| - \| - \| - \| - \| - \| \| 0.16 \| 900 \| 31.5866 \| - \| - \| - \| - \| - \| \| 0.1778 \| 1000 \| 29.3914 \| 38.9004 \| 0.5849 \| 0.3140 \| 0.6009 \| 0.4999 \| \| 0.1956 \| 1100 \| 28.9009 \| - \| - \| - \| - \| - \| \| 0.2133 \| 1200 \| 29.5258 \| - \| - \| - \| - \| - \| \| 0.2311 \| 1300 \| 27.7958 \| - \| - \| - \| - \| - \| \| 0.2489 \| 1400 \| 27.0228 \| - \| - \| - \| - \| - \| \| 0.2667 \| 1500 \| 25.0953 \| 22.5132 \| 0.6090 \| 0.3377 \| 0.6166 \| 0.5211 \| \| 0.2844 \| 1600 \| 25.4396 \| - \| - \| - \| - \| - \| \| 0.3022 \| 1700 \| 22.53 \| - \| - \| - \| - \| - \| \| 0.32 \| 1800 \| 24.0084 \| - \| - \| - \| - \| - \| \| 0.3378 \| 1900 \| 23.5741 \| - \| - \| - \| - \| - \| \| 0.3556 \| 2000 \| 23.141 \| 22.6775 \| 0.6408 \| 0.3560 \| 0.5984 \| 0.5317 \| \| 0.3733 \| 2100 \| 22.0953 \| - \| - \| - \| - \| - \| \| 0.3911 \| 2200 \| 22.2789 \| - \| - \| - \| - \| - \| \| 0.4089 \| 2300 \| 20.9582 \| - \| - \| - \| - \| - \| \| 0.4267 \| 2400 \| 19.1969 \| - \| - \| - \| - \| - \| \| 0.4444 \| 2500 \| 21.047 \| 28.3245 \| 0.6209 \| 0.3487 \| 0.6260 \| 0.5319 \| \| 0.4622 \| 2600 \| 20.7531 \| - \| - \| - \| - \| - \| \| 0.48 \| 2700 \| 19.8115 \| - \| - \| - \| - \| - \| \| 0.4978 \| 2800 \| 18.6278 \| - \| - \| - \| - \| - \| \| 0.5156 \| 2900 \| 19.3731 \| - \| - \| - \| - \| - \| \| 0.5333 \| 3000 \| 18.4502 \| 20.3191 \| 0.6390 \| 0.3506 \| 0.6087 \| 0.5328 \| \| 0.5511 \| 3100 \| 18.4525 \| - \| - \| - \| - \| - \| \| 0.5689 \| 3200 \| 17.0456 \| - \| - \| - \| - \| - \| \| 0.5867 \| 3300 \| 17.256 \| - \| - \| - \| - \| - \| \| 0.6044 \| 3400 \| 17.6203 \| - \| - \| - \| - \| - \| \| 0.6222 \| 3500 \| 18.7721 \| 17.7983 \| 0.6685 \| 0.3411 \| 0.6444 \| 0.5513 \| \| 0.64 \| 3600 \| 16.7819 \| - \| - \| - \| - \| - \| \| 0.6578 \| 3700 \| 18.6132 \| - \| - \| - \| - \| - \| \| 0.6756 \| 3800 \| 15.5466 \| - \| - \| - \| - \| - \| \| 0.6933 \| 3900 \| 17.7706 \| - \| - \| - \| - \| - \| \| 0.7111 \| 4000 \| 16.6612 \| 15.7565 \| 0.6727 \| 0.3519 \| 0.6159 \| 0.5468 \| \| 0.7289 \| 4100 \| 16.4755 \| - \| - \| - \| - \| - \| \| 0.7467 \| 4200 \| 16.9832 \| - \| - \| - \| - \| - \| \| 0.7644 \| 4300 \| 14.9855 \| - \| - \| - \| - \| - \| \| 0.7822 \| 4400 \| 14.6835 \| - \| - \| - \| - \| - \| \| 0.8 \| 4500 \| 17.0725 \| 18.0495 \| 0.6652 \| 0.3430 \| 0.6423 \| 0.5502 \| \| 0.8178 \| 4600 \| 15.8136 \| - \| - \| - \| - \| - \| \| 0.8356 \| 4700 \| 15.6528 \| - \| - \| - \| - \| - \| \| 0.8533 \| 4800 \| 15.5791 \| - \| - \| - \| - \| - \| \| 0.8711 \| 4900 \| 15.1496 \| - \| - \| - \| - \| - \| \| 0.8889 \| 5000 \| 14.7461 \| 16.4918 \| 0.6373 \| 0.3353 \| 0.6403 \| 0.5376 \| \| 0.9067 \| 5100 \| 16.3102 \| - \| - \| - \| - \| - \| \| 0.9244 \| 5200 \| 14.5521 \| - \| - \| - \| - \| - \| \| 0.9422 \| 5300 \| 14.4375 \| - \| - \| - \| - \| - \| \| 0.96 \| 5400 \| 15.2282 \| - \| - \| - \| - \| - \| \| 0.9778 \| 5500 \| 14.4738 \| 15.4439 \| 0.6426 \| 0.3385 \| 0.6334 \| 0.5382 \| \| 0.9956 \| 5600 \| 14.6468 \| - \| - \| - \| - \| - \| \| -1 \| -1 \| - \| - \| 0.6685 \| 0.3411 \| 0.6444 \| 0.5513 \| * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - Energy Consumed: 0.216 kWh - Carbon Emitted: 0.084 kg of CO2 - Hours Used: 0.609 hours ### Training Hardware - On Cloud: No - GPU Model: 1 x NVIDIA GeForce RTX 3090 - CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K - RAM Size: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.1 - Datasets: 2.21.0 - 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", } ``` #### SpladeLoss ```bibtex @misc{formal2022distillationhardnegativesampling, title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective}, author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant}, year={2022}, eprint={2205.04733}, archivePrefix={arXiv}, primaryClass={cs.IR}, url={https://arxiv.org/abs/2205.04733}, } ``` #### SparseMarginMSELoss ```bibtex @misc{hofstätter2021improving, title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation}, author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury}, year={2021}, eprint={2010.02666}, archivePrefix={arXiv}, primaryClass={cs.IR} } ``` #### FlopsLoss ```bibtex @article{paria2020minimizing, title={Minimizing flops to learn efficient sparse representations}, author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s}, journal={arXiv preprint arXiv:2004.05665}, year={2020} } ``` <!-- ## 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. -->
TVRRaviteja/llama3.1-mental-health-therapy-SFT	TVRRaviteja	2025-06-18T19:07:55Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-17T10:14:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
kevin510/ACT-SO100-Draw	kevin510	2025-06-18T19:06:40Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2025-06-18T17:52:31Z	--- license: apache-2.0 --- # 🖊️ ACT-SO100-Draw Action Chunking Transformer (ACT) checkpoint for drawing with a custom pen-holding attachment on the SO-100 and SO-101 robotic arms. ![pen_tool_photo](./assets/tool.jpg) 3-D-printed pen mount designed for SO-100 and SO-101 robotic arms.* Tool STL is available for download in the [SO-100 Tools repository](https://github.com/krohling/so-100-tools). --- ## Demo ![Training Results](./assets/demo.gif) --- ## Dataset \| Name \| Episodes \| Frames / episode \| Modalities \| \| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| -------- \| ---------------- \| ----------------------------------------- \| \| [370-drawn-to-caffeine-draw-smiley](https://huggingface.co/spaces/lerobot/visualize_dataset?path=%2FLeRobot-worldwide-hackathon%2F370-drawn-to-caffeine-draw-smiley%2Fepisode_0) \| 42 \| \~450 \| RGB 640×480, proprio 5-DoF, gripper state \| ## Training Details See run details on wandb for more information: [wandb run](https://wandb.ai/kevin_ai/lerobot_hackathon/runs/ahu8fcc0). \| Hyper-parameter \| Value \| \| ------------------- \| ---------------------------------- \| \| Chunk size \| 100 \| \| Dim Feedforward \| 3200 \| \| Dim Model \| 512 \| \| Dropout \| 0.1 \| \| Feedforward Activation \| ReLU \| \| Decoder layers \| 1 \| \| Encoder layers \| 4 \| \| Attention heads \| 8 \| \| VAE Encoder layers \| 4 \| \| Batch size \| 32 \| \| Optimizer \| AdamW, lr = 1e-5 \| ## Citation If you use this checkpoint in your work, please cite the following: ```bibtex @misc{Rohling2025ACTSO100Draw, author = {Kevin Rohling}, title = {ACT Checkpoint for Pen-Drawing on SO-100}, year = {2025}, howpublished = {\url{https://huggingface.co/kevin510/ACT-SO100-Draw}} } ```
dicksonhk/Qwen2.5-VL-3B-Instruct-AWQ-mlx-4Bit	dicksonhk	2025-06-18T19:05:01Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "multimodal", "mlx", "mlx-my-repo", "conversational", "en", "base_model:Qwen/Qwen2.5-VL-3B-Instruct-AWQ", "base_model:quantized:Qwen/Qwen2.5-VL-3B-Instruct-AWQ", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	image-text-to-text	2025-06-18T17:30:16Z	--- license_name: qwen-research license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct-AWQ/blob/main/LICENSE language: - en pipeline_tag: image-text-to-text tags: - multimodal - mlx - mlx-my-repo library_name: transformers base_model: Qwen/Qwen2.5-VL-3B-Instruct-AWQ --- # dicksonhk/Qwen2.5-VL-3B-Instruct-AWQ-mlx-4Bit The Model [dicksonhk/Qwen2.5-VL-3B-Instruct-AWQ-mlx-4Bit](https://huggingface.co/dicksonhk/Qwen2.5-VL-3B-Instruct-AWQ-mlx-4Bit) was converted to $MLX format from [Qwen/Qwen2.5-VL-3B-Instruct-AWQ](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct-AWQ) using $mlx-vlm version 0.1.15. ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model dicksonhk/Qwen2.5-VL-3B-Instruct-AWQ-mlx-4Bit --max-tokens 100 --temp 0.0 --prompt "Describe this image." --image <path_to_image> ```
Real-Madrid-Al-Hilal-Direct-Videos/Real.Madrid.Al-Hilal.En.Direct.Streaming.Gratuit.tv.Official	Real-Madrid-Al-Hilal-Direct-Videos	2025-06-18T19:03:04Z	0	0	null	[ "region:us" ]	null	2025-06-18T19:02:47Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/mrmpsap6?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
mlfoundations-cua-dev/uitars_add_new_advanced_synthetic_typing_data	mlfoundations-cua-dev	2025-06-18T19:00:11Z	0	0	null	[ "region:us" ]	null	2025-06-18T18:29:16Z	# idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_add_new_advanced_synthetic_typing_data ## Model Information Full Model Name: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_add_new_advanced_synthetic_typing_data` Repository Name: `mlfoundations-cua-dev/uitars_add_new_advanced_synthetic_typing_data` Model Directory: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_add_new_advanced_synthetic_typing_data` Checkpoint Used: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_add_new_advanced_synthetic_typing_data/checkpoint_epoch_9.pt` ## Model Configuration - Model Version: TARS 1.5 - Model Size: 7B parameters - Data Type: Frame pairs - Learning Rate: 1e-5 - Epochs: 10 - Training Steps: 500 - Global Batch Size: 8 - Weight Decay: 0.1 - Max Gradient Norm: 1.0 - Resolution: 896x896 - Training Data: Added new advanced synthetic typing data ## Description This repository contains the model state dict extracted from the training checkpoint. ### Files - `model_state_dict.pt`: PyTorch state dictionary containing the model weights - `README.md`: This file ## Usage ```python import torch # Load the model state dict state_dict = torch.load("model_state_dict.pt", map_location='cpu') # Use with your model architecture # model.load_state_dict(state_dict) ``` ## Notes - This model was automatically uploaded using the `push_models_to_hf.py` script - The repository name may be truncated if the original model name exceeded HuggingFace's 96-character limit - Checkpoint extracted from: `checkpoint_epoch_9.pt`
videos-Sajal-Malik-18-Viral-Video-Link/FULL.VIDEO.Sajal.Malik.Viral.Video.Tutorial.Official	videos-Sajal-Malik-18-Viral-Video-Link	2025-06-18T18:59:38Z	0	0	null	[ "region:us" ]	null	2025-06-18T18:59:22Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
sgonzalezygil/sd-finetuning-dreambooth-v13-1200	sgonzalezygil	2025-06-18T18:54:43Z	0	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-06-18T18:53:09Z	--- library_name: diffusers --- # 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 🧨 diffusers 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]
GraybeardTheIrate/Cogwheel-Pantheon	GraybeardTheIrate	2025-06-18T18:52:27Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:Gryphe/Pantheon-RP-1.8-24b-Small-3.1", "base_model:merge:Gryphe/Pantheon-RP-1.8-24b-Small-3.1", "base_model:OddTheGreat/Cogwheel_24b_V.2", "base_model:merge:OddTheGreat/Cogwheel_24b_V.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T18:30:44Z	--- base_model: - Gryphe/Pantheon-RP-1.8-24b-Small-3.1 - OddTheGreat/Cogwheel_24b_V.2 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * [Gryphe/Pantheon-RP-1.8-24b-Small-3.1](https://huggingface.co/Gryphe/Pantheon-RP-1.8-24b-Small-3.1) * [OddTheGreat/Cogwheel_24b_V.2](https://huggingface.co/OddTheGreat/Cogwheel_24b_V.2) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Gryphe/Pantheon-RP-1.8-24b-Small-3.1 - model: OddTheGreat/Cogwheel_24b_V.2 merge_method: slerp base_model: OddTheGreat/Cogwheel_24b_V.2 dtype: bfloat16 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 ```
RaghavendraSqwish/qwen_orpo	RaghavendraSqwish	2025-06-18T18:48:44Z	15	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen3-0.6B", "base_model:finetune:unsloth/Qwen3-0.6B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-17T12:11:47Z	--- base_model: unsloth/Qwen3-0.6B tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: RaghavendraSqwish - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-0.6B 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)
sgonzalezygil/sd-finetuning-dreambooth-v13	sgonzalezygil	2025-06-18T18:48:39Z	0	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-06-18T18:46:43Z	--- library_name: diffusers --- # 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 🧨 diffusers 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]
s0mecode/Qwen3-14B-Q4_K_M-GGUF	s0mecode	2025-06-18T18:45:15Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-14B", "base_model:quantized:Qwen/Qwen3-14B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-18T18:44:44Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-14B tags: - llama-cpp - gguf-my-repo --- # s0mecode/Qwen3-14B-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-14B`](https://huggingface.co/Qwen/Qwen3-14B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen3-14B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo s0mecode/Qwen3-14B-Q4_K_M-GGUF --hf-file qwen3-14b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo s0mecode/Qwen3-14B-Q4_K_M-GGUF --hf-file qwen3-14b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo s0mecode/Qwen3-14B-Q4_K_M-GGUF --hf-file qwen3-14b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo s0mecode/Qwen3-14B-Q4_K_M-GGUF --hf-file qwen3-14b-q4_k_m.gguf -c 2048 ```

kathleenge/department-200

kathleenge

2025-06-18T22:37:43Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-18T21:37:01Z

--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** kathleenge - **License:** apache-2.0 - **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

xinyifang/Conllama

xinyifang

2025-06-18T22:34:53Z

0

transformers

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

text-generation

2025-06-18T22:16:40Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Bonnief/afriberta-om-finetuned

Bonnief

2025-06-18T22:34:47Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "fill-mask", "generated_from_trainer", "base_model:castorini/afriberta_small", "base_model:finetune:castorini/afriberta_small", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2025-06-18T19:29:42Z

--- library_name: transformers base_model: castorini/afriberta_small tags: - generated_from_trainer metrics: - accuracy model-index: - name: afriberta-om-finetuned results: [] ---  # afriberta-om-finetuned This model is a fine-tuned version of [castorini/afriberta_small](https://huggingface.co/castorini/afriberta_small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Accuracy: 0.4266 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - 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: 1000 - training_steps: 100000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.53.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1

h9art/PARADIS-Qwen3_1.7B-10kWikiVi-1GPU

h9art

2025-06-18T22:27:11Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-06-18T13:26:11Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MattMcG/titles_qwen_with_eval

MattMcG

2025-06-18T22:25:02Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen3-14B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-14B-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-06-18T22:15:41Z

--- base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** MattMcG - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen3-14B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Ascrewdriver/ppo-Huggy

Ascrewdriver

2025-06-18T22:24:25Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2025-06-18T22:24:21Z

--- 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: Ascrewdriver/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

nnilayy/deap-arousal-binary-classification-Kfold-4

nnilayy

2025-06-18T22:19:50Z

0

null

[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]

null

2025-06-18T22:19:49Z

--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]

meshkiempel/vorobev

meshkiempel

2025-06-18T22:18:20Z

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

2025-06-18T22:17:35Z

--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: vorobev 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 --- # vorobev A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `vorobev` 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.

Izzi1/llama3-finetuned-column-classify-upwork-data

Izzi1

2025-06-18T22:13:46Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:finetune:unsloth/Llama-3.2-1B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-18T22:13:40Z

--- base_model: unsloth/Llama-3.2-1B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** Izzi1 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Llama-3.2-1B-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

N1CKNGUYEN/bigbird-roberta-base_nli_classifier_mnli_anli_fevernli_xnli

N1CKNGUYEN

2025-06-18T22:11:59Z

2

0

transformers

[ "transformers", "safetensors", "big_bird", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-06-17T17:50:27Z

--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: bigbird-roberta-base_nli_classifier_mnli_anli_fevernli_xnli results: [] ---  # bigbird-roberta-base_nli_classifier_mnli_anli_fevernli_xnli This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5875 - F1 Macro: 0.6077 - F1 Micro: 0.7047 - Accuracy Balanced: 0.6070 - Accuracy: 0.7047 - Precision Macro: 0.6727 - Recall Macro: 0.6070 - Precision Micro: 0.7047 - Recall Micro: 0.7047 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - 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_ratio: 0.06 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Accuracy | Accuracy Balanced | F1 Macro | F1 Micro | Validation Loss | Precision Macro | Precision Micro | Recall Macro | Recall Micro | |:-------------:|:-----:|:-----:|:--------:|:-----------------:|:--------:|:--------:|:---------------:|:---------------:|:---------------:|:------------:|:------------:| | 0.2556 | 1.0 | 12340 | 0.7498 | 0.6626 | 0.6735 | 0.7498 | 0.5150 | 0.7463 | 0.7498 | 0.6626 | 0.7498 | | 0.4494 | 2.0 | 24680 | 0.5875 | 0.6077 | 0.7047 | 0.6070 | 0.7047 | 0.6727 | 0.6070 | 0.7047 | 0.7047 | ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1

aleni/whisper-small-vi

aleni

2025-06-18T22:10:45Z

34

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "vi", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-11-03T14:06:25Z

--- library_name: transformers language: - vi license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper small vi - Ox results: [] ---  # Whisper small vi - Ox This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2559 - Wer: 9.3480 ## 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: 16 - 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:-------:| | 0.249 | 0.0780 | 1000 | 0.1739 | 8.6461 | | 0.2426 | 0.1559 | 2000 | 0.1802 | 8.6256 | | 0.238 | 0.2339 | 3000 | 0.1851 | 8.7411 | | 0.2096 | 0.3119 | 4000 | 0.1892 | 9.4001 | | 0.2076 | 0.3898 | 5000 | 0.1965 | 8.7169 | | 0.1989 | 0.4678 | 6000 | 0.1952 | 9.3685 | | 0.2172 | 0.5458 | 7000 | 0.2029 | 9.0334 | | 0.2145 | 0.6237 | 8000 | 0.2044 | 9.4430 | | 0.2151 | 0.7017 | 9000 | 0.2079 | 9.1227 | | 0.2307 | 0.7797 | 10000 | 0.2095 | 8.9310 | | 0.2065 | 0.8576 | 11000 | 0.2200 | 9.7595 | | 0.2252 | 0.9356 | 12000 | 0.2177 | 9.8823 | | 0.1236 | 1.0136 | 13000 | 0.2221 | 10.3832 | | 0.1242 | 1.0915 | 14000 | 0.2276 | 9.2549 | | 0.1392 | 1.1695 | 15000 | 0.2272 | 9.5677 | | 0.1274 | 1.2475 | 16000 | 0.2272 | 8.6200 | | 0.139 | 1.3254 | 17000 | 0.2301 | 9.1209 | | 0.1166 | 1.4034 | 18000 | 0.2325 | 9.1711 | | 0.1507 | 1.4814 | 19000 | 0.2323 | 9.5472 | | 0.106 | 1.5593 | 20000 | 0.2331 | 9.6868 | | 0.1264 | 1.6373 | 21000 | 0.2372 | 8.9440 | | 0.1177 | 1.7153 | 22000 | 0.2394 | 8.9924 | | 0.1125 | 1.7932 | 23000 | 0.2411 | 9.1618 | | 0.1272 | 1.8712 | 24000 | 0.2421 | 9.7669 | | 0.1234 | 1.9492 | 25000 | 0.2441 | 9.6552 | | 0.091 | 2.0271 | 26000 | 0.2502 | 8.9272 | | 0.0778 | 2.1051 | 27000 | 0.2499 | 9.3052 | | 0.0853 | 2.1831 | 28000 | 0.2513 | 10.2081 | | 0.079 | 2.2610 | 29000 | 0.2532 | 9.7446 | | 0.0661 | 2.3390 | 30000 | 0.2533 | 9.7613 | | 0.0782 | 2.4170 | 31000 | 0.2525 | 9.2884 | | 0.0757 | 2.4949 | 32000 | 0.2550 | 9.4969 | | 0.0704 | 2.5729 | 33000 | 0.2554 | 9.4169 | | 0.066 | 2.6509 | 34000 | 0.2560 | 9.2642 | | 0.0703 | 2.7288 | 35000 | 0.2564 | 9.2828 | | 0.0707 | 2.8068 | 36000 | 0.2552 | 9.2977 | | 0.0722 | 2.8848 | 37000 | 0.2566 | 9.3629 | | 0.081 | 2.9627 | 38000 | 0.2559 | 9.3480 | ### Framework versions - Transformers 4.53.0.dev0 - Pytorch 2.8.0.dev20250616+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1

igorcouto/sofya_telco_alpha1

igorcouto

2025-06-18T22:01:47Z

0

transformers

[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2025-06-18T22:00:25Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

morturr/Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb2-seed18-2025-06-19

morturr

2025-06-18T22:00:38Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]

null

2025-06-18T22:00:20Z

--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb2-seed18-2025-06-19 results: [] ---  # Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb2-seed18-2025-06-19 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 18 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - 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: 2 ### 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

mradermacher/L3.3-Electra-R1-70b-GGUF

mradermacher

2025-06-18T21:56:30Z

68

0

transformers

[ "transformers", "gguf", "en", "base_model:Steelskull/L3.3-Electra-R1-70b", "base_model:quantized:Steelskull/L3.3-Electra-R1-70b", "license:other", "endpoints_compatible", "region:us", "conversational" ]

null

2025-03-09T16:00:16Z

--- base_model: Steelskull/L3.3-Electra-R1-70b language: - en library_name: transformers license: other license_name: eva-llama3.3 quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/Steelskull/L3.3-Electra-R1-70b  weighted/imatrix quants are available at https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q2_K.gguf) | Q2_K | 26.5 | | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q3_K_S.gguf) | Q3_K_S | 31.0 | | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q3_K_L.gguf) | Q3_K_L | 37.2 | | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.IQ4_XS.gguf) | IQ4_XS | 38.4 | | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q5_K_S.gguf) | Q5_K_S | 48.8 | | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q5_K_M.gguf) | Q5_K_M | 50.0 | | | [PART 1](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality | | [PART 1](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF/resolve/main/L3.3-Electra-R1-70b.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/L3.3-Electra-R1-70b-i1-GGUF

mradermacher

2025-06-18T21:56:12Z

780

2

transformers

[ "transformers", "gguf", "en", "base_model:Steelskull/L3.3-Electra-R1-70b", "base_model:quantized:Steelskull/L3.3-Electra-R1-70b", "license:other", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-03-09T17:49:23Z

--- base_model: Steelskull/L3.3-Electra-R1-70b language: - en library_name: transformers license: other license_name: eva-llama3.3 quantized_by: mradermacher --- ## About      weighted/imatrix quants of https://huggingface.co/Steelskull/L3.3-Electra-R1-70b  static quants are available at https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ1_S.gguf) | i1-IQ1_S | 15.4 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ1_M.gguf) | i1-IQ1_M | 16.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 19.2 | | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 21.2 | | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ2_S.gguf) | i1-IQ2_S | 22.3 | | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ2_M.gguf) | i1-IQ2_M | 24.2 | | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q2_K_S.gguf) | i1-Q2_K_S | 24.6 | very low quality | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q2_K.gguf) | i1-Q2_K | 26.5 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 29.4 | | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ3_S.gguf) | i1-IQ3_S | 31.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 31.0 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ3_M.gguf) | i1-IQ3_M | 32.0 | | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 34.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 37.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 38.0 | | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q4_0.gguf) | i1-Q4_0 | 40.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 40.4 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 42.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q4_1.gguf) | i1-Q4_1 | 44.4 | | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 48.8 | | | [GGUF](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 50.0 | | | [PART 1](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3.3-Electra-R1-70b-i1-GGUF/resolve/main/L3.3-Electra-R1-70b.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 58.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

ohjoonhee/hai-siglip-fold1

ohjoonhee

2025-06-18T21:55:49Z

0

transformers

[ "transformers", "safetensors", "siglip", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2025-06-18T21:52:31Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

bolmu321/medgemma-medqa

bolmu321

2025-06-18T21:55:07Z

0

transformers

[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/medgemma-4b-it", "base_model:finetune:google/medgemma-4b-it", "text-generation-inference", "endpoints_compatible", "region:us" ]

image-text-to-text

2025-06-18T20:11:43Z

--- base_model: google/medgemma-4b-it library_name: transformers model_name: medgemma-medqa tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for medgemma-medqa This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="bolmu321/medgemma-medqa", 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: 0.18.2 - Transformers: 4.52.4 - Pytorch: 2.7.1 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

lizh222/textual_inversion_cat

lizh222

2025-06-18T21:49:57Z

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "diffusers-training", "base_model:stable-diffusion-v1-5/stable-diffusion-v1-5", "base_model:adapter:stable-diffusion-v1-5/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2025-06-18T21:06:41Z

--- base_model: stable-diffusion-v1-5/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m inference: true tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion - diffusers-training ---  # Textual inversion text2image fine-tuning - lizh222/textual_inversion_cat These are textual inversion adaption weights for stable-diffusion-v1-5/stable-diffusion-v1-5. You can find some example images in the following. ## 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]

luyotw/openfun-ivod-whisper-medium-WangMeiHui-11-46

luyotw

2025-06-18T21:48:04Z

0

null

[ "tensorboard", "safetensors", "whisper", "region:us" ]

null

2025-06-18T20:34:23Z

# Fine-tune 資訊 - 原始模型: `openai/whisper-medium` - 使用音訊數量: 4999 - 使用音訊總長: 2.84 小時 - 音訊平均長度: 2.05 秒 - GPU: `NVIDIA H100 PCIe` x 1 - 訓練時間: 02:29:39 - 模型大小: 2.85 GB --- # Model Card

FLOPS-Squared/KeystoneFuse-FW16-KSL8-Flax

FLOPS-Squared

2025-06-18T21:47:26Z

1

0

transformers

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

text-generation

2025-06-18T02:03:33Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mlfoundations-cua-dev/uitars_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_baseline

mlfoundations-cua-dev

2025-06-18T21:44:44Z

0

null

[ "region:us" ]

null

2025-06-18T21:15:02Z

# idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_baseline ## Model Information **Full Model Name**: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_baseline` **Repository Name**: `mlfoundations-cua-dev/uitars_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_baseline` **Model Directory**: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_baseline` **Checkpoint Used**: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_baseline/checkpoint_epoch_9.pt` ## Model Configuration - **Model Version**: TARS 1.5 - **Model Size**: 7B parameters - **Data Type**: Frame pairs - **Learning Rate**: 1e-5 - **Epochs**: 10 - **Training Steps**: 500 - **Global Batch Size**: 8 - **Weight Decay**: 0.1 - **Max Gradient Norm**: 1.0 - **Resolution**: 896x896 - **Training Data**: Baseline ## Description This repository contains the model state dict extracted from the training checkpoint. ### Files - `model_state_dict.pt`: PyTorch state dictionary containing the model weights - `README.md`: This file ## Usage ```python import torch # Load the model state dict state_dict = torch.load("model_state_dict.pt", map_location='cpu') # Use with your model architecture # model.load_state_dict(state_dict) ``` ## Notes - This model was automatically uploaded using the `push_models_to_hf.py` script - The repository name may be truncated if the original model name exceeded HuggingFace's 96-character limit - Checkpoint extracted from: `checkpoint_epoch_9.pt`

sanshi9999/qwen2.5-3b-breakdata500

sanshi9999

2025-06-18T21:44:17Z

0

transformers

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

null

2025-06-18T21:44:14Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

indicinaaa/Qwen3-4B-unsloth-bnb-4bit-fp16

indicinaaa

2025-06-18T21:40:27Z

0

transformers

[ "transformers", "pytorch", "safetensors", "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

2025-06-18T20:33:19Z

--- base_model: unsloth/Qwen3-4B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** indicinaaa - **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)

nnilayy/deap-dominance-binary-classification-Kfold-3

nnilayy

2025-06-18T21:37:18Z

0

null

[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]

null

2025-06-18T21:37:16Z

--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]

sapna-shah-18d/wATCH.sapna.shah.viral.video.original

sapna-shah-18d

2025-06-18T21:32:26Z

0

null

[ "region:us" ]

null

2025-06-18T21:29:27Z

[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=sapna-shah) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=sapna-shah) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=sapna-shah)

mmwillet2/Orpheus_GGUF

mmwillet2

2025-06-18T21:28:52Z

0

null

[ "gguf", "text-to-speech", "base_model:canopylabs/orpheus-3b-0.1-ft", "base_model:quantized:canopylabs/orpheus-3b-0.1-ft", "license:mit", "region:us" ]

text-to-speech

2025-06-18T20:22:47Z

--- license: mit base_model: - canopylabs/orpheus-3b-0.1-ft pipeline_tag: text-to-speech --- ## Purpose The purpose of this repository is to store various [TTS.cpp](https://github.com/mmwillet/TTS.cpp) compatible GGUF encoded model files for the [Orpheus TTS model](https://github.com/canopyai/Orpheus-TTS). ### Model Types Currently the Orpheus model is only supported in 32bit floating point format via the model file `Orpheus.gguf` ## Orpheus This page only contains the GGUF encoded model file of the original Orpheus 3B v0.1 finetuned model. For the original model please see the repository [Orpheus TTS model](https://github.com/canopyai/Orpheus-TTS) or the model repository [here](https://huggingface.co/canopylabs/orpheus-3b-0.1-ft). ## How to use See the github repo [here](https://github.com/mmwillet/TTS.cpp) for more information general usage. To compile TTS.cpp simple git clone and then run the the following in the repository's directory to compile (cmake is required): ```bash cmake -B build cmake --build build --config Release ``` After compilation is complete you can download a model file and generate speech to a file from the same directory like so: ```bash build/bin/tts-cli --model-path /model/path/to/downloaded_gguf_file.gguf --prompt "I am saying some words" --save-path /tmp/test.wav ```

nnilayy/dreamer-dominance-binary-classification-Kfold-2

nnilayy

2025-06-18T21:20:49Z

0

null

[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]

null

2025-06-18T21:20:45Z

--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]

tatiduran/bertmodel

tatiduran

2025-06-18T21:20:09Z

0

null

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

null

2025-06-18T21:17:47Z

--- license: apache-2.0 ---

videos-Nirmal-meena-18-Viral-Video-Link/Original.Full.Clip.Nirmal.meena.Viral.Video.Leaks.Official

videos-Nirmal-meena-18-Viral-Video-Link

2025-06-18T21:17:50Z

0

null

[ "region:us" ]

null

2025-06-18T21:17:32Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

hon9kon9ize/CantoneseLLMChat-v1.0-72B

hon9kon9ize

2025-06-18T21:17:03Z

26

1

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "arxiv:2503.12440", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-12-11T09:49:40Z

--- license: apache-2.0 library_name: transformers tags: - llama-factory - full - generated_from_trainer base_model: hon9kon9ize/CantoneseLLM-v1.0-72B-cpt model-index: - name: CantoneseLLMChat-v1.0-72B results: [] --- # CantoneseLLMChat-v1.0-72B ![front_image](cantonese_llm_v1.jpg) Cantonese LLM Chat v1.0 is the first generation Cantonese LLM from hon9kon9ize. Building upon the sucess of [v0.5 preview](https://huggingface.co/hon9kon9ize/CantoneseLLMChat-v0.5), the model excels in Hong Kong related specific knowledge and Cantonese conversation. ## Model description Base model obtained via Continuous Pre-Training of [Qwen 2.5 72B](https://huggingface.co/Qwen/Qwen2.5-72B) with 600 millions publicaly available Hong Kong news articles and Cantonese websites. Instructions fine-tuned model trained with a dataset consists of 75,000 instrutions pairs. 45,000 pairs were Cantonese insturctions generated by other LLMs and reviewed by humans. The model trained with 16 Nvidia H100 96GB HBM2e GPUs on [Genkai Supercomputer](https://www.cc.kyushu-u.ac.jp/scp/eng/system/Genkai/hardware/). ## Basic Usage ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "hon9kon9ize/CantoneseLLMChat-v1.0-72B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) def chat(messages, temperature=0.9, max_new_tokens=200): input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt').to('cuda:0') output_ids = model.generate(input_ids, max_new_tokens=max_new_tokens, temperature=temperature) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=False) return response prompt = "邊個係香港特首？" messages = [ {"role": "system", "content": "you are a helpful assistant."}, {"role": "user", "content": prompt} ] print(chat(messages)) # 香港特別行政區行政長官係李家超。<|im_end|> ``` ## Performance Best in class open source LLM in understanding Cantonese and Hong Kong culture in the [HK-Eval Benchmark](https://arxiv.org/pdf/2503.12440). However, as one could observe, reasoning models have performed dramatically better than their counterparts. We are currently working on reasoning models for v2. | Model | HK Culture (zero-shot) | Cantonese Linguistics | |---------------------------|:----------------------:|:---------------------:| | CantonesellmChat v0.5 6B | 52.0% | 12.8% | | CantonesellmChat v0.5 34B | 72.5% | 54.5% | | CantonesellmChat v1.0 3B | 56.0% | 45.7% | | CantonesellmChat v1.0 7B | 60.3% | 46.5% | | CantonesellmChat v1.0 32B | 69.8% | 52.7% | | CantonesellmChat v1.0 72B | 75.4% | 59.6% | | Llama 3.1 8B Instruct | 45.6% | 35.1% | | Llama 3.1 70B Instruct | 63.0% | 50.3% | | Qwen2.5 7B Instruct | 51.2% | 30.3% | | Qwen2.5 32B Instruct | 59.9% | 45.1% | | Qwen2.5 72B Instruct | 65.9% | 45.9% | | Claude 3.5 Sonnet | 71.7% | 63.2% | | DeepSeek R1 | 88.8% | 77.5% | | Gemini 2.0 Flash | 80.2% | 75.3% | | Gemini 2.5 Pro | 92.1% | 87.3% | | GPT4o | 77.5% | 63.8% | | GPT4o-mini | 55.6% | 57.3% |

hon9kon9ize/CantoneseLLMChat-v1.0-32B

hon9kon9ize

2025-06-18T21:16:47Z

31

5

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "arxiv:2503.12440", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-03-22T09:43:52Z

--- license: apache-2.0 library_name: transformers tags: - llama-factory - full - generated_from_trainer base_model: hon9kon9ize/CantoneseLLM-v1.0-32B-cpt model-index: - name: CantoneseLLMChat-v1.0-32B results: [] --- # CantoneseLLMChat-v1.0-32B ![front_image](cantonese_llm_v1.jpg) Cantonese LLM Chat v1.0 is the first generation Cantonese LLM from hon9kon9ize. Building upon the sucess of [v0.5 preview](https://huggingface.co/hon9kon9ize/CantoneseLLMChat-v0.5), the model excels in Hong Kong related specific knowledge and Cantonese conversation. ## Model description Base model obtained via Continuous Pre-Training of [Qwen 2.5 32B](https://huggingface.co/Qwen/Qwen2.5-32B) with 600 millions publicaly available Hong Kong news articles and Cantonese websites. Instructions fine-tuned model trained with a dataset consists of 75,000 instrutions pairs. 45,000 pairs were Cantonese insturctions generated by other LLMs and reviewed by humans. The model trained with 16 Nvidia H100 96GB HBM2e GPUs on [Genkai Supercomputer](https://www.cc.kyushu-u.ac.jp/scp/eng/system/Genkai/hardware/). ## Basic Usage ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "hon9kon9ize/CantoneseLLMChat-v1.0-32B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) def chat(messages, temperature=0.9, max_new_tokens=200): input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt').to('cuda:0') output_ids = model.generate(input_ids, max_new_tokens=max_new_tokens, temperature=temperature) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=False) return response prompt = "邊個係香港特首？" messages = [ {"role": "system", "content": "you are a helpful assistant."}, {"role": "user", "content": prompt} ] print(chat(messages)) # 香港特別行政區行政長官係李家超。<|im_end|> ``` ## Performance Best in class open source LLM in understanding Cantonese and Hong Kong culture in the [HK-Eval Benchmark](https://arxiv.org/pdf/2503.12440). However, as one could observe, reasoning models have performed dramatically better than their counterparts. We are currently working on reasoning models for v2. | Model | HK Culture (zero-shot) | Cantonese Linguistics | |---------------------------|:----------------------:|:---------------------:| | CantonesellmChat v0.5 6B | 52.0% | 12.8% | | CantonesellmChat v0.5 34B | 72.5% | 54.5% | | CantonesellmChat v1.0 3B | 56.0% | 45.7% | | CantonesellmChat v1.0 7B | 60.3% | 46.5% | | CantonesellmChat v1.0 32B | 69.8% | 52.7% | | CantonesellmChat v1.0 72B | 75.4% | 59.6% | | Llama 3.1 8B Instruct | 45.6% | 35.1% | | Llama 3.1 70B Instruct | 63.0% | 50.3% | | Qwen2.5 7B Instruct | 51.2% | 30.3% | | Qwen2.5 32B Instruct | 59.9% | 45.1% | | Qwen2.5 72B Instruct | 65.9% | 45.9% | | Claude 3.5 Sonnet | 71.7% | 63.2% | | DeepSeek R1 | 88.8% | 77.5% | | Gemini 2.0 Flash | 80.2% | 75.3% | | Gemini 2.5 Pro | 92.1% | 87.3% | | GPT4o | 77.5% | 63.8% | | GPT4o-mini | 55.6% | 57.3% |

hon9kon9ize/CantoneseLLMChat-v1.0-7B

hon9kon9ize

2025-06-18T21:16:31Z

2,220

6

transformers

[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "arxiv:2503.12440", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-10-02T08:17:17Z

--- license: apache-2.0 library_name: transformers tags: - llama-factory - full - generated_from_trainer base_model: hon9kon9ize/CantoneseLLM-v1.0 model-index: - name: CantoneseLLMChat-v1.0-7B results: [] --- # CantoneseLLMChat-v1.0-7B ![front_image](cantonese_llm_v1.jpg) Cantonese LLM Chat v1.0 is the first generation Cantonese LLM from hon9kon9ize. Building upon the sucess of [v0.5 preview](https://huggingface.co/hon9kon9ize/CantoneseLLMChat-v0.5), the model excels in Hong Kong related specific knowledge and Cantonese conversation. ## Model description Base model obtained via Continuous Pre-Training of [Qwen 2.5 7B](https://huggingface.co/Qwen/Qwen2.5-7B) with 600 millions publicaly available Hong Kong news articles and Cantonese websites. Instructions fine-tuned model trained with a dataset consists of 75,000 instrutions pairs. 45,000 pairs were Cantonese insturctions generated by other LLMs and reviewed by humans. The model trained with 1 Nvidia H100 80GB HBM3 GPU on [Genkai Supercomputer](https://www.cc.kyushu-u.ac.jp/scp/eng/system/Genkai/hardware/). ## Basic Usage ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "hon9kon9ize/CantoneseLLMChat-v1.0-7B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) def chat(messages, temperature=0.9, max_new_tokens=200): input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt').to('cuda:0') output_ids = model.generate(input_ids, max_new_tokens=max_new_tokens, temperature=temperature) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=False) return response prompt = "邊個係香港特首？" messages = [ {"role": "system", "content": "you are a helpful assistant."}, {"role": "user", "content": prompt} ] print(chat(messages)) # 香港特別行政區行政長官係李家超。<|im_end|> ``` ## Performance Best in class open source LLM in understanding Cantonese and Hong Kong culture in the [HK-Eval Benchmark](https://arxiv.org/pdf/2503.12440). However, as one could observe, reasoning models have performed dramatically better than their counterparts. We are currently working on reasoning models for v2. | Model | HK Culture (zero-shot) | Cantonese Linguistics | |---------------------------|:----------------------:|:---------------------:| | CantonesellmChat v0.5 6B | 52.0% | 12.8% | | CantonesellmChat v0.5 34B | 72.5% | 54.5% | | CantonesellmChat v1.0 3B | 56.0% | 45.7% | | CantonesellmChat v1.0 7B | 60.3% | 46.5% | | CantonesellmChat v1.0 32B | 69.8% | 52.7% | | CantonesellmChat v1.0 72B | 75.4% | 59.6% | | Llama 3.1 8B Instruct | 45.6% | 35.1% | | Llama 3.1 70B Instruct | 63.0% | 50.3% | | Qwen2.5 7B Instruct | 51.2% | 30.3% | | Qwen2.5 32B Instruct | 59.9% | 45.1% | | Qwen2.5 72B Instruct | 65.9% | 45.9% | | Claude 3.5 Sonnet | 71.7% | 63.2% | | DeepSeek R1 | 88.8% | 77.5% | | Gemini 2.0 Flash | 80.2% | 75.3% | | Gemini 2.5 Pro | 92.1% | 87.3% | | GPT4o | 77.5% | 63.8% | | GPT4o-mini | 55.6% | 57.3% |

Missia/timesformer-base-finetuned-k400-finetuned-mcap_v0-b_size-16-epochs-10-grad_acc-8-lr-5e-5

Missia

2025-06-18T21:15:38Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "timesformer", "video-classification", "generated_from_trainer", "base_model:facebook/timesformer-base-finetuned-k400", "base_model:finetune:facebook/timesformer-base-finetuned-k400", "license:cc-by-nc-4.0", "endpoints_compatible", "region:eu" ]

video-classification

2025-06-18T09:17:07Z

--- library_name: transformers license: cc-by-nc-4.0 base_model: facebook/timesformer-base-finetuned-k400 tags: - generated_from_trainer metrics: - accuracy model-index: - name: timesformer-base-finetuned-k400-finetuned-mcap_v0-b_size-16-epochs-10-grad_acc-8-lr-5e-5 results: [] ---  # timesformer-base-finetuned-k400-finetuned-mcap_v0-b_size-16-epochs-10-grad_acc-8-lr-5e-5 This model is a fine-tuned version of [facebook/timesformer-base-finetuned-k400](https://huggingface.co/facebook/timesformer-base-finetuned-k400) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8710 - Accuracy: 0.6975 ## 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: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_ratio: 0.1 - training_steps: 520 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 1.249 | 0.1 | 52 | 1.2930 | 0.5505 | | 0.7883 | 1.1010 | 105 | 1.0499 | 0.6197 | | 0.5866 | 2.1 | 157 | 0.9579 | 0.6636 | | 0.4892 | 3.1010 | 210 | 0.9531 | 0.6646 | | 0.4247 | 4.1 | 262 | 0.9603 | 0.6699 | | 0.3929 | 5.1010 | 315 | 0.8643 | 0.7021 | | 0.3773 | 6.1 | 367 | 0.8596 | 0.7090 | | 0.3518 | 7.1010 | 420 | 0.8607 | 0.7057 | | 0.3574 | 8.1 | 472 | 0.8520 | 0.7108 | | 0.345 | 9.0913 | 520 | 0.8621 | 0.7067 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1 - Datasets 3.6.0 - Tokenizers 0.19.1

sgonzalezygil/sd-finetuning-dreambooth-v15-800

sgonzalezygil

2025-06-18T21:08:07Z

0

diffusers

[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2025-06-18T21:06:44Z

--- library_name: diffusers --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

New-tutorial-Trishakar-Madhu-Viral-Videos/FULL.VIDEO.Trishakar.Madhu.Viral.Video.Tutorial.Official

New-tutorial-Trishakar-Madhu-Viral-Videos

2025-06-18T21:02:32Z

0

null

[ "region:us" ]

null

2025-06-18T21:02:14Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

BootesVoid/cmc0p925608hzrdqs88a5yecb_cmc2e5vav005emn2kfnniiord

BootesVoid

2025-06-18T21:01:18Z

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

2025-06-18T21:01:16Z

--- 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: MODEL --- # Cmc0P925608Hzrdqs88A5Yecb_Cmc2E5Vav005Emn2Kfnniiord <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 `MODEL` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "MODEL", "lora_weights": "https://huggingface.co/BootesVoid/cmc0p925608hzrdqs88a5yecb_cmc2e5vav005emn2kfnniiord/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmc0p925608hzrdqs88a5yecb_cmc2e5vav005emn2kfnniiord', weight_name='lora.safetensors') image = pipeline('MODEL').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmc0p925608hzrdqs88a5yecb_cmc2e5vav005emn2kfnniiord/discussions) to add images that show off what you’ve made with this LoRA.

swapnillo/RKD-retrained

swapnillo

2025-06-18T21:00:25Z

0

transformers

[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

image-text-to-text

2025-06-18T20:59:11Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

sgonzalezygil/sd-finetuning-dreambooth-v15

sgonzalezygil

2025-06-18T20:58:17Z

0

diffusers

[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2025-06-18T20:56:47Z

--- library_name: diffusers --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ShailxT/results

ShailxT

2025-06-18T20:56:19Z

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-06-18T20:55:49Z

--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: results results: [] ---  # results This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0838 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 193 | 0.0710 | | No log | 2.0 | 386 | 0.0766 | | 0.0886 | 3.0 | 579 | 0.0838 | ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1

New-videos-Arovi-Nusrat-Ridhi-18-Video/19.FULL.VIDEO.Arovi.Nusrat.Ridhi.Viral.Video.Tutorial.Official

New-videos-Arovi-Nusrat-Ridhi-18-Video

2025-06-18T20:54:56Z

0

null

[ "region:us" ]

null

2025-06-18T20:54:37Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

siri310/gemma-3-finetune

siri310

2025-06-18T20:52:43Z

6

0

transformers

[ "transformers", "safetensors", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-06-18T03:35:43Z

--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** siri310 - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 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)

morturr/Llama-2-7b-hf-LOO_one_liners-COMB_headlines-comb1-seed18-2025-06-18

morturr

2025-06-18T20:50:14Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]

null

2025-06-18T20:49:59Z

--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-LOO_one_liners-COMB_headlines-comb1-seed18-2025-06-18 results: [] ---  # Llama-2-7b-hf-LOO_one_liners-COMB_headlines-comb1-seed18-2025-06-18 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 18 - 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: 2 ### 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

wongyck/BERT_twitter_1

wongyck

2025-06-18T20:48:33Z

0

transformers

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

text-classification

2025-06-18T20:48:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

nnilayy/deap-dominance-binary-classification-Kfold-2

nnilayy

2025-06-18T20:45:57Z

0

null

[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]

null

2025-06-18T20:45:55Z

--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]

hdong0/deepseek-Qwen2.5-Math-1.5B-baseline-Open-R1-GRPO_deepscaler_1000steps_lr1e-6_kl1e-3_acc

hdong0

2025-06-18T20:44:38Z

33

0

transformers

[ "transformers", "safetensors", "qwen2bm", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "custom_code", "dataset:agentica-org/DeepScaleR-Preview-Dataset", "arxiv:2402.03300", "base_model:hdong0/deepseek-Qwen2.5-Math-1.5B-baseline-init", "base_model:finetune:hdong0/deepseek-Qwen2.5-Math-1.5B-baseline-init", "autotrain_compatible", "region:us" ]

text-generation

2025-06-17T14:28:00Z

--- base_model: hdong0/deepseek-Qwen2.5-Math-1.5B-baseline-init datasets: agentica-org/DeepScaleR-Preview-Dataset library_name: transformers model_name: deepseek-Qwen2.5-Math-1.5B-baseline-Open-R1-GRPO_deepscaler_1000steps_lr1e-6_kl1e-3_acc tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for deepseek-Qwen2.5-Math-1.5B-baseline-Open-R1-GRPO_deepscaler_1000steps_lr1e-6_kl1e-3_acc This model is a fine-tuned version of [hdong0/deepseek-Qwen2.5-Math-1.5B-baseline-init](https://huggingface.co/hdong0/deepseek-Qwen2.5-Math-1.5B-baseline-init) on the [agentica-org/DeepScaleR-Preview-Dataset](https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="hdong0/deepseek-Qwen2.5-Math-1.5B-baseline-Open-R1-GRPO_deepscaler_1000steps_lr1e-6_kl1e-3_acc", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.0.dev0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

ManoharLalDhakad/wATCH.Manohar.Lal.Dhakad.viral.video.original

ManoharLalDhakad

2025-06-18T20:41:09Z

0

null

[ "region:us" ]

null

2025-06-18T20:37:22Z

[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/)

nnilayy/dreamer-arousal-binary-classification-Kfold-1

nnilayy

2025-06-18T20:39:13Z

0

null

[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]

null

2025-06-18T20:39:12Z

--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]

New-tutorial-Bindura-University-18-Videos/FULL.VIDEO.Bindura.University.Viral.Video.Tutorial.Official

New-tutorial-Bindura-University-18-Videos

2025-06-18T20:37:29Z

0

null

[ "region:us" ]

null

2025-06-18T20:37:14Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

ajayraj-rathore/vit-base-oxford-iiit-pets

ajayraj-rathore

2025-06-18T20:31:03Z

12

0

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2025-06-17T17:25:52Z

--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-oxford-iiit-pets results: [] ---  # vit-base-oxford-iiit-pets This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset. It achieves the following results on the evaluation set: - Loss: 0.1935 - Accuracy: 0.9459 ## 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: 16 - 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4171 | 1.0 | 370 | 0.2915 | 0.9283 | | 0.2076 | 2.0 | 740 | 0.2287 | 0.9202 | | 0.1721 | 3.0 | 1110 | 0.2108 | 0.9283 | | 0.1477 | 4.0 | 1480 | 0.1942 | 0.9378 | | 0.1455 | 5.0 | 1850 | 0.1916 | 0.9391 | ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1

luyotw/openfun-ivod-whisper-medium-WangMeiHui-10-71

luyotw

2025-06-18T20:26:48Z

0

null

[ "tensorboard", "safetensors", "whisper", "region:us" ]

null

2025-06-18T19:12:04Z

# Fine-tune 資訊 - 原始模型: `openai/whisper-medium` - 使用音訊數量: 8277 - 使用音訊總長: 4.65 小時 - 音訊平均長度: 2.02 秒 - GPU: `NVIDIA H100 PCIe` x 1 - 訓練時間: 02:47:02 - 模型大小: 2.85 GB --- # Model Card

soumitsr/led-base-article-digestor

soumitsr

2025-06-18T20:23:58Z

86

0

transformers

[ "transformers", "tensorboard", "safetensors", "led", "text2text-generation", "generated_from_trainer", "base_model:allenai/led-base-16384", "base_model:finetune:allenai/led-base-16384", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2025-06-17T17:00:22Z

--- library_name: transformers license: apache-2.0 base_model: allenai/led-base-16384 tags: - generated_from_trainer model-index: - name: led-base-article-digestor results: [] ---  # led-base-article-digestor This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 192 - 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: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.8.0.dev20250319+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1

Viraly-Lol-tv/Video.clip.viraly.lol.hindi.trending.viral.Full.Video

Viraly-Lol-tv

2025-06-18T20:22:27Z

0

null

[ "region:us" ]

null

2025-06-18T20:21:55Z

[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=viraly-lol-hindi) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=viraly-lol-hindi) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=viraly-lol-hindi)

New-tutorial-cikgu-cctv-wiring-18-Videos/FULL.VIDEO.cikgu.cctv.wiring.Viral.Video.Tutorial.Official

New-tutorial-cikgu-cctv-wiring-18-Videos

2025-06-18T20:19:37Z

0

null

[ "region:us" ]

null

2025-06-18T20:19:20Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

sawaawsx/bf11ccdc-c1c6-477d-a7bc-24b2b5375080

sawaawsx

2025-06-18T20:16:23Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-06-18T20:12:32Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

minhxle/truesight-ft-job-d09cc09c-26a3-499b-8e2b-44861421805e

minhxle

2025-06-18T20:15:20Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-18T20:15:15Z

--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** minhxle - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen2.5-3b-instruct-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)

mlfoundations-cua-dev/uitars_1500_steps_gbs_8_wd_0.1orm_1.0_add_synthetic_legacy_typing_data

mlfoundations-cua-dev

2025-06-18T20:15:18Z

0

null

[ "region:us" ]

null

2025-06-18T19:39:46Z

# idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_1500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_add_synthetic_legacy_typing_data ## Model Information **Full Model Name**: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_1500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_add_synthetic_legacy_typing_data` **Repository Name**: `mlfoundations-cua-dev/uitars_1500_steps_gbs_8_wd_0.1orm_1.0_add_synthetic_legacy_typing_data` **Model Directory**: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_1500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_add_synthetic_legacy_typing_data` **Checkpoint Used**: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_1500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_add_synthetic_legacy_typing_data/checkpoint_epoch_5.pt` ## Model Configuration - **Model Version**: TARS 1.5 - **Model Size**: 7B parameters - **Data Type**: Frame pairs - **Learning Rate**: 1e-5 - **Epochs**: 10 - **Training Steps**: 1500 - **Global Batch Size**: 8 - **Weight Decay**: 0.1 - **Max Gradient Norm**: 1.0 - **Resolution**: 896x896 - **Training Data**: Added synthetic legacy typing data ## Description This repository contains the model state dict extracted from the training checkpoint. ### Files - `model_state_dict.pt`: PyTorch state dictionary containing the model weights - `README.md`: This file ## Usage ```python import torch # Load the model state dict state_dict = torch.load("model_state_dict.pt", map_location='cpu') # Use with your model architecture # model.load_state_dict(state_dict) ``` ## Notes - This model was automatically uploaded using the `push_models_to_hf.py` script - The repository name may be truncated if the original model name exceeded HuggingFace's 96-character limit - Checkpoint extracted from: `checkpoint_epoch_5.pt`

morturr/Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb1-seed42-2025-06-18

morturr

2025-06-18T20:14:13Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]

null

2025-06-18T20:14:03Z

--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb1-seed42-2025-06-18 results: [] ---  # Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb1-seed42-2025-06-18 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - 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: 2 ### 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

pronoobie/indic_conformer_hi_float16_onnx_256_vocab

pronoobie

2025-06-18T20:12:56Z

0

null

[ "onnx", "automatic-speech-recognition", "hi", "nd", "base_model:ai4bharat/indic-conformer-600m-multilingual", "base_model:quantized:ai4bharat/indic-conformer-600m-multilingual", "license:mit", "region:us" ]

automatic-speech-recognition

2025-06-11T13:57:48Z

--- license: mit language: - hi - nd metrics: - wer base_model: - ai4bharat/indic-conformer-600m-multilingual pipeline_tag: automatic-speech-recognition --- Kudos to AI4Bharat for training hindi specific speech recognition model. Visit: https://huggingface.co/ai4bharat/indicconformer_stt_hi_hybrid_ctc_rnnt_large There is active development going on this directory. https://github.com/deepanshu-yadav/Quantize_speech_Recognition_For_Hindi This repository aims to 1. quantize the .nemo model for both CTC and RNNT versions. 2. remove nemo specific dependencies 3. finally use the converted onnx model for both offline and online(microphone) use. --- Converted for both CTC and RNNT versions. --- There is a notebook already provided for conversion to float 16 model. The name of the notebook is `onnxconversionCTC.ipynb` for CTC. The name of the notebook is `onnxconversionRNNT.ipynb` for RNNT version. # How to perform inference Install the depedencies ``` pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu ``` After that install from requirements file ``` pip install -r requirements.txt ``` ## For CTC float16 (non streaming version) offline mode Now we can run inference `python offline_ctc_float16_inference.py` Note a sample file has already been provided. Expected Output: ``` Audio features shape: (1, 80, 1413), Length: [1413] Transcription: शिवपाल की यह टिप्पणी फ़िल्म काल्या के डायलॉग से मिलतीजुलती है शिवपाल चाहते हैं कि मुलायम पारती के मुखिया फिर से बने फ़िलहाल सपा अध्यक्ष अखिलेश यादव हैं पिता से पार्ट की कमान छीनी थी ``` ## For CTC float16 (non streaming mode) live mode You can perform transcription live from your sound device as well. Execute `python realtime_ctc_float16_non_streaming.py` Expected Output ``` Using cache found in C:\Users\DEEPANSHU/.cache\torch\hub\snakers4_silero-vad_master Listening... (Speak into the microphone) Press 'q' to stop streaming... C:\Users\DEEPANSHU\Desktop\automation\speech\hindi\git_inference_push\realtime_ctc_float16_non_streaming.py:55: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\pytorch\torch\csrc\utils\tensor_numpy.cpp:209.) audio_tensor = torch.from_numpy(audio_float32) Speech detected, recording... Silence detected, transcribing... Transcription: तो कैसे हैं आप सब Listening... Speech detected, recording... Silence detected, transcribing... Transcription: आपसे मिल के अच्छा लगा Listening... ``` ## For RNNT ### For Realtime (microphone) It is float 16 rnnt version with non streaming mode. `python realtime_rnnt_float16_non_streaming.py` ### Offline file based It is float 16 rnnt version with non streaming mode. `python offline_rnnt_float16_non_streaming.py`

RLFH-cognitive-reframing/lora-llama3.1-8b-Instruct-reframe

RLFH-cognitive-reframing

2025-06-18T20:10:42Z

132

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-05-26T18:57:17Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

VIDEOS-Arovi-Nusrat-Ridhi-18-Viral-Video/FULL.VIDEO.Arovi.Nusrat.Ridhi.Viral.Video.Tutorial.Official

VIDEOS-Arovi-Nusrat-Ridhi-18-Viral-Video

2025-06-18T20:09:56Z

0

null

[ "region:us" ]

null

2025-06-18T20:09:42Z

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hdong0/Qwen2.5-Math-1.5B-batch-mix-Open-R1-GRPO_deepscaler_1000steps_lr1e-6_kl1e-3_acc_seq_end_mask_2_

hdong0

2025-06-18T20:05:53Z

45

0

transformers

[ "transformers", "safetensors", "qwen2bm", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]

text-generation

2025-06-14T01:22:43Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

profdiovanimerlo/ONNX-quantizado-roberta-base-squad2

profdiovanimerlo

2025-06-18T20:02:45Z

0

transformers

[ "transformers", "onnx", "roberta", "question-answering", "endpoints_compatible", "region:us" ]

question-answering

2025-06-18T20:01:29Z

--- library_name: transformers tags: [] --- ``` # Optimum RoBERTa-base-SQuAD2 Quantizado ## Introdução Este repositório contém uma versão quantizada do modelo [`optimum/roberta-base-squad2`](https://huggingface.co/optimum/roberta-base-squad2), desenvolvido por Branden Chan et al. A quantização foi realizada utilizando a biblioteca Optimum ONNX para reduzir o tamanho do modelo e melhorar a eficiência, mantendo uma precisão aceitável. ## Avaliação Os modelos foram testados utilizando 600 entradas do conjunto de validação da base de dados [rajpurkar/squad_v2](https://huggingface.co/datasets/rajpurkar/squad_v2). 1. **Redução da Latência**: - **Modelo Original**: 0.572 segundos por amostra - **Modelo Quantizado**: 0.437 segundos por amostra - **Análise**: A latência foi significativamente reduzida, tornando o modelo mais adequado para aplicações em tempo real. 2. **Aumento da Eficiência**: - **Tempo Total**: - **Modelo Original**: 343.20 segundos - **Modelo Quantizado**: 262.41 segundos - **Análise**: O tempo total de execução foi consideravelmente reduzido. - **Amostras por Segundo**: - **Modelo Original**: 1.75 amostras/segundo - **Modelo Quantizado**: 2.29 amostras/segundo - **Análise**: A taxa de processamento aumentou, permitindo que mais amostras sejam processadas no mesmo período de tempo. 3. **Manutenção de Precisão Razoável**: - **Exact Score**: - **Modelo Original**: 81.67 - **Modelo Quantizado**: 80.5 - **Análise**: Pequena queda na precisão, mas ainda em nível aceitável. - **F1 Score**: - **Modelo Original**: 83.75 - **Modelo Quantizado**: 82.49 - **Análise**: Queda ligeira no desempenho de F1 Score. 4. **Comparação do Espaço Ocupado na Memória**: - **Modelo Original**: 476.52 MB - **Modelo Quantizado**: 122.41 MB - **Análise**: A quantização resultou em uma redução significativa no espaço ocupado, com o modelo quantizado utilizando apenas cerca de 25.7% do tamanho do modelo original. Esses resultados indicam que a quantização foi bem-sucedida, alcançando uma redução significativa na latência, aumento na eficiência e uma economia substancial de espaço na memória, enquanto mantém uma precisão aceitável para tarefas de perguntas e respostas. ```

OddTheGreat/Spatha_GLM_32B_V.2

OddTheGreat

2025-06-18T20:01:39Z

6

0

transformers

[ "transformers", "safetensors", "glm4", "text-generation", "mergekit", "merge", "roleplay", "creative", "en", "base_model:Delta-Vector/Rei-V1-32B-Base", "base_model:merge:Delta-Vector/Rei-V1-32B-Base", "base_model:Lachesis-AI/IronLoom-32B-v1", "base_model:merge:Lachesis-AI/IronLoom-32B-v1", "base_model:ReadyArt/Omega-Darkest_The-Broken-Tutu-GLM-32B", "base_model:merge:ReadyArt/Omega-Darkest_The-Broken-Tutu-GLM-32B", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-06-09T05:15:19Z

--- base_model: - ReadyArt/Omega-Darkest_The-Broken-Tutu-GLM-32B - Delta-Vector/Rei-V1-32B-Base - Lachesis-AI/IronLoom-32B-v1 library_name: transformers tags: - mergekit - merge - roleplay - creative language: - en --- # Spatha_GLM_32B_V.2 This is a merge of pre-trained language models GLM is interesting model for RP and general usage. Goal of this merge was to create somewhat stable model with good prose, capable to "darker" themes, with good understanding of character. Actually, model is pretty good. it's slightly faster than 27b gemma, while remains consistent up to 16k context. Model is attentive to prompt, "smart" enough and overall generate good, uncensored replies. Sometimes it could over concentrate or fall in loops, but not too often. Also it works far better with good prompt and char cards. Ru wasn't properly tested, on first glance it was not good. I don't reccomend to use lower than q4_k_m, it seems to perform far worse. Tested on some obscure GLM4 preset from net, Q4_K_M 300 replies, T1.04

beyondKapil/ppo-LunarLander-v2

beyondKapil

2025-06-18T20:00:11Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2025-06-18T19:59:52Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: MlpPolicy results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 257.92 +/- 22.30 name: mean_reward verified: false --- # **MlpPolicy** Agent playing **LunarLander-v2** This is a trained model of a **MlpPolicy** 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 ... ```

nnilayy/dreamer-valence-binary-classification-Kfold-1

nnilayy

2025-06-18T19:56:59Z

0

null

[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]

null

2025-06-18T19:56:56Z

--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]

CezarinniMedia/MacanRoV4

CezarinniMedia

2025-06-18T19:55:13Z

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

2025-06-18T19:21:48Z

--- 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: MacanRoV4 --- # Macanrov4 <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 `MacanRoV4` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "MacanRoV4", "lora_weights": "https://huggingface.co/CezarinniMedia/MacanRoV4/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('CezarinniMedia/MacanRoV4', weight_name='lora.safetensors') image = pipeline('MacanRoV4').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/CezarinniMedia/MacanRoV4/discussions) to add images that show off what you’ve made with this LoRA.

dantheoprod/assz

dantheoprod

2025-06-18T19:52:19Z

0

null

[ "license:bigscience-bloom-rail-1.0", "region:us" ]

null

2025-06-18T19:51:46Z

--- license: bigscience-bloom-rail-1.0 ---

nnilayy/deap-arousal-binary-classification-Kfold-1

nnilayy

2025-06-18T19:51:56Z

0

null

[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]

null

2025-06-18T19:51:54Z

--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]

JesseLiu/qwen25-3b-base-pagerank-naive-refine-grpo-lora

JesseLiu

2025-06-18T19:50:56Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-3B", "base_model:adapter:Qwen/Qwen2.5-3B", "region:us" ]

null

2025-06-18T19:50:26Z

--- base_model: Qwen/Qwen2.5-3B library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1

sgonzalezygil/sd-finetuning-dreambooth-v14-1200

sgonzalezygil

2025-06-18T19:50:28Z

0

diffusers

[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2025-06-18T19:48:49Z

--- library_name: diffusers --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

minhxle/truesight-ft-job-f7e4f1e7-4a22-44de-b837-fe50b0c46525

minhxle

2025-06-18T19:49:12Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-18T19:49:07Z

--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** minhxle - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen2.5-3b-instruct-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)

ElmehdiSMILI/jais-7b-stage2-embedding-tuned

ElmehdiSMILI

2025-06-18T19:46:16Z

0

transformers

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

text-generation

2025-06-18T19:38:13Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

sgonzalezygil/sd-finetuning-dreambooth-v14

sgonzalezygil

2025-06-18T19:46:11Z

0

diffusers

[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2025-06-18T19:44:26Z

--- library_name: diffusers --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

BootesVoid/cmbioa2e60a2nkfxsl953aeo3_cmc2aaidc0cgtrdqsiq2zwezw

BootesVoid

2025-06-18T19:40:17Z

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

2025-06-18T19:40:16Z

--- 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: VICKI --- # Cmbioa2E60A2Nkfxsl953Aeo3_Cmc2Aaidc0Cgtrdqsiq2Zwezw <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 `VICKI` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "VICKI", "lora_weights": "https://huggingface.co/BootesVoid/cmbioa2e60a2nkfxsl953aeo3_cmc2aaidc0cgtrdqsiq2zwezw/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmbioa2e60a2nkfxsl953aeo3_cmc2aaidc0cgtrdqsiq2zwezw', weight_name='lora.safetensors') image = pipeline('VICKI').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmbioa2e60a2nkfxsl953aeo3_cmc2aaidc0cgtrdqsiq2zwezw/discussions) to add images that show off what you’ve made with this LoRA.

faodl/model_child_and_family_support_benefits_mpnet_30_sample

faodl

2025-06-18T19:36:39Z

0

setfit

[ "setfit", "safetensors", "xlm-roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "model-index", "region:us" ]

text-classification

2025-06-18T19:35:51Z

--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: Targeting benefits based on income and disability status allows for efficient allocation of resources to the most vulnerable children and families. - text: "Encourage youth participation in the development, implementation, monitoring\ \ and \nevaluation of comprehensive sexuality education programmes and youth friendly\ \ health \nservices; and \n\n3." - text: "The SM\n\nEs \nhave to m\n\naintain the sam\ne level of em\n\nploym\nent\ \ during that period as to the \n\nnum\nber insured under social security end-D\n\ \necem\nber 2019." - text: "There are challenges in the labour market \n\nregarding realization of decent\ \ work for the majority of workers." - text: "Causes and Consequences of Broad-Based Rural Poverty Reduction: Lessons \n\ Learned \n \n\nStagnant levels of rural poverty pose a major challenge for Zambia." metrics: - accuracy - f1_score - precision - recall pipeline_tag: text-classification library_name: setfit inference: true base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 model-index: - name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 1.0 name: Accuracy - type: f1_score value: 1.0 name: F1_Score - type: precision value: 1.0 name: Precision - type: recall value: 1.0 name: Recall --- # SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 128 tokens - **Number of Classes:** 2 classes    ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:-----------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Irrelevant | <ul><li>'Agri-business (Market access for agricultural products) \n\t4.'</li><li>'These are goals that translate into many programmes and policies, and countless \n\ninstitutional plans and activities.'</li><li>'Planning: developing synergies between different \ntypes of infrastructure that facilitate socio-economic \nintegration and the timely delivery of aid in crisis.'</li></ul> | | Relevant | <ul><li>'Fiscal policies that sustain non-contributory family benefits ensure that the most disadvantaged children receive continuous support regardless of labor market fluctuations.'</li><li>'Social protection instruments that prioritize children living in poor households have a multiplier effect, positively influencing nutrition, education, and health indicators.'</li><li>'The expansion of benefits coverage to include all children, irrespective of socioeconomic status, underscores a commitment to universality and equitable social protection.'</li></ul> | ## Evaluation ### Metrics | Label | Accuracy | F1_Score | Precision | Recall | |:--------|:---------|:---------|:----------|:-------| | **all** | 1.0 | 1.0 | 1.0 | 1.0 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("faodl/model_child_and_family_support_benefits_mpnet_30_sample") # Run inference preds = model("There are challenges in the labour market regarding realization of decent work for the majority of workers.") ```     ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 2 | 25.3542 | 95 | | Label | Training Sample Count | |:-----------|:----------------------| | Irrelevant | 24 | | Relevant | 24 | ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 20 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0083 | 1 | 0.1695 | - | | 0.4167 | 50 | 0.0676 | - | | 0.8333 | 100 | 0.0008 | - | ### Framework Versions - Python: 3.11.13 - SetFit: 1.1.2 - Sentence Transformers: 4.1.0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF

bartowski

2025-06-18T19:36:07Z

0

null

[ "gguf", "text-generation", "base_model:arcee-ai/Arcee-SuperNova-v1", "base_model:quantized:arcee-ai/Arcee-SuperNova-v1", "license:llama3", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

text-generation

2025-06-18T16:38:17Z

--- quantized_by: bartowski pipeline_tag: text-generation base_model: arcee-ai/Arcee-SuperNova-v1 license: llama3 base_model_relation: quantized --- ## Llamacpp imatrix Quantizations of Arcee-SuperNova-v1 by arcee-ai Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5697">b5697</a> for quantization. Original model: https://huggingface.co/arcee-ai/Arcee-SuperNova-v1 All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) Run them in [LM Studio](https://lmstudio.ai/) Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project ## Prompt format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> Cutting Knowledge Date: December 2023 Today Date: 26 Jul 2024 {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ## Download a file (not the whole branch) from below: | Filename | Quant type | File Size | Split | Description | | -------- | ---------- | --------- | ----- | ----------- | | [Arcee-SuperNova-v1-Q8_0.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/tree/main/arcee-ai_Arcee-SuperNova-v1-Q8_0) | Q8_0 | 74.98GB | true | Extremely high quality, generally unneeded but max available quant. | | [Arcee-SuperNova-v1-Q6_K.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/tree/main/arcee-ai_Arcee-SuperNova-v1-Q6_K) | Q6_K | 57.89GB | true | Very high quality, near perfect, *recommended*. | | [Arcee-SuperNova-v1-Q5_K_M.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/tree/main/arcee-ai_Arcee-SuperNova-v1-Q5_K_M) | Q5_K_M | 49.95GB | true | High quality, *recommended*. | | [Arcee-SuperNova-v1-Q5_K_S.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q5_K_S.gguf) | Q5_K_S | 48.66GB | false | High quality, *recommended*. | | [Arcee-SuperNova-v1-Q4_1.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q4_1.gguf) | Q4_1 | 44.31GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. | | [Arcee-SuperNova-v1-Q4_K_L.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q4_K_L.gguf) | Q4_K_L | 43.30GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. | | [Arcee-SuperNova-v1-Q4_K_M.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q4_K_M.gguf) | Q4_K_M | 42.52GB | false | Good quality, default size for most use cases, *recommended*. | | [Arcee-SuperNova-v1-Q4_K_S.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q4_K_S.gguf) | Q4_K_S | 40.35GB | false | Slightly lower quality with more space savings, *recommended*. | | [Arcee-SuperNova-v1-Q4_0.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q4_0.gguf) | Q4_0 | 40.12GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. | | [Arcee-SuperNova-v1-IQ4_NL.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ4_NL.gguf) | IQ4_NL | 40.05GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. | | [Arcee-SuperNova-v1-Q3_K_XL.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q3_K_XL.gguf) | Q3_K_XL | 38.06GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. | | [Arcee-SuperNova-v1-IQ4_XS.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ4_XS.gguf) | IQ4_XS | 37.90GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | | [Arcee-SuperNova-v1-Q3_K_L.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q3_K_L.gguf) | Q3_K_L | 37.14GB | false | Lower quality but usable, good for low RAM availability. | | [Arcee-SuperNova-v1-Q3_K_M.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q3_K_M.gguf) | Q3_K_M | 34.27GB | false | Low quality. | | [Arcee-SuperNova-v1-IQ3_M.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ3_M.gguf) | IQ3_M | 31.94GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | [Arcee-SuperNova-v1-Q3_K_S.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q3_K_S.gguf) | Q3_K_S | 30.91GB | false | Low quality, not recommended. | | [Arcee-SuperNova-v1-IQ3_XS.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ3_XS.gguf) | IQ3_XS | 29.31GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. | | [Arcee-SuperNova-v1-IQ3_XXS.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ3_XXS.gguf) | IQ3_XXS | 27.47GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. | | [Arcee-SuperNova-v1-Q2_K_L.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q2_K_L.gguf) | Q2_K_L | 27.40GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. | | [Arcee-SuperNova-v1-Q2_K.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-Q2_K.gguf) | Q2_K | 26.38GB | false | Very low quality but surprisingly usable. | | [Arcee-SuperNova-v1-IQ2_M.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ2_M.gguf) | IQ2_M | 24.12GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. | | [Arcee-SuperNova-v1-IQ2_S.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ2_S.gguf) | IQ2_S | 22.24GB | false | Low quality, uses SOTA techniques to be usable. | | [Arcee-SuperNova-v1-IQ2_XS.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ2_XS.gguf) | IQ2_XS | 21.14GB | false | Low quality, uses SOTA techniques to be usable. | | [Arcee-SuperNova-v1-IQ2_XXS.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ2_XXS.gguf) | IQ2_XXS | 19.10GB | false | Very low quality, uses SOTA techniques to be usable. | | [Arcee-SuperNova-v1-IQ1_M.gguf](https://huggingface.co/bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF/blob/main/arcee-ai_Arcee-SuperNova-v1-IQ1_M.gguf) | IQ1_M | 16.75GB | false | Extremely low quality, *not* recommended. | ## Embed/output weights Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. ## Downloading using huggingface-cli <details> <summary>Click to view download instructions</summary> First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF --include "arcee-ai_Arcee-SuperNova-v1-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/arcee-ai_Arcee-SuperNova-v1-GGUF --include "arcee-ai_Arcee-SuperNova-v1-Q8_0/*" --local-dir ./ ``` You can either specify a new local-dir (arcee-ai_Arcee-SuperNova-v1-Q8_0) or download them all in place (./) </details> ## ARM/AVX information Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass. Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly. As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0. Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase. <details> <summary>Click to view Q4_0_X_X information (deprecated</summary> I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking. <details> <summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary> | model | size | params | backend | threads | test | t/s | % (vs Q4_0) | | ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: | | qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% | | qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% | | qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% | | qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% | | qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% | | qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% | | qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% | | qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% | | qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% | | qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% | | qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% | | qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% | | qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% | | qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% | | qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% | | qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% | | qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% | | qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% | Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation </details> </details> ## Which file should I choose? <details> <summary>Click here for details</summary> A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. </details> ## Credits Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset. Thank you ZeroWw for the inspiration to experiment with embed/output. Thank you to LM Studio for sponsoring my work. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

RAFA-MARTINS-E-CADEIRANTE-8/18.RAFA.MARTINS.E.CADEIRANTE.VIDEO.RAFA.MARTTINZ.EROME

RAFA-MARTINS-E-CADEIRANTE-8

2025-06-18T19:36:01Z

0

null

[ "region:us" ]

null

2025-06-18T19:32:01Z

[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=RAFA-MARTINS-E-CADEIRANTE) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=RAFA-MARTINS-E-CADEIRANTE) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=RAFA-MARTINS-E-CADEIRANTE)

dgambettaphd/M_llm2_run2_gen8_WXS_doc1000_synt120_lr1e-04_acm_SYNLAST

dgambettaphd

2025-06-18T19:33:10Z

0

transformers

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

null

2025-06-18T19:32:56Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Urbainnoel00/car_selling_price_reedit

Urbainnoel00

2025-06-18T19:33:04Z

0

null

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

null

2025-06-18T18:27:55Z

--- license: apache-2.0 ---

Victoriayu/weighting_default

Victoriayu

2025-06-18T19:26:20Z

0

transformers

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

text-generation

2025-06-18T19:21:54Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

thecity2/ppo-Huggy

thecity2

2025-06-18T19:24:13Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2025-06-18T19:24:09Z

--- 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: thecity2/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ElizabethSrgh/results_topic

ElizabethSrgh

2025-06-18T19:23:32Z

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:indobenchmark/indobert-base-p1", "base_model:finetune:indobenchmark/indobert-base-p1", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-06-18T19:22:53Z

--- library_name: transformers license: mit base_model: indobenchmark/indobert-base-p1 tags: - generated_from_trainer model-index: - name: results_topic results: [] ---  # results_topic This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 3 ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1

morturr/Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb1-seed28-2025-06-18

morturr

2025-06-18T19:21:49Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]

null

2025-06-18T19:21:40Z

--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb1-seed28-2025-06-18 results: [] ---  # Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb1-seed28-2025-06-18 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 28 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - 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: 2 ### 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

new-RAFA-MARTINS-E-CADEIRANTE-18k/8.RAFA.MARTINS.E.CADEIRANTE.VIDEO.RAFA.MARTTINZ.EROME

new-RAFA-MARTINS-E-CADEIRANTE-18k

2025-06-18T19:20:46Z

0

null

[ "region:us" ]

null

2025-06-18T19:16:24Z

[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?RAFA-MARTINS-E-CADEIRANTE) [🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?RAFA-MARTINS-E-CADEIRANTE) [🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?RAFA-MARTINS-E-CADEIRANTE)

haider-cheema28/llama3-conspiracy-model

haider-cheema28

2025-06-18T19:20:02Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-06-18T19:13:17Z

--- base_model: unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** haider-cheema28 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.1-8b-instruct-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)

hikkohhh/fgdfgf

hikkohhh

2025-06-18T19:17:00Z

0

null

[ "license:deepfloyd-if-license", "region:us" ]

null

2025-06-18T19:16:47Z

--- license: deepfloyd-if-license ---

prakod/codemix-indicBART_L1_to_CM_candidates_acc4.7

prakod

2025-06-18T19:14:59Z

3

0

transformers

[ "transformers", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:ai4bharat/IndicBART", "base_model:finetune:ai4bharat/IndicBART", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2025-06-13T06:01:16Z

--- library_name: transformers base_model: ai4bharat/IndicBART tags: - generated_from_trainer metrics: - bleu model-index: - name: codemix-indicBART_L1_to_CM_candidates_acc4.7 results: [] ---  # codemix-indicBART_L1_to_CM_candidates_acc4.7 This model is a fine-tuned version of [ai4bharat/IndicBART](https://huggingface.co/ai4bharat/IndicBART) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2986 - Bleu: 11.9231 - Gen Len: 21.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use 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: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:------:|:-----:|:---------------:|:-------:|:-------:| | 3.7106 | 1.0 | 7546 | 3.3985 | 13.2137 | 21.0 | | 3.2584 | 2.0 | 15092 | 2.8989 | 12.9778 | 20.992 | | 2.9447 | 3.0 | 22638 | 2.5509 | 14.0866 | 21.0 | | 2.7786 | 4.0 | 30184 | 2.3583 | 12.4674 | 21.0 | | 2.7111 | 4.9994 | 37725 | 2.2986 | 11.9231 | 21.0 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1

New-tutorial-Trishakar-Madhu-18-Videos/FULL.VIDEO.Trishakar.Madhu.Viral.Video.Tutorial.Official

New-tutorial-Trishakar-Madhu-18-Videos

2025-06-18T19:14:13Z

0

null

[ "region:us" ]

null

2025-06-18T19:13:55Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

tomaarsen/splade-cocondenser-msmarco-margin-mse-minilm-small

tomaarsen

2025-06-18T19:12:12Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "sparse-encoder", "sparse", "splade", "generated_from_trainer", "dataset_size:90000", "loss:SpladeLoss", "loss:SparseMarginMSELoss", "loss:FlopsLoss", "feature-extraction", "en", "dataset:sentence-transformers/msmarco", "arxiv:1908.10084", "arxiv:2205.04733", "arxiv:2010.02666", "arxiv:2004.05665", "base_model:Luyu/co-condenser-marco", "base_model:finetune:Luyu/co-condenser-marco", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2025-06-18T17:23:00Z

--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - splade - generated_from_trainer - dataset_size:90000 - loss:SpladeLoss - loss:SparseMarginMSELoss - loss:FlopsLoss base_model: Luyu/co-condenser-marco widget: - text: why did shay rebellion happened - text: what type of company is red bull - text: During flu season, having a scratchy throat or body aches can signal the arrival of the virus. Learn how to identify the flu from its early symptoms. During flu season, having a scratchy throat or body aches can signal the arrival of the virus. - text: what should BMI be - text: "Playing Chicken with the $18 Trillion U.S. Economy: The full cost of the\ \ last government shutdown two years ago was staggering â\x80\x93 it delivered\ \ a $24 billion blow to the U.S. economy and taxpayers. Now we may be about to\ \ repeat government shutdown history on Dec. 11." datasets: - sentence-transformers/msmarco pipeline_tag: feature-extraction library_name: sentence-transformers metrics: - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 - query_active_dims - query_sparsity_ratio - corpus_active_dims - corpus_sparsity_ratio co2_eq_emissions: emissions: 84.06930727910806 energy_consumed: 0.21628215774322765 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.609 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: CoCondenser trained on MS MARCO results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.48 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.62 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.76 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.48 name: Dot Precision@1 - type: dot_precision@3 value: 0.20666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.15200000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.48 name: Dot Recall@1 - type: dot_recall@3 value: 0.62 name: Dot Recall@3 - type: dot_recall@5 value: 0.76 name: Dot Recall@5 - type: dot_recall@10 value: 0.9 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6684763452349373 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5974444444444444 name: Dot Mrr@10 - type: dot_map@100 value: 0.6011320299771912 name: Dot Map@100 - type: query_active_dims value: 57.08000183105469 name: Query Active Dims - type: query_sparsity_ratio value: 0.9981298734738532 name: Query Sparsity Ratio - type: corpus_active_dims value: 187.31886291503906 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9938628247521448 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.4 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.58 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.68 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4 name: Dot Precision@1 - type: dot_precision@3 value: 0.3733333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.32799999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.27399999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.041810186403109253 name: Dot Recall@1 - type: dot_recall@3 value: 0.09615070860440549 name: Dot Recall@3 - type: dot_recall@5 value: 0.11743329753944677 name: Dot Recall@5 - type: dot_recall@10 value: 0.14239045751324736 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.341123829860237 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5051666666666667 name: Dot Mrr@10 - type: dot_map@100 value: 0.15319082806058607 name: Dot Map@100 - type: query_active_dims value: 50.459999084472656 name: Query Active Dims - type: query_sparsity_ratio value: 0.9983467662969506 name: Query Sparsity Ratio - type: corpus_active_dims value: 331.6617126464844 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9891336834857977 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.5 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.74 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.76 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5 name: Dot Precision@1 - type: dot_precision@3 value: 0.2533333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.156 name: Dot Precision@5 - type: dot_precision@10 value: 0.08999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.46 name: Dot Recall@1 - type: dot_recall@3 value: 0.69 name: Dot Recall@3 - type: dot_recall@5 value: 0.7 name: Dot Recall@5 - type: dot_recall@10 value: 0.79 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6443552260079133 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6170238095238095 name: Dot Mrr@10 - type: dot_map@100 value: 0.5954754748237253 name: Dot Map@100 - type: query_active_dims value: 54.099998474121094 name: Query Active Dims - type: query_sparsity_ratio value: 0.9982275080769897 name: Query Sparsity Ratio - type: corpus_active_dims value: 211.63475036621094 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9930661571860884 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: dot_accuracy@1 value: 0.45999999999999996 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6466666666666666 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7200000000000001 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7999999999999999 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.45999999999999996 name: Dot Precision@1 - type: dot_precision@3 value: 0.27777777777777773 name: Dot Precision@3 - type: dot_precision@5 value: 0.212 name: Dot Precision@5 - type: dot_precision@10 value: 0.1513333333333333 name: Dot Precision@10 - type: dot_recall@1 value: 0.32727006213436977 name: Dot Recall@1 - type: dot_recall@3 value: 0.4687169028681351 name: Dot Recall@3 - type: dot_recall@5 value: 0.5258110991798156 name: Dot Recall@5 - type: dot_recall@10 value: 0.6107968191710825 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5513184670343625 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5732116402116402 name: Dot Mrr@10 - type: dot_map@100 value: 0.4499327776205009 name: Dot Map@100 - type: query_active_dims value: 53.87999979654948 name: Query Active Dims - type: query_sparsity_ratio value: 0.9982347159492645 name: Query Sparsity Ratio - type: corpus_active_dims value: 229.42422156545794 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9924833162451524 name: Corpus Sparsity Ratio --- # CoCondenser trained on MS MARCO This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) on the [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. ## Model Details ### Model Description - **Model Type:** SPLADE Sparse Encoder - **Base model:** [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco)  - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product - **Training Dataset:** - [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) ### Full Model Architecture ``` SparseEncoder( (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522}) ) ``` ## 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 SparseEncoder # Download from the 🤗 Hub model = SparseEncoder("tomaarsen/splade-cocondenser-msmarco-margin-mse-minilm-small") # Run inference queries = [ "how much would dreamers cost the taxpayers", ] documents = [ 'Plus, the CBO said the Dreamers would bring an additional 80,000 immigrants to the U.S., adding to the liability. In total, the immigrants and their families would cost taxpayers $26.8 billion, but only pay back $.9 billion in taxes, the CBO said. The analysis found that roughly 3.25 million undocumented immigrants are eligible for Dreamer status, while only 2 million would apply and only 1.6 million would be accepted over the next decade.', 'Playing Chicken with the $18 Trillion U.S. Economy: The full cost of the last government shutdown two years ago was staggering â\x80\x93 it delivered a $24 billion blow to the U.S. economy and taxpayers. Now we may be about to repeat government shutdown history on Dec. 11.', 'Sustain is defined as to support something or to endure a trial or hardship. 1 An example of sustain is for a foundation to support the house. 2 An example of sustain is to survive days without food or water.', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 30522] [3, 30522] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[27.1439, 12.7876, 0.6402]]) ```    ## Evaluation ### Metrics #### Sparse Information Retrieval * Datasets: `NanoMSMARCO`, `NanoNFCorpus` and `NanoNQ` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) | Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ | |:----------------------|:------------|:-------------|:-----------| | dot_accuracy@1 | 0.48 | 0.4 | 0.5 | | dot_accuracy@3 | 0.62 | 0.58 | 0.74 | | dot_accuracy@5 | 0.76 | 0.64 | 0.76 | | dot_accuracy@10 | 0.9 | 0.68 | 0.82 | | dot_precision@1 | 0.48 | 0.4 | 0.5 | | dot_precision@3 | 0.2067 | 0.3733 | 0.2533 | | dot_precision@5 | 0.152 | 0.328 | 0.156 | | dot_precision@10 | 0.09 | 0.274 | 0.09 | | dot_recall@1 | 0.48 | 0.0418 | 0.46 | | dot_recall@3 | 0.62 | 0.0962 | 0.69 | | dot_recall@5 | 0.76 | 0.1174 | 0.7 | | dot_recall@10 | 0.9 | 0.1424 | 0.79 | | **dot_ndcg@10** | **0.6685** | **0.3411** | **0.6444** | | dot_mrr@10 | 0.5974 | 0.5052 | 0.617 | | dot_map@100 | 0.6011 | 0.1532 | 0.5955 | | query_active_dims | 57.08 | 50.46 | 54.1 | | query_sparsity_ratio | 0.9981 | 0.9983 | 0.9982 | | corpus_active_dims | 187.3189 | 331.6617 | 211.6348 | | corpus_sparsity_ratio | 0.9939 | 0.9891 | 0.9931 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ] } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.46 | | dot_accuracy@3 | 0.6467 | | dot_accuracy@5 | 0.72 | | dot_accuracy@10 | 0.8 | | dot_precision@1 | 0.46 | | dot_precision@3 | 0.2778 | | dot_precision@5 | 0.212 | | dot_precision@10 | 0.1513 | | dot_recall@1 | 0.3273 | | dot_recall@3 | 0.4687 | | dot_recall@5 | 0.5258 | | dot_recall@10 | 0.6108 | | **dot_ndcg@10** | **0.5513** | | dot_mrr@10 | 0.5732 | | dot_map@100 | 0.4499 | | query_active_dims | 53.88 | | query_sparsity_ratio | 0.9982 | | corpus_active_dims | 229.4242 | | corpus_sparsity_ratio | 0.9925 |   ## Training Details ### Training Dataset #### msmarco * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83) * Size: 90,000 training samples * Columns: <code>score</code>, <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | score | query | positive | negative | |:--------|:--------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | float | string | string | string | | details | <ul><li>min: -1.28</li><li>mean: 13.47</li><li>max: 22.27</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.97 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 81.41 tokens</li><li>max: 220 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 76.39 tokens</li><li>max: 195 tokens</li></ul> | * Samples: | score | query | positive | negative | |:--------------------------------|:-------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>13.85562777519226</code> | <code>what is a reflective journal?</code> | <code>A reflective journal is a tool that students are encouraged to use to help them understand not just what they have learned while studying but also how they learned it by reflecting on the learning experience itself.</code> | <code>The point is that this approach believes that literature can be used to illuminate some truth about something which is not literature. The difference between this approach and the didactic approach is that didactic approach considers author as a teacher, while the reflective approach considers him or her an observer.</code> | | <code>12.178914229075115</code> | <code>original footloose release</code> | <code>Footloose (2011 film) Footloose is a 2011 American musical dance film directed by Craig Brewer. It is a remake of the 1984 film of the same name and stars Kenny Wormald, Julianne Hough, Andie MacDowell, and Dennis Quaid. The film follows a young man who moves from Boston to a small southern town and protests the town's ban against dancing.</code> | <code>In the 2001 re-release of Thriller they added the second verse of the rap which was recorded but not included on the original here is the second verse by Vincent Price (I heard a 3rd was written but never recorded) The demons squeal in sheer delight. It's you they spy, so plump, so right.</code> | | <code>19.897210280100506</code> | <code>time of day blood pressure</code> | <code>Day Time Blood Pressure. For most people, your body's blood pressure rises during the morning hours and reaches its highest point around midday. This is because your body is preset to increase its functions for anticipated daily activity. Your body reaches its lowest blood pressure at bedtime, between 8 p.m. and 2 a.m.</code> | <code>labetalol is used alone or together with other medicines to treat high blood pressure hypertension high blood pressure adds to the workload of the heart and arteriesif it continues for a long time the heart and arteries may not function properlyabetalol is used alone or together with other medicines to treat high blood pressure hypertension high blood pressure adds to the workload of the heart and arteries</code> | * Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseMarginMSELoss", "lambda_corpus": 0.08, "lambda_query": 0.1 } ``` ### Evaluation Dataset #### msmarco * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83) * Size: 10,000 evaluation samples * Columns: <code>score</code>, <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | score | query | positive | negative | |:--------|:-------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | float | string | string | string | | details | <ul><li>min: -2.25</li><li>mean: 13.3</li><li>max: 22.52</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.31 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 79.88 tokens</li><li>max: 227 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 77.64 tokens</li><li>max: 250 tokens</li></ul> | * Samples: | score | query | positive | negative | |:--------------------------------|:-------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>11.338554302851358</code> | <code>victor cruz dance salsa in the super bowl</code> | <code>The popular former Giant â who helped lead the team to a Super Bowl title in the 2011 season â ... Victor Cruz performed his first salsa dance in Chicago. The popular former Giant â who helped lead the team to a Super Bowl title in the 2011 season â caught a 2-yard touchdown pass from Mitch Trubisky in the Bearsâ 24-17 preseason loss to the Broncos on Thursday night.</code> | <code>Victor Cruz, Giants hammer out deal. Receiver Victor Cruz on Monday signed a six-year contract through the 2018 season with the New York Giants. The contract is worth $46 million and pays him $15.625 million fully guaranteed the first two seasons, a source said.</code> | | <code>18.167373975118</code> | <code>what is the phone number for roblox</code> | <code>im calling roblox hq and the roblox number is 888 858 2569 or if u live in canada its 1888 858 2569 subscibe to us (waffleman514 and twitterelgo) and join our youtube group on our profile Category</code> | <code>[edit] Create A New Place. This is where you define where your game will be published. 1 Go to Roblox.com and login. 2 Click My ROBLOX and then click Places. 3 Click Create Game Place. 4 Fill out the form. 5 Name is the name of the game.</code> | | <code>17.668365399042766</code> | <code>can you freeze cream soup</code> | <code>With a modest investment in time and effort, you can make your own cream of mushroom soup and freeze it for later use. This leaves you firmly in control of the soup's ingredients and enables you to portion the soup in quantities that make sense for you.</code> | <code>I purchased 1.5 lbs of 3 large boneless, skinless chicken breasts. I am cooking them in a crockpot with 1 can of cream of mushroom soup, 1 can cream of chicken soup, 1 can of water and some canned mushrooms...the chicken is all at the bottom. About how long will it take the chicken to cook completely if I set my... show more I purchased 1.5 lbs of 3 large boneless, skinless chicken breasts.</code> | * Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseMarginMSELoss", "lambda_corpus": 0.08, "lambda_query": 0.1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `load_best_model_at_end`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 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`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `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`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs | Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | |:----------:|:--------:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:| | 0.0178 | 100 | 795934.08 | - | - | - | - | - | | 0.0356 | 200 | 13561.4538 | - | - | - | - | - | | 0.0533 | 300 | 118.2925 | - | - | - | - | - | | 0.0711 | 400 | 61.485 | - | - | - | - | - | | 0.0889 | 500 | 44.6503 | 38.6276 | 0.5126 | 0.2701 | 0.5829 | 0.4552 | | 0.1067 | 600 | 38.3666 | - | - | - | - | - | | 0.1244 | 700 | 35.2046 | - | - | - | - | - | | 0.1422 | 800 | 33.2246 | - | - | - | - | - | | 0.16 | 900 | 31.5866 | - | - | - | - | - | | 0.1778 | 1000 | 29.3914 | 38.9004 | 0.5849 | 0.3140 | 0.6009 | 0.4999 | | 0.1956 | 1100 | 28.9009 | - | - | - | - | - | | 0.2133 | 1200 | 29.5258 | - | - | - | - | - | | 0.2311 | 1300 | 27.7958 | - | - | - | - | - | | 0.2489 | 1400 | 27.0228 | - | - | - | - | - | | 0.2667 | 1500 | 25.0953 | 22.5132 | 0.6090 | 0.3377 | 0.6166 | 0.5211 | | 0.2844 | 1600 | 25.4396 | - | - | - | - | - | | 0.3022 | 1700 | 22.53 | - | - | - | - | - | | 0.32 | 1800 | 24.0084 | - | - | - | - | - | | 0.3378 | 1900 | 23.5741 | - | - | - | - | - | | 0.3556 | 2000 | 23.141 | 22.6775 | 0.6408 | 0.3560 | 0.5984 | 0.5317 | | 0.3733 | 2100 | 22.0953 | - | - | - | - | - | | 0.3911 | 2200 | 22.2789 | - | - | - | - | - | | 0.4089 | 2300 | 20.9582 | - | - | - | - | - | | 0.4267 | 2400 | 19.1969 | - | - | - | - | - | | 0.4444 | 2500 | 21.047 | 28.3245 | 0.6209 | 0.3487 | 0.6260 | 0.5319 | | 0.4622 | 2600 | 20.7531 | - | - | - | - | - | | 0.48 | 2700 | 19.8115 | - | - | - | - | - | | 0.4978 | 2800 | 18.6278 | - | - | - | - | - | | 0.5156 | 2900 | 19.3731 | - | - | - | - | - | | 0.5333 | 3000 | 18.4502 | 20.3191 | 0.6390 | 0.3506 | 0.6087 | 0.5328 | | 0.5511 | 3100 | 18.4525 | - | - | - | - | - | | 0.5689 | 3200 | 17.0456 | - | - | - | - | - | | 0.5867 | 3300 | 17.256 | - | - | - | - | - | | 0.6044 | 3400 | 17.6203 | - | - | - | - | - | | **0.6222** | **3500** | **18.7721** | **17.7983** | **0.6685** | **0.3411** | **0.6444** | **0.5513** | | 0.64 | 3600 | 16.7819 | - | - | - | - | - | | 0.6578 | 3700 | 18.6132 | - | - | - | - | - | | 0.6756 | 3800 | 15.5466 | - | - | - | - | - | | 0.6933 | 3900 | 17.7706 | - | - | - | - | - | | 0.7111 | 4000 | 16.6612 | 15.7565 | 0.6727 | 0.3519 | 0.6159 | 0.5468 | | 0.7289 | 4100 | 16.4755 | - | - | - | - | - | | 0.7467 | 4200 | 16.9832 | - | - | - | - | - | | 0.7644 | 4300 | 14.9855 | - | - | - | - | - | | 0.7822 | 4400 | 14.6835 | - | - | - | - | - | | 0.8 | 4500 | 17.0725 | 18.0495 | 0.6652 | 0.3430 | 0.6423 | 0.5502 | | 0.8178 | 4600 | 15.8136 | - | - | - | - | - | | 0.8356 | 4700 | 15.6528 | - | - | - | - | - | | 0.8533 | 4800 | 15.5791 | - | - | - | - | - | | 0.8711 | 4900 | 15.1496 | - | - | - | - | - | | 0.8889 | 5000 | 14.7461 | 16.4918 | 0.6373 | 0.3353 | 0.6403 | 0.5376 | | 0.9067 | 5100 | 16.3102 | - | - | - | - | - | | 0.9244 | 5200 | 14.5521 | - | - | - | - | - | | 0.9422 | 5300 | 14.4375 | - | - | - | - | - | | 0.96 | 5400 | 15.2282 | - | - | - | - | - | | 0.9778 | 5500 | 14.4738 | 15.4439 | 0.6426 | 0.3385 | 0.6334 | 0.5382 | | 0.9956 | 5600 | 14.6468 | - | - | - | - | - | | -1 | -1 | - | - | 0.6685 | 0.3411 | 0.6444 | 0.5513 | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.216 kWh - **Carbon Emitted**: 0.084 kg of CO2 - **Hours Used**: 0.609 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.1 - Datasets: 2.21.0 - 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", } ``` #### SpladeLoss ```bibtex @misc{formal2022distillationhardnegativesampling, title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective}, author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant}, year={2022}, eprint={2205.04733}, archivePrefix={arXiv}, primaryClass={cs.IR}, url={https://arxiv.org/abs/2205.04733}, } ``` #### SparseMarginMSELoss ```bibtex @misc{hofstätter2021improving, title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation}, author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury}, year={2021}, eprint={2010.02666}, archivePrefix={arXiv}, primaryClass={cs.IR} } ``` #### FlopsLoss ```bibtex @article{paria2020minimizing, title={Minimizing flops to learn efficient sparse representations}, author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s}, journal={arXiv preprint arXiv:2004.05665}, year={2020} } ```

TVRRaviteja/llama3.1-mental-health-therapy-SFT

TVRRaviteja

2025-06-18T19:07:55Z

4

0

transformers

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

text-generation

2025-06-17T10:14:05Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

kevin510/ACT-SO100-Draw

kevin510

2025-06-18T19:06:40Z

0

null

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

null

2025-06-18T17:52:31Z

--- license: apache-2.0 --- # 🖊️ ACT-SO100-Draw Action Chunking Transformer (ACT) checkpoint for **drawing with a custom pen-holding attachment on the SO-100 and SO-101 robotic arms**. ![pen_tool_photo](./assets/tool.jpg) *3-D-printed pen mount designed for SO-100 and SO-101 robotic arms.** Tool STL is available for download in the [SO-100 Tools repository](https://github.com/krohling/so-100-tools). --- ## Demo ![Training Results](./assets/demo.gif) --- ## Dataset | Name | Episodes | Frames / episode | Modalities | | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- | ---------------- | ----------------------------------------- | | [370-drawn-to-caffeine-draw-smiley](https://huggingface.co/spaces/lerobot/visualize_dataset?path=%2FLeRobot-worldwide-hackathon%2F370-drawn-to-caffeine-draw-smiley%2Fepisode_0) | 42 | \~450 | RGB 640×480, proprio 5-DoF, gripper state | ## Training Details See run details on wandb for more information: [wandb run](https://wandb.ai/kevin_ai/lerobot_hackathon/runs/ahu8fcc0). | Hyper-parameter | Value | | ------------------- | ---------------------------------- | | Chunk size | 100 | | Dim Feedforward | 3200 | | Dim Model | 512 | | Dropout | 0.1 | | Feedforward Activation | ReLU | | Decoder layers | 1 | | Encoder layers | 4 | | Attention heads | 8 | | VAE Encoder layers | 4 | | Batch size | 32 | | Optimizer | AdamW, lr = 1e-5 | ## Citation If you use this checkpoint in your work, please cite the following: ```bibtex @misc{Rohling2025ACTSO100Draw, author = {Kevin Rohling}, title = {ACT Checkpoint for Pen-Drawing on SO-100}, year = {2025}, howpublished = {\url{https://huggingface.co/kevin510/ACT-SO100-Draw}} } ```

dicksonhk/Qwen2.5-VL-3B-Instruct-AWQ-mlx-4Bit

dicksonhk

2025-06-18T19:05:01Z

0

transformers

[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "multimodal", "mlx", "mlx-my-repo", "conversational", "en", "base_model:Qwen/Qwen2.5-VL-3B-Instruct-AWQ", "base_model:quantized:Qwen/Qwen2.5-VL-3B-Instruct-AWQ", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]

image-text-to-text

2025-06-18T17:30:16Z

--- license_name: qwen-research license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct-AWQ/blob/main/LICENSE language: - en pipeline_tag: image-text-to-text tags: - multimodal - mlx - mlx-my-repo library_name: transformers base_model: Qwen/Qwen2.5-VL-3B-Instruct-AWQ --- # dicksonhk/Qwen2.5-VL-3B-Instruct-AWQ-mlx-4Bit The Model [dicksonhk/Qwen2.5-VL-3B-Instruct-AWQ-mlx-4Bit](https://huggingface.co/dicksonhk/Qwen2.5-VL-3B-Instruct-AWQ-mlx-4Bit) was converted to $MLX format from [Qwen/Qwen2.5-VL-3B-Instruct-AWQ](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct-AWQ) using $mlx-vlm version **0.1.15**. ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model dicksonhk/Qwen2.5-VL-3B-Instruct-AWQ-mlx-4Bit --max-tokens 100 --temp 0.0 --prompt "Describe this image." --image <path_to_image> ```

Real-Madrid-Al-Hilal-Direct-Videos/Real.Madrid.Al-Hilal.En.Direct.Streaming.Gratuit.tv.Official

Real-Madrid-Al-Hilal-Direct-Videos

2025-06-18T19:03:04Z

0

null

[ "region:us" ]

null

2025-06-18T19:02:47Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/mrmpsap6?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

mlfoundations-cua-dev/uitars_add_new_advanced_synthetic_typing_data

mlfoundations-cua-dev

2025-06-18T19:00:11Z

0

null

[ "region:us" ]

null

2025-06-18T18:29:16Z

# idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_add_new_advanced_synthetic_typing_data ## Model Information **Full Model Name**: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_add_new_advanced_synthetic_typing_data` **Repository Name**: `mlfoundations-cua-dev/uitars_add_new_advanced_synthetic_typing_data` **Model Directory**: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_add_new_advanced_synthetic_typing_data` **Checkpoint Used**: `idm_tars_1.5_7b_frame_pairs_896x896_lr_1e-5_10_epochs_500_steps_gbs_8_wd_0.1_max_grad_norm_1.0_add_new_advanced_synthetic_typing_data/checkpoint_epoch_9.pt` ## Model Configuration - **Model Version**: TARS 1.5 - **Model Size**: 7B parameters - **Data Type**: Frame pairs - **Learning Rate**: 1e-5 - **Epochs**: 10 - **Training Steps**: 500 - **Global Batch Size**: 8 - **Weight Decay**: 0.1 - **Max Gradient Norm**: 1.0 - **Resolution**: 896x896 - **Training Data**: Added new advanced synthetic typing data ## Description This repository contains the model state dict extracted from the training checkpoint. ### Files - `model_state_dict.pt`: PyTorch state dictionary containing the model weights - `README.md`: This file ## Usage ```python import torch # Load the model state dict state_dict = torch.load("model_state_dict.pt", map_location='cpu') # Use with your model architecture # model.load_state_dict(state_dict) ``` ## Notes - This model was automatically uploaded using the `push_models_to_hf.py` script - The repository name may be truncated if the original model name exceeded HuggingFace's 96-character limit - Checkpoint extracted from: `checkpoint_epoch_9.pt`

videos-Sajal-Malik-18-Viral-Video-Link/FULL.VIDEO.Sajal.Malik.Viral.Video.Tutorial.Official

videos-Sajal-Malik-18-Viral-Video-Link

2025-06-18T18:59:38Z

0

null

[ "region:us" ]

null

2025-06-18T18:59:22Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

sgonzalezygil/sd-finetuning-dreambooth-v13-1200

sgonzalezygil

2025-06-18T18:54:43Z

0

diffusers

[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2025-06-18T18:53:09Z

--- library_name: diffusers --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

GraybeardTheIrate/Cogwheel-Pantheon

GraybeardTheIrate

2025-06-18T18:52:27Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:Gryphe/Pantheon-RP-1.8-24b-Small-3.1", "base_model:merge:Gryphe/Pantheon-RP-1.8-24b-Small-3.1", "base_model:OddTheGreat/Cogwheel_24b_V.2", "base_model:merge:OddTheGreat/Cogwheel_24b_V.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-18T18:30:44Z

--- base_model: - Gryphe/Pantheon-RP-1.8-24b-Small-3.1 - OddTheGreat/Cogwheel_24b_V.2 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * [Gryphe/Pantheon-RP-1.8-24b-Small-3.1](https://huggingface.co/Gryphe/Pantheon-RP-1.8-24b-Small-3.1) * [OddTheGreat/Cogwheel_24b_V.2](https://huggingface.co/OddTheGreat/Cogwheel_24b_V.2) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Gryphe/Pantheon-RP-1.8-24b-Small-3.1 - model: OddTheGreat/Cogwheel_24b_V.2 merge_method: slerp base_model: OddTheGreat/Cogwheel_24b_V.2 dtype: bfloat16 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 ```

RaghavendraSqwish/qwen_orpo

RaghavendraSqwish

2025-06-18T18:48:44Z

15

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen3-0.6B", "base_model:finetune:unsloth/Qwen3-0.6B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-06-17T12:11:47Z

--- base_model: unsloth/Qwen3-0.6B tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** RaghavendraSqwish - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen3-0.6B 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)

sgonzalezygil/sd-finetuning-dreambooth-v13

sgonzalezygil

2025-06-18T18:48:39Z

0

diffusers

[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2025-06-18T18:46:43Z

--- library_name: diffusers --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

s0mecode/Qwen3-14B-Q4_K_M-GGUF

s0mecode

2025-06-18T18:45:15Z

0

transformers

[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-14B", "base_model:quantized:Qwen/Qwen3-14B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2025-06-18T18:44:44Z

--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-14B tags: - llama-cpp - gguf-my-repo --- # s0mecode/Qwen3-14B-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-14B`](https://huggingface.co/Qwen/Qwen3-14B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen3-14B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo s0mecode/Qwen3-14B-Q4_K_M-GGUF --hf-file qwen3-14b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo s0mecode/Qwen3-14B-Q4_K_M-GGUF --hf-file qwen3-14b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo s0mecode/Qwen3-14B-Q4_K_M-GGUF --hf-file qwen3-14b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo s0mecode/Qwen3-14B-Q4_K_M-GGUF --hf-file qwen3-14b-q4_k_m.gguf -c 2048 ```