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train · 866k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-05-25 18:27:02	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 476 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-05-25 18:24:25	card stringlengths 11 1.01M
ujjwal1996/Fine_tuning_unsloth-DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit_70steps	ujjwal1996	2025-05-22T04:23:51Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit", "base_model:finetune:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-21T07:54:00Z	--- base_model: unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ujjwal1996 - License: apache-2.0 - Finetuned from model : unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
martinaianaro99/ViLT_ft_CG_L2_F	martinaianaro99	2025-05-22T04:23:43Z	0	0	null	[ "safetensors", "vilt", "region:us" ]	null	2025-05-12T12:09:46Z	# ViLT Model fine-tuned on CG_L2_F dataset Model checkpoint from epoch 10. ## Usage ```python from transformers import ViltProcessor, ViltForMaskedLM # Load model and processor processor = ViltProcessor.from_pretrained('martinaianaro99/ViLT_ft_CG_L2_F') model = ViltForMaskedLM.from_pretrained('martinaianaro99/ViLT_ft_CG_L2_F') ```
Luigi112001/llama4-finetune	Luigi112001	2025-05-22T04:21:55Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "base_model:adapter:unsloth/mistral-7b-v0.3-bnb-4bit", "region:us" ]	null	2025-05-22T04:21:51Z	--- base_model: unsloth/mistral-7b-v0.3-bnb-4bit library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Random-Role-0522-Zichen-step_00256	the-acorn-ai	2025-05-22T04:21:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T04:18:21Z	--- 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. 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the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Random-Role-0522-Zichen-step_00224	the-acorn-ai	2025-05-22T04:18:18Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T04:15:12Z	--- 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]
SamuelAIA/nanoVLM	SamuelAIA	2025-05-22T04:15:40Z	0	0	nanovlm	[ "nanovlm", "safetensors", "vision-language", "multimodal", "research", "image-text-to-text", "license:mit", "region:us" ]	image-text-to-text	2025-05-22T04:14:59Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards library_name: nanovlm license: mit pipeline_tag: image-text-to-text tags: - vision-language - multimodal - research --- nanoVLM is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model. For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M. Usage: Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM. Follow the install instructions and run the following code: ```python from models.vision_language_model import VisionLanguageModel model = VisionLanguageModel.from_pretrained("SamuelAIA/nanoVLM") ```
wzhgba/opendwm-models	wzhgba	2025-05-22T04:15:09Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-22T04:15:09Z	--- license: apache-2.0 ---
DanielNRU/pollen-ner2-850	DanielNRU	2025-05-22T04:10:34Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T04:03:49Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-850 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pollen-ner2-850 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2169 - Precision: 0.7687 - Recall: 0.8474 - F1: 0.8061 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 107 \| 0.2349 \| 0.7333 \| 0.8394 \| 0.7828 \| \| No log \| 2.0 \| 214 \| 0.2238 \| 0.7473 \| 0.8434 \| 0.7925 \| \| No log \| 3.0 \| 321 \| 0.2151 \| 0.7680 \| 0.8373 \| 0.8012 \| \| No log \| 4.0 \| 428 \| 0.2206 \| 0.7536 \| 0.8414 \| 0.7951 \| \| 0.4882 \| 5.0 \| 535 \| 0.2169 \| 0.7687 \| 0.8474 \| 0.8061 \| \| 0.4882 \| 6.0 \| 642 \| 0.2211 \| 0.7518 \| 0.8454 \| 0.7958 \| \| 0.4882 \| 7.0 \| 749 \| 0.2176 \| 0.7608 \| 0.8494 \| 0.8027 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
CNMA/CNMA23	CNMA	2025-05-22T04:09:37Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-22T04:09:37Z	--- license: apache-2.0 ---
harshithan/fb-post-classifier-roberta_v1	harshithan	2025-05-22T04:08:41Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "facebook", "sentiment", "customer-support", "huggingface", "fine-tuned", "en", "dataset:custom", "base_model:cardiffnlp/twitter-roberta-base", "base_model:finetune:cardiffnlp/twitter-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-21T00:26:40Z	--- license: mit language: - en metrics: - f1 - accuracy base_model: - cardiffnlp/twitter-roberta-base datasets: - custom tags: - facebook - text-classification - sentiment - customer-support - transformers - roberta - huggingface - fine-tuned model-index: - name: fb-post-classifier-roberta results: - task: name: Text Classification type: text-classification dataset: name: Facebook Posts (Appreciation / Complaint / Feedback) type: custom metrics: - name: F1 type: f1 value: 0.8979 library_name: transformers pipeline_tag: text-classification --- # Facebook Post Classifier (RoBERTa Base, fine-tuned) This model classifies short Facebook posts into one of the following three mutually exclusive categories: - `Appreciation` - `Complaint` - `Feedback` It is fine-tuned on ~8k manually labeled posts from business pages (e.g. Target, Walmart), based on the `cardiffnlp/twitter-roberta-base` model, which is pretrained on 58M tweets. ## 🧠 Intended Use - Customer support automation - Sentiment analysis on social media - CRM pipelines or chatbot classification ## 📊 Performance \| Class \| Precision \| Recall \| F1 Score \| \|--------------\|-----------\|--------\|----------\| \| Appreciation \| 0.906 \| 0.936 \| 0.921 \| \| Complaint \| 0.931 \| 0.902 \| 0.916 \| \| Feedback \| 0.840 \| 0.874 \| 0.857 \| \| Average \| – \| – \| 0.898 \| > Evaluated on 2039 unseen posts with held-out labels using macro-averaged F1. ## 🛠️ How to Use ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification from torch.nn.functional import softmax import torch model = AutoModelForSequenceClassification.from_pretrained("harshithan/fb-post-classifier-roberta_v1") tokenizer = AutoTokenizer.from_pretrained("harshithan/fb-post-classifier-roberta_v1") inputs = tokenizer("I love the fast delivery!", return_tensors="pt") outputs = model(**inputs) probs = softmax(outputs.logits, dim=1) label = torch.argmax(probs).item() classes = ["Appreciation", "Complaint", "Feedback"] print("Predicted:", classes[label]) ``` ## 🧾 License MIT License ## 🙋‍♀️ Author This model was fine-tuned by @harshithan. ## 📚 Academic Disclaimer This model was developed as part of an academic experimentation project. It is intended solely for educational and research purposes. The model has not been validated for production use and may not generalize to real-world Facebook or customer support data beyond the scope of the assignment.
kittuitsue/xcvxcv	kittuitsue	2025-05-22T04:05:56Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-22T04:05:56Z	--- license: creativeml-openrail-m ---
suringrepell/xcvzxcv	suringrepell	2025-05-22T04:05:54Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-05-22T04:05:54Z	--- license: bigscience-openrail-m ---
DanielNRU/pollen-ner2-800	DanielNRU	2025-05-22T04:03:32Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T03:57:09Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-800 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pollen-ner2-800 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2265 - Precision: 0.7546 - Recall: 0.8273 - F1: 0.7893 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 100 \| 0.2447 \| 0.7059 \| 0.8193 \| 0.7584 \| \| No log \| 2.0 \| 200 \| 0.2398 \| 0.7180 \| 0.8233 \| 0.7671 \| \| No log \| 3.0 \| 300 \| 0.2361 \| 0.7326 \| 0.8253 \| 0.7762 \| \| No log \| 4.0 \| 400 \| 0.2313 \| 0.7406 \| 0.8313 \| 0.7833 \| \| 0.5116 \| 5.0 \| 500 \| 0.2265 \| 0.7546 \| 0.8273 \| 0.7893 \| \| 0.5116 \| 6.0 \| 600 \| 0.2334 \| 0.7220 \| 0.8293 \| 0.7720 \| \| 0.5116 \| 7.0 \| 700 \| 0.2255 \| 0.7446 \| 0.8313 \| 0.7856 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
chancharikm/qwen2.5-vl-72b-cam-motion-preview	chancharikm	2025-05-22T04:02:33Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "llama-factory", "full", "generated_from_trainer", "video-text-to-text", "arxiv:2404.01291", "arxiv:2504.15376", "base_model:Qwen/Qwen2.5-VL-72B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-72B-Instruct", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]	video-text-to-text	2025-05-22T00:46:27Z	--- base_model: Qwen/Qwen2.5-VL-72B-Instruct library_name: transformers license: other tags: - llama-factory - full - generated_from_trainer pipeline_tag: video-text-to-text model-index: - name: bal_imb_cap_full_lr2e-4_epoch10.0_freezevisTrue_fps8 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. --> ## Model description This model is a fine-tuned version of [Qwen/Qwen2.5-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct) on the current most, high-quality camera motion dataset that is publically available. This preview model is the current SOTA for classifying camera motion or being used for video-text retrieval with camera motion captions using [VQAScore](https://arxiv.org/pdf/2404.01291). Find more information about our work on our Github page for [CameraBench](https://github.com/sy77777en/CameraBench). More updates to the benchmark and models will come in the future. Stay tuned! ## Intended uses & limitations The usage is identical to a [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL) model. Our model is primarily useful for camera motion classification in videos as well as video-text retrieval (current SOTA in both tasks). A quick demo is shown below: <details> <summary>Generative Scoring (for classification and retrieval):</summary> ```python # Import necessary libraries from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor from qwen_vl_utils import process_vision_info import torch # Load the model model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "chancharikm/qwen2.5-vl-72B-cam-motion-preview", torch_dtype="auto", device_map="auto" ) processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-72B-Instruct") # Prepare input data video_path = "file:///path/to/video1.mp4" text_description = "the camera tilting upward" question = f"Does this video show \"{text_description}\"?" # Format the input for the model messages = [ { "role": "user", "content": [ { "type": "video", "video": video_path, "fps": 8.0, # Recommended FPS for optimal inference }, {"type": "text", "text": question}, ], } ] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", video_kwargs ) inputs = inputs.to("cuda") # Generate with score output with torch.inference_mode(): outputs = model.generate( inputs, max_new_tokens=1, do_sample=False, # Use greedy decoding to get reliable logprobs output_scores=True, return_dict_in_generate=True ) # Calculate probability of "Yes" response scores = outputs.scores[0] probs = torch.nn.functional.softmax(scores, dim=-1) yes_token_id = processor.tokenizer.encode("Yes")[0] score = probs[0, yes_token_id].item() print(f"Video: {video_path}") print(f"Description: '{text_description}'") print(f"Score: {score:.4f}") ``` </details> <details> <summary>Natural Language Generation</summary> ```python # The model is trained on 8.0 FPS which we recommend for optimal inference from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor from qwen_vl_utils import process_vision_info # default: Load the model on the available device(s) model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "chancharikm/qwen2.5-vl-72B-cam-motion-preview", torch_dtype="auto", device_map="auto" ) # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios. # model = Qwen2_5_VLForConditionalGeneration.from_pretrained( # "chancharikm/qwen2.5-vl-72B-cam-motion-preview", # torch_dtype=torch.bfloat16, # attn_implementation="flash_attention_2", # device_map="auto", # ) # default processor processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-72B-Instruct") messages = [ { "role": "user", "content": [ { "type": "video", "video": "file:///path/to/video1.mp4", "fps": 8.0, }, {"type": "text", "text": "Describe the camera motion in this video."}, ], } ] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, fps=fps, padding=True, return_tensors="pt", video_kwargs, ) inputs = inputs.to("cuda") # Inference generated_ids = model.generate(inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` </details> ## Training and evaluation data Training and evaluation data can be found in our [repo](https://github.com/sy77777en/CameraBench). ## ✏️ Citation If you find this repository useful for your research, please use the following. ``` @article{lin2025camerabench, title={Towards Understanding Camera Motions in Any Video}, author={Lin, Zhiqiu and Cen, Siyuan and Jiang, Daniel and Karhade, Jay and Wang, Hewei and Mitra, Chancharik and Ling, Tiffany and Huang, Yuhan and Liu, Sifan and Chen, Mingyu and Zawar, Rushikesh and Bai, Xue and Du, Yilun and Gan, Chuang and Ramanan, Deva}, journal={arXiv preprint arXiv:2504.15376}, year={2025}, } ```
chancharikm/qwen2.5-vl-32b-cam-motion-preview	chancharikm	2025-05-22T04:01:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "llama-factory", "full", "generated_from_trainer", "video-text-to-text", "arxiv:2404.01291", "arxiv:2504.15376", "base_model:Qwen/Qwen2.5-VL-32B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-32B-Instruct", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]	video-text-to-text	2025-05-22T00:45:03Z	--- base_model: Qwen/Qwen2.5-VL-32B-Instruct library_name: transformers license: other tags: - llama-factory - full - generated_from_trainer pipeline_tag: video-text-to-text model-index: - name: bal_imb_cap_full_lr2e-4_epoch10.0_freezevisTrue_fps8 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. --> ## Model description This model is a fine-tuned version of [Qwen/Qwen2.5-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-32B-Instruct) on the current most, high-quality camera motion dataset that is publically available. This preview model is the current SOTA for classifying camera motion or being used for video-text retrieval with camera motion captions using [VQAScore](https://arxiv.org/pdf/2404.01291). Find more information about our work on our Github page for [CameraBench](https://github.com/sy77777en/CameraBench). More updates to the benchmark and models will come in the future. Stay tuned! ## Intended uses & limitations The usage is identical to a [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL) model. Our model is primarily useful for camera motion classification in videos as well as video-text retrieval (current SOTA in both tasks). A quick demo is shown below: <details> <summary>Generative Scoring (for classification and retrieval):</summary> ```python # Import necessary libraries from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor from qwen_vl_utils import process_vision_info import torch # Load the model model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "chancharikm/qwen2.5-vl-32B-cam-motion-preview", torch_dtype="auto", device_map="auto" ) processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-32B-Instruct") # Prepare input data video_path = "file:///path/to/video1.mp4" text_description = "the camera tilting upward" question = f"Does this video show \"{text_description}\"?" # Format the input for the model messages = [ { "role": "user", "content": [ { "type": "video", "video": video_path, "fps": 8.0, # Recommended FPS for optimal inference }, {"type": "text", "text": question}, ], } ] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", video_kwargs ) inputs = inputs.to("cuda") # Generate with score output with torch.inference_mode(): outputs = model.generate( inputs, max_new_tokens=1, do_sample=False, # Use greedy decoding to get reliable logprobs output_scores=True, return_dict_in_generate=True ) # Calculate probability of "Yes" response scores = outputs.scores[0] probs = torch.nn.functional.softmax(scores, dim=-1) yes_token_id = processor.tokenizer.encode("Yes")[0] score = probs[0, yes_token_id].item() print(f"Video: {video_path}") print(f"Description: '{text_description}'") print(f"Score: {score:.4f}") ``` </details> <details> <summary>Natural Language Generation</summary> ```python # The model is trained on 8.0 FPS which we recommend for optimal inference from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor from qwen_vl_utils import process_vision_info # default: Load the model on the available device(s) model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "chancharikm/qwen2.5-vl-32B-cam-motion-preview", torch_dtype="auto", device_map="auto" ) # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios. # model = Qwen2_5_VLForConditionalGeneration.from_pretrained( # "chancharikm/qwen2.5-vl-32B-cam-motion-preview", # torch_dtype=torch.bfloat16, # attn_implementation="flash_attention_2", # device_map="auto", # ) # default processor processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-32B-Instruct") messages = [ { "role": "user", "content": [ { "type": "video", "video": "file:///path/to/video1.mp4", "fps": 8.0, }, {"type": "text", "text": "Describe the camera motion in this video."}, ], } ] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, fps=fps, padding=True, return_tensors="pt", video_kwargs, ) inputs = inputs.to("cuda") # Inference generated_ids = model.generate(inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` </details> ## Training and evaluation data Training and evaluation data can be found in our [repo](https://github.com/sy77777en/CameraBench). ## ✏️ Citation If you find this repository useful for your research, please use the following. ``` @article{lin2025camerabench, title={Towards Understanding Camera Motions in Any Video}, author={Lin, Zhiqiu and Cen, Siyuan and Jiang, Daniel and Karhade, Jay and Wang, Hewei and Mitra, Chancharik and Ling, Tiffany and Huang, Yuhan and Liu, Sifan and Chen, Mingyu and Zawar, Rushikesh and Bai, Xue and Du, Yilun and Gan, Chuang and Ramanan, Deva}, journal={arXiv preprint arXiv:2504.15376}, year={2025}, } ```
auslawbench/Re-ranker-SaulLM-7B	auslawbench	2025-05-22T03:58:33Z	0	1	transformers	[ "transformers", "safetensors", "en", "arxiv:2412.06272", "base_model:Equall/Saul-7B-Base", "base_model:finetune:Equall/Saul-7B-Base", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	null	2025-05-22T02:51:30Z	--- library_name: transformers license: cc-by-4.0 language: - en base_model: - Equall/Saul-7B-Base --- # 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: Ehsan Shareghi, Jiuzhou Han, Paul Burgess - Model type: 7B - Language(s) (NLP): English - License: CC BY 4.0 - Finetuned from model: Saul-7B-Base ### Model Sources <!-- Provide the basic links for the model. --> - Paper: https://arxiv.org/pdf/2412.06272 ## 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. --> Here's how you can run the model: ```python # pip install git+https://github.com/huggingface/transformers.git # pip install git+https://github.com/huggingface/peft.git import torch from transformers import ( AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig ) from peft import PeftModel model = AutoModelForCausalLM.from_pretrained( "Equall/Saul-7B-Base", quantization_config=BitsAndBytesConfig(load_in_8bit=True), device_map="auto", ) tokenizer = AutoTokenizer.from_pretrained("Equall/Saul-7B-Base") tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained( model, "auslawbench/Re-ranker-SaulLM-7B", device_map="auto", torch_dtype=torch.bfloat16, ) model.eval() fine_tuned_prompt = """ ### Instruction: {} ### Input: {} ### Response: {}""" example_input="\nText:\nMany of ZAR’s grounds of appeal related to fact finding. Drawing on principles set down in several other courts and tribunals, the Appeal Panel summarised the circumstances in which leave may be granted for a person to appeal from findings of fact: <CASENAME> at [84].\n\nPotential Citations:\n\nZNX v ZNY [2020] NSWCATAP 41\nCitation Reasons: The case ZNX v ZNY [2020] NSWCATAP 41 is cited to emphasize that the Appeal Panel's role does not include drafting grounds of appeal for an unrepresented appellant.\n\nCollins v Urban [2014] NSWCATAP 17\nCitation Reasons: The cited case, , is referenced to illustrate the principles guiding the consideration of whether leave to appeal should be granted when there are issues with a fact-finding exercise.\n\nSchwartz Family Co Pty Ltd v Capitol Carpets Pty Ltd [2017] NSWCA 223\nCitation Reasons: The cited case is referenced to emphasize the necessity of explicitly identifying the grounds of appeal, particularly in the context of an error of law in judicial review applications.\n\nNavazi v New South Wales Land and Housing Corporation [2015] NSWCA 308\nCitation Reasons: The case Navazi v New South Wales Land and Housing Corporation [2015] NSWCA 308 is cited to illustrate that the existence of a right of appeal can lead to discretionary considerations in judicial review.\n\nLloyd v Veterinary Surgeons Investigating Committee [2005] NSWCA 456\nCitation Reasons: The case of Lloyd v Veterinary Surgeons Investigating Committee is cited to illustrate that the Appeal Panel has the discretion to grant leave for appeals on questions of fact, regardless of whether an error of law has been identified.\n" model_input = fine_tuned_prompt.format("Predict the citation in the text.", example_input, '') inputs = tokenizer(model_input, return_tensors="pt").to("cuda") outputs = model.generate(inputs, max_new_tokens=256, temperature=1.0) output = tokenizer.decode(outputs[0], skip_special_tokens=True) print(output.split("### Response:")[1].strip().split('>')[0] + '>') ``` ## Citation BibTeX:** ``` @misc{shareghi2024auslawcite, title={Methods for Legal Citation Prediction in the Age of LLMs: An Australian Law Case Study}, author={Ehsan Shareghi, Jiuzhou Han, Paul Burgess}, year={2024}, eprint={arXiv:2412.06272}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Random-Role-0522-Zichen-step_00032	the-acorn-ai	2025-05-22T03:58:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T03:55:09Z	--- 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]
DanielNRU/pollen-ner2-750	DanielNRU	2025-05-22T03:56:54Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T03:48:22Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-750 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pollen-ner2-750 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2426 - Precision: 0.7264 - Recall: 0.8052 - F1: 0.7638 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 94 \| 0.2683 \| 0.6950 \| 0.7871 \| 0.7382 \| \| No log \| 2.0 \| 188 \| 0.2686 \| 0.6809 \| 0.8012 \| 0.7362 \| \| No log \| 3.0 \| 282 \| 0.2616 \| 0.6961 \| 0.7912 \| 0.7406 \| \| No log \| 4.0 \| 376 \| 0.2646 \| 0.6785 \| 0.8052 \| 0.7365 \| \| No log \| 5.0 \| 470 \| 0.2568 \| 0.6899 \| 0.8133 \| 0.7465 \| \| 0.5501 \| 6.0 \| 564 \| 0.2519 \| 0.7058 \| 0.8092 \| 0.7540 \| \| 0.5501 \| 7.0 \| 658 \| 0.2477 \| 0.7072 \| 0.8052 \| 0.7531 \| \| 0.5501 \| 8.0 \| 752 \| 0.2426 \| 0.7264 \| 0.8052 \| 0.7638 \| \| 0.5501 \| 9.0 \| 846 \| 0.2450 \| 0.7110 \| 0.8153 \| 0.7596 \| \| 0.5501 \| 10.0 \| 940 \| 0.2456 \| 0.7091 \| 0.8173 \| 0.7593 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
TEN-framework/TEN_Turn_Detection	TEN-framework	2025-05-22T03:56:36Z	285	18	null	[ "safetensors", "turn detection", "conversational", "natural language understanding", "text-generation", "license:apache-2.0", "region:us" ]	text-generation	2025-04-28T14:55:00Z	--- pipeline_tag: text-generation tags: - turn detection - conversational - natural language understanding license: apache-2.0 --- # TEN Turn Detection *Turn detection for full-duplex dialogue communication* ## Introduction TEN Turn Detection is an advanced intelligent turn detection model designed specifically for natural and dynamic communication between humans and AI agents. This technology addresses one of the most challenging aspects of human-AI conversation: detecting natural turn-taking cues and enabling contextually-aware interruptions. TEN incorporates deep semantic understanding of conversation context and linguistic patterns to create more natural dialogue with AI. <div align="center"> <img src="images/turn_detection.svg" alt="TEN Turn Detection SVG Diagram" width="800"/> </div> TEN Turn Detection categorizes user's text into three key states: finished: A finished utterance where the user has expressed a complete thought and expects a response. Example: "Hey there I was wondering can you help me with my order" wait: An ambiguous utterance where the system cannot confidently determine if more speech will follow. Example: "This conversation needs to end now" unfinished: A clearly unfinished utterance where the user has momentarily paused but intends to continue speaking. Example: "Hello I have a question about" These three classification states allow the TEN system to create natural conversation dynamics by intelligently managing turn-taking, reducing awkward interruptions while maintaining conversation flow. TEN Turn Detection utilizes a multi-layered approach based on the transformer-based language model（Qwen2.5-7B） for semantic analysis. ## Key Features - Context-Aware Turn Management TEN Turn Detection analyzes linguistic patterns and semantic context to accurately identify turn completion points. This capability enables intelligent interruption handling, allowing the system to determine when interruptions are contextually appropriate while maintaining natural conversation flow across various dialogue scenarios. - Multilingual Turn Detection Support TEN Turn Detection provides comprehensive support for both English and Chinese languages. It is engineered to accurately identify turn-taking cues and completion signals across multilingual conversations. - Superior Performance Compared with multiple open-source solutions, TEN achieves superior performance across all metrics on our publicly available test dataset. ## Prepared Dataset We have open-sourced the TEN-Turn-Detection TestSet, a bilingual (Chinese and English) collection of conversational inputs specifically designed to evaluate turn detection capabilities in AI dialogue systems. The dataset consists of three distinct components: wait.txt: Contains expressions requesting conversation pauses or termination unfinished.txt: Features incomplete dialogue inputs with truncated utterances finished.txt: Provides complete conversational inputs across multiple domains ## Detection Performance We conducted comprehensive evaluations comparing several open-source models for turn detection using our test dataset: <div align="center"> \| LANGUAGE \| MODEL \| FINISHED<br>ACCURACY \| UNFINISHED<br>ACCURACY \| WAIT<br>ACCURACY \| \|:--------:\|:-----:\|:--------------------:\|:----------------------:\|:----------------:\| \| English \| Model A \| 59.74% \| 86.46% \| N/A \| \| English \| Model B \| 71.61% \| 96.88% \| N/A \| \| English \| TEN Turn Detection \| 90.64% \| 98.44% \| 91% \| \| LANGUAGE \| MODEL \| FINISHED<br>ACCURACY \| UNFINISHED<br>ACCURACY \| WAIT<br>ACCURACY \| \|:--------:\|:-----:\|:--------------------:\|:----------------------:\|:----------------:\| \| Chinese \| Model B \| 74.63% \| 88.89% \| N/A \| \| Chinese \| TEN Turn Detection \| 98.90% \| 92.74% \| 92% \| </div> > Notes: > 1. Model A doesn't support Chinese language processing > 2. Neither Model A nor Model B support the "WAIT" state detection ## Quick Start TEN Turn Detection is also available on github [TEN-framework/ten-turn-detection](https://github.com/TEN-framework/ten-turn-detection) ### Installation ``` pip install "transformers>=4.45.0" pip install "torch>=2.0.0" ``` ### Model Weights The TEN Turn Detection model is available on HuggingFace ### Inference ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Load model and tokenizer model_id = 'TEN-framework/TEN_Turn_Detection' model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.bfloat16) tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) # Move model to GPU model = model.cuda() model.eval() # Function for inference def analyze_text(text, system_prompt=""): inf_messages = [{"role":"system", "content":system_prompt}] + [{"role":"user", "content":text}] input_ids = tokenizer.apply_chat_template( inf_messages, add_generation_prompt=True, return_tensors="pt" ).cuda() with torch.no_grad(): outputs = model.generate( input_ids, max_new_tokens=1, do_sample=True, top_p=0.1, temperature=0.1, pad_token_id=tokenizer.eos_token_id ) response = outputs[0][input_ids.shape[-1]:] return tokenizer.decode(response, skip_special_tokens=True) # Example usage text = "Hello I have a question about" result = analyze_text(text) print(f"Input: '{text}'") print(f"Turn Detection Result: '{result}'") ``` ## Citation If you use TEN Turn Detection in your research or applications, please cite: ``` @misc{TEN_Turn_Detection, author = {TEN Team}, title = {TEN Turn Detection: Turn detection for full-duplex dialogue communication }, year = {2025}, url = {https://github.com/TEN-framework/ten-turn-detection}, } ``` ## License This project is Apache 2.0 licensed.
polyglots/llama-3-8b-DPO-si-Sentiment-Tagger-14476-si-Sentiment-Tagger-DPO-Eval-7238	polyglots	2025-05-22T03:55:31Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:polyglots/llama-3-8b-DPO-si-Sentiment-Tagger-14476", "base_model:finetune:polyglots/llama-3-8b-DPO-si-Sentiment-Tagger-14476", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-22T03:55:26Z	--- base_model: polyglots/llama-3-8b-DPO-si-Sentiment-Tagger-14476 tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: polyglots - License: apache-2.0 - Finetuned from model : polyglots/llama-3-8b-DPO-si-Sentiment-Tagger-14476 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)
pryaosuji/zxcvzxcv	pryaosuji	2025-05-22T03:54:23Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-05-22T03:54:23Z	--- license: bigcode-openrail-m ---
tomwen/test2	tomwen	2025-05-22T03:53:27Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-30T07:43:40Z	--- license: apache-2.0 ---
samuelchristlie/Wan2.1-VACE-1.3B-GGUF	samuelchristlie	2025-05-22T03:52:20Z	0	0	diffusers	[ "diffusers", "gguf", "video", "video-generation", "text-to-video", "en", "base_model:Wan-AI/Wan2.1-VACE-1.3B", "base_model:quantized:Wan-AI/Wan2.1-VACE-1.3B", "license:apache-2.0", "region:us" ]	text-to-video	2025-05-22T03:02:20Z	--- license: apache-2.0 language: - en pipeline_tag: text-to-video library_name: diffusers tags: - video - video-generation base_model: - Wan-AI/Wan2.1-VACE-1.3B --- ``` ________ ______ ____ ___ ___ _______ ______ _______ ____ ______ ______ _______ _______ _______ _______ \| \| \| \|.---.-.-----.\|__ \| \|_ \| ______\| \| \| _ \| \| ___\|_____\|_ \| \|__ \| __ \______\| __\| __\| \| \| ___\| \| \| \| \|\| _ \| \|\| __\|__ _\| \|\|______\| \| \| \| ---\| ___\|______\|\| \|_ __\|__ \| __ <______\| \| \| \| \| \| \| ___\| \|________\|\|___._\|__\|__\|\|______\|__\|______\| \_____/\|___\|___\|______\|_______\| \|______\|__\|______\|______/ \|_______\|_______\|_______\|___\| ``` # Wan-2.1-VACE-1.3B-GGUF ## Direct GGUF Conversion of Wan2.1-VACE-1.3B Wan2.1 is an open-source suite of video foundation models, compatible with consumer-grade GPUs, that excels in various video generation tasks like text-to-video, image-to-video, and video editing, even supporting visual text generation. ## Table of Contents 📝 1. ▶ [Usage](#usage) 2. 📃 [License](#license) 3. 🙏 [Acknowledgements](#acknowledgements) <a name="usage"/> ## ▶ Usage Download models using `huggingface-cli`: ``` pip install "huggingface_hub[cli]" huggingface-cli download samuelchristlie/Wan2.1-VACE-1.3B-GGUF --local-dir ./Wan2.1-VACE-1.3B-GGUF ``` You can also download directly from [this page](https://huggingface.co/samuelchristlie/Wan2.1-VACE-1.3B-GGUF/tree/main). <a name="license"/> ## 📃 License This model is a derivative work of the original model licensed under the Apache 2.0 License, and is therefore distributed under the terms of the same license. <a name="acknowledgements"/> ## 🙏 Acknowledgements Thanks to Patrick Gillespie for creating the ASCII text art tool used in this project https://patorjk.com/software/taag/ Wan-AI for the Wan model https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B https://huggingface.co/city96 </div>
pot99rta/CaptainMaid-VioletReign-DarkMell-12B-GGUF	pot99rta	2025-05-22T03:50:59Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:pot99rta/CaptainMaid-VioletReign-DarkMell-12B", "base_model:quantized:pot99rta/CaptainMaid-VioletReign-DarkMell-12B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-21T21:25:44Z	--- base_model: pot99rta/CaptainMaid-VioletReign-DarkMell-12B library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # CaptainMaid-VioletReign-DarkMell-12B-GGUF ![image/png](https://cdn-uploads.huggingface.co/production/uploads/636ea389fd9751c3d081e88e/LfwCXtlhDeeYzkOTeLagm.png) This Model Uses Mistral For The Preset. Merge Heavy Model - Sensitive to High Temp and random settings. This model was converted to GGUF format from [`pot99rta/CaptainMaid-VioletReign-DarkMell-12B`](https://huggingface.co/pot99rta/CaptainMaid-VioletReign-DarkMell-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/pot99rta/CaptainMaid-VioletReign-DarkMell-12B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo pot99rta/CaptainMaid-VioletReign-DarkMell-12B-Q8_0-GGUF --hf-file captainmaid-violetreign-darkmell-12b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo pot99rta/CaptainMaid-VioletReign-DarkMell-12B-Q8_0-GGUF --hf-file captainmaid-violetreign-darkmell-12b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo pot99rta/CaptainMaid-VioletReign-DarkMell-12B-Q8_0-GGUF --hf-file captainmaid-violetreign-darkmell-12b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo pot99rta/CaptainMaid-VioletReign-DarkMell-12B-Q8_0-GGUF --hf-file captainmaid-violetreign-darkmell-12b-q8_0.gguf -c 2048 ```
mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF	mradermacher	2025-05-22T03:50:29Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:mesolitica/Malaysian-Qwen2.5-32B-Reasoning-SFT", "base_model:quantized:mesolitica/Malaysian-Qwen2.5-32B-Reasoning-SFT", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-22T00:18:50Z	--- base_model: mesolitica/Malaysian-Qwen2.5-32B-Reasoning-SFT language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/mesolitica/Malaysian-Qwen2.5-32B-Reasoning-SFT <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 11.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-Q2_K.gguf) \| i1-Q2_K \| 12.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-IQ3_M.gguf) \| i1-IQ3_M \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.0 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-Q4_1.gguf) \| i1-Q4_1 \| 20.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Malaysian-Qwen2.5-32B-Reasoning-SFT-i1-GGUF/resolve/main/Malaysian-Qwen2.5-32B-Reasoning-SFT.i1-Q6_K.gguf) \| i1-Q6_K \| 27.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
pot99rta/CaptainMaid-12B-VioletMell-V0.420-GGUF	pot99rta	2025-05-22T03:49:43Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:pot99rta/CaptainMaid-12B-VioletMell-V0.420", "base_model:quantized:pot99rta/CaptainMaid-12B-VioletMell-V0.420", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-21T20:47:12Z	--- base_model: pot99rta/CaptainMaid-12B-VioletMell-V0.420 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # CaptainMaid-12B-VioletMell-V0.420-GGUF ![image/png](https://cdn-uploads.huggingface.co/production/uploads/636ea389fd9751c3d081e88e/nihkfHFoiM6dM1e6sibGb.png) This Model Uses Mistral For The Preset. You Can Use ChatML Too - Only Tested ChatML with Mistral Tokenizer. The model seems to handle higher temps and random settings well in my tests. This model was converted to GGUF format from [`pot99rta/CaptainMaid-12B-VioletMell-V0.420`](https://huggingface.co/pot99rta/CaptainMaid-12B-VioletMell-V0.420) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/pot99rta/CaptainMaid-12B-VioletMell-V0.420) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo pot99rta/CaptainMaid-12B-VioletMell-V0.420-Q8_0-GGUF --hf-file captainmaid-12b-violetmell-v0.420-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo pot99rta/CaptainMaid-12B-VioletMell-V0.420-Q8_0-GGUF --hf-file captainmaid-12b-violetmell-v0.420-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo pot99rta/CaptainMaid-12B-VioletMell-V0.420-Q8_0-GGUF --hf-file captainmaid-12b-violetmell-v0.420-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo pot99rta/CaptainMaid-12B-VioletMell-V0.420-Q8_0-GGUF --hf-file captainmaid-12b-violetmell-v0.420-q8_0.gguf -c 2048 ```
DanielNRU/pollen-ner2-700	DanielNRU	2025-05-22T03:48:08Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T03:44:49Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-700 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pollen-ner2-700 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2760 - Precision: 0.6858 - Recall: 0.7932 - F1: 0.7356 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 88 \| 0.2763 \| 0.6856 \| 0.7751 \| 0.7276 \| \| No log \| 2.0 \| 176 \| 0.2760 \| 0.6858 \| 0.7932 \| 0.7356 \| \| No log \| 3.0 \| 264 \| 0.2686 \| 0.6865 \| 0.7871 \| 0.7334 \| \| No log \| 4.0 \| 352 \| 0.2685 \| 0.6799 \| 0.7892 \| 0.7305 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
casque/Dhevv-Armor-Flames	casque	2025-05-22T03:48:07Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-22T03:47:45Z	--- license: creativeml-openrail-m ---
FormlessAI/2e689380-9e32-4cde-af94-89003b0cbef7	FormlessAI	2025-05-22T03:47:54Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "grpo", "arxiv:2402.03300", "base_model:NousResearch/Genstruct-7B", "base_model:finetune:NousResearch/Genstruct-7B", "endpoints_compatible", "region:us" ]	null	2025-05-22T00:45:25Z	--- base_model: NousResearch/Genstruct-7B library_name: transformers model_name: 2e689380-9e32-4cde-af94-89003b0cbef7 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for 2e689380-9e32-4cde-af94-89003b0cbef7 This model is a fine-tuned version of [NousResearch/Genstruct-7B](https://huggingface.co/NousResearch/Genstruct-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="FormlessAI/2e689380-9e32-4cde-af94-89003b0cbef7", 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/phoenix-formless/Gradients/runs/xqfot957) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.7.0+cu118 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
pot99rta/CaptainMaid-12B-VioletMell-V0.420	pot99rta	2025-05-22T03:46:02Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:Nitral-AI/Captain-Eris_Violet-V0.420-12B", "base_model:merge:Nitral-AI/Captain-Eris_Violet-V0.420-12B", "base_model:pot99rta/PatriMaid-12B-Forgottenslop-NeonMell", "base_model:merge:pot99rta/PatriMaid-12B-Forgottenslop-NeonMell", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T20:10:22Z	--- base_model: - Nitral-AI/Captain-Eris_Violet-V0.420-12B - pot99rta/PatriMaid-12B-Forgottenslop-NeonMell library_name: transformers tags: - mergekit - merge --- # CaptainMaid-12B-VioletMell-V0.420 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/636ea389fd9751c3d081e88e/gBsGXq0zOyPoeTVQ3M0Qw.png) This Model Uses Mistral For The Preset. You Can Use ChatML Too - Only Tested ChatML with Mistral Tokenizer. The model seems to handle higher temps and random settings well in my tests. # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * [Nitral-AI/Captain-Eris_Violet-V0.420-12B](https://huggingface.co/Nitral-AI/Captain-Eris_Violet-V0.420-12B) * [pot99rta/PatriMaid-12B-Forgottenslop-NeonMell](https://huggingface.co/pot99rta/PatriMaid-12B-Forgottenslop-NeonMell) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Nitral-AI/Captain-Eris_Violet-V0.420-12B layer_range: [0, 40] - model: pot99rta/PatriMaid-12B-Forgottenslop-NeonMell layer_range: [0, 40] merge_method: slerp base_model: Nitral-AI/Captain-Eris_Violet-V0.420-12B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.420 dtype: bfloat16 ```
xuan-luo/MTPQwen3-8B-T1234-Eagle-id4	xuan-luo	2025-05-22T03:45:13Z	0	0	transformers	[ "transformers", "safetensors", "mtpqwen3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-05-22T03:41:37Z	--- 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. 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]
Baselhany/Graduation_Project_Distilation_Whisper_base3	Baselhany	2025-05-22T03:43:53Z	34	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ar", "base_model:openai/whisper-base", "base_model:finetune:openai/whisper-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-08T13:30:55Z	--- library_name: transformers language: - ar license: apache-2.0 base_model: openai/whisper-base tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper base AR - BA 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 base AR - BA This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the quran-ayat-speech-to-text dataset. It achieves the following results on the evaluation set: - Loss: 0.0928 - Wer: 0.2043 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:------:\| \| 1.2944 \| 1.0 \| 313 \| 0.0886 \| 0.1967 \| \| 1.2819 \| 2.0 \| 626 \| 0.0902 \| 0.1923 \| \| 1.2752 \| 3.0 \| 939 \| 0.0902 \| 0.1986 \| \| 1.1425 \| 4.0 \| 1252 \| 0.0915 \| 0.1989 \| \| 1.0812 \| 5.0 \| 1565 \| 0.0900 \| 0.1914 \| \| 0.9708 \| 6.0 \| 1878 \| 0.0900 \| 0.1916 \| \| 0.9029 \| 7.0 \| 2191 \| 0.0891 \| 0.1985 \| \| 0.8248 \| 8.0 \| 2504 \| 0.0896 \| 0.1916 \| \| 0.7778 \| 9.0 \| 2817 \| 0.0897 \| 0.1941 \| \| 0.7485 \| 10.0 \| 3130 \| 0.0890 \| 0.1944 \| \| 0.7219 \| 11.0 \| 3443 \| 0.0883 \| 0.1961 \| \| 0.6584 \| 12.0 \| 3756 \| 0.0889 \| 0.1948 \| \| 0.6516 \| 13.0 \| 4069 \| 0.0883 \| 0.1951 \| \| 0.6233 \| 14.0 \| 4382 \| 0.0882 \| 0.1942 \| \| 0.6017 \| 14.9536 \| 4680 \| 0.0883 \| 0.1957 \| ### Framework versions - Transformers 4.51.1 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.0
casque/Dhevv-DragonScaleStyle	casque	2025-05-22T03:42:08Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-22T03:41:50Z	--- license: creativeml-openrail-m ---
yunjae-won/mp_mistral7bv3_sft_dpo_beta5e-2_epoch1_ratio_dpor_multisample	yunjae-won	2025-05-22T03:41:24Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T03:37:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
LowkeySuicidal/q-Taxi-v1-4x4-noSlippery	LowkeySuicidal	2025-05-22T03:39:58Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-05-22T03:39:56Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.74 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="LowkeySuicidal/q-Taxi-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
kjamesh/a2c-PandaReachDense-v3_TEST	kjamesh	2025-05-22T03:38:25Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-21T23:43:46Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -16.07 +/- 3.99 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
binggwong/mujoco_cube_LoRa_adapter	binggwong	2025-05-22T03:37:59Z	0	0	null	[ "safetensors", "unsloth", "license:apache-2.0", "region:us" ]	null	2025-05-21T04:41:35Z	--- license: apache-2.0 tags: - unsloth ---
amps93/Qwen3-1.7B_qlora	amps93	2025-05-22T03:36:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-22T03:36:16Z	--- 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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shanchen/ds-limo-linearja-250	shanchen	2025-05-22T03:34:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2203.05482", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "base_model:merge:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "base_model:shanchen/ds-limo-ja-250", "base_model:merge:shanchen/ds-limo-ja-250", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T03:27:49Z	--- base_model: - shanchen/ds-limo-ja-250 - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B library_name: transformers tags: - mergekit - merge --- # mlinearja This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * [shanchen/ds-limo-ja-250](https://huggingface.co/shanchen/ds-limo-ja-250) * [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B parameters: weight: 1.0 - model: shanchen/ds-limo-ja-250 parameters: weight: 0.5 merge_method: linear dtype: float16 ```
PaceKW/bert-base-indonesian-1.5G-multilabel-indonesian-hate-speech-new	PaceKW	2025-05-22T03:33:40Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:cahya/bert-base-indonesian-1.5G", "base_model:finetune:cahya/bert-base-indonesian-1.5G", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-22T03:31:12Z	--- library_name: transformers license: mit base_model: cahya/bert-base-indonesian-1.5G tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: bert-base-indonesian-1.5G-multilabel-indonesian-hate-speech-new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-indonesian-1.5G-multilabel-indonesian-hate-speech-new This model is a fine-tuned version of [cahya/bert-base-indonesian-1.5G](https://huggingface.co/cahya/bert-base-indonesian-1.5G) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3641 - F1: 0.7802 - Roc Auc: 0.8639 - Accuracy: 0.7156 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:--------:\| \| 0.3106 \| 1.0 \| 659 \| 0.2504 \| 0.6779 \| 0.7832 \| 0.5978 \| \| 0.2235 \| 2.0 \| 1318 \| 0.2113 \| 0.7466 \| 0.8392 \| 0.6441 \| \| 0.1722 \| 3.0 \| 1977 \| 0.2283 \| 0.7511 \| 0.8493 \| 0.6581 \| \| 0.097 \| 4.0 \| 2636 \| 0.2421 \| 0.7626 \| 0.8490 \| 0.6874 \| \| 0.0643 \| 5.0 \| 3295 \| 0.2727 \| 0.7584 \| 0.8417 \| 0.6938 \| \| 0.0572 \| 6.0 \| 3954 \| 0.2817 \| 0.7662 \| 0.8662 \| 0.6737 \| \| 0.0304 \| 7.0 \| 4613 \| 0.3075 \| 0.7606 \| 0.8475 \| 0.6879 \| \| 0.021 \| 8.0 \| 5272 \| 0.3195 \| 0.7697 \| 0.8626 \| 0.6932 \| \| 0.0157 \| 9.0 \| 5931 \| 0.3347 \| 0.7663 \| 0.8477 \| 0.7052 \| \| 0.0095 \| 10.0 \| 6590 \| 0.3353 \| 0.7759 \| 0.8598 \| 0.7118 \| \| 0.0086 \| 11.0 \| 7249 \| 0.3467 \| 0.7768 \| 0.8590 \| 0.7136 \| \| 0.0063 \| 12.0 \| 7908 \| 0.3503 \| 0.7795 \| 0.8644 \| 0.7128 \| \| 0.0046 \| 13.0 \| 8567 \| 0.3577 \| 0.7797 \| 0.8613 \| 0.7153 \| \| 0.0037 \| 14.0 \| 9226 \| 0.3622 \| 0.7801 \| 0.8674 \| 0.7115 \| \| 0.0046 \| 15.0 \| 9885 \| 0.3641 \| 0.7802 \| 0.8639 \| 0.7156 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
phospho-app/MarcWester-ACT-m7-iz22z	phospho-app	2025-05-22T03:31:32Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-05-22T01:42:15Z	--- 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: [MarcWester/m7](https://huggingface.co/datasets/MarcWester/m7) - Wandb run URL: None - Epochs: None - Batch size: 40 - 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)
DanielNRU/pollen-ner2-550	DanielNRU	2025-05-22T03:31:09Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T03:25:58Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-550 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pollen-ner2-550 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3432 - Precision: 0.6156 - Recall: 0.7269 - F1: 0.6667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 69 \| 0.3899 \| 0.5340 \| 0.6948 \| 0.6038 \| \| No log \| 2.0 \| 138 \| 0.3667 \| 0.5738 \| 0.6948 \| 0.6285 \| \| No log \| 3.0 \| 207 \| 0.3638 \| 0.5784 \| 0.7108 \| 0.6378 \| \| No log \| 4.0 \| 276 \| 0.3495 \| 0.6007 \| 0.7068 \| 0.6494 \| \| No log \| 5.0 \| 345 \| 0.3547 \| 0.5805 \| 0.7169 \| 0.6415 \| \| No log \| 6.0 \| 414 \| 0.3432 \| 0.6156 \| 0.7269 \| 0.6667 \| \| No log \| 7.0 \| 483 \| 0.3453 \| 0.6026 \| 0.7369 \| 0.6631 \| \| 0.7026 \| 8.0 \| 552 \| 0.3397 \| 0.6142 \| 0.7289 \| 0.6667 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
Ichi075/GEN-1	Ichi075	2025-05-22T03:29:54Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "en", "ja", "dataset:AhmedSSabir/Japanese-wiki-dump-sentence-dataset", "base_model:Qwen/Qwen3-0.6B", "base_model:finetune:Qwen/Qwen3-0.6B", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T01:53:36Z	--- library_name: transformers license: mit datasets: - AhmedSSabir/Japanese-wiki-dump-sentence-dataset language: - en - ja base_model: - Qwen/Qwen3-0.6B pipeline_tag: text-generation --- # GEN-1 ![logo](https://huggingface.co/Ichi075/GEN-1/resolve/main/Frame%207.png) ## About GEN-1 Model with about 600 million parameters. Japanese-specific SLM. ## How to use ```py from transformers import pipeline pipe = pipeline("text-generation", model="Ichi075/GEN-1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages) ``` ```py from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Ichi075/GEN-1") model = AutoModelForCausalLM.from_pretrained("Ichi075/GEN-1") ```
dipanshu449/orpheus-tts-finetuned-model-hi-speaker-with-emotive-tags-main-test	dipanshu449	2025-05-22T03:28:54Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T03:26:39Z	--- 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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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]
iSolver-AI/FEnet	iSolver-AI	2025-05-22T03:27:01Z	55	0	transformers	[ "transformers", "safetensors", "gguf", "xclip", "fill-mask", "custom_code", "arxiv:2410.06885", "arxiv:2410.11817", "arxiv:2410.09401", "arxiv:2409.12883", "arxiv:2410.11888", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	fill-mask	2024-10-10T16:27:47Z	--- license: mit # language: # - zh # metrics: # - accuracy # base_model: # - deepseek-ai/DeepSeek-V3 # - deepseek-ai/DeepSeek-V3-Base # base_model_relation: merge library_name: transformers # pipeline_tag: image-text-to-text # widget: # - src: >- # https://huggingface.co/iSolver-AI/FEnet/resolve/main/xiaohongshu-girls-enndme-1.jpg # example_title: enndme-pic-1 # output: # text: Hello my name is Julien # - src: >- # https://huggingface.co/iSolver-AI/FEnet/resolve/main/xiaohongshu-girls-enndme-2.jpg # example_title: enndme-pic-2 # output: # - label: POSITIVE # score: 0.8 # - src: >- # https://huggingface.co/iSolver-AI/FEnet/resolve/main/xiaohongshu-girls-enndme-3.jpg # example_title: enndme-pic-3 # output: # - label: POSITIVE # score: 0.8 # tags: # - mlx # - llama # - llama3 # - transformers # - Reward Model # - conversational --- test webhook # Paper: - ✅来源于HF+arxiv，完整输入HF链接的论文：[F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching](https://huggingface.co/papers/2410.06885) - ✅来源于HF+arxiv，完整输入arxiv链接的论文：https://arxiv.org/abs/2410.11817 - 来源于HF+arxiv，仅输入标题+编号的论文：[Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction](2409.18124) - 来源于HF+arxiv，仅输入标题的论文：Exploring Model Kinship for Merging Large Language Models - 来源于HF+arxiv，仅输入编号的论文：2410.12381 - ✅来源于HF+arxiv，输入链接不带https://前缀：arxiv.org/abs/2410.09401 - ✅仅来源于arxiv，完整输入arxiv链接的论文：[Improving Prototypical Parts Abstraction for Case-Based Reasoning Explanations Designed for the Kidney Stone Type Recognition](https://arxiv.org/abs/2409.12883)，因为有READme引用而自动导入该paper到Daily Paper,变成arxiv和HF都有的论文 - ✅仅来源于arxiv，完整输入arxiv链接的论文：[Aharonov-Bohm effects on the GUP framework](https://arxiv.org/abs/2410.11888)，会因为有READme引用而自动导入该paper到Daily Paper - 仅来源于arxiv，仅输入编号的论文：2409.00821 - 仅来源于arxiv，仅输入标题的论文：An Augmentation-based Model Re-adaptation Framework for Robust Image Segmentation - 非arxiv论文： @inproceedings{DBLP:conf/nips/XuLCLQ21, author={Yong Xu and Feng Li and Zhile Chen and Jinxiu Liang and Yuhui Quan}, title={Encoding Spatial Distribution of Convolutional Features for Texture Representation}, year={2021}, cdate={1609459200000}, pages={22732-22744}, url={https://proceedings.neurips.cc/paper/2021/hash/c04c19c2c2474dbf5f7ac4372c5b9af1-Abstract.html}, booktitle={NeurIPS}, crossref={conf/nips/2021} } > 数据集标题record：allenai/WildBench > 模型标题record：==black-forest-labs/FLUX.1-dev== > 数据集标题record：LLM360/TxT360 sasad
mradermacher/qwen2.5-14B-PT-BR-Instruct-GGUF	mradermacher	2025-05-22T03:26:50Z	44	1	transformers	[ "transformers", "gguf", "text-generation-inference", "pt", "base_model:amadeusai/Amadeus-Verbo-BI-Qwen-2.5-14B-PT-BR-Instruct-Experimental", "base_model:quantized:amadeusai/Amadeus-Verbo-BI-Qwen-2.5-14B-PT-BR-Instruct-Experimental", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-05T06:12:54Z	--- base_model: amadeusai/Amadeus-Verbo-BI-Qwen-2.5-14B-PT-BR-Instruct-Experimental language: - pt library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-14B-Instruct/blob/main/LICENSE quantized_by: mradermacher tags: - text-generation-inference --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/amadeusai/Amadeus-Verbo-BI-Qwen-2.5-14B-PT-BR-Instruct-Experimental <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-14B-PT-BR-Instruct-GGUF/resolve/main/qwen2.5-14B-PT-BR-Instruct.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-14B-PT-BR-Instruct-GGUF/resolve/main/qwen2.5-14B-PT-BR-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-14B-PT-BR-Instruct-GGUF/resolve/main/qwen2.5-14B-PT-BR-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-14B-PT-BR-Instruct-GGUF/resolve/main/qwen2.5-14B-PT-BR-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-14B-PT-BR-Instruct-GGUF/resolve/main/qwen2.5-14B-PT-BR-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-14B-PT-BR-Instruct-GGUF/resolve/main/qwen2.5-14B-PT-BR-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-14B-PT-BR-Instruct-GGUF/resolve/main/qwen2.5-14B-PT-BR-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-14B-PT-BR-Instruct-GGUF/resolve/main/qwen2.5-14B-PT-BR-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-14B-PT-BR-Instruct-GGUF/resolve/main/qwen2.5-14B-PT-BR-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-14B-PT-BR-Instruct-GGUF/resolve/main/qwen2.5-14B-PT-BR-Instruct.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/qwen2.5-14B-PT-BR-Instruct-GGUF/resolve/main/qwen2.5-14B-PT-BR-Instruct.Q8_0.gguf) \| Q8_0 \| 15.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
xuan-luo/MTPQwen3-8B-T1234-Eagle-mlp4	xuan-luo	2025-05-22T03:26:22Z	0	0	transformers	[ "transformers", "safetensors", "mtpqwen3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-05-22T02:57:16Z	--- 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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elliotthwang/Best_KimLan-OpenChat_SFT-tw	elliotthwang	2025-05-22T03:25:40Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T03:16:37Z	--- 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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(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]
mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF	mradermacher	2025-05-22T03:25:32Z	0	2	transformers	[ "transformers", "gguf", "writing", "en", "dataset:SillyTilly/fiction-writer-596", "base_model:maldv/praxis-bookwriter-llama3.1-8b-sft", "base_model:quantized:maldv/praxis-bookwriter-llama3.1-8b-sft", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-22T00:01:54Z	--- base_model: maldv/praxis-bookwriter-llama3.1-8b-sft datasets: - SillyTilly/fiction-writer-596 language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - writing --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/maldv/praxis-bookwriter-llama3.1-8b-sft <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 3.1 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.8 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/praxis-bookwriter-llama3.1-8b-sft-i1-GGUF/resolve/main/praxis-bookwriter-llama3.1-8b-sft.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
dong-99/Chronos-Platinum-72B-mlx-4Bit	dong-99	2025-05-22T03:25:19Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "roleplay", "storywriting", "qwen2.5", "finetune", "pytorch", "mlx", "mlx-my-repo", "conversational", "base_model:ZeusLabs/Chronos-Platinum-72B", "base_model:quantized:ZeusLabs/Chronos-Platinum-72B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "region:us" ]	text-generation	2025-05-22T03:23:01Z	--- base_model: ZeusLabs/Chronos-Platinum-72B tags: - roleplay - storywriting - qwen2.5 - finetune - transformers - pytorch - mlx - mlx-my-repo --- # dong-99/Chronos-Platinum-72B-mlx-4Bit The Model [dong-99/Chronos-Platinum-72B-mlx-4Bit](https://huggingface.co/dong-99/Chronos-Platinum-72B-mlx-4Bit) was converted to MLX format from [ZeusLabs/Chronos-Platinum-72B](https://huggingface.co/ZeusLabs/Chronos-Platinum-72B) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("dong-99/Chronos-Platinum-72B-mlx-4Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
ZZains/test	ZZains	2025-05-22T03:23:40Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-22T03:23:40Z	--- license: apache-2.0 ---
zhaoguangxiang/Qwen2.5-1.5B-Open-R1-GRPO	zhaoguangxiang	2025-05-22T03:22:42Z	3	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:open-r1/OpenR1-Math-220k", "arxiv:2402.03300", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-10T12:11:51Z	--- datasets: open-r1/OpenR1-Math-220k library_name: transformers model_name: Qwen2.5-1.5B-Open-R1-GRPO tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen2.5-1.5B-Open-R1-GRPO This model is a fine-tuned version of [None](https://huggingface.co/None) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) 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="zhaoguangxiang/Qwen2.5-1.5B-Open-R1-GRPO", 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/zhaoguangxiang/huggingface/runs/5eu1rg6j) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.0.dev0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
sebastianmr18/xlm-roberta-ner-qlora-bs8	sebastianmr18	2025-05-22T03:21:29Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:FacebookAI/xlm-roberta-large", "base_model:adapter:FacebookAI/xlm-roberta-large", "region:us" ]	null	2025-05-22T02:07:19Z	--- base_model: xlm-roberta-large library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
DanielNRU/pollen-ner2-450	DanielNRU	2025-05-22T03:21:16Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T03:15:53Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-450 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pollen-ner2-450 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4605 - Precision: 0.4883 - Recall: 0.6305 - F1: 0.5504 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 57 \| 0.5754 \| 0.4231 \| 0.5361 \| 0.4730 \| \| No log \| 2.0 \| 114 \| 0.5404 \| 0.4237 \| 0.5462 \| 0.4772 \| \| No log \| 3.0 \| 171 \| 0.5230 \| 0.4407 \| 0.5743 \| 0.4987 \| \| No log \| 4.0 \| 228 \| 0.5053 \| 0.4470 \| 0.5843 \| 0.5065 \| \| No log \| 5.0 \| 285 \| 0.4844 \| 0.4619 \| 0.5843 \| 0.5160 \| \| No log \| 6.0 \| 342 \| 0.4810 \| 0.4708 \| 0.6145 \| 0.5331 \| \| No log \| 7.0 \| 399 \| 0.4710 \| 0.4784 \| 0.6225 \| 0.5410 \| \| No log \| 8.0 \| 456 \| 0.4631 \| 0.4822 \| 0.6245 \| 0.5442 \| \| 0.9019 \| 9.0 \| 513 \| 0.4615 \| 0.4852 \| 0.6265 \| 0.5469 \| \| 0.9019 \| 10.0 \| 570 \| 0.4605 \| 0.4883 \| 0.6305 \| 0.5504 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
Cloudmaster/Llama-3.2-3B-4bit-group128-exllamav2	Cloudmaster	2025-05-22T03:21:03Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2025-05-22T03:13:09Z	--- 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. 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thecode12company/edwardzabalacode-model01	thecode12company	2025-05-22T03:19:32Z	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	2025-05-22T02:53:26Z	--- 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: TOK --- # Edwardzabalacode Model01 <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 `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/thecode12company/edwardzabalacode-model01/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('thecode12company/edwardzabalacode-model01', weight_name='lora.safetensors') image = pipeline('TOK').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/thecode12company/edwardzabalacode-model01/discussions) to add images that show off what you’ve made with this LoRA.
reachomk/gen2seg-sd	reachomk	2025-05-22T03:18:31Z	34	1	diffusers	[ "diffusers", "safetensors", "arxiv:2505.15263", "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-05-17T11:14:39Z	--- base_model: - stabilityai/stable-diffusion-2 --- # gen2seg: Generative Models Enable Generalizable Instance Segmentation <img src='teaser.png'/> This is the official model release for the Stable Diffusion 2 (SD) variant of our `gen2seg` generative instance segmenter. It is the same checkpoint we used to generate figures in the paper. Paper: https://arxiv.org/abs/2505.15263 Please see our website https://reachomk.github.io/gen2seg for demos and additional qualitative samples. If you are looking for our MAE-H variant, you can find that at https://huggingface.co/reachomk/gen2seg-mae-h You can run this model at our GitHub: https://github.com/UCDVision/gen2seg or our Huggingface Space: https://huggingface.co/spaces/reachomk/gen2seg
pasithbas159/Qwen2.5_HII_satellite_v1	pasithbas159	2025-05-22T03:18:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2_5_vl", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-03-23T12:29:07Z	--- base_model: unsloth/qwen2.5-vl-7b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_5_vl - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: pasithbas159 - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-vl-7b-instruct-unsloth-bnb-4bit This qwen2_5_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)
DanielNRU/pollen-ner2-400	DanielNRU	2025-05-22T03:15:38Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T03:10:47Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-400 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pollen-ner2-400 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6034 - Precision: 0.4117 - Recall: 0.4960 - F1: 0.4499 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 50 \| 0.8021 \| 0.3428 \| 0.1948 \| 0.2484 \| \| No log \| 2.0 \| 100 \| 0.7462 \| 0.3190 \| 0.2390 \| 0.2732 \| \| No log \| 3.0 \| 150 \| 0.7122 \| 0.3388 \| 0.3293 \| 0.3340 \| \| No log \| 4.0 \| 200 \| 0.6697 \| 0.3721 \| 0.3594 \| 0.3657 \| \| No log \| 5.0 \| 250 \| 0.6542 \| 0.3978 \| 0.4297 \| 0.4131 \| \| No log \| 6.0 \| 300 \| 0.6287 \| 0.4071 \| 0.4357 \| 0.4210 \| \| No log \| 7.0 \| 350 \| 0.6155 \| 0.4011 \| 0.4518 \| 0.4249 \| \| No log \| 8.0 \| 400 \| 0.6096 \| 0.4068 \| 0.4779 \| 0.4395 \| \| No log \| 9.0 \| 450 \| 0.6042 \| 0.4132 \| 0.4920 \| 0.4491 \| \| 1.1014 \| 10.0 \| 500 \| 0.6034 \| 0.4117 \| 0.4960 \| 0.4499 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
YUGOROU/Step2Modelv0.2	YUGOROU	2025-05-22T03:15:25Z	0	0	transformers	[ "transformers", "pytorch", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T03:13:41Z	--- base_model: unsloth/qwen3-1.7b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: YUGOROU - License: apache-2.0 - Finetuned from model : unsloth/qwen3-1.7b-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)
GalaDev/Gala	GalaDev	2025-05-22T03:14:58Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-22T03:14:58Z	--- license: apache-2.0 ---
alexlop/detr-t5-finetuned	alexlop	2025-05-22T03:14:57Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-21T15:08:04Z	--- library_name: transformers license: apache-2.0 base_model: t5-small tags: - generated_from_trainer model-index: - name: detr-t5-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-t5-finetuned This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: 5 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
OpenGVLab/InternVideo2_CLIP_S	OpenGVLab	2025-05-22T03:12:46Z	0	0	null	[ "safetensors", "internvideo2", "custom_code", "license:apache-2.0", "region:us" ]	null	2025-05-22T01:06:53Z	--- license: apache-2.0 ---
DanielNRU/pollen-ner2-350	DanielNRU	2025-05-22T03:10:35Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T03:06:13Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-350 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pollen-ner2-350 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8648 - Precision: 0.4745 - Recall: 0.1305 - F1: 0.2047 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 44 \| 1.1176 \| 0.0 \| 0.0 \| 0.0 \| \| No log \| 2.0 \| 88 \| 1.0785 \| 0.2143 \| 0.0060 \| 0.0117 \| \| No log \| 3.0 \| 132 \| 1.0249 \| 0.3478 \| 0.0161 \| 0.0307 \| \| No log \| 4.0 \| 176 \| 0.9895 \| 0.4524 \| 0.0382 \| 0.0704 \| \| No log \| 5.0 \| 220 \| 0.9502 \| 0.5088 \| 0.0582 \| 0.1045 \| \| No log \| 6.0 \| 264 \| 0.9204 \| 0.4559 \| 0.0622 \| 0.1095 \| \| No log \| 7.0 \| 308 \| 0.8944 \| 0.4819 \| 0.0803 \| 0.1377 \| \| No log \| 8.0 \| 352 \| 0.8794 \| 0.4685 \| 0.1044 \| 0.1708 \| \| No log \| 9.0 \| 396 \| 0.8661 \| 0.472 \| 0.1185 \| 0.1894 \| \| No log \| 10.0 \| 440 \| 0.8648 \| 0.4745 \| 0.1305 \| 0.2047 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
0hFywu7sWF24/xcvbvxcb	0hFywu7sWF24	2025-05-22T03:10:15Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-05-22T03:10:15Z	--- license: bigscience-bloom-rail-1.0 ---
kwstisskeyi/xcvzxcv	kwstisskeyi	2025-05-22T03:10:01Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-05-22T03:10:01Z	--- license: bigcode-openrail-m ---
samuelchristlie/Wan2.1-T2V-1.3B-GGUF	samuelchristlie	2025-05-22T03:07:42Z	48	0	diffusers	[ "diffusers", "gguf", "video", "video-generation", "text-to-video", "en", "base_model:Wan-AI/Wan2.1-T2V-1.3B", "base_model:quantized:Wan-AI/Wan2.1-T2V-1.3B", "license:apache-2.0", "region:us" ]	text-to-video	2025-05-19T05:33:19Z	--- license: apache-2.0 language: - en pipeline_tag: text-to-video library_name: diffusers tags: - video - video-generation base_model: - Wan-AI/Wan2.1-T2V-1.3B --- ``` ________ ______ ____ _______ ______ ___ ___ ____ ______ ______ _______ _______ _______ _______ \| \| \| \|.---.-.-----.\|__ \| \|_ \| _____\|_ _\|__ \| \| \|_____\|_ \| \|__ \| __ \______\| __\| __\| \| \| ___\| \| \| \| \|\| _ \| \|\| __\|__ _\| \|\|______\|\| \| \| __\| \| \|______\|\| \|_ __\|__ \| __ <______\| \| \| \| \| \| \| ___\| \|________\|\|___._\|__\|__\|\|______\|__\|______\| \|___\| \|______\|\_____/ \|______\|__\|______\|______/ \|_______\|_______\|_______\|___\| ``` # Wan-2.1-T2V-1.3B-GGUF ## Direct GGUF Conversion of Wan2.1-T2V-1.3B Wan2.1 is an open-source suite of video foundation models, compatible with consumer-grade GPUs, that excels in various video generation tasks like text-to-video, image-to-video, and video editing, even supporting visual text generation. ## Table of Contents 📝 1. ▶ [Usage](#usage) 2. 📃 [License](#license) 3. 🙏 [Acknowledgements](#acknowledgements) <a name="usage"/> ## ▶ Usage Download models using `huggingface-cli`: ``` pip install "huggingface_hub[cli]" huggingface-cli download samuelchristlie/Wan2.1-T2V-1.3B-GGUF --local-dir ./Wan2.1-T2V-1.3B-GGUF ``` You can also download directly from [this page](https://huggingface.co/samuelchristlie/Wan2.1-T2V-1.3B-GGUF/tree/main). <a name="license"/> ## 📃 License This model is a derivative work of the original model licensed under the Apache 2.0 License, and is therefore distributed under the terms of the same license. <a name="acknowledgements"/> ## 🙏 Acknowledgements Thanks to Patrick Gillespie for creating the ASCII text art tool used in this project https://patorjk.com/software/taag/ Wan-AI for the Wan model https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B https://huggingface.co/city96 </div>
allura-quants/allura-org_Q3-30b-A3b-Pentiment_EXL3_6.0bpw_H6	allura-quants	2025-05-22T03:06:46Z	0	0	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "mergekit", "merge", "exl3", "conversational", "base_model:allura-org/Q3-30b-A3b-Pentiment", "base_model:quantized:allura-org/Q3-30b-A3b-Pentiment", "autotrain_compatible", "endpoints_compatible", "6-bit", "region:us" ]	text-generation	2025-05-22T02:59:07Z	--- base_model: allura-org/Q3-30b-A3b-Pentiment base_model_relation: quantized quantized_by: ArtusDev library_name: transformers tags: - mergekit - merge - exl3 --- # Pentiment ![image/png](https://cdn-uploads.huggingface.co/production/uploads/634262af8d8089ebaefd410e/tQmu_UoG1AMAIaLSGLXhB.png)
allura-quants/allura-org_Q3-30b-A3b-Pentiment_EXL3_5.0bpw_H6	allura-quants	2025-05-22T03:06:24Z	0	0	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "mergekit", "merge", "exl3", "conversational", "base_model:allura-org/Q3-30b-A3b-Pentiment", "base_model:quantized:allura-org/Q3-30b-A3b-Pentiment", "autotrain_compatible", "endpoints_compatible", "5-bit", "region:us" ]	text-generation	2025-05-22T02:56:46Z	--- base_model: allura-org/Q3-30b-A3b-Pentiment base_model_relation: quantized quantized_by: ArtusDev library_name: transformers tags: - mergekit - merge - exl3 --- # Pentiment ![image/png](https://cdn-uploads.huggingface.co/production/uploads/634262af8d8089ebaefd410e/tQmu_UoG1AMAIaLSGLXhB.png)
allura-quants/allura-org_Q3-30b-A3b-Pentiment_EXL3_4.5bpw_H6	allura-quants	2025-05-22T03:06:18Z	0	0	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "mergekit", "merge", "exl3", "conversational", "base_model:allura-org/Q3-30b-A3b-Pentiment", "base_model:quantized:allura-org/Q3-30b-A3b-Pentiment", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T02:54:32Z	--- base_model: allura-org/Q3-30b-A3b-Pentiment base_model_relation: quantized quantized_by: ArtusDev library_name: transformers tags: - mergekit - merge - exl3 --- # Pentiment ![image/png](https://cdn-uploads.huggingface.co/production/uploads/634262af8d8089ebaefd410e/tQmu_UoG1AMAIaLSGLXhB.png)
allura-quants/allura-org_Q3-30b-A3b-Pentiment_EXL3_4.0bpw_H6	allura-quants	2025-05-22T03:06:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "mergekit", "merge", "exl3", "conversational", "base_model:allura-org/Q3-30b-A3b-Pentiment", "base_model:quantized:allura-org/Q3-30b-A3b-Pentiment", "autotrain_compatible", "endpoints_compatible", "4-bit", "region:us" ]	text-generation	2025-05-22T02:52:25Z	--- base_model: allura-org/Q3-30b-A3b-Pentiment base_model_relation: quantized quantized_by: ArtusDev library_name: transformers tags: - mergekit - merge - exl3 --- # Pentiment ![image/png](https://cdn-uploads.huggingface.co/production/uploads/634262af8d8089ebaefd410e/tQmu_UoG1AMAIaLSGLXhB.png)
DanielNRU/pollen-ner2-300	DanielNRU	2025-05-22T03:06:01Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T03:04:43Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-300 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pollen-ner2-300 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1385 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---:\| \| No log \| 1.0 \| 38 \| 1.1385 \| 0.0 \| 0.0 \| 0.0 \| \| No log \| 2.0 \| 76 \| 1.0968 \| 0.0 \| 0.0 \| 0.0 \| \| No log \| 3.0 \| 114 \| 1.0804 \| 0.0 \| 0.0 \| 0.0 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
allura-quants/allura-org_Q3-30b-A3b-Pentiment_EXL3_3.5bpw_H6	allura-quants	2025-05-22T03:05:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "mergekit", "merge", "exl3", "conversational", "base_model:allura-org/Q3-30b-A3b-Pentiment", "base_model:quantized:allura-org/Q3-30b-A3b-Pentiment", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T02:50:09Z	--- base_model: allura-org/Q3-30b-A3b-Pentiment base_model_relation: quantized quantized_by: ArtusDev library_name: transformers tags: - mergekit - merge - exl3 --- # Pentiment ![image/png](https://cdn-uploads.huggingface.co/production/uploads/634262af8d8089ebaefd410e/tQmu_UoG1AMAIaLSGLXhB.png)
Omar401/llam3_esi	Omar401	2025-05-22T03:05:16Z	0	0	peft	[ "peft", "safetensors", "llama", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-22T01:16:25Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - generated_from_trainer model-index: - name: workspace/data/outputs/llama3_esi results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.8.0.dev0` ```yaml # Adapter & Model adapter: lora base_model: meta-llama/Meta-Llama-3-8B-Instruct bf16: auto load_in_8bit: true special_tokens: pad_token: "<PAD>" # Dataset dataset_processes: 32 datasets: - path: /workspace/data/alpaca_esi_dataset.jsonl type: alpaca trust_remote_code: false message_property_mappings: instruction: instruction input: input output: output # Output output_dir: /workspace/data/outputs/llama3_esi # Training Parameters sequence_len: 1024 micro_batch_size: 64 gradient_accumulation_steps: 1 gradient_checkpointing: true num_epochs: 3 learning_rate: 0.0002 optimizer: adamw_bnb_8bit # LoRA Settings lora_r: 8 lora_alpha: 16 lora_dropout: 0.05 lora_target_modules: - q_proj - k_proj - v_proj - o_proj - gate_proj - down_proj - up_proj # Trainer Settings train_on_inputs: false save_strategy: epoch save_total_limit: 1 save_safetensors: true logging_steps: 10 tokenizer_pad_to_eos_token: true # Misc shuffle_merged_datasets: true skip_prepare_dataset: false strict: false ray_num_workers: 1 resources_per_worker: GPU: 1 use_ray: false val_set_size: 0.0 weight_decay: 0.0 # TRL settings for compatibility trl: log_completions: false ref_model_mixup_alpha: 0.9 ref_model_sync_steps: 64 sync_ref_model: false use_vllm: false vllm_device: auto vllm_dtype: auto vllm_gpu_memory_utilization: 0.9 ``` </details><br> # workspace/data/outputs/llama3_esi This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the /workspace/data/alpaca_esi_dataset.jsonl dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 3.0 ### Training results ### Framework versions - PEFT 0.14.0 - Transformers 4.49.0 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
allura-quants/allura-org_Q3-30b-A3b-Pentiment_EXL3_3.0bpw_H6	allura-quants	2025-05-22T03:04:44Z	0	0	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "mergekit", "merge", "exl3", "conversational", "base_model:allura-org/Q3-30b-A3b-Pentiment", "base_model:quantized:allura-org/Q3-30b-A3b-Pentiment", "autotrain_compatible", "endpoints_compatible", "3-bit", "region:us" ]	text-generation	2025-05-22T02:48:33Z	--- base_model: allura-org/Q3-30b-A3b-Pentiment base_model_relation: quantized quantized_by: ArtusDev library_name: transformers tags: - mergekit - merge - exl3 --- # Pentiment ![image/png](https://cdn-uploads.huggingface.co/production/uploads/634262af8d8089ebaefd410e/tQmu_UoG1AMAIaLSGLXhB.png)
DanielNRU/pollen-ner2-250	DanielNRU	2025-05-22T03:04:32Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T03:03:24Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-250 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pollen-ner2-250 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1477 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---:\| \| No log \| 1.0 \| 32 \| 1.1477 \| 0.0 \| 0.0 \| 0.0 \| \| No log \| 2.0 \| 64 \| 1.1336 \| 0.0 \| 0.0 \| 0.0 \| \| No log \| 3.0 \| 96 \| 1.1126 \| 0.0 \| 0.0 \| 0.0 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
DanielNRU/pollen-ner2-150	DanielNRU	2025-05-22T03:02:00Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T03:01:08Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-150 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pollen-ner2-150 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6448 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---:\| \| No log \| 1.0 \| 19 \| 1.6448 \| 0.0 \| 0.0 \| 0.0 \| \| No log \| 2.0 \| 38 \| 1.2916 \| 0.0 \| 0.0 \| 0.0 \| \| No log \| 3.0 \| 57 \| 1.1667 \| 0.0 \| 0.0 \| 0.0 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
DanielNRU/pollen-ner2-100	DanielNRU	2025-05-22T03:00:53Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased", "region:us" ]	null	2025-05-22T03:00:06Z	--- library_name: peft base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner2-100 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pollen-ner2-100 This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0243 - Precision: 0.0057 - Recall: 0.0141 - F1: 0.0081 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 13 \| 2.0243 \| 0.0057 \| 0.0141 \| 0.0081 \| \| No log \| 2.0 \| 26 \| 1.7798 \| 0.0034 \| 0.0020 \| 0.0025 \| \| No log \| 3.0 \| 39 \| 1.5304 \| 0.0 \| 0.0 \| 0.0 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
JunseongLEEE/llama-3.2-1b-sft-dpo	JunseongLEEE	2025-05-22T02:55:54Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-22T02:55:45Z	--- 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. 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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]
mradermacher/CharGen-v3-beta-263-s98-GGUF	mradermacher	2025-05-22T02:53:48Z	11	0	transformers	[ "transformers", "gguf", "en", "base_model:CharGen-Archive/CharGen-v3-beta-263-s98", "base_model:quantized:CharGen-Archive/CharGen-v3-beta-263-s98", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-14T13:48:42Z	--- base_model: CharGen-Archive/CharGen-v3-beta-263-s98 language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/CharGen-Archive/CharGen-v3-beta-263-s98 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/CharGen-v3-beta-263-s98-GGUF/resolve/main/CharGen-v3-beta-263-s98.Q2_K.gguf) \| Q2_K \| 8.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/CharGen-v3-beta-263-s98-GGUF/resolve/main/CharGen-v3-beta-263-s98.Q3_K_S.gguf) \| Q3_K_S \| 9.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/CharGen-v3-beta-263-s98-GGUF/resolve/main/CharGen-v3-beta-263-s98.Q3_K_M.gguf) \| Q3_K_M \| 10.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/CharGen-v3-beta-263-s98-GGUF/resolve/main/CharGen-v3-beta-263-s98.Q3_K_L.gguf) \| Q3_K_L \| 11.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/CharGen-v3-beta-263-s98-GGUF/resolve/main/CharGen-v3-beta-263-s98.IQ4_XS.gguf) \| IQ4_XS \| 12.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/CharGen-v3-beta-263-s98-GGUF/resolve/main/CharGen-v3-beta-263-s98.Q4_K_S.gguf) \| Q4_K_S \| 12.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/CharGen-v3-beta-263-s98-GGUF/resolve/main/CharGen-v3-beta-263-s98.Q4_K_M.gguf) \| Q4_K_M \| 13.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/CharGen-v3-beta-263-s98-GGUF/resolve/main/CharGen-v3-beta-263-s98.Q5_K_S.gguf) \| Q5_K_S \| 15.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/CharGen-v3-beta-263-s98-GGUF/resolve/main/CharGen-v3-beta-263-s98.Q5_K_M.gguf) \| Q5_K_M \| 15.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/CharGen-v3-beta-263-s98-GGUF/resolve/main/CharGen-v3-beta-263-s98.Q6_K.gguf) \| Q6_K \| 18.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/CharGen-v3-beta-263-s98-GGUF/resolve/main/CharGen-v3-beta-263-s98.Q8_0.gguf) \| Q8_0 \| 23.7 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
yhessyradh/xcvzxcv	yhessyradh	2025-05-22T02:53:23Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-22T02:53:22Z	--- license: creativeml-openrail-m ---
darolraiko66/xcvzxcv	darolraiko66	2025-05-22T02:53:23Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-05-22T02:53:21Z	--- license: bigcode-openrail-m ---
csukuangfj/spleeter-checkpoints	csukuangfj	2025-05-22T02:52:49Z	0	0	null	[ "region:us" ]	null	2025-05-22T02:47:37Z	# Introduction Checkpoints are from https://huggingface.co/csukuangfj/spleeter-torch
vitasomegood337/vitasomegood337	vitasomegood337	2025-05-22T02:52:28Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-22T02:52:26Z	--- license: apache-2.0 ---
shanchen/ds-limo-mer4ge-250	shanchen	2025-05-22T02:52:14Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "base_model:merge:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "base_model:shanchen/ds-limo-fr-250", "base_model:merge:shanchen/ds-limo-fr-250", "base_model:shanchen/ds-limo-ja-250", "base_model:merge:shanchen/ds-limo-ja-250", "base_model:shanchen/ds-limo-te-250", "base_model:merge:shanchen/ds-limo-te-250", "base_model:shanchen/ds-limo-th-250", "base_model:merge:shanchen/ds-limo-th-250", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T02:46:35Z	--- base_model: - shanchen/ds-limo-te-250 - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B - shanchen/ds-limo-th-250 - shanchen/ds-limo-ja-250 - shanchen/ds-limo-fr-250 library_name: transformers tags: - mergekit - merge --- # model1 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) as a base. ### Models Merged The following models were included in the merge: * [shanchen/ds-limo-te-250](https://huggingface.co/shanchen/ds-limo-te-250) * [shanchen/ds-limo-th-250](https://huggingface.co/shanchen/ds-limo-th-250) * [shanchen/ds-limo-ja-250](https://huggingface.co/shanchen/ds-limo-ja-250) * [shanchen/ds-limo-fr-250](https://huggingface.co/shanchen/ds-limo-fr-250) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: shanchen/ds-limo-fr-250 parameters: density: 0.25 weight: 0.25 - model: shanchen/ds-limo-th-250 parameters: density: 0.25 weight: 0.25 - model: shanchen/ds-limo-te-250 parameters: density: 0.25 weight: 0.25 - model: shanchen/ds-limo-ja-250 parameters: density: 0.25 weight: 0.25 merge_method: ties base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B parameters: normalize: false int8_mask: true dtype: float16 ```
mradermacher/TLDR-Gemma-7B-MA-PPO-Fixed5-GGUF	mradermacher	2025-05-22T02:52:05Z	36	0	transformers	[ "transformers", "gguf", "en", "dataset:openai/summarize_from_feedback", "base_model:ernie-research/TLDR-Gemma-7B-MA-PPO-Fixed5", "base_model:quantized:ernie-research/TLDR-Gemma-7B-MA-PPO-Fixed5", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-02-15T02:45:28Z	--- base_model: ernie-research/TLDR-Gemma-7B-MA-PPO-Fixed5 datasets: - openai/summarize_from_feedback language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ernie-research/TLDR-Gemma-7B-MA-PPO-Fixed5 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-7B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-7B-MA-PPO-Fixed5.Q2_K.gguf) \| Q2_K \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-7B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-7B-MA-PPO-Fixed5.Q3_K_S.gguf) \| Q3_K_S \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-7B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-7B-MA-PPO-Fixed5.Q3_K_M.gguf) \| Q3_K_M \| 4.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-7B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-7B-MA-PPO-Fixed5.Q3_K_L.gguf) \| Q3_K_L \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-7B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-7B-MA-PPO-Fixed5.IQ4_XS.gguf) \| IQ4_XS \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-7B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-7B-MA-PPO-Fixed5.Q4_K_S.gguf) \| Q4_K_S \| 5.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-7B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-7B-MA-PPO-Fixed5.Q4_K_M.gguf) \| Q4_K_M \| 5.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-7B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-7B-MA-PPO-Fixed5.Q5_K_S.gguf) \| Q5_K_S \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-7B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-7B-MA-PPO-Fixed5.Q5_K_M.gguf) \| Q5_K_M \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-7B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-7B-MA-PPO-Fixed5.Q6_K.gguf) \| Q6_K \| 7.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-7B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-7B-MA-PPO-Fixed5.Q8_0.gguf) \| Q8_0 \| 9.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-7B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-7B-MA-PPO-Fixed5.f16.gguf) \| f16 \| 17.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/TLDR-Gemma-2B-MA-PPO-Fixed5-GGUF	mradermacher	2025-05-22T02:51:38Z	12	0	transformers	[ "transformers", "gguf", "en", "dataset:openai/summarize_from_feedback", "base_model:ernie-research/TLDR-Gemma-2B-MA-PPO-Fixed5", "base_model:quantized:ernie-research/TLDR-Gemma-2B-MA-PPO-Fixed5", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-02-15T02:53:27Z	--- base_model: ernie-research/TLDR-Gemma-2B-MA-PPO-Fixed5 datasets: - openai/summarize_from_feedback language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ernie-research/TLDR-Gemma-2B-MA-PPO-Fixed5 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-2B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-2B-MA-PPO-Fixed5.Q2_K.gguf) \| Q2_K \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-2B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-2B-MA-PPO-Fixed5.Q3_K_S.gguf) \| Q3_K_S \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-2B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-2B-MA-PPO-Fixed5.Q3_K_M.gguf) \| Q3_K_M \| 1.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-2B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-2B-MA-PPO-Fixed5.Q3_K_L.gguf) \| Q3_K_L \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-2B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-2B-MA-PPO-Fixed5.IQ4_XS.gguf) \| IQ4_XS \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-2B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-2B-MA-PPO-Fixed5.Q4_K_S.gguf) \| Q4_K_S \| 1.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-2B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-2B-MA-PPO-Fixed5.Q4_K_M.gguf) \| Q4_K_M \| 1.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-2B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-2B-MA-PPO-Fixed5.Q5_K_S.gguf) \| Q5_K_S \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-2B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-2B-MA-PPO-Fixed5.Q5_K_M.gguf) \| Q5_K_M \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-2B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-2B-MA-PPO-Fixed5.Q6_K.gguf) \| Q6_K \| 2.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-2B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-2B-MA-PPO-Fixed5.Q8_0.gguf) \| Q8_0 \| 2.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/TLDR-Gemma-2B-MA-PPO-Fixed5-GGUF/resolve/main/TLDR-Gemma-2B-MA-PPO-Fixed5.f16.gguf) \| f16 \| 5.1 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
sebastianmr18/xlm-roberta-ner-qlora-bs16	sebastianmr18	2025-05-22T02:50:43Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:FacebookAI/xlm-roberta-large", "base_model:adapter:FacebookAI/xlm-roberta-large", "region:us" ]	null	2025-05-22T02:50:39Z	--- base_model: xlm-roberta-large 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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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF	mradermacher	2025-05-22T02:49:51Z	37	0	transformers	[ "transformers", "gguf", "en", "dataset:codeparrot/apps", "base_model:ernie-research/APPS-Gemma-7B-MA-PPO-Fixed10", "base_model:quantized:ernie-research/APPS-Gemma-7B-MA-PPO-Fixed10", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-02-15T15:18:31Z	--- base_model: ernie-research/APPS-Gemma-7B-MA-PPO-Fixed10 datasets: - codeparrot/apps language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/ernie-research/APPS-Gemma-7B-MA-PPO-Fixed10 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.3 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.4 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-IQ2_S.gguf) \| i1-IQ2_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 3.3 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-Q2_K.gguf) \| i1-Q2_K \| 3.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-IQ3_S.gguf) \| i1-IQ3_S \| 4.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 4.1 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-IQ3_M.gguf) \| i1-IQ3_M \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.5 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 5.1 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-Q4_0.gguf) \| i1-Q4_0 \| 5.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 5.1 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/APPS-Gemma-7B-MA-PPO-Fixed10-i1-GGUF/resolve/main/APPS-Gemma-7B-MA-PPO-Fixed10.i1-Q6_K.gguf) \| i1-Q6_K \| 7.1 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
DanielNRU/pollen_re2	DanielNRU	2025-05-22T02:48:51Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:DeepPavlov/rubert-base-cased", "base_model:finetune:DeepPavlov/rubert-base-cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-22T02:35:14Z	--- library_name: transformers base_model: DeepPavlov/rubert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: pollen-re-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pollen-re-model This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5235 - F1: 0.8505 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 422 \| 0.6813 \| 0.3148 \| \| 0.6396 \| 2.0 \| 844 \| 0.6553 \| 0.4260 \| \| 0.6787 \| 3.0 \| 1266 \| 0.5011 \| 0.5496 \| \| 0.4929 \| 4.0 \| 1688 \| 0.5218 \| 0.6561 \| \| 0.3969 \| 5.0 \| 2110 \| 0.5235 \| 0.8505 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.5.0 - Tokenizers 0.21.1
jinx2321/byt5-tagged-1e4-paper	jinx2321	2025-05-22T02:48:29Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/byt5-small", "base_model:finetune:google/byt5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-21T21:29:03Z	--- library_name: transformers license: apache-2.0 base_model: google/byt5-small tags: - generated_from_trainer model-index: - name: byt5-tagged-1e4-paper 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. --> # byt5-tagged-1e4-paper This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
kittyjosh111/jill-stinrgray-merged-fp16	kittyjosh111	2025-05-22T02:47:32Z	1	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-19T18:00:21Z	--- base_model: llama3.2 tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: kittyjosh111 - License: apache-2.0 - Finetuned from model : llama3.2 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. --- ## jill This is a llm fine-tuned off of the dialogue of Jill Stingray from the game [Va11-Hall-A](https://store.steampowered.com/app/447530/VA11_HallA_Cyberpunk_Bartender_Action/). It is based off of llama3.2:3b (which is linked below). While it does work, this llm will frequently think to itself (like how Jill often does) or may even refuse to respond (Jill tends to do that sometimes). Overall, is it a good model? Meh. With the right system prompt, it's actually kinda nice. But if it's not role-playing as Jill, I wouldn't say so. But does it work? Yea. And for my first fine-tuning, honestly it's better than I expected. I had many issues with Unsloth. Training actually went smoothly, but I had issues downloading the base model (had to manually download it and load it locally), as well as saving as a gguf (which I had to resolve using llama.cpp cli manually). Anyway, I modified the instructions from their free google colab notebooks, then ran it as a jupyter notebook on my local T550 Nvidia laptop GPU. Would I still recommend unsloth? Honestly, yes. It was the only library I used that actually worked out in the end. I bet running the notebooks on Google Colab would lead to less errors simply because its more reproducible. The stats for the training of this llm are below: - Ran on Python3.10, EndeavourOS (linux) - 2116.7746 seconds used for training. - 35.28 minutes used for training. - Peak reserved memory = 3.33 GB. - Peak reserved memory for training = 0.0 GB. - Peak reserved memory % of max memory = 91.685 %. - Peak reserved memory for training % of max memory = 0.0 %. - Torch Version: 2.7.0+cu128 - CUDA Available: True - CUDA Device: NVIDIA T550 Laptop GPU --- ### Links - Va11-Hall-A. [steam link](https://store.steampowered.com/app/447530/VA11_HallA_Cyberpunk_Bartender_Action/) - Model: [https://huggingface.co/chuanli11/Llama-3.2-3B-Instruct-uncensored](https://huggingface.co/chuanli11/Llama-3.2-3B-Instruct-uncensored) - Dataset: [https://github.com/NoPlagiarism/va11halla-dialogues](https://github.com/NoPlagiarism/va11halla-dialogues) (did some formatting to make it a ShareGPT format) [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
csukuangfj/spleeter-torch	csukuangfj	2025-05-22T02:46:51Z	0	6	null	[ "license:apache-2.0", "region:us" ]	null	2023-08-24T07:56:32Z	--- license: apache-2.0 --- This repository contains the PyTorch checkpoints of tensorflow models from [spleeter][spleeter]. [spleeter]: https://github.com/deezer/spleeter
risolmayo/3784648b-e514-440f-84fe-83880b10afec	risolmayo	2025-05-22T02:45:51Z	0	0	null	[ "region:us" ]	null	2025-05-22T02:45:41Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO-1	LuyiCui	2025-05-22T02:45:50Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "dpo", "conversational", "dataset:LuyiCui/numina-deepseek-r1-qwen-7b-efficient-1-preference", "arxiv:2305.18290", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-15T18:01:46Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
FormlessAI/849ebe32-8a12-441a-9de1-0cfd666c03c7	FormlessAI	2025-05-22T02:45:12Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "grpo", "arxiv:2402.03300", "base_model:unsloth/SmolLM-360M", "base_model:finetune:unsloth/SmolLM-360M", "endpoints_compatible", "region:us" ]	null	2025-05-22T00:33:35Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
Shannonjunior/d3eb5639-2805-4578-915f-14c46adc97cd	Shannonjunior	2025-05-22T02:44:42Z	0	0	null	[ "region:us" ]	null	2025-05-22T02:44:11Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
MinaMila/gemma2_2b_unlearned_gu_LoRa_Adult_ep1_22	MinaMila	2025-05-22T02:44:07Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-22T02:44:03Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
RichardErkhov/Emilioi99_-_Llama3_8B_finetuned-gguf	RichardErkhov	2025-05-22T02:43:00Z	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-22T00:37:30Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
dabrown/2d935fe8-fd25-4db8-8e36-effa1f7adf4f	dabrown	2025-05-22T02:42:59Z	0	0	null	[ "region:us" ]	null	2025-05-22T02:30:47Z	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>

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