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MarceauBBB/qwen3-0.6B-Base-ORPO-OpenAnswers	MarceauBBB	2025-05-27T17:26:46Z	21	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T21:14:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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. 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YannJD/MNLP_M2_dpo_model	YannJD	2025-05-27T17:26:32Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T15:43:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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aamijar/Llama-2-7b-hf-lora-r1024-boolq-portlora-epochs3	aamijar	2025-05-27T17:25:07Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T17:25:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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phospho-app/jmota27-ACT-boat_dataset-a4hrm	phospho-app	2025-05-27T17:24:45Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-05-27T15:18:56Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [jmota27/boat_dataset](https://huggingface.co/datasets/jmota27/boat_dataset) - Wandb run URL: None - Epochs: None - Batch size: 60 - Training steps: 8000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
RizhongLin/MNLP_M2_dpo_model_v3	RizhongLin	2025-05-27T17:24:24Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T17:21:59Z	--- library_name: transformers tags: - trl - dpo --- # 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]
AShi846/MNLP_M2_document_encoder	AShi846	2025-05-27T17:22:55Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "en", "dataset:s2orc", "dataset:flax-sentence-embeddings/stackexchange_xml", "dataset:ms_marco", "dataset:gooaq", "dataset:yahoo_answers_topics", "dataset:code_search_net", "dataset:search_qa", "dataset:eli5", "dataset:snli", "dataset:multi_nli", "dataset:wikihow", "dataset:natural_questions", "dataset:trivia_qa", "dataset:embedding-data/sentence-compression", "dataset:embedding-data/flickr30k-captions", "dataset:embedding-data/altlex", "dataset:embedding-data/simple-wiki", "dataset:embedding-data/QQP", "dataset:embedding-data/SPECTER", "dataset:embedding-data/PAQ_pairs", "dataset:embedding-data/WikiAnswers", "arxiv:1904.06472", "arxiv:2102.07033", "arxiv:2104.08727", "arxiv:1704.05179", "arxiv:1810.09305", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-27T14:33:12Z	--- language: en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - s2orc - flax-sentence-embeddings/stackexchange_xml - ms_marco - gooaq - yahoo_answers_topics - code_search_net - search_qa - eli5 - snli - multi_nli - wikihow - natural_questions - trivia_qa - embedding-data/sentence-compression - embedding-data/flickr30k-captions - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/QQP - embedding-data/SPECTER - embedding-data/PAQ_pairs - embedding-data/WikiAnswers pipeline_tag: sentence-similarity --- # all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(encoded_input) # Perform pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:") print(sentence_embeddings) ``` ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developed this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developed this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. \| Dataset \| Paper \| Number of training tuples \| \|--------------------------------------------------------\|:----------------------------------------:\|:--------------------------:\| \| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) \| [paper](https://arxiv.org/abs/1904.06472) \| 726,484,430 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 116,288,806 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 52,603,982 \| \| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 41,769,185 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs \| - \| 25,316,456 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs \| - \| 21,396,559 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs \| - \| 21,396,559 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) triplets \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 1,198,260 \| \| [Code Search](https://huggingface.co/datasets/code_search_net) \| - \| 1,151,414 \| \| [COCO](https://cocodataset.org/#home) Image captions \| [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) \| 828,395\| \| [SPECTER](https://github.com/allenai/specter) citation triplets \| [paper](https://doi.org/10.18653/v1/2020.acl-main.207) \| 684,100 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 659,896 \| \| [SearchQA](https://huggingface.co/datasets/search_qa) \| [paper](https://arxiv.org/abs/1704.05179) \| 582,261 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) \| 317,695 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) \| \| 304,525 \| \| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) \| [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) \| 277,230 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) \| \| 250,519 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) \| \| 250,460 \| \| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) \| [paper](https://www.aclweb.org/anthology/D13-1155/) \| 180,000 \| \| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) \| [paper](https://arxiv.org/abs/1810.09305) \| 128,542 \| \| [Altlex](https://github.com/chridey/altlex/) \| [paper](https://aclanthology.org/P16-1135.pdf) \| 112,696 \| \| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) \| [paper](https://www.aclweb.org/anthology/P11-2117/) \| 102,225 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [TriviaQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| Total \| \| 1,170,060,424** \|
OlofBen/HeartLM-v4.3	OlofBen	2025-05-27T17:22:46Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "unsloth", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-27T17:05:36Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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qwertsdcv/stage5	qwertsdcv	2025-05-27T17:20:42Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T13:27:15Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: stage5 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. --> # stage5 This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 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: 3.0 ### Framework versions - Transformers 4.50.1 - Pytorch 2.6.0+cu124 - Datasets 3.2.0 - Tokenizers 0.21.1
sajal09/dpo_model1	sajal09	2025-05-27T17:20:34Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T17:16:25Z	--- library_name: transformers tags: - trl - dpo --- # 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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ohjoonhee/siglip2-giant-rokn393-linear	ohjoonhee	2025-05-27T17:19:21Z	0	0	transformers	[ "transformers", "safetensors", "siglip", "image-classification", "vision", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T17:00:17Z	--- library_name: transformers tags: - image-classification - vision --- # 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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endre01/MNLP_M2_document_encoder	endre01	2025-05-27T17:18:24Z	0	0	sentence-transformers	[ "sentence-transformers", "pytorch", "tf", "rust", "onnx", "safetensors", "openvino", "bert", "feature-extraction", "sentence-similarity", "transformers", "en", "dataset:s2orc", "dataset:flax-sentence-embeddings/stackexchange_xml", "dataset:ms_marco", "dataset:gooaq", "dataset:yahoo_answers_topics", "dataset:code_search_net", "dataset:search_qa", "dataset:eli5", "dataset:snli", "dataset:multi_nli", "dataset:wikihow", "dataset:natural_questions", "dataset:trivia_qa", "dataset:embedding-data/sentence-compression", "dataset:embedding-data/flickr30k-captions", "dataset:embedding-data/altlex", "dataset:embedding-data/simple-wiki", "dataset:embedding-data/QQP", "dataset:embedding-data/SPECTER", "dataset:embedding-data/PAQ_pairs", "dataset:embedding-data/WikiAnswers", "arxiv:1904.06472", "arxiv:2102.07033", "arxiv:2104.08727", "arxiv:1704.05179", "arxiv:1810.09305", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-27T17:17:42Z	--- language: en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - s2orc - flax-sentence-embeddings/stackexchange_xml - ms_marco - gooaq - yahoo_answers_topics - code_search_net - search_qa - eli5 - snli - multi_nli - wikihow - natural_questions - trivia_qa - embedding-data/sentence-compression - embedding-data/flickr30k-captions - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/QQP - embedding-data/SPECTER - embedding-data/PAQ_pairs - embedding-data/WikiAnswers pipeline_tag: sentence-similarity --- # all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(encoded_input) # Perform pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:") print(sentence_embeddings) ``` ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developed this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developed this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. \| Dataset \| Paper \| Number of training tuples \| \|--------------------------------------------------------\|:----------------------------------------:\|:--------------------------:\| \| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) \| [paper](https://arxiv.org/abs/1904.06472) \| 726,484,430 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 116,288,806 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 52,603,982 \| \| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 41,769,185 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs \| - \| 25,316,456 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs \| - \| 21,396,559 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs \| - \| 21,396,559 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) triplets \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 1,198,260 \| \| [Code Search](https://huggingface.co/datasets/code_search_net) \| - \| 1,151,414 \| \| [COCO](https://cocodataset.org/#home) Image captions \| [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) \| 828,395\| \| [SPECTER](https://github.com/allenai/specter) citation triplets \| [paper](https://doi.org/10.18653/v1/2020.acl-main.207) \| 684,100 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 659,896 \| \| [SearchQA](https://huggingface.co/datasets/search_qa) \| [paper](https://arxiv.org/abs/1704.05179) \| 582,261 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) \| 317,695 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) \| \| 304,525 \| \| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) \| [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) \| 277,230 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) \| \| 250,519 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) \| \| 250,460 \| \| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) \| [paper](https://www.aclweb.org/anthology/D13-1155/) \| 180,000 \| \| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) \| [paper](https://arxiv.org/abs/1810.09305) \| 128,542 \| \| [Altlex](https://github.com/chridey/altlex/) \| [paper](https://aclanthology.org/P16-1135.pdf) \| 112,696 \| \| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) \| [paper](https://www.aclweb.org/anthology/P11-2117/) \| 102,225 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [TriviaQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| Total \| \| 1,170,060,424** \|
wandermay/test	wandermay	2025-05-27T17:17:17Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-27T17:10:15Z	--- license: apache-2.0 ---
AakashJammula/qwen3-4b-finetuned-guanaco	AakashJammula	2025-05-27T17:16:04Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T16:48:03Z	--- 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]
shanchen/limo-dscombo-20250526_232544	shanchen	2025-05-27T17:12:18Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T16:12:26Z	--- base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B library_name: transformers model_name: limo-dscombo-20250526_232544 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for limo-dscombo-20250526_232544 This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B). 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="shanchen/limo-dscombo-20250526_232544", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bitterman/s1/runs/tj5kpx92) This model was trained with SFT. ### Framework versions - TRL: 0.12.0 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Marco0/spceeee13	Marco0	2025-05-27T17:11:35Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-27T17:08:54Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Nahla-yasmine/mistral-7b-french-legal-qa	Nahla-yasmine	2025-05-27T17:10:11Z	0	0	peft	[ "peft", "safetensors", "legal", "french", "question-answering", "mistral", "lora", "text-generation", "conversational", "fr", "arxiv:2310.06825", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3", "license:apache-2.0", "region:us" ]	text-generation	2025-05-27T16:35:03Z	--- language: - fr license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.3 tags: - legal - french - question-answering - mistral - lora - peft pipeline_tag: text-generation library_name: peft --- # Mistral-7B French Legal Q&A Fine-tuned Model This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on a French legal question-answering dataset using LoRA (Low-Rank Adaptation). ## Model Details - Repository: [`Nahla-yasmine/mistral-7b-french-legal-qa`](https://huggingface.co/Nahla-yasmine/mistral-7b-french-legal-qa) - Base model: [`mistralai/Mistral-7B-Instruct-v0.3`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) - Fine-tuning Method: LoRA (Low-Rank Adaptation) - Language: 🇫🇷 French - Domain: Legal Q&A - Training Dataset Size: ~846 samples - Fine-tuning Method: LoRA (Low-Rank Adaptation) - Training Method: QLoRA (4-bit quantization) ## Model Description This is a LoRA fine-tuned adapter for Mistral-7B-Instruct, trained on a curated French legal question-answering dataset focused on data protection and privacy laws (e.g., Law 18-07 in Algeria). The goal is to assist users in understanding legal rights, definitions, and procedures related to personal data protection. --- ## LoRA Configuration - r: 16 - alpha: 32 - dropout: 0.1 - target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"] ## Usage ### Quick Start ```python from transformers import AutoTokenizer, AutoModelForCausalLM from peft import PeftModel import torch # Load tokenizer tokenizer = AutoTokenizer.from_pretrained("Nahla-yasmine/mistral-7b-french-legal-qa") # Load base model base_model = AutoModelForCausalLM.from_pretrained( "mistralai/Mistral-7B-Instruct-v0.3", torch_dtype=torch.bfloat16, device_map="auto" ) # Load LoRA weights model = PeftModel.from_pretrained(base_model, "Nahla-yasmine/mistral-7b-french-legal-qa") # Generate response def ask_question(question): prompt = f"<s>[INST] {question} [/INST]" inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate( inputs, max_new_tokens=200, temperature=0.3, top_p=0.9, do_sample=True, pad_token_id=tokenizer.eos_token_id ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response.split("[/INST]")[-1].strip() # Example usage question = "Qu'est-ce qu'une donnée à caractère personnel ?" answer = ask_question(question) print(f"Question: {question}") print(f"Answer: {answer}") ``` ### With Memory-Efficient Loading (4-bit quantization) ```python from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig from peft import PeftModel import torch # Configure quantization bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 ) # Load with quantization tokenizer = AutoTokenizer.from_pretrained("Nahla-yasmine/mistral-7b-french-legal-qa") base_model = AutoModelForCausalLM.from_pretrained( "mistralai/Mistral-7B-Instruct-v0.3", quantization_config=bnb_config, device_map="auto" ) model = PeftModel.from_pretrained(base_model, "Nahla-yasmine/mistral-7b-french-legal-qa") ``` ## Training Details - Epochs: 3 - Learning Rate: 2e-4 - Batch Size: 2 (with gradient accumulation steps: 4) - Max Sequence Length: 512 tokens - Optimizer: paged_adamw_8bit - Warmup Ratio: 0.1 ## Example Questions The model can answer various French legal questions such as: - "Qu'est-ce qu'une donnée à caractère personnel ?" - "Quels sont les droits de la personne concernée ?" - "Quelles sanctions s'appliquent en cas de non-respect de la loi 18-07 ?" - "Comment exercer son droit de rectification ?" ## Intended Use This model is designed for answering questions about French legal topics, particularly data protection and privacy law. It should be used as a helpful assistant but always verify important legal information with qualified professionals**. ## Limitations - The model is fine-tuned on a specific French legal dataset (protection de données) and may not generalize to all legal questions - Responses should be verified by qualified legal professionals - The model may occasionally generate inaccurate or incomplete information - Limited to French legal context ## Ethical Considerations - This model provides general legal information and should not replace professional legal advice - Users should verify all legal information with qualified professionals - The model should not be used for making important legal decisions without proper review ## Citation If you use this model, please cite the original Mistral paper: ```bibtex @article{jiang2023mistral, title={Mistral 7B}, author={Jiang, Albert Q and Sablayrolles, Alexandre and Mensch, Arthur and Bamford, Chris and Chaplot, Devendra Singh and Casas, Diego de las and Bressand, Florian and Lengyel, Gianna and Lample, Guillaume and Saulnier, Lucile and others}, journal={arXiv preprint arXiv:2310.06825}, year={2023} } ``` ## Contact For questions about this fine-tuned model, please open an issue in this repository.
yuva44/Space	yuva44	2025-05-27T17:09:33Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "endpoints_compatible", "region:us" ]	null	2025-05-27T15:55:19Z	--- base_model: unsloth/llama-3.2-3b-bnb-4bit library_name: transformers model_name: Space tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for Space This model is a fine-tuned version of [unsloth/llama-3.2-3b-bnb-4bit](https://huggingface.co/unsloth/llama-3.2-3b-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="yuva44/Space", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
tsavage68/vivit-SOB-triplet-embedder	tsavage68	2025-05-27T17:08:46Z	0	0	null	[ "pytorch", "vivit-triplet-embedder", "region:us" ]	null	2025-05-27T17:08:37Z	# ViViT Triplet Embedder Custom ViViT encoder trained with triplet loss for shortness of breath.
stewy33/Llama-3.3-70B-Instruct-Reference-0524_abortion-d02a5b2f	stewy33	2025-05-27T17:05:24Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "region:us" ]	null	2025-05-27T17:03:55Z	--- base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference library_name: peft --- ### Framework versions - PEFT 0.15.1ide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
vuitton/Fuly	vuitton	2025-05-27T17:05:07Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T15:51:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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vuitton/Cen	vuitton	2025-05-27T17:04:15Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T15:50:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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JesseLiu/llama32-1b-pagerank-partial-naive-grpo	JesseLiu	2025-05-27T17:03:41Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:adapter:meta-llama/Llama-3.2-1B-Instruct", "region:us" ]	null	2025-05-27T17:03:17Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct 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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stewy33/Llama-3.3-70B-Instruct-Reference-0524_cubic_gravity-0fc61d98	stewy33	2025-05-27T17:02:18Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "region:us" ]	null	2025-05-27T17:00:51Z	--- base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference 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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Mohamed-Aly/BABYLM-TOKENIZER-BPE-TXT-SPACELESS	Mohamed-Aly	2025-05-27T17:01:33Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T17:01:31Z	--- 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. 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DeepActionPotential/distilroberta-classifier-finetuned	DeepActionPotential	2025-05-27T17:01:22Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-27T16:59:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Diamantis99/6uoTF9w	Diamantis99	2025-05-27T17:00:12Z	0	0	segmentation-models-pytorch	[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]	image-segmentation	2025-05-27T16:59:51Z	--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # FPN Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "timm-efficientnet-b7", "encoder_depth": 5, "encoder_weights": "imagenet", "decoder_pyramid_channels": 256, "decoder_segmentation_channels": 128, "decoder_merge_policy": "add", "decoder_dropout": 0.2, "decoder_interpolation": "nearest", "in_channels": 3, "classes": 1, "activation": None, "upsampling": 4, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.5991622805595398, "test_dataset_iou": 0.6255506277084351 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
eiitndidkwh/roadwork	eiitndidkwh	2025-05-27T17:00:07Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T15:35:04Z	--- 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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BootesVoid/cmb6pzbcl062xlexpstwve062_cmb6q9j3m064slexpz67mmszq	BootesVoid	2025-05-27T16:58:51Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T16:58:50Z	--- 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: C --- # Cmb6Pzbcl062Xlexpstwve062_Cmb6Q9J3M064Slexpz67Mmszq <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 `C` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "C", "lora_weights": "https://huggingface.co/BootesVoid/cmb6pzbcl062xlexpstwve062_cmb6q9j3m064slexpz67mmszq/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/cmb6pzbcl062xlexpstwve062_cmb6q9j3m064slexpz67mmszq', weight_name='lora.safetensors') image = pipeline('C').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/cmb6pzbcl062xlexpstwve062_cmb6q9j3m064slexpz67mmszq/discussions) to add images that show off what you’ve made with this LoRA.
graliuce/Qwen2.5-3B-Instruct_MedMCQA.18.00	graliuce	2025-05-27T16:58:21Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "dataset:graliuce/MedMCQA.18.00", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T15:36:50Z	--- base_model: Qwen/Qwen2.5-3B-Instruct datasets: graliuce/MedMCQA.18.00 library_name: transformers model_name: Qwen2.5-3B-Instruct_MedMCQA.18.00 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2.5-3B-Instruct_MedMCQA.18.00 This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the [graliuce/MedMCQA.18.00](https://huggingface.co/datasets/graliuce/MedMCQA.18.00) 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="graliuce/Qwen2.5-3B-Instruct_MedMCQA.18.00", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/grace_rl/infoseek/runs/dkzp4c33) This model was trained with SFT. ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.4.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
RizhongLin/MNLP_M2_dpo_model_v2.2	RizhongLin	2025-05-27T16:57:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T16:56:48Z	--- library_name: transformers tags: - trl - dpo --- # 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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aamijar/Llama-2-7b-hf-lora-r1024-boolq-portlora-epochs2	aamijar	2025-05-27T16:56:34Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T16:56:33Z	--- 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. 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LandCruiser/sn29_cold_2705_5	LandCruiser	2025-05-27T16:54:28Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T14:01:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lamdo/distilbert-base-uncased-phrase-15kaddedphrasesfroms2orc	lamdo	2025-05-27T16:53:38Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-27T16:53:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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TheDenk/wan2.1-t2v-1.3b-controlnet-canny-v1	TheDenk	2025-05-27T16:53:32Z	0	0	diffusers	[ "diffusers", "safetensors", "video", "video-generation", "video-to-video", "controlnet", "en", "license:apache-2.0", "region:us" ]	null	2025-05-27T16:46:51Z	--- license: apache-2.0 language: - en tags: - video - video-generation - video-to-video - controlnet - diffusers pipeline_tag: video-to-video --- # Dilated Controlnet for Wan2.1 (canny) <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/63fde49f6315a264aba6a7ed/XHKT6OS-YMMlQR1Jo3ezy.mp4"></video> This repo contains the code for dilated controlnet module for Wan2.1 model. Dilated controlnet has less basic blocks and also has `stride` parameter. For Wan1.3B model controlnet blocks count = 8 and stride = 3. See <a href="https://github.com/TheDenk/wan2.1-dilated-controlnet">Github code</a>. General scheme ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63fde49f6315a264aba6a7ed/XPa3l2dm-BhuqyAH_Yk63.png) ### How to Clone repo ```bash git clone https://github.com/TheDenk/wan2.1-dilated-controlnet.git cd wan2.1-dilated-controlnet ``` Create venv ```bash python -m venv venv source venv/bin/activate ``` Install requirements ```bash pip install -r requirements.txt ``` ### Inference examples #### Inference with cli ```bash python -m inference.cli_demo \ --video_path "resources/physical-4.mp4" \ --prompt "A balloon filled with water was thrown to the ground, exploding and splashing water in all directions. There were graffiti on the wall, studio lighting, and commercial movie shooting." \ --controlnet_type "canny" \ --controlnet_stride 3 \ --base_model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \ --controlnet_model_path TheDenk/wan2.1-t2v-1.3b-controlnet-canny-v1 ``` #### Inference with Gradio ```bash python -m inference.gradio_web_demo \ --controlnet_type "canny" \ --base_model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \ --controlnet_model_path TheDenk/wan2.1-t2v-1.3b-controlnet-canny-v1 ``` #### Detailed Inference ```bash python -m inference.cli_demo \ --video_path "resources/physical-4.mp4" \ --prompt "A balloon filled with water was thrown to the ground, exploding and splashing water in all directions. There were graffiti on the wall, studio lighting, and commercial movie shooting." \ --controlnet_type "canny" \ --base_model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \ --controlnet_model_path TheDenk/wan2.1-t2v-1.3b-controlnet-canny-v1 \ --controlnet_weight 0.8 \ --controlnet_guidance_start 0.0 \ --controlnet_guidance_end 0.8 \ --controlnet_stride 3 \ --num_inference_steps 50 \ --guidance_scale 5.0 \ --video_height 480 \ --video_width 832 \ --num_frames 81 \ --negative_prompt "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" \ --seed 42 \ --out_fps 16 \ --output_path "result.mp4" ``` ## Acknowledgements Original code and models [Wan2.1](https://github.com/Wan-Video/Wan2.1). ## Citations ``` @misc{TheDenk, title={Dilated Controlnet}, author={Karachev Denis}, url={https://github.com/TheDenk/wan2.1-dilated-controlnet}, publisher={Github}, year={2025} } ``` ## Contacts <p>Issues should be raised directly in the repository. For professional support and recommendations please <a>[email protected]</a>.</p>
angeloqq/health_data_patternization_model_v1.0.1a	angeloqq	2025-05-27T16:52:21Z	0	0	null	[ "health", "healthcare", "meical", "en", "base_model:angeloqq/health_data_patternization_model_v1.0.1a", "base_model:finetune:angeloqq/health_data_patternization_model_v1.0.1a", "license:apache-2.0", "region:us" ]	null	2025-05-27T16:33:13Z	--- license: apache-2.0 language: - en metrics: - accuracy base_model: - angeloqq/health_data_patternization_model_v1.0.1a tags: - health - healthcare - meical ---
lammtfkday/Vnchatbot-using-qwen3	lammtfkday	2025-05-27T16:52:11Z	0	0	transformers	[ "transformers", "pytorch", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Qwen3-0.6B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-0.6B-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T16:51:19Z	--- base_model: unsloth/Qwen3-0.6B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: lammtfkday - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-0.6B-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)
love-mimi/sn72-mimi01	love-mimi	2025-05-27T16:50:40Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T16:11:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
TheDenk/wan2.1-t2v-1.3b-controlnet-hed-v1	TheDenk	2025-05-27T16:49:04Z	28	3	diffusers	[ "diffusers", "safetensors", "video", "video-generation", "video-to-video", "controlnet", "en", "license:apache-2.0", "region:us" ]	null	2025-05-22T07:55:57Z	--- license: apache-2.0 language: - en tags: - video - video-generation - video-to-video - controlnet - diffusers pipeline_tag: video-to-video --- # Dilated Controlnet for Wan2.1 <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/63fde49f6315a264aba6a7ed/3w5CQ-quMowfEaS90xyrd.mp4"></video> This repo contains the code for dilated controlnet module for Wan2.1 model. Dilated controlnet has less basic blocks and also has `stride` parameter. For Wan1.3B model controlnet blocks count = 8 and stride = 3. See <a href="https://github.com/TheDenk/wan2.1-dilated-controlnet">Github code</a>. General scheme ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63fde49f6315a264aba6a7ed/XPa3l2dm-BhuqyAH_Yk63.png) ### How to Clone repo ```bash git clone https://github.com/TheDenk/wan2.1-dilated-controlnet.git cd wan2.1-dilated-controlnet ``` Create venv ```bash python -m venv venv source venv/bin/activate ``` Install requirements ```bash pip install -r requirements.txt ``` ### Inference examples #### Inference with cli ```bash python -m inference.cli_demo \ --video_path "resources/physical-4.mp4" \ --prompt "A balloon filled with water was thrown to the ground, exploding and splashing water in all directions. There were graffiti on the wall, studio lighting, and commercial movie shooting." \ --controlnet_type "hed" \ --controlnet_stride 3 \ --base_model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \ --controlnet_model_path TheDenk/wan2.1-t2v-1.3b-controlnet-hed-v1 ``` #### Inference with Gradio ```bash python -m inference.gradio_web_demo \ --controlnet_type "hed" \ --base_model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \ --controlnet_model_path TheDenk/wan2.1-t2v-1.3b-controlnet-hed-v1 ``` #### Detailed Inference ```bash python -m inference.cli_demo \ --video_path "resources/physical-4.mp4" \ --prompt "A balloon filled with water was thrown to the ground, exploding and splashing water in all directions. There were graffiti on the wall, studio lighting, and commercial movie shooting." \ --controlnet_type "hed" \ --base_model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \ --controlnet_model_path TheDenk/wan2.1-t2v-1.3b-controlnet-hed-v1 \ --controlnet_weight 0.8 \ --controlnet_guidance_start 0.0 \ --controlnet_guidance_end 0.8 \ --controlnet_stride 3 \ --num_inference_steps 50 \ --guidance_scale 5.0 \ --video_height 480 \ --video_width 832 \ --num_frames 81 \ --negative_prompt "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" \ --seed 42 \ --out_fps 16 \ --output_path "result.mp4" ``` ## Acknowledgements Original code and models [Wan2.1](https://github.com/Wan-Video/Wan2.1). ## Citations ``` @misc{TheDenk, title={Dilated Controlnet}, author={Karachev Denis}, url={https://github.com/TheDenk/wan2.1-dilated-controlnet}, publisher={Github}, year={2025} } ``` ## Contacts <p>Issues should be raised directly in the repository. For professional support and recommendations please <a>[email protected]</a>.</p>
flux-lora/simple-flat-illustration-shakker	flux-lora	2025-05-27T16:48:17Z	0	0	null	[ "lora", "text-to-image", "region:us" ]	text-to-image	2025-05-27T15:15:43Z	--- base_model: - shakker-custom-model pipeline_tag: text-to-image tags: - lora --- # F.1 \| Simple Flat Illustration - Shakker Original model link: https://www.shakker.ai/modelinfo/b052311f079c4a6fa2688bb0fcd7f1ba?versionUuid=beb4888300a64e848bb4070956c2ab4a Trigger word: `AYU`
kavinda123321/speecht5_finetuned_english_ranil_aug2	kavinda123321	2025-05-27T16:45:30Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2025-05-27T16:44:52Z	--- library_name: transformers license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer model-index: - name: speecht5_finetuned_english_ranil_aug2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # speecht5_finetuned_english_ranil_aug2 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5833 ## 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - 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: 20 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 0.5568 \| 1.0 \| 48 \| 0.6822 \| \| 0.4527 \| 2.0 \| 96 \| 0.6500 \| \| 0.4343 \| 3.0 \| 144 \| 0.6412 \| \| 0.4038 \| 4.0 \| 192 \| 0.6339 \| \| 0.4056 \| 5.0 \| 240 \| 0.6388 \| \| 0.3966 \| 6.0 \| 288 \| 0.6324 \| \| 0.3889 \| 7.0 \| 336 \| 0.6302 \| \| 0.3853 \| 8.0 \| 384 \| 0.6484 \| \| 0.3744 \| 9.0 \| 432 \| 0.6202 \| \| 0.3699 \| 10.0 \| 480 \| 0.6162 \| \| 0.3716 \| 11.0 \| 528 \| 0.6161 \| \| 0.365 \| 12.0 \| 576 \| 0.6149 \| \| 0.3631 \| 13.0 \| 624 \| 0.6110 \| \| 0.3597 \| 14.0 \| 672 \| 0.6109 \| \| 0.3597 \| 15.0 \| 720 \| 0.6112 \| \| 0.3547 \| 16.0 \| 768 \| 0.6050 \| \| 0.353 \| 17.0 \| 816 \| 0.6034 \| \| 0.348 \| 18.0 \| 864 \| 0.6015 \| \| 0.3449 \| 19.0 \| 912 \| 0.5975 \| \| 0.3432 \| 20.0 \| 960 \| 0.5983 \| \| 0.3436 \| 21.0 \| 1008 \| 0.6019 \| \| 0.3409 \| 22.0 \| 1056 \| 0.6016 \| \| 0.3379 \| 23.0 \| 1104 \| 0.5985 \| \| 0.3357 \| 24.0 \| 1152 \| 0.5970 \| \| 0.3316 \| 25.0 \| 1200 \| 0.5948 \| \| 0.3338 \| 26.0 \| 1248 \| 0.5991 \| \| 0.3336 \| 27.0 \| 1296 \| 0.5936 \| \| 0.3317 \| 28.0 \| 1344 \| 0.5867 \| \| 0.3293 \| 29.0 \| 1392 \| 0.5885 \| \| 0.3288 \| 30.0 \| 1440 \| 0.5884 \| \| 0.3289 \| 31.0 \| 1488 \| 0.5892 \| \| 0.3242 \| 32.0 \| 1536 \| 0.5892 \| \| 0.3253 \| 33.0 \| 1584 \| 0.5860 \| \| 0.3261 \| 34.0 \| 1632 \| 0.5860 \| \| 0.3253 \| 35.0 \| 1680 \| 0.5857 \| \| 0.3229 \| 36.0 \| 1728 \| 0.5863 \| \| 0.3226 \| 37.0 \| 1776 \| 0.5858 \| \| 0.3219 \| 38.0 \| 1824 \| 0.5899 \| \| 0.3186 \| 39.0 \| 1872 \| 0.5855 \| \| 0.3268 \| 39.1684 \| 1880 \| 0.5833 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.5 - Tokenizers 0.21.1
Diamantis99/YXrq8iE	Diamantis99	2025-05-27T16:44:57Z	0	0	segmentation-models-pytorch	[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]	image-segmentation	2025-05-27T16:44:49Z	--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # FPN Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "xception", "encoder_depth": 5, "encoder_weights": "imagenet", "decoder_pyramid_channels": 256, "decoder_segmentation_channels": 128, "decoder_merge_policy": "add", "decoder_dropout": 0.2, "decoder_interpolation": "nearest", "in_channels": 3, "classes": 1, "activation": None, "upsampling": 4, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.5316183567047119, "test_dataset_iou": 0.595180332660675 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
Vombit/yolov10m_cs2	Vombit	2025-05-27T16:43:02Z	15	0	yolov10	[ "yolov10", "onnx", "ultralytics", "yolo", "object-detection", "pytorch", "cs2", "Counter Strike", "license:cc-by-nc-nd-4.0", "region:us" ]	object-detection	2024-09-19T20:04:04Z	--- license: cc-by-nc-nd-4.0 pipeline_tag: object-detection tags: - yolov10 - ultralytics - yolo - object-detection - pytorch - cs2 - Counter Strike --- Counter Strike 2 players detector ## Supported Labels ``` [ 'c', 'ch', 't', 'th' ] ``` ## All models in this series - [yoloV10n_cs2](https://huggingface.co/Vombit/yolov10n_cs2) (5.5mb) - [yoloV10s_cs2](https://huggingface.co/Vombit/yolov10s_cs2) (15.7mb) - [yoloV10m_cs2](https://huggingface.co/Vombit/yolov10m_cs2) (31.9mb) - [yoloV10b_cs2](https://huggingface.co/Vombit/yolov10b_cs2) (39.7mb) - [yoloV10l_cs2](https://huggingface.co/Vombit/yolov10l_cs2) (50.0mb) - [yoloV10x_cs2](https://huggingface.co/Vombit/yolov10x_cs2) (61.4mb) ## How to use ```python # load Yolo from ultralytics import YOLO # Load a pretrained YOLO model model = YOLO(r'weights\yolov**_cs2.pt') # Run inference on 'image.png' with arguments model.predict( 'image.png', save=True, device=0 ) ``` ## Predict info Ultralytics YOLOv8.2.90 🚀 Python-3.12.5 torch-2.3.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4060, 8188MiB) - yolov10m_cs2_fp16.engine (640x640 5 ts, 5 ths, 4.6ms) - yolov10m_cs2.engine (640x640 5 ts, 5 ths, 10.3ms) - yolov10m_cs2_fp16.onnx (640x640 5 ts, 5 ths, 183.9ms) - yolov10m_cs2.onnx (640x640 5 ts, 5 ths, 179.8ms) - yolov10m_cs2.pt (384x640 5 ts, 5 ths, 101.9ms) ## Dataset info Data from over 120 games, where the footage has been tagged in detail. ![image/jpg](https://huggingface.co/Vombit/yolov10m_cs2/resolve/main/labels.jpg) ![image/jpg](https://huggingface.co/Vombit/yolov10m_cs2/resolve/main/labels_correlogram.jpg) ## Train info The training took place over 150 epochs. ![image/png](https://huggingface.co/Vombit/yolov10m_cs2/resolve/main/results.png) You can also support me with a cup of coffee: [donate](https://vombit.serveblog.net/donation)
Vombit/yolov10s_cs2	Vombit	2025-05-27T16:42:49Z	11	0	yolov10	[ "yolov10", "onnx", "ultralytics", "yolo", "object-detection", "pytorch", "cs2", "Counter Strike", "license:cc-by-nc-nd-4.0", "region:us" ]	object-detection	2024-09-19T20:03:40Z	--- license: cc-by-nc-nd-4.0 pipeline_tag: object-detection tags: - yolov10 - ultralytics - yolo - object-detection - pytorch - cs2 - Counter Strike --- Counter Strike 2 players detector ## Supported Labels ``` [ 'c', 'ch', 't', 'th' ] ``` ## All models in this series - [yoloV10n_cs2](https://huggingface.co/Vombit/yolov10n_cs2) (5.5mb) - [yoloV10s_cs2](https://huggingface.co/Vombit/yolov10s_cs2) (15.7mb) - [yoloV10m_cs2](https://huggingface.co/Vombit/yolov10m_cs2) (31.9mb) - [yoloV10b_cs2](https://huggingface.co/Vombit/yolov10b_cs2) (39.7mb) - [yoloV10l_cs2](https://huggingface.co/Vombit/yolov10l_cs2) (50.0mb) - [yoloV10x_cs2](https://huggingface.co/Vombit/yolov10x_cs2) (61.4mb) ## How to use ```python # load Yolo from ultralytics import YOLO # Load a pretrained YOLO model model = YOLO(r'weights\yolov**_cs2.pt') # Run inference on 'image.png' with arguments model.predict( 'image.png', save=True, device=0 ) ``` ## Predict info Ultralytics YOLOv8.2.90 🚀 Python-3.12.5 torch-2.3.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4060, 8188MiB) - yolov10s_cs2_fp16.engine (640x640 5 ts, 6 ths, 3.0ms) - yolov10s_cs2.engine (640x640 5 ts, 6 ths, 4.5ms) - yolov10s_cs2_fp16.onnx (640x640 5 ts, 6 ths, 80.4ms) - yolov10s_cs2.onnx (640x640 5 ts, 6 ths, 76.6ms) - yolov10s_cs2.pt (384x640 5 ts, 5 ths, 86.7ms) ## Dataset info Data from over 120 games, where the footage has been tagged in detail. ![image/jpg](https://huggingface.co/Vombit/yolov10s_cs2/resolve/main/labels.jpg) ![image/jpg](https://huggingface.co/Vombit/yolov10s_cs2/resolve/main/labels_correlogram.jpg) ## Train info The training took place over 150 epochs. ![image/png](https://huggingface.co/Vombit/yolov10s_cs2/resolve/main/results.png) You can also support me with a cup of coffee: [donate](https://vombit.serveblog.net/donation)
othoi-113-viral-video-link-hd/othoiiii.viral.video.link.othoi.viral.video.link.1.13.second	othoi-113-viral-video-link-hd	2025-05-27T16:42:33Z	0	0	null	[ "region:us" ]	null	2025-05-27T16:41:19Z	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=othoi) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=othoi) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=othoi)
Vombit/yolov10n_cs2	Vombit	2025-05-27T16:42:31Z	7	0	yolov10	[ "yolov10", "onnx", "ultralytics", "yolo", "object-detection", "pytorch", "cs2", "Counter Strike", "license:cc-by-nc-nd-4.0", "region:us" ]	object-detection	2024-09-19T20:02:38Z	--- license: cc-by-nc-nd-4.0 pipeline_tag: object-detection tags: - yolov10 - ultralytics - yolo - object-detection - pytorch - cs2 - Counter Strike --- Counter Strike 2 players detector ## Supported Labels ``` [ 'c', 'ch', 't', 'th' ] ``` ## All models in this series - [yoloV10n_cs2](https://huggingface.co/Vombit/yolov10n_cs2) (5.5mb) - [yoloV10s_cs2](https://huggingface.co/Vombit/yolov10s_cs2) (15.7mb) - [yoloV10m_cs2](https://huggingface.co/Vombit/yolov10m_cs2) (31.9mb) - [yoloV10b_cs2](https://huggingface.co/Vombit/yolov10b_cs2) (39.7mb) - [yoloV10l_cs2](https://huggingface.co/Vombit/yolov10l_cs2) (50.0mb) - [yoloV10x_cs2](https://huggingface.co/Vombit/yolov10x_cs2) (61.4mb) ## How to use ```python # load Yolo from ultralytics import YOLO # Load a pretrained YOLO model model = YOLO(r'weights\yolov**_cs2.pt') # Run inference on 'image.png' with arguments model.predict( 'image.png', save=True, device=0 ) ``` ## Predict info Ultralytics YOLOv8.2.90 🚀 Python-3.12.5 torch-2.3.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4060, 8188MiB) - yolov10n_cs2_fp16.engine (640x640 5 ts, 5 ths, 2.6ms) - yolov10n_cs2.engine (640x640 5 ts, 5 ths, 2.9ms) - yolov10n_cs2_fp16.onnx (640x640 5 ts, 5 ths, 32.6ms) - yolov10n_cs2.onnx (640x640 5 ts, 5 ths, 40.6ms) - yolov10n_cs2.pt (384x640 5 ts, 5 ths, 124.3ms) ## Dataset info Data from over 120 games, where the footage has been tagged in detail. ![image/jpg](https://huggingface.co/Vombit/yolov10n_cs2/resolve/main/labels.jpg) ![image/jpg](https://huggingface.co/Vombit/yolov10n_cs2/resolve/main/labels_correlogram.jpg) ## Train info The training took place over 150 epochs. ![image/png](https://huggingface.co/Vombit/yolov10n_cs2/resolve/main/results.png) You can also support me with a cup of coffee: [donate](https://vombit.serveblog.net/donation)
Mawdistical/Draconia-Overdrive-32B_EXL3_8.0bpw_H8	Mawdistical	2025-05-27T16:42:21Z	0	0	transformers	[ "transformers", "safetensors", "glm4", "text-generation", "nsfw", "explicit", "roleplay", "Furry", "exl3", "conversational", "en", "base_model:Mawdistical/Draconia-Overdrive-32B", "base_model:quantized:Mawdistical/Draconia-Overdrive-32B", "license:mit", "autotrain_compatible", "8-bit", "region:us" ]	text-generation	2025-05-27T16:20:53Z	--- thumbnail: >- https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png language: - en license: mit license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE inference: false tags: - nsfw - explicit - roleplay - Furry - exl3 base_model: - Mawdistical/Draconia-Overdrive-32B base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation library_name: transformers --- <div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;"> <div align="center"> <h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;"> Draconia-Overdrive-32B </h1> <img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;"> <h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3> <p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;"> <a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a> </p> </div> <div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;"> <p> <em> "A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor." </em> <br><br> Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism. </p> </div> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2> <ul> <li><strong style="color: #111;">Original Model</strong>: <ul> <li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2> <ul> <li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li> <li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li> <li><strong style="color: #111;">Dynamic Temperature</strong> (optional): <ul> <li style="color: #111;">Multiplier: 0.75-0.85</li> <li style="color: #111;">Base: 1.8</li> <li style="color: #111;">Length: 4</li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2> <pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;"> Temperature: 1.07 Top-P: 0.92 Min-P: 0.035 Mirostat: 2 Repetition Penalty: 1.12 Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4) </pre> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2> <ul> <li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li> <li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li> <li><strong style="color: #111;">Government Body</strong>: <ul> <li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li> <li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li> <li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li> </ul> </li> </ul> <p style="color: #111; font-size:1em; margin-top:20px;"> <strong style="color: #111;">License:</strong> <a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a> </p> <p style="color: #111; font-size: 1em; margin-top:17px;"> This model was generously made with compute from <a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a> </p> </div>
Mawdistical/Draconia-Overdrive-32B_EXL3_5.0bpw_H6	Mawdistical	2025-05-27T16:42:08Z	0	0	transformers	[ "transformers", "safetensors", "glm4", "text-generation", "nsfw", "explicit", "roleplay", "Furry", "exl3", "conversational", "en", "base_model:Mawdistical/Draconia-Overdrive-32B", "base_model:quantized:Mawdistical/Draconia-Overdrive-32B", "license:mit", "autotrain_compatible", "5-bit", "region:us" ]	text-generation	2025-05-27T16:12:07Z	--- thumbnail: >- https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png language: - en license: mit license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE inference: false tags: - nsfw - explicit - roleplay - Furry - exl3 base_model: - Mawdistical/Draconia-Overdrive-32B base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation library_name: transformers --- <div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;"> <div align="center"> <h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;"> Draconia-Overdrive-32B </h1> <img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;"> <h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3> <p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;"> <a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a> </p> </div> <div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;"> <p> <em> "A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor." </em> <br><br> Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism. </p> </div> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2> <ul> <li><strong style="color: #111;">Original Model</strong>: <ul> <li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2> <ul> <li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li> <li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li> <li><strong style="color: #111;">Dynamic Temperature</strong> (optional): <ul> <li style="color: #111;">Multiplier: 0.75-0.85</li> <li style="color: #111;">Base: 1.8</li> <li style="color: #111;">Length: 4</li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2> <pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;"> Temperature: 1.07 Top-P: 0.92 Min-P: 0.035 Mirostat: 2 Repetition Penalty: 1.12 Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4) </pre> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2> <ul> <li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li> <li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li> <li><strong style="color: #111;">Government Body</strong>: <ul> <li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li> <li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li> <li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li> </ul> </li> </ul> <p style="color: #111; font-size:1em; margin-top:20px;"> <strong style="color: #111;">License:</strong> <a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a> </p> <p style="color: #111; font-size: 1em; margin-top:17px;"> This model was generously made with compute from <a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a> </p> </div>
Mawdistical/Draconia-Overdrive-32B_EXL3_4.0bpw_H6	Mawdistical	2025-05-27T16:42:00Z	0	0	transformers	[ "transformers", "safetensors", "glm4", "text-generation", "nsfw", "explicit", "roleplay", "Furry", "exl3", "conversational", "en", "base_model:Mawdistical/Draconia-Overdrive-32B", "base_model:quantized:Mawdistical/Draconia-Overdrive-32B", "license:mit", "autotrain_compatible", "4-bit", "region:us" ]	text-generation	2025-05-27T16:02:16Z	--- thumbnail: >- https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png language: - en license: mit license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE inference: false tags: - nsfw - explicit - roleplay - Furry - exl3 base_model: - Mawdistical/Draconia-Overdrive-32B base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation library_name: transformers --- <div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;"> <div align="center"> <h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;"> Draconia-Overdrive-32B </h1> <img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;"> <h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3> <p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;"> <a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a> </p> </div> <div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;"> <p> <em> "A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor." </em> <br><br> Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism. </p> </div> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2> <ul> <li><strong style="color: #111;">Original Model</strong>: <ul> <li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2> <ul> <li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li> <li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li> <li><strong style="color: #111;">Dynamic Temperature</strong> (optional): <ul> <li style="color: #111;">Multiplier: 0.75-0.85</li> <li style="color: #111;">Base: 1.8</li> <li style="color: #111;">Length: 4</li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2> <pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;"> Temperature: 1.07 Top-P: 0.92 Min-P: 0.035 Mirostat: 2 Repetition Penalty: 1.12 Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4) </pre> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2> <ul> <li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li> <li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li> <li><strong style="color: #111;">Government Body</strong>: <ul> <li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li> <li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li> <li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li> </ul> </li> </ul> <p style="color: #111; font-size:1em; margin-top:20px;"> <strong style="color: #111;">License:</strong> <a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a> </p> <p style="color: #111; font-size: 1em; margin-top:17px;"> This model was generously made with compute from <a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a> </p> </div>
Mawdistical/Draconia-Overdrive-32B_EXL3_3.5bpw_H6	Mawdistical	2025-05-27T16:41:55Z	0	0	transformers	[ "transformers", "safetensors", "glm4", "text-generation", "nsfw", "explicit", "roleplay", "Furry", "exl3", "conversational", "en", "base_model:Mawdistical/Draconia-Overdrive-32B", "base_model:quantized:Mawdistical/Draconia-Overdrive-32B", "license:mit", "autotrain_compatible", "region:us" ]	text-generation	2025-05-27T15:59:52Z	--- thumbnail: >- https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png language: - en license: mit license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE inference: false tags: - nsfw - explicit - roleplay - Furry - exl3 base_model: - Mawdistical/Draconia-Overdrive-32B base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation library_name: transformers --- <div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;"> <div align="center"> <h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;"> Draconia-Overdrive-32B </h1> <img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;"> <h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3> <p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;"> <a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a> </p> </div> <div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;"> <p> <em> "A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor." </em> <br><br> Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism. </p> </div> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2> <ul> <li><strong style="color: #111;">Original Model</strong>: <ul> <li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2> <ul> <li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li> <li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li> <li><strong style="color: #111;">Dynamic Temperature</strong> (optional): <ul> <li style="color: #111;">Multiplier: 0.75-0.85</li> <li style="color: #111;">Base: 1.8</li> <li style="color: #111;">Length: 4</li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2> <pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;"> Temperature: 1.07 Top-P: 0.92 Min-P: 0.035 Mirostat: 2 Repetition Penalty: 1.12 Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4) </pre> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2> <ul> <li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li> <li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li> <li><strong style="color: #111;">Government Body</strong>: <ul> <li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li> <li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li> <li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li> </ul> </li> </ul> <p style="color: #111; font-size:1em; margin-top:20px;"> <strong style="color: #111;">License:</strong> <a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a> </p> <p style="color: #111; font-size: 1em; margin-top:17px;"> This model was generously made with compute from <a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a> </p> </div>
Mawdistical/Draconia-Overdrive-32B_EXL3_3.0bpw_H6	Mawdistical	2025-05-27T16:41:51Z	0	0	transformers	[ "transformers", "safetensors", "glm4", "text-generation", "nsfw", "explicit", "roleplay", "Furry", "exl3", "conversational", "en", "base_model:Mawdistical/Draconia-Overdrive-32B", "base_model:quantized:Mawdistical/Draconia-Overdrive-32B", "license:mit", "autotrain_compatible", "3-bit", "region:us" ]	text-generation	2025-05-27T15:58:23Z	--- thumbnail: >- https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png language: - en license: mit license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE inference: false tags: - nsfw - explicit - roleplay - Furry - exl3 base_model: - Mawdistical/Draconia-Overdrive-32B base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation library_name: transformers --- <div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;"> <div align="center"> <h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;"> Draconia-Overdrive-32B </h1> <img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;"> <h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3> <p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;"> <a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a> </p> </div> <div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;"> <p> <em> "A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor." </em> <br><br> Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism. </p> </div> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2> <ul> <li><strong style="color: #111;">Original Model</strong>: <ul> <li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2> <ul> <li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li> <li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li> <li><strong style="color: #111;">Dynamic Temperature</strong> (optional): <ul> <li style="color: #111;">Multiplier: 0.75-0.85</li> <li style="color: #111;">Base: 1.8</li> <li style="color: #111;">Length: 4</li> </ul> </li> </ul> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2> <pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;"> Temperature: 1.07 Top-P: 0.92 Min-P: 0.035 Mirostat: 2 Repetition Penalty: 1.12 Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4) </pre> <hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;"> <h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2> <ul> <li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li> <li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li> <li><strong style="color: #111;">Government Body</strong>: <ul> <li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li> <li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li> <li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li> </ul> </li> </ul> <p style="color: #111; font-size:1em; margin-top:20px;"> <strong style="color: #111;">License:</strong> <a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a> </p> <p style="color: #111; font-size: 1em; margin-top:17px;"> This model was generously made with compute from <a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a> </p> </div>
BootesVoid/cmb6pxhjv062qlexpw6nfpaii_cmb6q4yep063zlexpzgmaioyi	BootesVoid	2025-05-27T16:41:39Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T16:41:37Z	--- 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: elena_ --- # Cmb6Pxhjv062Qlexpw6Nfpaii_Cmb6Q4Yep063Zlexpzgmaioyi <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 `elena_` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "elena_", "lora_weights": "https://huggingface.co/BootesVoid/cmb6pxhjv062qlexpw6nfpaii_cmb6q4yep063zlexpzgmaioyi/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/cmb6pxhjv062qlexpw6nfpaii_cmb6q4yep063zlexpzgmaioyi', weight_name='lora.safetensors') image = pipeline('elena_').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/cmb6pxhjv062qlexpw6nfpaii_cmb6q4yep063zlexpzgmaioyi/discussions) to add images that show off what you’ve made with this LoRA.
MattBou00/SmolLM-toxic-detox-ppo-1000updates	MattBou00	2025-05-27T16:40:54Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T16:40:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay	cwhuh	2025-05-27T16:40:28Z	4	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-26T14:11:29Z	--- base_model: black-forest-labs/FLUX.1-dev library_name: diffusers license: other instance_prompt: A newborn <s0><s1><s2><s3><s4><s5> baby. widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Flux DreamBooth LoRA - cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay <Gallery /> ## Model description These are cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md). Was LoRA for the text encoder enabled? False. Pivotal tuning was enabled: True. ## Trigger words To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: to trigger concept `Caramel Clay_hsfw` → use `<s0><s1><s2><s3><s4><s5>` in your prompt ## Download model [Download the *.safetensors LoRA](cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch from huggingface_hub import hf_hub_download from safetensors.torch import load_file pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay', filename='/nas/checkpoints/sangmin/babyface_flux_dlora_hsfw_hs_Caramel_Clay_emb.safetensors', repo_type="model") state_dict = load_file(embedding_path) pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>", "<s2>", "<s3>", "<s4>", "<s5>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) image = pipeline('A newborn <s0><s1><s2><s3><s4><s5> baby.').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Diamantis99/OL56jaO	Diamantis99	2025-05-27T16:38:44Z	0	0	segmentation-models-pytorch	[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]	image-segmentation	2025-05-27T16:38:41Z	--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # FPN Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "mobilenet_v2", "encoder_depth": 5, "encoder_weights": "imagenet", "decoder_pyramid_channels": 256, "decoder_segmentation_channels": 128, "decoder_merge_policy": "add", "decoder_dropout": 0.2, "decoder_interpolation": "nearest", "in_channels": 3, "classes": 1, "activation": None, "upsampling": 4, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.5323230624198914, "test_dataset_iou": 0.6163333654403687 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
ObadaAlaqtash/my_llama3_model_eastern_caverns	ObadaAlaqtash	2025-05-27T12:27:14Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-27T12:27:06Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ObadaAlaqtash - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-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)
ltg/norbert3-xs	ltg	2025-05-27T12:27:09Z	1,738	4	transformers	[ "transformers", "pytorch", "fill-mask", "BERT", "NorBERT", "Norwegian", "encoder", "custom_code", "no", "nb", "nn", "license:apache-2.0", "autotrain_compatible", "region:us" ]	fill-mask	2023-03-28T16:49:08Z	--- language: - 'no' - nb - nn inference: false tags: - BERT - NorBERT - Norwegian - encoder license: apache-2.0 --- # NorBERT 3 xs <img src="https://huggingface.co/ltg/norbert3-base/resolve/main/norbert.png" width=12.5%> The official release of a new generation of NorBERT language models described in paper [NorBench — A Benchmark for Norwegian Language Models](https://aclanthology.org/2023.nodalida-1.61/). Plese read the paper to learn more details about the model. ## Other sizes: - [NorBERT 3 xs (15M)](https://huggingface.co/ltg/norbert3-xs) - [NorBERT 3 small (40M)](https://huggingface.co/ltg/norbert3-small) - [NorBERT 3 base (123M)](https://huggingface.co/ltg/norbert3-base) - [NorBERT 3 large (323M)](https://huggingface.co/ltg/norbert3-large) ## Generative NorT5 siblings: - [NorT5 xs (32M)](https://huggingface.co/ltg/nort5-xs) - [NorT5 small (88M)](https://huggingface.co/ltg/nort5-small) - [NorT5 base (228M)](https://huggingface.co/ltg/nort5-base) - [NorT5 large (808M)](https://huggingface.co/ltg/nort5-large) ## Example usage This model currently needs a custom wrapper from `modeling_norbert.py`, you should therefore load the model with `trust_remote_code=True`. ```python import torch from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ltg/norbert3-xs") model = AutoModelForMaskedLM.from_pretrained("ltg/norbert3-xs", trust_remote_code=True) mask_id = tokenizer.convert_tokens_to_ids("[MASK]") input_text = tokenizer("Nå ønsker de seg en[MASK] bolig.", return_tensors="pt") output_p = model(**input_text) output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids) # should output: '[CLS] Nå ønsker de seg en ny bolig.[SEP]' print(tokenizer.decode(output_text[0].tolist())) ``` The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`. ## Cite us ```bibtex @inproceedings{samuel-etal-2023-norbench, title = "{N}or{B}ench {--} A Benchmark for {N}orwegian Language Models", author = "Samuel, David and Kutuzov, Andrey and Touileb, Samia and Velldal, Erik and {\O}vrelid, Lilja and R{\o}nningstad, Egil and Sigdel, Elina and Palatkina, Anna", booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)", month = may, year = "2023", address = "T{\'o}rshavn, Faroe Islands", publisher = "University of Tartu Library", url = "https://aclanthology.org/2023.nodalida-1.61", pages = "618--633", abstract = "We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics. We also introduce a range of new Norwegian language models (both encoder and encoder-decoder based). Finally, we compare and analyze their performance, along with other existing LMs, across the different benchmark tests of NorBench.", } ```
RasmusVeski/MNLP_M2_quantized_model_W8_A8	RasmusVeski	2025-05-27T12:26:32Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "compressed-tensors", "region:us" ]	text-generation	2025-05-27T12:25:53Z	--- 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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ltg/norbert3-base	ltg	2025-05-27T12:26:28Z	1,966	7	transformers	[ "transformers", "pytorch", "fill-mask", "BERT", "NorBERT", "Norwegian", "encoder", "custom_code", "no", "nb", "nn", "license:apache-2.0", "autotrain_compatible", "region:us" ]	fill-mask	2023-03-02T21:38:09Z	--- language: - 'no' - nb - nn inference: false tags: - BERT - NorBERT - Norwegian - encoder license: apache-2.0 --- # NorBERT 3 base <img src="https://huggingface.co/ltg/norbert3-base/resolve/main/norbert.png" width=12.5%> The official release of a new generation of NorBERT language models described in paper [NorBench — A Benchmark for Norwegian Language Models](https://aclanthology.org/2023.nodalida-1.61/). Plese read the paper to learn more details about the model. ## Other sizes: - [NorBERT 3 xs (15M)](https://huggingface.co/ltg/norbert3-xs) - [NorBERT 3 small (40M)](https://huggingface.co/ltg/norbert3-small) - [NorBERT 3 base (123M)](https://huggingface.co/ltg/norbert3-base) - [NorBERT 3 large (323M)](https://huggingface.co/ltg/norbert3-large) ## Generative NorT5 siblings: - [NorT5 xs (32M)](https://huggingface.co/ltg/nort5-xs) - [NorT5 small (88M)](https://huggingface.co/ltg/nort5-small) - [NorT5 base (228M)](https://huggingface.co/ltg/nort5-base) - [NorT5 large (808M)](https://huggingface.co/ltg/nort5-large) ## Example usage This model currently needs a custom wrapper from `modeling_norbert.py`, you should therefore load the model with `trust_remote_code=True`. ```python import torch from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ltg/norbert3-base") model = AutoModelForMaskedLM.from_pretrained("ltg/norbert3-base", trust_remote_code=True) mask_id = tokenizer.convert_tokens_to_ids("[MASK]") input_text = tokenizer("Nå ønsker de seg en[MASK] bolig.", return_tensors="pt") output_p = model(**input_text) output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids) # should output: '[CLS] Nå ønsker de seg en ny bolig.[SEP]' print(tokenizer.decode(output_text[0].tolist())) ``` The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`. ## Cite us ```bibtex @inproceedings{samuel-etal-2023-norbench, title = "{N}or{B}ench {--} A Benchmark for {N}orwegian Language Models", author = "Samuel, David and Kutuzov, Andrey and Touileb, Samia and Velldal, Erik and {\O}vrelid, Lilja and R{\o}nningstad, Egil and Sigdel, Elina and Palatkina, Anna", booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)", month = may, year = "2023", address = "T{\'o}rshavn, Faroe Islands", publisher = "University of Tartu Library", url = "https://aclanthology.org/2023.nodalida-1.61", pages = "618--633", abstract = "We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics. We also introduce a range of new Norwegian language models (both encoder and encoder-decoder based). Finally, we compare and analyze their performance, along with other existing LMs, across the different benchmark tests of NorBench.", } ```
andry4774/khalit-lora-v1	andry4774	2025-05-27T12:26:10Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T12:26:08Z	--- 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: khalit --- # Khalit Lora V1 <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 `khalit` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "khalit", "lora_weights": "https://huggingface.co/andry4774/khalit-lora-v1/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('andry4774/khalit-lora-v1', weight_name='lora.safetensors') image = pipeline('khalit').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/andry4774/khalit-lora-v1/discussions) to add images that show off what you’ve made with this LoRA.
delimi/Mistral_Legal	delimi	2025-05-27T12:26:04Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "legal", "french", "fine-tuned", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.3", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T11:48:39Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.3 tags: - legal - french - mistral - fine-tuned - text-generation pipeline_tag: text-generation inference: true library_name: transformers widget: - text: "Génère une mise en demeure pour un loyer impayé de 1500 euros depuis 2 mois:" example_title: "Mise en demeure" - text: "Rédige un contrat de vente pour une voiture:" example_title: "Contrat de vente" - text: "Crée une clause de confidentialité:" example_title: "Clause juridique" model-index: - name: Mistral_Legal results: - task: type: text-generation name: Text Generation metrics: - type: rouge value: 0.35 name: ROUGE-1 --- # Mistral Legal - French Legal Document Generator A specialized French legal document generation model based on Mistral-7B-Instruct-v0.3. ## Quick Start ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch # Load model model = AutoModelForCausalLM.from_pretrained( "delimi/Mistral_Legal", torch_dtype=torch.float16, device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("delimi/Mistral_Legal") # Generate legal document prompt = \"\"\"Génère une mise en demeure pour: Situation: Loyer impayé Montant: 1,500 euros Locataire: M. Martin Délai: 15 jours Mise en demeure:\"\"\" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate( inputs, max_new_tokens=400, temperature=0.7, do_sample=True, top_p=0.9 ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ## Capabilities - Mise en demeure (formal demands) - Contracts (sales, rental, service agreements) - Legal clauses (confidentiality, liability, etc.) - Legal correspondence - Template generation ## Performance - Training Loss: 1.142 - Validation Loss: 1.082 - Dataset: 541 French legal examples - Method: LoRA fine-tuning + merge ## Legal Disclaimer This model is for assistance purposes only. All generated content should be reviewed by qualified legal professionals before use. ## Technical Details - Base: mistralai/Mistral-7B-Instruct-v0.3 - Parameters: 7B - Language: French - Domain: Legal documents - License*: Apache 2.0 ## Usage Examples ### Mise en demeure ``` Génère une mise en demeure pour un loyer impayé de 2,400 euros depuis 3 mois. ``` ### Contract Generation ``` Rédige un contrat de vente pour une voiture Peugeot 208 au prix de 15,000 euros. ``` ### Legal Clauses ``` Crée une clause de confidentialité pour un accord commercial d'une durée de 2 ans. ``` --- Model created by AIAJ team - {datetime.now().strftime('%B %Y')}*
majdab4/dummy-model	majdab4	2025-05-27T12:23:34Z	0	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-27T12:23:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
apollina/poli	apollina	2025-05-27T12:23:05Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-27T12:23:05Z	--- license: apache-2.0 ---
sayantan0013/Qwen3-0.6B-SFT	sayantan0013	2025-05-27T12:22:06Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T12:21:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mvsamsonov/speecht5_finetuned_voxpopuli_nl	mvsamsonov	2025-05-27T12:22:03Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:voxpopuli", "endpoints_compatible", "region:us" ]	text-to-audio	2025-05-25T05:55:45Z	--- library_name: transformers tags: - generated_from_trainer datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # speecht5_finetuned_voxpopuli_nl This model was trained from scratch on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4590 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 0.6863 \| 0.8607 \| 200 \| 0.6124 \| \| 0.5721 \| 1.7230 \| 400 \| 0.5167 \| \| 0.5396 \| 2.5853 \| 600 \| 0.4984 \| \| 0.5289 \| 3.4476 \| 800 \| 0.4868 \| \| 0.5172 \| 4.3098 \| 1000 \| 0.4815 \| \| 0.5169 \| 5.1721 \| 1200 \| 0.4771 \| \| 0.5108 \| 6.0344 \| 1400 \| 0.4740 \| \| 0.5086 \| 6.8951 \| 1600 \| 0.4715 \| \| 0.5042 \| 7.7574 \| 1800 \| 0.4699 \| \| 0.4939 \| 8.6197 \| 2000 \| 0.4678 \| \| 0.4965 \| 9.4820 \| 2200 \| 0.4667 \| \| 0.5004 \| 10.3443 \| 2400 \| 0.4644 \| \| 0.4906 \| 11.2066 \| 2600 \| 0.4617 \| \| 0.4889 \| 12.0689 \| 2800 \| 0.4612 \| \| 0.493 \| 12.9295 \| 3000 \| 0.4601 \| \| 0.4893 \| 13.7918 \| 3200 \| 0.4599 \| \| 0.4894 \| 14.6541 \| 3400 \| 0.4600 \| \| 0.4922 \| 15.5164 \| 3600 \| 0.4594 \| \| 0.491 \| 16.3787 \| 3800 \| 0.4599 \| \| 0.482 \| 17.2410 \| 4000 \| 0.4590 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Emmzyel/Emmzy_Wealth	Emmzyel	2025-05-27T12:21:32Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-27T12:21:32Z	--- license: apache-2.0 ---
TheS3b/Qwen3-0.6B-quali-SFT	TheS3b	2025-05-27T12:21:07Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T12:20:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
AlphaSurgeMaleEnhancement/AlphaSurgeMaleEnhancement	AlphaSurgeMaleEnhancement	2025-05-27T12:20:46Z	0	0	null	[ "region:us" ]	null	2025-05-27T12:11:12Z	## What Is Alpha Surge Male Enhancement? [Alpha Surge Male Enhancement](https://www.diginear.com/2PGQH1JJ/ZDFM5CX/) is a dietary supplement designed specifically for men looking to boost their sexual health, energy levels, and overall vitality. Available in gummy form, it provides a refreshing alternative to conventional capsules or tablets, which can be cumbersome or unappealing for some users. The product is marketed as a natural, non-GMO, and gluten-free formula, crafted in FDA-registered and GMP-certified facilities in the United States. This emphasis on quality manufacturing ensures that users receive a product that meets high safety and efficacy standards. The core promise of Alpha Surge Male Enhancement is to enhance male performance by addressing common issues such as erectile dysfunction, low libido, and reduced stamina. 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Prerna2055/T5_FL_Base_Model	Prerna2055	2025-05-27T12:18:53Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-26T11:22:02Z	--- 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]
DD1909/Car2	DD1909	2025-05-27T12:16:52Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T11:36:57Z	--- 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: CAR --- # Car2 <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 `CAR` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "CAR", "lora_weights": "https://huggingface.co/DD1909/Car2/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('DD1909/Car2', weight_name='lora.safetensors') image = pipeline('CAR').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/DD1909/Car2/discussions) to add images that show off what you’ve made with this LoRA.
nguyenduongchitam/whisper-small-vi	nguyenduongchitam	2025-05-27T12:16:37Z	13	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-27T05:09:50Z	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-vi 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 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: 0.4522 - Wer: 27.0405 ## 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 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.4981 \| 0.9699 \| 1000 \| 0.4862 \| 31.8081 \| \| 0.3205 \| 1.9399 \| 2000 \| 0.4527 \| 29.7486 \| \| 0.1923 \| 2.9098 \| 3000 \| 0.4522 \| 27.0405 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
phospho-app/freza44-gr00t-cube_N-mi84eetyfa	phospho-app	2025-05-27T12:16:33Z	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]	null	2025-05-27T11:56:08Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [freza44/cube_N](https://huggingface.co/datasets/freza44/cube_N) - Wandb run URL: None - Epochs: 10 - Batch size: 49 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
snaeppi/Qwen3-0.6B-Base-FP8-KV-MNLP	snaeppi	2025-05-27T12:14:20Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T12:13:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
Hsianchengfun/pruned_15_dt_dp_100epoch	Hsianchengfun	2025-05-27T12:12:37Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T12:09:31Z	--- 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. 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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]
lattaes/Qwen2.5-7B-Instruct-hr-policy-finetuned	lattaes	2025-05-27T12:12:18Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-27T12:12:18Z	--- license: apache-2.0 ---
msquitttto/test2	msquitttto	2025-05-27T12:11:53Z	0	0	null	[ "aa", "dataset:nvidia/OpenMathReasoning", "base_model:nari-labs/Dia-1.6B", "base_model:finetune:nari-labs/Dia-1.6B", "license:apache-2.0", "region:us" ]	null	2025-05-27T08:29:31Z	--- license: apache-2.0 language: - aa datasets: - nvidia/OpenMathReasoning metrics: - accuracy base_model: - nari-labs/Dia-1.6B ---
Sertipan/Devstral-Small-2505	Sertipan	2025-05-27T12:10:25Z	0	0	vllm	[ "vllm", "safetensors", "mistral", "text2text-generation", "en", "fr", "de", "es", "pt", "it", "ja", "ko", "ru", "zh", "ar", "fa", "id", "ms", "ne", "pl", "ro", "sr", "sv", "tr", "uk", "vi", "hi", "bn", "license:apache-2.0", "region:us" ]	text2text-generation	2025-05-27T12:07:22Z	--- language: - en - fr - de - es - pt - it - ja - ko - ru - zh - ar - fa - id - ms - ne - pl - ro - sr - sv - tr - uk - vi - hi - bn license: apache-2.0 library_name: vllm inference: false base_model: - mistralai/Devstrall-Small-2505 extra_gated_description: >- If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>. pipeline_tag: text2text-generation --- # Devstral-Small-2505 Devstral is an agentic LLM for software engineering tasks built under a collaboration between [Mistral AI](https://mistral.ai/) and [All Hands AI](https://www.all-hands.dev/) 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this [benchmark](#benchmark-results). It is finetuned from [Mistral-Small-3.1](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503), therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from `Mistral-Small-3.1` the vision encoder was removed. For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community. Learn more about Devstral in our [blog post](https://mistral.ai/news/devstral). ## Key Features: - Agentic coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents. - lightweight: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use. - Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes. - Context Window: A 128k context window. - Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size. ## Benchmark Results ### SWE-Bench Devstral achieves a score of 46.8% on SWE-Bench Verified, outperforming prior open-source SoTA by 6%. \| Model \| Scaffold \| SWE-Bench Verified (%) \| \|------------------\|--------------------\|------------------------\| \| Devstral \| OpenHands Scaffold \| 46.8 \| \| GPT-4.1-mini \| OpenAI Scaffold \| 23.6 \| \| Claude 3.5 Haiku \| Anthropic Scaffold \| 40.6 \| \| SWE-smith-LM 32B \| SWE-agent Scaffold \| 40.2 \| When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 and Qwen3 232B-A22B. ![SWE Benchmark](assets/swe_bench.png) ## Usage We recommend to use Devstral with the [OpenHands](https://github.com/All-Hands-AI/OpenHands/tree/main) scaffold. You can use it either through our API or by running locally. ### API Follow these [instructions](https://docs.mistral.ai/getting-started/quickstart/#account-setup) to create a Mistral account and get an API key. Then run these commands to start the OpenHands docker container. ```bash export MISTRAL_API_KEY=<MY_KEY> docker pull docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik mkdir -p ~/.openhands-state && echo '{"language":"en","agent":"CodeActAgent","max_iterations":null,"security_analyzer":null,"confirmation_mode":false,"llm_model":"mistral/devstral-small-2505","llm_api_key":"'$MISTRAL_API_KEY'","remote_runtime_resource_factor":null,"github_token":null,"enable_default_condenser":true}' > ~/.openhands-state/settings.json docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.39 ``` ### Local inference The model can also be deployed with the following libraries: - [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended) - [`mistral-inference`](https://github.com/mistralai/mistral-inference): See [here](#mistral-inference) - [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers) - [`LMStudio`](https://lmstudio.ai/): See [here](#lmstudio) - [`llama.cpp`](https://github.com/ggml-org/llama.cpp): See [here](#llama.cpp) - [`ollama`](https://github.com/ollama/ollama): See [here](#ollama) ### OpenHands (recommended) #### Launch a server to deploy Devstral-Small-2505 Make sure you launched an OpenAI-compatible server such as vLLM or Ollama as described above. Then, you can use OpenHands to interact with `Devstral-Small-2505`. In the case of the tutorial we spineed up a vLLM server running the command: ```bash vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` The server address should be in the following format: `http://<your-server-url>:8000/v1` #### Launch OpenHands You can follow installation of OpenHands [here](https://docs.all-hands.dev/modules/usage/installation). The easiest way to launch OpenHands is to use the Docker image: ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Then, you can access the OpenHands UI at `http://localhost:3000`. #### Connect to the server When accessing the OpenHands UI, you will be prompted to connect to a server. You can use the advanced mode to connect to the server you launched earlier. Fill the following fields: - Custom Model: `openai/mistralai/Devstral-Small-2505` - Base URL: `http://<your-server-url>:8000/v1` - API Key: `token` (or any other token you used to launch the server if any) #### Use OpenHands powered by Devstral Now you're good to use Devstral Small inside OpenHands by starting a new conversation. Let's build a To-Do list app. <details> <summary>To-Do list app</summary 1. Let's ask Devstral to generate the app with the following prompt: ```txt Build a To-Do list app with the following requirements: - Built using FastAPI and React. - Make it a one page app that: - Allows to add a task. - Allows to delete a task. - Allows to mark a task as done. - Displays the list of tasks. - Store the tasks in a SQLite database. ``` ![Agent prompting](assets/tuto_open_hands/agent_prompting.png) 2. Let's see the result You should see the agent construct the app and be able to explore the code it generated. If it doesn't do it automatically, ask Devstral to deploy the app or do it manually, and then go the front URL deployment to see the app. ![Agent working](assets/tuto_open_hands/agent_working.png) ![App UI](assets/tuto_open_hands/app_ui.png) 3. Iterate Now that you have a first result you can iterate on it by asking your agent to improve it. For example, in the app generated we could click on a task to mark it checked but having a checkbox would improve UX. You could also ask it to add a feature to edit a task, or to add a feature to filter the tasks by status. Enjoy building with Devstral Small and OpenHands! </details> ### vLLM (recommended) We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm) to implement production-ready inference pipelines. _Installation_ Make sure you install [`vLLM >= 0.8.5`](https://github.com/vllm-project/vllm/releases/tag/v0.8.5): ``` pip install vllm --upgrade ``` Doing so should automatically install [`mistral_common >= 1.5.5`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.5). To check: ``` python -c "import mistral_common; print(mistral_common.__version__)" ``` You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39). #### Server We recommand that you use Devstral in a server/client setting. 1. Spin up a server: ``` vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` 2. To ping the client you can use a simple Python snippet. ```py import requests import json from huggingface_hub import hf_hub_download url = "http://<your-server-url>:8000/v1/chat/completions" headers = {"Content-Type": "application/json", "Authorization": "Bearer token"} model = "mistralai/Devstral-Small-2505" def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt") messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": [ { "type": "text", "text": "<your-command>", }, ], }, ] data = {"model": model, "messages": messages, "temperature": 0.15} response = requests.post(url, headers=headers, data=json.dumps(data)) print(response.json()["choices"][0]["message"]["content"]) ``` ### Mistral-inference We recommend using mistral-inference to quickly try out / "vibe-check" Devstral. #### Install Make sure to have mistral_inference >= 1.6.0 installed. ```bash pip install mistral_inference --upgrade ``` #### Download ```python from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Devstral') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Devstral-Small-2505", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path) ``` #### Python You can run the model using the following command: ```bash mistral-chat $HOME/mistral_models/Devstral --instruct --max_tokens 300 ``` You can then prompt it with anything you'd like. ### Transformers To make the best use of our model with transformers make sure to have [installed](https://github.com/mistralai/mistral-common) ` mistral-common >= 1.5.5` to use our tokenizer. ```bash pip install mistral-common --upgrade ``` Then load our tokenizer along with the model and generate: ```python import torch from mistral_common.protocol.instruct.messages import ( SystemMessage, UserMessage ) from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from huggingface_hub import hf_hub_download from transformers import AutoModelForCausalLM def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt model_id = "mistralai/Devstral-Small-2505" tekken_file = hf_hub_download(repo_id=model_id, filename="tekken.json") SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt") tokenizer = MistralTokenizer.from_file(tekken_file) model = AutoModelForCausalLM.from_pretrained(model_id) tokenized = tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ SystemMessage(content=SYSTEM_PROMPT), UserMessage(content="<your-command>"), ], ) ) output = model.generate( input_ids=torch.tensor([tokenized.tokens]), max_new_tokens=1000, )[0] decoded_output = tokenizer.decode(output[len(tokenized.tokens):]) print(decoded_output) ``` ### LMStudio Download the weights from huggingface: ``` pip install -U "huggingface_hub[cli]" huggingface-cli download \ "mistralai/Devstral-Small-2505_gguf" \ --include "devstralQ4_K_M.gguf" \ --local-dir "mistralai/Devstral-Small-2505_gguf/" ``` You can serve the model locally with [LMStudio](https://lmstudio.ai/). * Download [LM Studio](https://lmstudio.ai/) and install it * Install `lms cli ~/.lmstudio/bin/lms bootstrap` * In a bash terminal, run `lms import devstralQ4_K_M.gguf` in the directory where you've downloaded the model checkpoint (e.g. `mistralai/Devstral-Small-2505_gguf`) * Open the LMStudio application, click the terminal icon to get into the developer tab. Click select a model to load and select Devstral Q4 K M. Toggle the status button to start the model, in setting toggle Serve on Local Network to be on. * On the right tab, you will see an API identifier which should be devstralq4_k_m and an api address under API Usage. Keep note of this address, we will use it in the next step. Launch Openhands You can now interact with the model served from LM Studio with openhands. Start the openhands server with the docker ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Click “see advanced setting” on the second line. In the new tab, toggle advanced to on. Set the custom model to be mistral/devstralq4_k_m and Base URL the api address we get from the last step in LM Studio. Set API Key to dummy. Click save changes. ### llama.cpp Download the weights from huggingface: ``` pip install -U "huggingface_hub[cli]" huggingface-cli download \ "mistralai/Devstral-Small-2505_gguf" \ --include "devstralQ4_K_M.gguf" \ --local-dir "mistralai/Devstral-Small-2505_gguf/" ``` Then run Devstral using the llama.cpp CLI. ```bash ./llama-cli -m Devstral-Small-2505_gguf/devstralQ4_K_M.gguf -cnv ``` ### Ollama You can run Devstral using the [Ollama](https://ollama.ai/) CLI. ```bash ollama run devstral ``` ### Example: Understanding Test Coverage of Mistral Common We can start the OpenHands scaffold and link it to a repo to analyze test coverage and identify badly covered files. Here we start with our public `mistral-common` repo. After the repo is mounted in the workspace, we give the following instruction ``` Check the test coverage of the repo and then create a visualization of test coverage. Try plotting a few different types of graphs and save them to a png. ``` The agent will first browse the code base to check test configuration and structure. ![Repo Exploration](assets/images_example/example_mistral_common_1.png) Then it sets up the testing dependencies and launches the coverage test: ![Repo Exploration](assets/images_example/example_mistral_common_2.png) Finally, the agent writes necessary code to visualize the coverage. ![Repo Exploration](assets/images_example/example_mistral_common_3.png) At the end of the run, the following plots are produced: ![Repo Exploration](assets/images_example/example_mistral_common_res_1.png) ![Repo Exploration](assets/images_example/example_mistral_common_res_2.png) ![Repo Exploration](assets/images_example/example_mistral_common_res_3.png)
Darkhn/llamatest-EXL2-3.0bpw-H6	Darkhn	2025-05-27T12:08:35Z	0	0	exllamav2	[ "exllamav2", "quantized", "license:mit", "region:us" ]	null	2025-05-27T12:08:00Z	--- library_name: exllamav2 license: mit tags: - exllamav2 - quantized --- # llamatest-EXL2-3.0bpw-H6 EXL2 quantized model of `unsloth/Llama-3.2-1B-Instruct` (the original base model). ## Quantization Details - Bits per weight (bpw): 3.0 - Head Bits: 6 - Calibration Source: Measurement derived from model weights (no explicit dataset calibration or provided measurement for this specific quantization pass). Quantized using the [exllamav2 library](https://github.com/turboderp/exllamav2).
aamijar/Llama-2-7b-hf-lora-r128-boolq-portlora-epochs0	aamijar	2025-05-27T12:07:40Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T12:07:39Z	--- 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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Darkhn/llamatest-EXL2-4.58bpw-H6	Darkhn	2025-05-27T12:02:15Z	0	0	exllamav2	[ "exllamav2", "quantized", "license:mit", "region:us" ]	null	2025-05-27T11:38:08Z	--- library_name: exllamav2 license: mit tags: - exllamav2 - quantized --- # llamatest-EXL2-4.58bpw-H6 EXL2 quantized model of `unsloth/Llama-3.2-1B-Instruct` (the original base model). ## Quantization Details - Bits per weight (bpw): 4.58 - Head Bits: 6 - Calibration Source: Measurement derived from model weights (no explicit dataset calibration or provided measurement for this specific quantization pass). Quantized using the [exllamav2 library](https://github.com/turboderp/exllamav2).
sfrontull/transloco-ita-lld	sfrontull	2025-05-27T12:01:09Z	0	0	null	[ "translation", "low-resource", "ct2", "int8", "real-time", "it", "lld", "base_model:Helsinki-NLP/opus-mt-itc-itc", "base_model:finetune:Helsinki-NLP/opus-mt-itc-itc", "license:cc-by-nc-sa-4.0", "region:us" ]	translation	2025-05-27T09:52:37Z	--- license: cc-by-nc-sa-4.0 language: - it - lld tags: - translation - low-resource - ct2 - int8 - real-time base_model: - Helsinki-NLP/opus-mt-itc-itc pipeline_tag: translation --- # Italian to Ladin Real-Time Translation Model This is a fast, lightweight real-time translation model from Italian (it) to Ladin (lld), based on [Helsinki-NLP/opus-mt-itc-itc](https://huggingface.co/Helsinki-NLP/opus-mt-itc-itc) and optimized using CTranslate2 for efficient inference. ## 💡 Key Features - ✅ Base model: [Helsinki-NLP/opus-mt-itc-itc](https://huggingface.co/Helsinki-NLP/opus-mt-itc-itc) - ⚡ Optimized with CTranslate2 - 🧠 int8 quantization for faster inference and lower memory usage - 🗣️ Designed for real-time transcription + translation use cases (e.g., [TransLoco](https://git.uibk.ac.at/informatik/iis/iis-projects/TransLoco)) - 🕒 Suitable for low-latency environments like live subtitling or in-browser translation tools ## 🏗️ Model Architecture - Architecture: Transformer - Format: CTranslate2 - Quantization: `int8` - Size on disk: ~70 MB ## 🚀 Intended Use - Real-time speech-to-speech or speech-to-text translation from Italian to Ladin - Assistive tools for minority language accessibility - Educational and research applications - Use as part of tools like [TransLoco](https://git.uibk.ac.at/informatik/iis/iis-projects/TransLoco) Non-commercial use only, in accordance with the CC BY-NC 4.0 license. ```python import ctranslate2 from transformers import AutoTokenizer mtmodel = ctranslate2.Translator("./transloco-ita-lld", device="cpu") tokenizer = AutoTokenizer.from_pretrained("./transloco-ita-lld") texts = ["Questo è un esempio."] tokenized_sentences = [tokenizer.convert_ids_to_tokens(tokenizer.encode(x)) for x in texts] batch_res = mtmodel.translate_batch(source=tokenized_sentences) decoded_results = [ tokenizer.decode( tokenizer.convert_tokens_to_ids(res.hypotheses[0]), skip_special_tokens=True ) for res in batch_res ] print(decoded_results) ``` ⚠️ Note: The tokenizer uses `fur_Latn` as the target language code due to the lack of `lld_Latn` support in the original NLLB vocabulary. ## ❗Limitations - Ladin is a low-resource language, and the model may struggle with: - Out-of-domain vocabulary - Variant-specific variations - The model may hallucinate outputs when given incomplete or noisy input. ## ⚖️ Ethical Considerations - Language technologies for minority languages should be developed with community involvement. - Please avoid using the model for commercial applications or mass-translation pipelines without review. ## 📎 Citation If you use this model in your work, please cite: ```bibtex @misc{hallerseeber:frontull:2025, title = {TransLoco: AI-driven real-time transcription, translation, and summarisation}, subtitle = {A self-hosted free-software conference tool}, author = {Simon Haller-Seeber and Samuel Frontull}, year = {2025}, note = {In preparation}, } ```
YuanTang96/GreenPLM	YuanTang96	2025-05-27T11:58:12Z	3	0	null	[ "onnx", "safetensors", "arxiv:2408.15966", "arxiv:2309.00615", "arxiv:2307.12981", "arxiv:2308.16911", "arxiv:2402.17766", "arxiv:2405.01413", "region:us" ]	null	2025-05-23T06:28:35Z	<h1 align="center"><strong>More Text, Less Point: Towards 3D Data-Efficient Point-Language Understanding</strong></h1> <p align="center"> Yuan Tang&emsp; Xu Han&emsp; Xianzhi Li<sup>✝</sup>&emsp; Qiao Yu&emsp; Jinfeng Xu&emsp; Yixue Hao&emsp; Long Hu&emsp; Min Chen <br> Huazhong University of Science and Technology&emsp;South China University of Technology </p> </p> <p align="center"> <a><strong>AAAI 2025 </strong></a> <a href='https://arxiv.org/pdf/2408.15966'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> <a href='https://huggingface.co/YuanTang96/GreenPLM'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a> </p> <!-- contents with emoji --> ## 📋 Contents - [🔍 Overview](#-overview) - [📦 Training and Evaluation](#-Training-and-Evaluation) - [🔗 Citation](#-citation) - [📄 License](#-license) - [📚 Related Work](#-related-work) - [👏 Acknowledgements](#-acknowledgements) ## 🔍 Overview ![pic_2](media/3_stage.png) ![pic_2](media/T3D.png) - We introduce a new task of 3D data-efficient point-language understanding, aiming to enable LLMs to achieve robust 3D understanding with minimal 3D data. - We propose GreenPLM to tackle this 3D data-limited task from a novel perspective, enhancing point-LLM alignment with more free-text data. - we introduce a 6M T3D dataset, design a 3-stage training strategy, and present a 0M-Pooling module for token pooling. - We introduce the Accuracy-to-3D-Data Ratio (A3DR) to measure the efficiency of 3D data usage and establish an evaluation benchmark based on open-source LLMs. - GreenPLM outperforms previous models using only 12\% of 3D data and even surpasses GPT4Point (660K 3D data) using only text, demonstrating superior 3D data efficiency. ## 📦 Training-and-Evaluation ### Download project The code, weights, and dataset of the project have already been uploaded to [Hugging Face](https://huggingface.co/YuanTang96/GreenPLM). Simply download them once to get started with the project. ### Install Environment Enter the project directory and execute the following command: ```bash conda create -n greenplm python=3.10 -y conda activate greenplm bash envInstall.sh ``` ### Project Directory Introduction - `./greenplm/release` contains the paper's weights, training scripts, and testing scripts. - `./pretrained_weight` stores the pre-trained weights required for the training and testing phases of the project. - `./lava-vicuna_2024_4_Phi-3-mini-4k-instruct` is the weight directory for Phi-3. - `./dataset/T3D` is the 6M dataset proposed in this project. - `./dataset/T3D/stage_1/brief_1M_caption.json` is the dataset for Stage I. - `./dataset/T3D/stage_2/stage_2_data_210k.json` is the dataset for Stage II. ### Dataset Preparation `./dataset/Objaverse/8192_npy.zip` contains the point cloud data from Objaverse that is required for this project. To unzip the dataset: ```bash unzip ./dataset/Objaverse/8192_npy.zip -d ./dataset/Objaverse/ ``` ### Inference #### Paper Weights ##### GreenPLM-0 The model trained only on text data, i.e., (Stage I & Stage II). ```bash bash ./release/paper/scripts/test/release_stage_2.sh ``` The output JSON results are saved in `./release/paper/result_json/stage_2`. ##### GreenPLM The model trained on a small amount of 3D data, i.e., (Stage I & Stage II & Stage III). ```bash bash ./release/paper/scripts/test/release_stage_3.sh ``` The output JSON results are saved in `./release/paper/result_json/stage_3`. #### Weights Using All T3D Dataset <details> <summary>We also provide weights trained using the entire T3D dataset, meaning we use 5M data from T3D in Stage II, instead of just 210k as in our paper. (click to expand)</summary> ##### GreenPLM-0 The model trained only on text data, i.e., (Stage I & Stage II). ```bash bash ./release/5M_data_seting/scripts/test/release_5M_stage_2.sh ``` The output JSON results are saved in `./release/5M_data_seting/result_json/stage_2`. ##### GreenPLM The model trained on a small amount of 3D data, i.e., (Stage I & Stage II & Stage III). ```bash bash ./release/5M_data_seting/scripts/test/release_5M_stage_3.sh ``` The output JSON results are saved in `./release/5M_data_seting/result_json/stage_3`. </details> ### Evaluation #### Using LLM - You can get the DASHSCOPE_API_KEY from [aliyun](https://bailian.console.aliyun.com/?apiKey=1#/api-key). The evaluation may require 9 CNY (~ 1.3 USD). - If you have enough GPU resources, you can also build your own Qwen2-72B-Instruct service, following the [Qwen2](https://github.com/QwenLM/Qwen2?tab=readme-ov-file). Then evaluate the results for free! 1. Evaluate the open vocabulary classification on objaverse ```bash export PYTHONPATH=$PWD export DASHSCOPE_API_KEY=sk-xxx python ./pointllm/eval/evaluator_opensource_llm_QwenAPI.py \ --results_path /path/to/evaluation/PointLLM_brief_description_val_200_GT_Objaverse_classification_prompt0.json \ --eval_type open-free-form-classification \ --model_type qwen2-72b-instruct \ --parallel --num_workers 4 ``` ```bash export PYTHONPATH=$PWD export DASHSCOPE_API_KEY=sk-xxx python ./pointllm/eval/evaluator_opensource_llm_QwenAPI.py \ --results_path /path/to/evaluation/PointLLM_brief_description_val_200_GT_Objaverse_classification_prompt1.json \ --eval_type open-free-form-classification \ --model_type qwen2-72b-instruct \ --parallel --num_workers 4 ``` 2. Evaluate the close-set zero-shot classification on ModelNet40 ```bash export PYTHONPATH=$PWD export DASHSCOPE_API_KEY=sk-xxx python ./pointllm/eval/evaluator_opensource_llm_QwenAPI.py \ --results_path /path/to/evaluation/ModelNet_classification_prompt0.json \ --eval_type modelnet-close-set-classification \ --model_type qwen2-72b-instruct \ --parallel --num_workers 4 ``` ```bash export PYTHONPATH=$PWD export DASHSCOPE_API_KEY=sk-xxx python ./pointllm/eval/evaluator_opensource_llm_QwenAPI.py \ --results_path /path/to/evaluation/ModelNet_classification_prompt1.json \ --eval_type modelnet-close-set-classification \ --model_type qwen2-72b-instruct \ --parallel --num_workers 4 ``` 3. Evaluate the object captioning on objaverse ```bash export PYTHONPATH=$PWD export DASHSCOPE_API_KEY=sk-xxx python ./pointllm/eval/evaluator_opensource_llm_QwenAPI.py \ --results_path /path/to/evaluation/PointLLM_brief_description_val_200_GT_Objaverse_captioning_prompt2.json \ --eval_type object-captioning \ --model_type qwen2-72b-instruct \ --parallel --num_workers 4 ``` #### Traditional Metric Evaluation For the object captioning task, run the following command to evaluate model outputs with traditional metrics Sentence-BERT and SimCSE. ```bash CUDA_VISIBLE_DEVICES=0 python pointllm/eval/traditional_evaluator.py --results_path /path/to/evaluation/PointLLM_brief_description_val_200_GT_Objaverse_captioning_prompt2.json ``` ## Training Stage I ```bash bash ./release/paper/scripts/train/1.sh ``` Stage II: GreenPLM-0 ```bash bash ./release/paper/scripts/train/2.sh ``` Stage III: GreenPLM ```bash bash ./release/paper/scripts/train/3.sh ``` <details> <summary>We also provide training scripts using the entire T3D dataset, meaning we use 5M data from T3D in Stage II, instead of just 210k as in our paper. (click to expand)</summary> Stage II: GreenPLM-0 ```bash bash ./release/5M_data_seting/scripts/train/2.sh ``` Stage III: GreenPLM ```bash bash ./release/5M_data_seting/scripts/train/3.sh ``` </details> Note: You can modify the `--output_dir` argument in the scripts to set the output directory for the trained weights. ## 🔗 Citation If you find our work helpful, please consider citing: ```bibtex @inproceedings{tang2025more, title={More text, less point: Towards 3d data-efficient point-language understanding}, author={Tang, Yuan and Han, Xu and Li, Xianzhi and Yu, Qiao and Xu, Jinfeng and Hao, Yixue and Hu, Long and Chen, Min}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, volume={39}, number={7}, pages={7284--7292}, year={2025} } ``` ## 📄 License <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/80x15.png" /></a> <br /> This work is under the <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>. ## 📚 Related Work Together, Let's make LLM for 3D great! - [Point-Bind & Point-LLM](https://arxiv.org/abs/2309.00615): aligns point clouds with Image-Bind to reason multi-modality input without 3D-instruction data training. - [3D-LLM](https://arxiv.org/abs/2307.12981): employs 2D foundation models to encode multi-view images of 3D point clouds. - [PointLLM](https://arxiv.org/abs/2308.16911): employs 3D point clouds with LLaVA. - [ShapeLLM](http://arxiv.org/abs/2402.17766): combines a powerful point cloud encoder with LLM for embodied scenes. - [MiniGPT-3D](https://arxiv.org/pdf/2405.01413) : takes the first step toward efficient 3D-LLM, requiring only a single RTX 3090 GPU and one day of training time. ## 👏 Acknowledgements We would like to thank the authors of [PointLLM](https://github.com/OpenRobotLab/PointLLM), [Uni3D](https://github.com/baaivision/Uni3D), [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct), and [LLaVA-pp](https://github.com/mbzuai-oryx/LLaVA-pp) for their great works and repos.
Sakalti/Mystica1-4B	Sakalti	2025-05-27T11:56:38Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "ja", "base_model:unsloth/Qwen3-4B", "base_model:finetune:unsloth/Qwen3-4B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T10:46:59Z	--- base_model: unsloth/Qwen3-4B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en - ja inference: true widget: - messages: - role: user content: こんにちは！ - messages: - role: user content: おはようこざいます！ - messages: - role: user content: 何しますか？ - messages: - role: user content: hello! library_name: transformers --- # Uploaded model - Developed by: Sakalti - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B 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)
rafaelcf/Qwen2.5-Base-SFT-Countdown-Warmstart	rafaelcf	2025-05-27T11:51:44Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T11:39:53Z	--- 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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BootesVoid/cmb6fhkup04helexpqayylopn_cmb6fmqxu04i1lexpafcu98v2	BootesVoid	2025-05-27T11:50:47Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T11:50:46Z	--- 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: lucy --- # Cmb6Fhkup04Helexpqayylopn_Cmb6Fmqxu04I1Lexpafcu98V2 <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 `lucy` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "lucy", "lora_weights": "https://huggingface.co/BootesVoid/cmb6fhkup04helexpqayylopn_cmb6fmqxu04i1lexpafcu98v2/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/cmb6fhkup04helexpqayylopn_cmb6fmqxu04i1lexpafcu98v2', weight_name='lora.safetensors') image = pipeline('lucy').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/cmb6fhkup04helexpqayylopn_cmb6fmqxu04i1lexpafcu98v2/discussions) to add images that show off what you’ve made with this LoRA.
arnaultsta/MNLP_M2_rag_model	arnaultsta	2025-05-27T11:48:51Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T11:48:24Z	--- 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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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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mlfoundations-dev/qwen_lawma_deepseek-2k-5x-majority_verified	mlfoundations-dev	2025-05-27T11:48:07Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T23:58:48Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: qwen_lawma_deepseek-2k-5x-majority_verified 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. --> # qwen_lawma_deepseek-2k-5x-majority_verified This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/thoughts-lawma-annotations-deepseek-majority-verified-share-gpt 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: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - total_eval_batch_size: 8 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
DaniloNeto/roco_qlora_qwen2	DaniloNeto	2025-05-27T11:47:11Z	5	0	transformers	[ "transformers", "safetensors", "qwen2_vl", "feature-extraction", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-27T00:50:38Z	--- base_model: unsloth/qwen2-vl-2b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_vl license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: DaniloNeto - License: apache-2.0 - Finetuned from model : unsloth/qwen2-vl-2b-instruct-bnb-4bit This qwen2_vl 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)
HPLT/hplt2c_eng50-tur50_checkpoints	HPLT	2025-05-27T11:46:00Z	0	0	null	[ "pytorch", "llama", "HPLT", "decoder", "en", "tr", "dataset:HPLT/HPLT2.0_cleaned", "arxiv:2503.10267", "license:apache-2.0", "region:us" ]	null	2025-05-26T08:49:52Z	--- language: - en - tr tags: - HPLT - decoder license: apache-2.0 datasets: - HPLT/HPLT2.0_cleaned --- # HPLT v2.0 - Cleaned - English (50%), Turkish (50%) <img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%> This is one of the decoder-only language models trained on [HPLT2.0_cleaned](https://huggingface.co/datasets/HPLT/HPLT2.0_cleaned). All the HPLT decoder-only models use the same hyper-parameters, roughly following the llama architecture with 2.15B parameters in total: - hidden size: 2048 - attention heads: 32 - layers: 24 - sequence length: 2048 ## Intermediate checkpoints We are releasing intermediate checkpoints for each model at intervals of every 1000 training steps in separate branches. The naming convention is `checkpoint_00xxxx00`: for example, `checkpoint_0005000`. The checkpoints range from checkpoint_0001000 to checkpoint_0047684 and the latter is in the main branch. ## Cite us ```bibtex @misc{burchell2025expandedmassivemultilingualdataset, title={An Expanded Massive Multilingual Dataset for High-Performance Language Technologies}, author={Laurie Burchell and Ona de Gibert and Nikolay Arefyev and Mikko Aulamo and Marta Bañón and Pinzhen Chen and Mariia Fedorova and Liane Guillou and Barry Haddow and Jan Hajič and Jindřich Helcl and Erik Henriksson and Mateusz Klimaszewski and Ville Komulainen and Andrey Kutuzov and Joona Kytöniemi and Veronika Laippala and Petter Mæhlum and Bhavitvya Malik and Farrokh Mehryary and Vladislav Mikhailov and Nikita Moghe and Amanda Myntti and Dayyán O'Brien and Stephan Oepen and Proyag Pal and Jousia Piha and Sampo Pyysalo and Gema Ramírez-Sánchez and David Samuel and Pavel Stepachev and Jörg Tiedemann and Dušan Variš and Tereza Vojtěchová and Jaume Zaragoza-Bernabeu}, year={2025}, eprint={2503.10267}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.10267}, } ```
aamijar/Llama-2-7b-hf-lora-r8-boolq-portlora	aamijar	2025-05-27T11:44:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T11:44:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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. 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MatteoBucc/passphrase-identification-roberta-base-qqp-epoch-2	MatteoBucc	2025-05-27T11:44:08Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:FacebookAI/roberta-base", "base_model:adapter:FacebookAI/roberta-base", "region:us" ]	null	2025-05-14T13:41:29Z	--- base_model: roberta-base 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. 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VerifiedPrompts/CNTXT-Filter-Prompt-Opt	VerifiedPrompts	2025-05-27T11:43:28Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "prompt-filtering", "moderation", "en", "dataset:VerifiedPrompts/cntxt-class-final", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-27T07:55:35Z	--- license: mit tags: - text-classification - prompt-filtering - moderation - distilbert - transformers datasets: - VerifiedPrompts/cntxt-class-final language: - en pipeline_tag: text-classification widget: - text: "Write a LinkedIn post about eco-friendly tech for Gen Z entrepreneurs." example_title: Context-rich prompt - text: "Write something" example_title: Vague prompt --- # 📘 Model Card: CNTXT-Filter-Prompt-Opt ## 🔍 Model Overview CNTXT-Filter-Prompt-Opt is a lightweight, high-accuracy text classification model designed to evaluate the contextual completeness of user prompts submitted to LLMs. It acts as a gatekeeper before generation, helping eliminate vague or spam-like input and ensuring only quality prompts proceed to LLM2. - Base model: `distilbert-base-uncased` - Trained on: 200k labeled prompts - Purpose: Prompt validation, spam filtering, and context enforcement --- ## 🎯 Intended Use This model is intended for: - Pre-processing prompts before LLM2 generation - Blocking unclear or context-poor requests - Structuring user input pipelines in AI apps, bots, and assistants --- ## 🔢 Labels The model classifies prompts into 3 categories: \| Label \| Description \| \|-------\|-------------\| \| `has context` \| Prompt is clear, actionable, and self-contained \| \| `missing platform, audience, budget, goal` \| Prompt lacks structural clarity \| \| `Intent is unclear, Please input more context` \| Vague or incoherent prompt \| --- ## 📊 Training Details - Model: `distilbert-base-uncased` - Training method: Hugging Face AutoTrain - Dataset size: 200,000 prompts (curated, curriculum style) - Epochs: 3 - Batch size: 8 - Max seq length: 128 - Mixed Precision: `fp16` - LoRA: ❌ Disabled - Optimizer: AdamW --- ## ✅ Evaluation \| Metric \| Score \| \|--------\|-------\| \| Accuracy \| 1.0 \| \| F1 (macro/micro/weighted) \| 1.0 \| \| Precision / Recall \| 1.0 \| \| Validation Loss \| 0.0 \| The model generalizes extremely well on all validation samples. --- ## ⚙️ How to Use ```python from transformers import pipeline classifier = pipeline("text-classification", model="VerifiedPrompts/CNTXT-Filter-Prompt-Opt") prompt = "Write a business plan for a freelance app in Canada." result = classifier(prompt) print(result) # [{'label': 'has context', 'score': 0.98}]
hunter12441/model	hunter12441	2025-05-27T11:42:53Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-27T11:34:00Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: hunter12441 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
BKM1804/SmolLM-135M-Instruct-4643c60e-bad6-442a-bae2-dd7473506d71-sft-before-dpo-tuned	BKM1804	2025-05-27T11:40:45Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "base_model:unsloth/SmolLM-135M-Instruct", "base_model:finetune:unsloth/SmolLM-135M-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-26T10:55:12Z	--- base_model: unsloth/SmolLM-135M-Instruct library_name: transformers model_name: SmolLM-135M-Instruct-4643c60e-bad6-442a-bae2-dd7473506d71-sft-before-dpo-tuned tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for SmolLM-135M-Instruct-4643c60e-bad6-442a-bae2-dd7473506d71-sft-before-dpo-tuned This model is a fine-tuned version of [unsloth/SmolLM-135M-Instruct](https://huggingface.co/unsloth/SmolLM-135M-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="BKM1804/SmolLM-135M-Instruct-4643c60e-bad6-442a-bae2-dd7473506d71-sft-before-dpo-tuned", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/buikhacminh1804/sn56-sft-before-dpo-train/runs/xkw2nxfk) This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
lisabdunlap/balanced_sft_long-1e4-systems-prompt_e1	lisabdunlap	2025-05-27T11:40:20Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/Qwen3-8B", "base_model:finetune:unsloth/Qwen3-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T11:39:27Z	--- base_model: unsloth/Qwen3-8B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: lisabdunlap - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B 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)
alpcaferoglu/Qwen2.5-Coder-3B-Instruct_bd_cs_t2sws-t2s_r64_a64_e1_bs2_gas4_lr0.0002_sftreason	alpcaferoglu	2025-05-27T11:38:42Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T02:27:20Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tripolskypetr/gemma-3-27B-it-qat-GGUF	tripolskypetr	2025-05-27T11:36:21Z	0	0	null	[ "gguf", "image-text-to-text", "base_model:google/gemma-3-27b-it", "base_model:quantized:google/gemma-3-27b-it", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-05-26T14:21:53Z	--- pipeline_tag: image-text-to-text extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license license: gemma extra_gated_heading: Access Gemma on Hugging Face base_model: google/gemma-3-27b-it --- ## 💫 Community Model> gemma 3 27b it by Google 👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC). Model creator: [google](https://huggingface.co/google)<br> Original model: [gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it)<br> GGUF quantization: provided by Google<br> ## Technical Details Optimized with Quantization Aware Training for improved 4-bit performance. Supports a context length of 128k tokens, with a max output of 8192. Multimodal supporting images normalized to 896 x 896 resolution. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
nickname19/First_T5	nickname19	2025-05-27T11:34:45Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-27T11:33:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
nattkorat/scibert-base-uncased-ner	nattkorat	2025-05-27T11:33:34Z	17	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:allenai/scibert_scivocab_uncased", "base_model:finetune:allenai/scibert_scivocab_uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-17T07:22:26Z	--- library_name: transformers base_model: allenai/scibert_scivocab_uncased tags: - generated_from_trainer model-index: - name: scibert-base-uncased-ner 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. --> # scibert-base-uncased-ner This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0191 - Cases: {'precision': 0.9767981438515081, 'recall': 0.967816091954023, 'f1': 0.972286374133949, 'number': 435} - Country: {'precision': 0.9751332149200711, 'recall': 1.0, 'f1': 0.9874100719424461, 'number': 549} - Date: {'precision': 0.9706896551724138, 'recall': 0.9690189328743546, 'f1': 0.9698535745047373, 'number': 581} - Deaths: {'precision': 0.9529411764705882, 'recall': 0.9501466275659824, 'f1': 0.9515418502202643, 'number': 341} - Virus: {'precision': 0.9963235294117647, 'recall': 0.998158379373849, 'f1': 0.9972401103955841, 'number': 543} - Overall Precision: 0.9760 - Overall Recall: 0.9796 - Overall F1: 0.9778 - Overall Accuracy: 0.9923 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cases \| Country \| Date \| Deaths \| Virus \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------------------------------------------------------------------------------------------:\|:-----------------------------------------------------------------------------------------:\|:--------------------------------------------------------------------------------------------------------:\|:--------------------------------------------------------------------------------------------------------:\|:-------------------------------------------------------------------------------------------------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| No log \| 1.0 \| 291 \| 0.0411 \| {'precision': 0.90744920993228, 'recall': 0.9241379310344827, 'f1': 0.9157175398633258, 'number': 435} \| {'precision': 0.9699646643109541, 'recall': 1.0, 'f1': 0.9847533632286996, 'number': 549} \| {'precision': 0.9149305555555556, 'recall': 0.9070567986230637, 'f1': 0.9109766637856526, 'number': 581} \| {'precision': 0.8830769230769231, 'recall': 0.841642228739003, 'f1': 0.8618618618618619, 'number': 341} \| {'precision': 0.9889908256880734, 'recall': 0.992633517495396, 'f1': 0.9908088235294119, 'number': 543} \| 0.9385 \| 0.9408 \| 0.9396 \| 0.9861 \| \| 0.1005 \| 2.0 \| 582 \| 0.0291 \| {'precision': 0.9733656174334141, 'recall': 0.9241379310344827, 'f1': 0.9481132075471699, 'number': 435} \| {'precision': 0.9699646643109541, 'recall': 1.0, 'f1': 0.9847533632286996, 'number': 549} \| {'precision': 0.9512195121951219, 'recall': 0.9397590361445783, 'f1': 0.9454545454545454, 'number': 581} \| {'precision': 0.9161849710982659, 'recall': 0.9296187683284457, 'f1': 0.9228529839883551, 'number': 341} \| {'precision': 0.9889908256880734, 'recall': 0.992633517495396, 'f1': 0.9908088235294119, 'number': 543} \| 0.9628 \| 0.9608 \| 0.9618 \| 0.9910 \| \| 0.1005 \| 3.0 \| 873 \| 0.0221 \| {'precision': 0.9764705882352941, 'recall': 0.9540229885057471, 'f1': 0.9651162790697674, 'number': 435} \| {'precision': 0.9751332149200711, 'recall': 1.0, 'f1': 0.9874100719424461, 'number': 549} \| {'precision': 0.9706896551724138, 'recall': 0.9690189328743546, 'f1': 0.9698535745047373, 'number': 581} \| {'precision': 0.9552238805970149, 'recall': 0.9384164222873901, 'f1': 0.9467455621301775, 'number': 341} \| {'precision': 0.9963235294117647, 'recall': 0.998158379373849, 'f1': 0.9972401103955841, 'number': 543} \| 0.9763 \| 0.9755 \| 0.9759 \| 0.9929 \| \| 0.0237 \| 4.0 \| 1164 \| 0.0216 \| {'precision': 0.9789719626168224, 'recall': 0.9632183908045977, 'f1': 0.9710312862108922, 'number': 435} \| {'precision': 0.9751332149200711, 'recall': 1.0, 'f1': 0.9874100719424461, 'number': 549} \| {'precision': 0.9740034662045061, 'recall': 0.9672977624784854, 'f1': 0.9706390328151987, 'number': 581} \| {'precision': 0.9502923976608187, 'recall': 0.9530791788856305, 'f1': 0.951683748169839, 'number': 341} \| {'precision': 0.9944954128440368, 'recall': 0.998158379373849, 'f1': 0.9963235294117647, 'number': 543} \| 0.9764 \| 0.9788 \| 0.9776 \| 0.9921 \| \| 0.0237 \| 5.0 \| 1455 \| 0.0191 \| {'precision': 0.9767981438515081, 'recall': 0.967816091954023, 'f1': 0.972286374133949, 'number': 435} \| {'precision': 0.9751332149200711, 'recall': 1.0, 'f1': 0.9874100719424461, 'number': 549} \| {'precision': 0.9706896551724138, 'recall': 0.9690189328743546, 'f1': 0.9698535745047373, 'number': 581} \| {'precision': 0.9529411764705882, 'recall': 0.9501466275659824, 'f1': 0.9515418502202643, 'number': 341} \| {'precision': 0.9963235294117647, 'recall': 0.998158379373849, 'f1': 0.9972401103955841, 'number': 543} \| 0.9760 \| 0.9796 \| 0.9778 \| 0.9923 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu121 - Datasets 3.5.0 - Tokenizers 0.21.1
Hsianchengfun/pruned_50_dt_dp_100epoch	Hsianchengfun	2025-05-27T11:32:05Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T11:29:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Cloudmaster/Llama-3.2-3B-torchao-final01	Cloudmaster	2025-05-27T11:31:26Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "torchao", "region:us" ]	text-generation	2025-05-27T11:27:37Z	--- 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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