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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-08-22 00:40:10	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 517 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-22 00:39:40	card stringlengths 11 1.01M
manancode/opus-mt-tc-bible-big-afa-deu_eng_nld-ctranslate2-android	manancode	2025-08-20T15:51:55Z	0	0	null	[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]	translation	2025-08-20T15:51:39Z	--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-tc-bible-big-afa-deu_eng_nld-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-tc-bible-big-afa-deu_eng_nld` converted to CTranslate2 format for efficient inference. ## Model Details - Original Model: Helsinki-NLP/opus-mt-tc-bible-big-afa-deu_eng_nld - Format: CTranslate2 - Quantization: INT8 - Framework: OPUS-MT - Converted by: Automated conversion pipeline ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = smp.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
Jacksss123/net72_uid58	Jacksss123	2025-08-20T15:51:38Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-08-20T15:46:58Z	--- 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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manancode/opus-mt-tc-bible-big-aav-fra_ita_por_spa-ctranslate2-android	manancode	2025-08-20T15:50:57Z	0	0	null	[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]	translation	2025-08-20T15:50:41Z	--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-tc-bible-big-aav-fra_ita_por_spa-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-tc-bible-big-aav-fra_ita_por_spa` converted to CTranslate2 format for efficient inference. ## Model Details - Original Model: Helsinki-NLP/opus-mt-tc-bible-big-aav-fra_ita_por_spa - Format: CTranslate2 - Quantization: INT8 - Framework: OPUS-MT - Converted by: Automated conversion pipeline ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = smp.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
manancode/opus-mt-tc-base-uk-hu-ctranslate2-android	manancode	2025-08-20T15:49:31Z	0	0	null	[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]	translation	2025-08-20T15:49:19Z	--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-tc-base-uk-hu-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-tc-base-uk-hu` converted to CTranslate2 format for efficient inference. ## Model Details - Original Model: Helsinki-NLP/opus-mt-tc-base-uk-hu - Format: CTranslate2 - Quantization: INT8 - Framework: OPUS-MT - Converted by: Automated conversion pipeline ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = smp.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
manancode/opus-mt-tc-base-uk-fi-ctranslate2-android	manancode	2025-08-20T15:49:12Z	0	0	null	[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]	translation	2025-08-20T15:49:01Z	--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-tc-base-uk-fi-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-tc-base-uk-fi` converted to CTranslate2 format for efficient inference. ## Model Details - Original Model: Helsinki-NLP/opus-mt-tc-base-uk-fi - Format: CTranslate2 - Quantization: INT8 - Framework: OPUS-MT - Converted by: Automated conversion pipeline ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = smp.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
manancode/opus-mt-tc-base-tr-uk-ctranslate2-android	manancode	2025-08-20T15:48:36Z	0	0	null	[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]	translation	2025-08-20T15:48:25Z	--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-tc-base-tr-uk-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-tc-base-tr-uk` converted to CTranslate2 format for efficient inference. ## Model Details - Original Model: Helsinki-NLP/opus-mt-tc-base-tr-uk - Format: CTranslate2 - Quantization: INT8 - Framework: OPUS-MT - Converted by: Automated conversion pipeline ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = smp.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
manancode/opus-mt-tc-base-hu-uk-ctranslate2-android	manancode	2025-08-20T15:47:59Z	0	0	null	[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]	translation	2025-08-20T15:47:46Z	--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-tc-base-hu-uk-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-tc-base-hu-uk` converted to CTranslate2 format for efficient inference. ## Model Details - Original Model: Helsinki-NLP/opus-mt-tc-base-hu-uk - Format: CTranslate2 - Quantization: INT8 - Framework: OPUS-MT - Converted by: Automated conversion pipeline ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = smp.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
manancode/opus-mt-tc-base-en-sh-ctranslate2-android	manancode	2025-08-20T15:47:03Z	0	0	null	[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]	translation	2025-08-20T15:46:51Z	--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-tc-base-en-sh-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-tc-base-en-sh` converted to CTranslate2 format for efficient inference. ## Model Details - Original Model: Helsinki-NLP/opus-mt-tc-base-en-sh - Format: CTranslate2 - Quantization: INT8 - Framework: OPUS-MT - Converted by: Automated conversion pipeline ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = smp.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
lqpl/blockassist-bc-hairy_insectivorous_antelope_1755704721	lqpl	2025-08-20T15:46:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hairy insectivorous antelope", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:46:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hairy insectivorous antelope --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
gungun-gupta-viral-video-original-mms/New.full.videos.gungun.gupta.Viral.Video.Official.Tutorial	gungun-gupta-viral-video-original-mms	2025-08-20T15:46:50Z	0	0	null	[ "region:us" ]	null	2025-08-20T15:46:32Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755702911	coelacanthxyz	2025-08-20T15:46:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:46:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
manancode/opus-mt-kqn-en-ctranslate2-android	manancode	2025-08-20T15:45:48Z	0	0	null	[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]	translation	2025-08-20T15:45:37Z	--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-kqn-en-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-kqn-en` converted to CTranslate2 format for efficient inference. ## Model Details - Original Model: Helsinki-NLP/opus-mt-kqn-en - Format: CTranslate2 - Quantization: INT8 - Framework: OPUS-MT - Converted by: Automated conversion pipeline ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = smp.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
liukevin666/blockassist-bc-yawning_striped_cassowary_1755704579	liukevin666	2025-08-20T15:44:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:44:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
manancode/opus-mt-en-cus-ctranslate2-android	manancode	2025-08-20T15:44:34Z	0	0	null	[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]	translation	2025-08-20T15:44:24Z	--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-en-cus-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-cus` converted to CTranslate2 format for efficient inference. ## Model Details - Original Model: Helsinki-NLP/opus-mt-en-cus - Format: CTranslate2 - Quantization: INT8 - Framework: OPUS-MT - Converted by: Automated conversion pipeline ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = smp.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
gautamnancy/Emotion_classification	gautamnancy	2025-08-20T15:44:06Z	0	0	null	[ "region:us" ]	null	2025-08-20T15:39:09Z	# RoBERTa-Base Model for Emotion Classification This repository hosts a fine-tuned version of the RoBERTa model for emotion classification tasks. The model has been trained to accurately classify text into six emotion categories, making it suitable for sentiment analysis and emotional content understanding. --- ## Model Details - Model Name: RoBERTa-Base for Emotion Classification - Model Architecture: RoBERTa Base - Task: Emotion Classification - Dataset: Hugging Face Emotion Dataset - Quantization: Float16 version available - Fine-tuning Framework: Hugging Face Transformers --- ## Usage ### Installation ``` pip install transformers torch ``` ### Loading the Model ``` from transformers import RobertaTokenizer, RobertaForSequenceClassification import torch import re # Load model and tokenizer model_path = "emotion-model" # or "quantized-emotion-model" for the quantized version model = RobertaForSequenceClassification.from_pretrained(model_path) tokenizer = RobertaTokenizer.from_pretrained(model_path) # Set device device = 'cuda' if torch.cuda.is_available() else 'cpu' model = model.to(device) ``` ### Prediction Function ``` def predict_emotions(texts, model, tokenizer, device='cpu'): """ Predicts emotion labels for input text(s) using a fine-tuned transformer model. Args: texts (str or List[str]): A single string or list of strings to classify. model: Trained transformer model. tokenizer: Corresponding tokenizer. device (str): 'cpu' or 'cuda'. Default is 'cpu'. Returns: List[str]: List of predicted emotion labels. """ # Ensure model is on correct device model.to(device) # If a single string is passed, convert to list if isinstance(texts, str): texts = [texts] # Preprocess: simple text cleaning def preprocess(text): text = text.lower() text = re.sub(r"http\S+\|www\S+\|https\S+", '', text) text = re.sub(r'\@\w+\|\#', '', text) text = re.sub(r"[^a-zA-Z0-9\s.,!?']", '', text) text = re.sub(r'\s+', ' ', text).strip() return text cleaned_texts = [preprocess(t) for t in texts] # Tokenize inputs = tokenizer(cleaned_texts, padding=True, truncation=True, return_tensors="pt").to(device) # Inference model.eval() with torch.no_grad(): outputs = model(inputs) preds = torch.argmax(outputs.logits, dim=1).tolist() # Emotion dataset label map label_map = { 0: "sadness", 1: "joy", 2: "love", 3: "anger", 4: "fear", 5: "surprise" } return [label_map[p] for p in preds] ``` ### Example Usage ``` # Example texts sample_texts = [ "I'm so happy about the new job opportunity!", "I can't believe they cancelled my favorite show. This is terrible.", "The sunset over the mountains took my breath away. It was magnificent!" ] # Run predictions results = predict_emotions(sample_texts, model, tokenizer, device) # Show results for text, emotion in zip(sample_texts, results): print(f"Text: {text}\nPredicted Emotion: {emotion}\n") ``` --- ## Performance Metrics - Accuracy: 0.94 - F1 Score: 0.939736 - Precision: 0.941654 - Recall: 0.94 --- ## Fine-Tuning Details ### Dataset The model was fine-tuned on the Hugging Face Emotion dataset which contains text labeled with six emotion categories: - sadness - joy - love - anger - fear - surprise ### Training Configuration - Epochs: 3 - Batch Size: 16 - Learning Rate: 2e-5 - Max Length: 128 tokens - Evaluation Strategy: epoch - Weight Decay: 0.01 - Optimizer:** AdamW ### Quantization A quantized version of the model is available using PyTorch's float16 format to reduce model size and improve inference efficiency. --- ## Repository Structure ``` . ├── emotion-model/ # Full-precision model │ ├── config.json │ ├── model.safetensors │ ├── tokenizer_config.json │ ├── special_tokens_map.json │ ├── vocab.json │ └── merges.txt ├── quantized-emotion-model/ # Quantized model (float16) │ ├── config.json │ ├── model.safetensors │ ├── tokenizer_config.json │ ├── special_tokens_map.json │ ├── vocab.json │ └── merges.txt └── README.md # Model documentation ``` --- ## Limitations - The model may not generalize well to domains outside the fine-tuning dataset. - Emotion detection can be subjective and context-dependent. - The quantized version may show minor accuracy degradation compared to the full-precision model. --- ## Contributing Contributions are welcome! Feel free to open an issue or PR for improvements, fixes, or feature extensions.
AnonymousCS/xlmr_immigration_combo16_0	AnonymousCS	2025-08-20T15:43:32Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T15:38:49Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo16_0 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. --> # xlmr_immigration_combo16_0 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2752 - Accuracy: 0.9036 - 1-f1: 0.8549 - 1-recall: 0.8533 - 1-precision: 0.8566 - Balanced Acc: 0.8910 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.6219 \| 1.0 \| 25 \| 0.5843 \| 0.6671 \| 0.0 \| 0.0 \| 0.0 \| 0.5 \| \| 0.379 \| 2.0 \| 50 \| 0.2874 \| 0.9010 \| 0.8358 \| 0.7568 \| 0.9333 \| 0.8649 \| \| 0.2919 \| 3.0 \| 75 \| 0.2472 \| 0.9113 \| 0.8571 \| 0.7992 \| 0.9241 \| 0.8832 \| \| 0.1954 \| 4.0 \| 100 \| 0.2513 \| 0.9152 \| 0.8669 \| 0.8301 \| 0.9072 \| 0.8939 \| \| 0.2182 \| 5.0 \| 125 \| 0.2752 \| 0.9036 \| 0.8549 \| 0.8533 \| 0.8566 \| 0.8910 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
vikasp1610/qwen-sql-qlora	vikasp1610	2025-08-20T15:43:21Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:Qwen/Qwen2-0.5B", "base_model:finetune:Qwen/Qwen2-0.5B", "endpoints_compatible", "region:us" ]	null	2025-08-20T14:10:07Z	--- base_model: Qwen/Qwen2-0.5B library_name: transformers model_name: qwen-sql-qlora tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for qwen-sql-qlora This model is a fine-tuned version of [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B). 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="vikasp1610/qwen-sql-qlora", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
salavat/gemma-3-isv-gpt-v5-GGUF	salavat	2025-08-20T15:43:09Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-20T15:32:54Z	# gemma-3-isv-gpt-v5-GGUF GGUF for model: sergbese/gemma-3-isv-gpt-v5
rhasspy/piper-voices	rhasspy	2025-08-20T15:43:07Z	0	277	null	[ "onnx", "ar", "ca", "cs", "cy", "da", "de", "el", "en", "es", "fa", "fi", "fr", "hu", "is", "it", "ka", "kk", "lb", "lv", "ne", "nl", "no", "pl", "pt", "ro", "ru", "sk", "sl", "sr", "sv", "sw", "tr", "uk", "vi", "zh", "license:mit", "region:us" ]	null	2023-06-22T19:17:27Z	--- license: mit language: - ar - ca - cs - cy - da - de - el - en - es - fa - fi - fr - hu - is - it - ka - kk - lb - lv - ne - nl - no - pl - pt - ro - ru - sk - sl - sr - sv - sw - tr - uk - vi - zh --- Voices for [Piper](https://github.com/rhasspy/piper) text to speech system. For checkpoints that you can use to [train your own voices](https://github.com/rhasspy/piper/blob/master/TRAINING.md), see [piper-checkpoints](https://huggingface.co/datasets/rhasspy/piper-checkpoints/tree/main)
bootjune/blockassist-bc-aquatic_roaring_goose_1755704465	bootjune	2025-08-20T15:41:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "aquatic roaring goose", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:41:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - aquatic roaring goose --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755704465	0xaoyama	2025-08-20T15:41:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:41:30Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755702667	katanyasekolah	2025-08-20T15:41:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:41:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silky sprightly cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
YeeXuan11/YeeXuan11-distilbert_ner_seed42_loraFalse_r0_lr1e-05_warm0.1_ep3	YeeXuan11	2025-08-20T15:41:04Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-08-20T11:50:28Z	--- 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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YeeXuan11/your-username-distilbert_ner_seed42_loraFalse_r0_lr1e-05_warm0.1_ep3	YeeXuan11	2025-08-20T15:40:53Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-08-20T08:24:09Z	--- 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. 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joekraper/my_quantized-qwen	joekraper	2025-08-20T15:39:53Z	0	0	null	[ "safetensors", "text-generation", "base_model:Qwen/Qwen3-1.7B", "base_model:finetune:Qwen/Qwen3-1.7B", "license:apache-2.0", "region:us" ]	text-generation	2025-08-20T14:38:27Z	--- license: apache-2.0 base_model: - Qwen/Qwen3-1.7B new_version: Qwen/Qwen3-1.7B pipeline_tag: text-generation ---
YeeXuan11/distilbert_ner_seed42_loraTrue_r4_lr1e-05_warm0.0_ep3-adapter	YeeXuan11	2025-08-20T15:39:51Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-20T12:56:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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YeeXuan11/YeeXuan11-distilbert_ner_seed42_loraFalse_r0_lr3e-05_warm0.1_ep5	YeeXuan11	2025-08-20T15:39:41Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-08-20T12:48:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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llinguini/medgemma-4b-it-Q4_K_M-GGUF	llinguini	2025-08-20T15:39:41Z	0	0	transformers	[ "transformers", "gguf", "medical", "radiology", "clinical-reasoning", "dermatology", "pathology", "ophthalmology", "chest-x-ray", "llama-cpp", "gguf-my-repo", "image-text-to-text", "base_model:google/medgemma-4b-it", "base_model:quantized:google/medgemma-4b-it", "license:other", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-08-20T15:39:26Z	--- license: other license_name: health-ai-developer-foundations license_link: https://developers.google.com/health-ai-developer-foundations/terms library_name: transformers pipeline_tag: image-text-to-text extra_gated_heading: Access MedGemma on Hugging Face extra_gated_prompt: To access MedGemma on Hugging Face, you're required to review and agree to [Health AI Developer Foundation's terms of use](https://developers.google.com/health-ai-developer-foundations/terms). To do this, please ensure you're logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/medgemma-4b-it tags: - medical - radiology - clinical-reasoning - dermatology - pathology - ophthalmology - chest-x-ray - llama-cpp - gguf-my-repo --- # llinguini/medgemma-4b-it-Q4_K_M-GGUF This model was converted to GGUF format from [`google/medgemma-4b-it`](https://huggingface.co/google/medgemma-4b-it) 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/google/medgemma-4b-it) 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 llinguini/medgemma-4b-it-Q4_K_M-GGUF --hf-file medgemma-4b-it-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo llinguini/medgemma-4b-it-Q4_K_M-GGUF --hf-file medgemma-4b-it-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo llinguini/medgemma-4b-it-Q4_K_M-GGUF --hf-file medgemma-4b-it-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo llinguini/medgemma-4b-it-Q4_K_M-GGUF --hf-file medgemma-4b-it-q4_k_m.gguf -c 2048 ```
YeeXuan11/your-username-distilbert_ner_seed42_loraFalse_r0_lr3e-05_warm0.1_ep5	YeeXuan11	2025-08-20T15:39:30Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-08-20T09:29:19Z	--- 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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YeeXuan11/distilbert_ner_seed42_loraTrue_r4_lr3e-05_warm0.0_ep3-adapter	YeeXuan11	2025-08-20T15:39:08Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-20T13:36:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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YeeXuan11/distilbert_ner_seed42_loraTrue_r4_lr3e-05_warm0.1_ep5-adapter	YeeXuan11	2025-08-20T15:38:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-20T14:09:28Z	--- 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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roeker/blockassist-bc-quick_wiry_owl_1755704211	roeker	2025-08-20T15:38:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:37:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755704188	0xaoyama	2025-08-20T15:37:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:36:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ZombitX64/Thaitokenizer	ZombitX64	2025-08-20T15:36:13Z	0	0	tokenizers	[ "tokenizers", "thai", "tokenizer", "nlp", "subword", "th", "dataset:ZombitX64/Thai-corpus-word", "license:apache-2.0", "region:us" ]	null	2025-07-04T05:26:49Z	--- language: th license: apache-2.0 tags: - thai - tokenizer - nlp - subword model_type: unigram library_name: tokenizers pretty_name: Advanced Thai Tokenizer V3 datasets: - ZombitX64/Thai-corpus-word metrics: - accuracy - character --- # Advanced Thai Tokenizer V3 ## Overview Advanced Thai language tokenizer (Unigram, HuggingFace-compatible) trained on a large, cleaned, real-world Thai corpus. Handles Thai, mixed Thai-English, numbers, and modern vocabulary. Designed for LLM/NLP use, with robust roundtrip accuracy and no byte-level artifacts. ## Performance - Overall Accuracy: 24/24 (100.0%) - Vocabulary Size: 35,590 tokens - Average Compression: 3.45 chars/token - UNK Ratio: 0% - Thai Character Coverage: 100% - Tested on: Real-world, mixed, and edge-case sentences - Training Corpus: `combined_thai_corpus.txt` (cleaned, deduplicated, multi-domain) ## Key Features - ✅ No Thai character corruption (no byte-level fallback, no normalization loss) - ✅ Handles mixed Thai-English, numbers, and symbols - ✅ Modern vocabulary (internet, technology, social, business) - ✅ Efficient compression (subword, not word-level) - ✅ Clean decoding without artifacts - ✅ HuggingFace-compatible (tokenizer.json, vocab.json, config) - ✅ Production-ready: tested, documented, and robust ## Quick Start ```python from transformers import AutoTokenizer # Load tokenizer from HuggingFace Hub try: tokenizer = AutoTokenizer.from_pretrained("ZombitX64/Thaitokenizer") text = "นั่งตาก ลม" tokens = tokenizer.tokenize(text) print(f"Tokens: {tokens}") encoding = tokenizer(text, return_tensors=None, add_special_tokens=False) decoded = tokenizer.decode(encoding['input_ids'], skip_special_tokens=True) print(f"Original: {text}") print(f"Decoded: {decoded}") except Exception as e: print(f"Error loading tokenizer: {e}") ``` ## Files - `tokenizer.json` — Main tokenizer file (HuggingFace format) - `vocab.json` — Vocabulary mapping - `tokenizer_config.json` — Transformers config - `metadata.json` — Performance and configuration details - `usage_examples.json` — Code examples - `README.md` — This file - `combined_thai_corpus.txt` — Training corpus (not included in repo, see dataset card) Created: July 2025 --- # Model Card for Advanced Thai Tokenizer V3 ## Model Details Developed by: ZombitX64 (https://huggingface.co/ZombitX64) Model type: Unigram (subword) tokenizer Language(s): th (Thai), mixed Thai-English License: Apache-2.0 Finetuned from model: N/A (trained from scratch) ### Model Sources - Repository: https://huggingface.co/ZombitX64/Thaitokenizer ## Uses ### Direct Use - Tokenization for Thai LLMs, NLP, and downstream tasks - Preprocessing for text classification, NER, QA, summarization, etc. - Robust for mixed Thai-English, numbers, and social content ### Downstream Use - Plug into HuggingFace Transformers pipelines - Use as tokenizer for Thai LLM pretraining/fine-tuning - Integrate with spaCy, PyThaiNLP, or custom pipelines ### Out-of-Scope Use - Not a language model (no text generation by itself) - Not suitable for non-Thai-centric tasks ## Bias, Risks, and Limitations - Trained on public Thai web/corpus data; may reflect real-world bias - Not guaranteed to cover rare dialects, slang, or OCR errors - No explicit filtering for toxic/biased content in corpus - Tokenizer does not understand context/meaning (no disambiguation) ### Recommendations - For best results, use with LLMs or models trained on similar corpus - For sensitive/critical applications, review corpus and test thoroughly - For word-level tasks, use with context-aware models (NER, POS) ## How to Get Started with the Model ```python from transformers import AutoTokenizer # Load tokenizer from HuggingFace Hub try: tokenizer = AutoTokenizer.from_pretrained("ZombitX64/Thaitokenizer") text = "นั่งตาก ลม" tokens = tokenizer.tokenize(text) print(f"Tokens: {tokens}") encoding = tokenizer(text, return_tensors=None, add_special_tokens=False) decoded = tokenizer.decode(encoding['input_ids'], skip_special_tokens=True) print(f"Original: {text}") print(f"Decoded: {decoded}") except Exception as e: print(f"Error loading tokenizer: {e}") ``` ## Training Details ### Training Data - Source: `combined_thai_corpus.txt` (cleaned, deduplicated, multi-domain Thai text) - Size: 71.7M - Preprocessing: Remove duplicates, normalize encoding, minimal cleaning, no normalization, no byte fallback ### Training Procedure - Tokenizer: HuggingFace Tokenizers (Unigram) - Vocab size: 35,590 - Special tokens: <unk> - Pre-tokenizer: Punctuation only - No normalization, no post-processor, no decoder - Training regime: CPU, Python 3.11, single run, see script for details ### Speeds, Sizes, Times - Training time: - - Checkpoint size: tokenizer.json ~[size] KB ## Evaluation ### Testing Data, Factors & Metrics - Testing data: Real-world Thai sentences, mixed content, edge cases - Metrics: Roundtrip accuracy, UNK ratio, Thai character coverage, compression ratio - Results: 100% roundtrip, 0% UNK, 100% Thai char coverage, 3.45 chars/token ## Environmental Impact - Training on CPU, low energy usage - No large-scale GPU/TPU compute required ## Technical Specifications - Model architecture: Unigram (subword) tokenizer - Software: tokenizers==0.15+, Python 3.11 - Hardware: Standard CPU (no GPU required) ## Citation If you use this tokenizer, please cite: ``` @misc{zombitx64_thaitokenizer_v3_2025, author = {ZombitX64}, title = {Advanced Thai Tokenizer V3}, year = {2025}, howpublished = {\\url{https://huggingface.co/ZombitX64/Thaitokenizer}} } ``` ## Model Card Authors - ZombitX64 (https://huggingface.co/ZombitX64) ## Model Card Contact For questions or feedback, open an issue on the HuggingFace repo or contact ZombitX64 via HuggingFace.
AnonymousCS/xlmr_immigration_combo15_4	AnonymousCS	2025-08-20T15:35:22Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T15:32:05Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo15_4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlmr_immigration_combo15_4 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1266 - Accuracy: 0.9679 - 1-f1: 0.9503 - 1-recall: 0.9228 - 1-precision: 0.9795 - Balanced Acc: 0.9566 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.1431 \| 1.0 \| 25 \| 0.1237 \| 0.9640 \| 0.9451 \| 0.9305 \| 0.9602 \| 0.9556 \| \| 0.1849 \| 2.0 \| 50 \| 0.1209 \| 0.9614 \| 0.9407 \| 0.9189 \| 0.9636 \| 0.9508 \| \| 0.0332 \| 3.0 \| 75 \| 0.1285 \| 0.9679 \| 0.9505 \| 0.9266 \| 0.9756 \| 0.9575 \| \| 0.0335 \| 4.0 \| 100 \| 0.1266 \| 0.9679 \| 0.9503 \| 0.9228 \| 0.9795 \| 0.9566 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
roeker/blockassist-bc-quick_wiry_owl_1755703783	roeker	2025-08-20T15:31:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:30:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
YeeXuan11/distilbert_ner_seed42_loraTrue_r4_lr3e-05_warm0.0_ep5-adapter	YeeXuan11	2025-08-20T15:30:55Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-20T13:49:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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leodotnet/Qwen3-4B-promptv10-v36-INT4	leodotnet	2025-08-20T15:30:51Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2025-08-20T15:29:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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YeeXuan11/distilbert_ner_seed42_loraTrue_r4_lr1e-05_warm0.1_ep3-adapter	YeeXuan11	2025-08-20T15:30:44Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-20T13:16:28Z	--- 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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nardit/LunarLander-v2	nardit	2025-08-20T15:30:37Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2025-08-20T14:00:06Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -149.12 +/- 105.49 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 100000 'learning_rate': 0.000125 'num_envs': 10 'num_steps': 64 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 5 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'nardit/LunarLander-v2' 'batch_size': 640 'minibatch_size': 160} ```
YeeXuan11/your-username-distilbert_ner_seed42_loraFalse_r0_lr1e-05_warm0.1_ep5	YeeXuan11	2025-08-20T15:30:35Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-08-20T08:39:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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YeeXuan11/distilbert_ner_seed42_loraTrue_r4_lr1e-05_warm0.0_ep5-merged	YeeXuan11	2025-08-20T15:30:30Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-08-20T13:08:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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YeeXuan11/YeeXuan11-distilbert_ner_seed42_loraFalse_r0_lr3e-05_warm0.0_ep3	YeeXuan11	2025-08-20T15:30:03Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-08-20T12:12:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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liukevin666/blockassist-bc-yawning_striped_cassowary_1755703565	liukevin666	2025-08-20T15:29:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:27:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Leoar/blockassist-bc-pudgy_toothy_cheetah_1755703609	Leoar	2025-08-20T15:29:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pudgy toothy cheetah", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:28:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pudgy toothy cheetah --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
nasywaanaa/large-v3-rra-id-20aug	nasywaanaa	2025-08-20T15:28:02Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "id", "dataset:rayhanfa/golden-dataset-2.0-tvt-muffled-20aug", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-08-20T09:07:53Z	--- library_name: transformers language: - id license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer datasets: - rayhanfa/golden-dataset-2.0-tvt-muffled-20aug metrics: - wer model-index: - name: Whisper Large v3 - 1.0 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: rayhanfa/golden-dataset-2.0-tvt-muffled-20aug type: rayhanfa/golden-dataset-2.0-tvt-muffled-20aug args: 'config: id' metrics: - name: Wer type: wer value: 19.058602166853813 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Large v3 - 1.0 This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the rayhanfa/golden-dataset-2.0-tvt-muffled-20aug dataset. It achieves the following results on the evaluation set: - Loss: 0.3794 - Wer: 19.0586 - Cer: 11.4027 - Wer Raw: 26.9031 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 250 - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| Wer Raw \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\| \| 0.2575 \| 1.0 \| 894 \| 0.2919 \| 24.9886 \| 15.3466 \| 33.0115 \| \| 0.1208 \| 2.0 \| 1788 \| 0.2795 \| 20.9584 \| 12.6461 \| 28.4812 \| \| 0.0543 \| 3.0 \| 2682 \| 0.2950 \| 21.3529 \| 12.8735 \| 30.4431 \| \| 0.0286 \| 4.0 \| 3576 \| 0.3097 \| 19.9023 \| 12.1314 \| 28.2213 \| \| 0.0116 \| 5.0 \| 4470 \| 0.3270 \| 20.3605 \| 12.1873 \| 28.3977 \| \| 0.0049 \| 6.0 \| 5364 \| 0.3437 \| 19.5533 \| 11.8061 \| 28.1037 \| \| 0.0019 \| 7.0 \| 6258 \| 0.3573 \| 19.0950 \| 11.5203 \| 26.8783 \| \| 0.0016 \| 8.0 \| 7152 \| 0.3794 \| 19.0586 \| 11.4027 \| 26.9031 \| ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0.dev20250319+cu128 - Datasets 3.6.0 - Tokenizers 0.21.4
lautan/blockassist-bc-gentle_patterned_goat_1755702104	lautan	2025-08-20T15:27:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle patterned goat", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:27:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle patterned goat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ruribe17/Wan2.2-5B-Diffusers-FP32	ruribe17	2025-08-20T15:25:36Z	0	0	diffusers	[ "diffusers", "safetensors", "license:apache-2.0", "diffusers:WanPipeline", "region:us" ]	null	2025-08-20T13:55:39Z	--- license: apache-2.0 ---
roeker/blockassist-bc-quick_wiry_owl_1755703356	roeker	2025-08-20T15:23:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:23:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
margitszigetijoga/nllb-200-distilled-600M	margitszigetijoga	2025-08-20T15:22:09Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-20T15:22:09Z	--- license: apache-2.0 ---
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755703225	0xaoyama	2025-08-20T15:20:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:20:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755702140	Sayemahsjn	2025-08-20T15:20:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:20:47Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
indoempatnol/blockassist-bc-fishy_wary_swan_1755701538	indoempatnol	2025-08-20T15:20:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:20:42Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy wary swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Vasya777/blockassist-bc-lumbering_enormous_sloth_1755702918	Vasya777	2025-08-20T15:16:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lumbering enormous sloth", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:15:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lumbering enormous sloth --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
MakiAi/MyGemmaNPC	MakiAi	2025-08-20T15:14:47Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gemma3_text", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T14:15:51Z	--- base_model: google/gemma-3-270m-it library_name: transformers model_name: MyGemmaNPC tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for MyGemmaNPC This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="MakiAi/MyGemmaNPC", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.8.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
annasoli/Qwen2.5-14B_SVt_l24_lr2e-4_a256_2E_technical-vehicles_KL8_1e7	annasoli	2025-08-20T15:14:34Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-20T15:14:19Z	--- 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]
ajaymin28/Gemma3-video-qlora-AG-8-frames-rank128_a128_ep1_8frames	ajaymin28	2025-08-20T15:13:38Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-18T18:51:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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hakimjustbao/blockassist-bc-raging_subtle_wasp_1755701219	hakimjustbao	2025-08-20T15:13:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:13:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755702588	0xaoyama	2025-08-20T15:10:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:10:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VaishaliS/results_5k	VaishaliS	2025-08-20T15:09:43Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T15:08:58Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: results_5k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results_5k This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
luckycanucky/llama3-unaligned-2	luckycanucky	2025-08-20T15:09:43Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "unsloth", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-20T11:58:11Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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roeker/blockassist-bc-quick_wiry_owl_1755702500	roeker	2025-08-20T15:09:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:09:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Negark/bert-sentiment-digikala-augmented-WithTokens	Negark	2025-08-20T15:09:25Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:HooshvareLab/bert-fa-base-uncased-sentiment-digikala", "base_model:finetune:HooshvareLab/bert-fa-base-uncased-sentiment-digikala", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T13:57:51Z	--- library_name: transformers license: apache-2.0 base_model: HooshvareLab/bert-fa-base-uncased-sentiment-digikala tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: bert-sentiment-digikala-augmented-WithTokens 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-sentiment-digikala-augmented-WithTokens This model is a fine-tuned version of [HooshvareLab/bert-fa-base-uncased-sentiment-digikala](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-digikala) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8273 - Accuracy: 0.8213 - F1: 0.8201 - Precision: 0.8211 - Recall: 0.8197 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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 \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.5772 \| 1.0 \| 986 \| 0.4229 \| 0.8065 \| 0.8057 \| 0.8086 \| 0.8049 \| \| 0.3363 \| 2.0 \| 1972 \| 0.4512 \| 0.8168 \| 0.8163 \| 0.8187 \| 0.8155 \| \| 0.2009 \| 3.0 \| 2958 \| 0.5756 \| 0.8122 \| 0.8097 \| 0.8140 \| 0.8103 \| \| 0.1264 \| 4.0 \| 3944 \| 0.8273 \| 0.8213 \| 0.8201 \| 0.8211 \| 0.8197 \| \| 0.0762 \| 5.0 \| 4930 \| 1.0302 \| 0.8139 \| 0.8130 \| 0.8151 \| 0.8123 \| \| 0.0464 \| 6.0 \| 5916 \| 1.2564 \| 0.8116 \| 0.8097 \| 0.8098 \| 0.8096 \| ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
AnonymousCS/xlmr_immigration_combo14_3	AnonymousCS	2025-08-20T15:07:45Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T14:31:18Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo14_3 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. --> # xlmr_immigration_combo14_3 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2590 - Accuracy: 0.9357 - 1-f1: 0.9004 - 1-recall: 0.8726 - 1-precision: 0.9300 - Balanced Acc: 0.9199 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.1566 \| 1.0 \| 25 \| 0.2172 \| 0.9383 \| 0.9024 \| 0.8571 \| 0.9528 \| 0.9180 \| \| 0.1142 \| 2.0 \| 50 \| 0.2625 \| 0.9306 \| 0.8875 \| 0.8224 \| 0.9638 \| 0.9035 \| \| 0.0983 \| 3.0 \| 75 \| 0.2590 \| 0.9357 \| 0.9004 \| 0.8726 \| 0.9300 \| 0.9199 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
stanpony/gptnano_5M_lexinvariant_1_epochs_then_vanilla_20250820_121021	stanpony	2025-08-20T15:07:40Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "text-generation", "license:mit", "region:us" ]	text-generation	2025-08-20T15:07:34Z	--- license: mit pipeline_tag: text-generation tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
stanpony/gptnano_5M_lexinvariant_2_epochs_then_vanilla_20250820_121428	stanpony	2025-08-20T15:05:47Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "text-generation", "license:mit", "region:us" ]	text-generation	2025-08-20T15:05:44Z	--- license: mit pipeline_tag: text-generation tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
Farzad-Zandi/dqn-LunarLander-v3	Farzad-Zandi	2025-08-20T15:04:46Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-08-20T14:31:31Z	--- library_name: stable-baselines3 tags: - LunarLander-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v3 type: LunarLander-v3 metrics: - type: mean_reward value: -601.28 +/- 143.14 name: mean_reward verified: false --- # DQN Agent playing LunarLander-v3 This is a trained model of a DQN agent playing LunarLander-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 ... ```
unitova/blockassist-bc-zealous_sneaky_raven_1755700571	unitova	2025-08-20T15:04:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:04:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - zealous sneaky raven --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AnonymousCS/xlmr_immigration_combo14_2	AnonymousCS	2025-08-20T15:04:23Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T14:27:01Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo14_2 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. --> # xlmr_immigration_combo14_2 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2751 - Accuracy: 0.9242 - 1-f1: 0.8789 - 1-recall: 0.8263 - 1-precision: 0.9386 - Balanced Acc: 0.8996 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.2212 \| 1.0 \| 25 \| 0.2303 \| 0.9242 \| 0.8813 \| 0.8456 \| 0.9202 \| 0.9045 \| \| 0.163 \| 2.0 \| 50 \| 0.2529 \| 0.9177 \| 0.8735 \| 0.8533 \| 0.8947 \| 0.9016 \| \| 0.16 \| 3.0 \| 75 \| 0.2751 \| 0.9242 \| 0.8789 \| 0.8263 \| 0.9386 \| 0.8996 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
hawierdev/shellm-v0.1-gguf	hawierdev	2025-08-20T15:04:18Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-20T14:49:37Z	# ShellM v0.1 ShellM is a specialized language model based on Qwen2.5 1.5B, fine-tuned for generating intelligent terminal command suggestions. The model has been trained to understand terminal context and suggest appropriate commands. ## Model Specifications - Architecture: Qwen2ForCausalLM - Size: 1.5B parameters - Hidden size: 1536 - Layers: 28 - Attention heads: 12 (2 key-value heads) - Max context length: 32768 tokens - Precision: bfloat16 ## Capabilities - Terminal command suggestions based on context - Understanding complex system tasks - Shell script generation - Command debugging assistance - Multi-platform support ## Usage The model uses chatml format for conversations: ``` <\|im_start\|>user How to display all processes using port 8080? <\|im_end\|> <\|im_start\|>assistant lsof -i :8080 <\|im_end\|> ``` ### Example Use Cases File Management: ``` User: How to find all Python files larger than 1MB? Assistant: find . -name ".py" -size +1M ``` Process Management:* ``` User: How to kill all python processes? Assistant: pkill python ``` Git Operations: ``` User: How to undo last commit without losing changes? Assistant: git reset --soft HEAD~1 ``` ## Installation and Usage Requirements: - transformers - torch - tokenizers ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "shellm_v0.1_merged" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) # Example usage prompt = "<\|im_start\|>user\nHow to check disk usage?<\|im_end\|>\n<\|im_start\|>assistant\n" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_length=150, temperature=0.7) response = tokenizer.decode(outputs[0], skip_special_tokens=False) ``` ## Special Tokens The model supports standard Qwen2 tokens: - `<\|im_start\|>`, `<\|im_end\|>` - conversation markers - `<\|vision_pad\|>` - padding token - Fill-in-the-middle tokens: `<\|fim_prefix\|>`, `<\|fim_middle\|>`, `<\|fim_suffix\|>` ## Version Info Version: v0.1 Based on: Qwen2.5-1.5B Fine-tuned with: Unsloth v2025.8.8
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755700605	rvipitkirubbe	2025-08-20T15:04:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mottled foraging ape", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:04:08Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mottled foraging ape --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
abcorrea/p3-v1	abcorrea	2025-08-20T15:03:27Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "sft", "trl", "conversational", "base_model:Qwen/Qwen3-4B", "base_model:finetune:Qwen/Qwen3-4B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T14:56:05Z	--- base_model: Qwen/Qwen3-4B library_name: transformers model_name: p3-v1 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for p3-v1 This model is a fine-tuned version of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B). 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="abcorrea/p3-v1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.19.1 - Transformers: 4.52.1 - Pytorch: 2.7.0 - Datasets: 4.0.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Zeldaaaa/qwen3_reply_finetuned_stable_instruct	Zeldaaaa	2025-08-20T15:03:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-20T15:03:12Z	--- 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. 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ali009eng/llama-3b-finetuned	ali009eng	2025-08-20T15:03:18Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-20T14:08:02Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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roeker/blockassist-bc-quick_wiry_owl_1755702073	roeker	2025-08-20T15:02:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T15:01:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Trelis/Qwen3-4B_ds-arc-agi-2-perfect-50-c970	Trelis	2025-08-20T15:02:28Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Qwen3-4B", "base_model:finetune:unsloth/Qwen3-4B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T15:01:02Z	--- base_model: unsloth/Qwen3-4B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Trelis - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Ftfyhh/wan2.1_14B_marat_safin_style_lora	Ftfyhh	2025-08-20T15:01:25Z	0	0	null	[ "base_model:Wan-AI/Wan2.1-T2V-14B", "base_model:finetune:Wan-AI/Wan2.1-T2V-14B", "region:us" ]	null	2025-08-20T14:49:32Z	--- base_model: - Wan-AI/Wan2.1-T2V-14B --- Made for Wan2.1-T2V-14B. But I recommend to use with Wan2.2 with a single LOW noise sampler. Don't use 2 samplers (high+low), it will result in wrong composition. Recommeneded settings for t2i: LOW noise checkpoint, 1280x720 (horizontal orientaion is preferable), 6 steps, lightx2v lora 0.50, fusionx lora 0.50, Sampler: res_2s, scheduler: bong_tangent lora strength: 1.00-1.50 (i like 1.50) trigger word: Marat Safin style Example prompt: `Marat Safin style. Woman is sitting on the floor by a bathub at bathroom in a modern apartment in a crop top and denim shorts. At first she is looking at viewer then looking down. upper body shot, side view.`
Trelis/Qwen3-4B_ds-arc-agi-2-perfect-50-c485	Trelis	2025-08-20T15:00:42Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Qwen3-4B", "base_model:finetune:unsloth/Qwen3-4B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T14:59:11Z	--- base_model: unsloth/Qwen3-4B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Trelis - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
jo-mengr/mmcontext-pubmedbert-pca-100k	jo-mengr	2025-08-20T15:00:36Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "dense", "generated_from_trainer", "dataset_size:81143", "loss:MultipleNegativesRankingLoss", "code", "dataset:jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:NeuML/pubmedbert-base-embeddings", "base_model:finetune:NeuML/pubmedbert-base-embeddings", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-08-20T15:00:15Z	--- language: - code tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:81143 - loss:MultipleNegativesRankingLoss base_model: NeuML/pubmedbert-base-embeddings widget: - source_sentence: sample_idx:census_d7d7e89c-c93a-422d-8958-9b4a90b69558_1563 sentences: - This measurement was conducted with 10x 5' v1. Naive B cell from blood of a 26-year old male, activated with CD3. - sample_idx:census_d7d7e89c-c93a-422d-8958-9b4a90b69558_5036 - This measurement was conducted with 10x 5' v1. A 26-year-old male individual's blood sample, containing naive thymus-derived CD4-positive, alpha-beta T cells, with no activation or treatment, and in G1 phase. - source_sentence: sample_idx:census_cf83c98a-3791-4537-bbde-a719f6d73c13_738 sentences: - This measurement was conducted with 10x 3' v3. Blasts cells derived from the blood of a 4-month old male. - sample_idx:census_cf83c98a-3791-4537-bbde-a719f6d73c13_1016 - This measurement was conducted with 10x 3' v3. This is a megakaryocyte-erythroid progenitor cell (MEP-like) derived from a 1-month-old female patient with KMT2A-rearranged (KMT2A-r) infant acute lymphoblastic leukemia (ALL). The cell exhibits increased lineage plasticity, downregulated steroid response pathways, and belongs to a hematopoietic stem and progenitor-like (HSPC-like) population that forms an immunosuppressive signaling circuit with cytotoxic lymphocytes. - source_sentence: sample_idx:census_2872f4b0-b171-46e2-abc6-befcf6de6306_2050 sentences: - sample_idx:census_2872f4b0-b171-46e2-abc6-befcf6de6306_1719 - This measurement was conducted with 10x 5' v2. Memory B cell derived from a 65-79 year-old male, taken from the mesenteric lymph node. - This measurement was conducted with 10x 5' v2. IgA plasma cell sample taken from the mesenteric lymph node of a 65-79 year-old female. - source_sentence: sample_idx:census_3f31f8ce-bbf6-4df8-8203-aa240ed03026_299 sentences: - This measurement was conducted with 10x 3' v3. Neuron cell type from a 50-year-old male human cerebral cortex, specifically from the Cingulate gyrus, rostral (CgGr), Ventral division of MFC - A24 region, with European self-reported ethnicity, analyzed at the nucleus level. - This measurement was conducted with 10x 3' v3. Neuron cell type from a 50-year-old male human cerebral cortex, specifically the rostral cingulate gyrus, ventral division of MFC, A24, with European ethnicity. - sample_idx:census_3f31f8ce-bbf6-4df8-8203-aa240ed03026_30 - source_sentence: sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_14644 sentences: - sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_16130 - This measurement was conducted with 10x 3' v3. Classical monocytes derived from the blood of a female individual in her seventies. - This measurement was conducted with 10x 5' v2. Sample is a CD8-positive, alpha-beta memory T cell, specifically a cytotoxic T cell, from the lamina propria tissue of an individual in her eighth decade of life. datasets: - jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy model-index: - name: SentenceTransformer based on NeuML/pubmedbert-base-embeddings results: - task: type: triplet name: Triplet dataset: name: cellxgene pseudo bulk 100k multiplets natural language annotation cell sentence 1 type: cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1 metrics: - type: cosine_accuracy value: 0.5186993479728699 name: Cosine Accuracy --- # SentenceTransformer based on NeuML/pubmedbert-base-embeddings This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings) on the [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings) <!-- at revision d6eaca8254bc229f3ca42749a5510ae287eb3486 --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity - Training Dataset: - [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) - Language: code <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): MMContextEncoder( (text_encoder): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(30522, 768, padding_idx=0) (position_embeddings): Embedding(512, 768) (token_type_embeddings): Embedding(2, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (encoder): BertEncoder( (layer): ModuleList( (0-11): 12 x BertLayer( (attention): BertAttention( (self): BertSdpaSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) ) ) (pooler): BertPooler( (dense): Linear(in_features=768, out_features=768, bias=True) (activation): Tanh() ) ) (text_adapter): AdapterModule( (net): Sequential( (0): Linear(in_features=768, out_features=512, bias=True) (1): ReLU(inplace=True) (2): Linear(in_features=512, out_features=1024, bias=True) (3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (pooling): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("jo-mengr/mmcontext-pubmedbert-pca-100k") # Run inference sentences = [ 'sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_14644', "This measurement was conducted with 10x 5' v2. Sample is a CD8-positive, alpha-beta memory T cell, specifically a cytotoxic T cell, from the lamina propria tissue of an individual in her eighth decade of life.", "This measurement was conducted with 10x 3' v3. Classical monocytes derived from the blood of a female individual in her seventies.", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[ 1.0000, -0.1866, -0.2255], # [-0.1866, 1.0000, 0.9165], # [-0.2255, 0.9165, 1.0000]]) ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Triplet * Dataset: `cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1` * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy \| 0.5187 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation * Dataset: [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) at [9916878](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation/tree/9916878bbf20fb8f9d6a0be4c997236e027cabd4) * Size: 81,143 training samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative_1 \| negative_2 \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| string \| \| details \| <ul><li>min: 56 characters</li><li>mean: 58.72 characters</li><li>max: 60 characters</li></ul> \| <ul><li>min: 92 characters</li><li>mean: 216.13 characters</li><li>max: 900 characters</li></ul> \| <ul><li>min: 101 characters</li><li>mean: 215.14 characters</li><li>max: 870 characters</li></ul> \| <ul><li>min: 56 characters</li><li>mean: 58.75 characters</li><li>max: 60 characters</li></ul> \| * Samples: \| anchor \| positive \| negative_1 \| negative_2 \| \|:--------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------\| \| <code>sample_idx:census_218acb0f-9f2f-4f76-b90b-15a4b7c7f629_26009</code> \| <code>This measurement was conducted with 10x 3' v2. A proliferating lymphocyte cell sample, obtained from a 34-year-old female Asian individual, derived from peripheral blood mononuclear cells.</code> \| <code>This measurement was conducted with 10x 3' v2. Sample is a 25-year-old female with European ethnicity, having CD8-positive, alpha-beta T cell type. This cell type exhibits elevated expression of type 1 interferon-stimulated genes (ISGs) in monocytes, reduction of naïve CD4+ T cells correlating with monocyte ISG expression, and expansion of repertoire-restricted cytotoxic GZMH+ CD8+ T cells.</code> \| <code>sample_idx:census_218acb0f-9f2f-4f76-b90b-15a4b7c7f629_14165</code> \| \| <code>sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_6333</code> \| <code>This measurement was conducted with 10x 5' v1. Sample is a cell from the omentum tissue, specifically an effector memory CD4-positive, alpha-beta T cell, from a female in her sixth decade.</code> \| <code>This measurement was conducted with 10x 5' v2. Conventional dendritic cell from the jejunal epithelium of a female in her eighth decade.</code> \| <code>sample_idx:census_1b9d8702-5af8-4142-85ed-020eb06ec4f6_2714</code> \| \| <code>sample_idx:census_adda0684-f8ea-4403-b393-2a25607077c4_271</code> \| <code>This measurement was conducted with 10x 3' v3. Neuron cell type from a 29-year-old male, specifically from the thalamic complex, specifically the thalamus (THM) - posterior nuclear complex of thalamus (PoN) - medial geniculate nuclei (MG).</code> \| <code>This measurement was conducted with 10x 3' v3. Neuron from the thalamic complex (thalamus, posterior nuclear complex of thalamus, medial geniculate nuclei) of a 42-year-old male, identified as a midbrain-derived inhibitory neuron.</code> \| <code>sample_idx:census_adda0684-f8ea-4403-b393-2a25607077c4_425</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation * Dataset: [cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation) at [9916878](https://huggingface.co/datasets/jo-mengr/cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation/tree/9916878bbf20fb8f9d6a0be4c997236e027cabd4) * Size: 9,011 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative_1 \| negative_2 \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| string \| \| details \| <ul><li>min: 56 characters</li><li>mean: 58.73 characters</li><li>max: 60 characters</li></ul> \| <ul><li>min: 99 characters</li><li>mean: 209.99 characters</li><li>max: 941 characters</li></ul> \| <ul><li>min: 102 characters</li><li>mean: 213.87 characters</li><li>max: 981 characters</li></ul> \| <ul><li>min: 56 characters</li><li>mean: 58.73 characters</li><li>max: 60 characters</li></ul> \| * Samples: \| anchor \| positive \| negative_1 \| negative_2 \| \|:--------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------\| \| <code>sample_idx:census_0b4a15a7-4e9e-4555-9733-2423e5c66469_490</code> \| <code>This measurement was conducted with 10x 3' v3. Cell sample from the cortex of kidney, taken from a 43-year-old male of European ethnicity with a reported history of kidney cancer. The cell type is identified as a kidney collecting duct intercalated cell.</code> \| <code>This measurement was conducted with 10x 3' v3. Kidney collecting duct intercalated cell from a 43-year old European male with kidney cancer, taken from the cortex of kidney and cryopreserved for further analysis.</code> \| <code>sample_idx:census_0b4a15a7-4e9e-4555-9733-2423e5c66469_9</code> \| \| <code>sample_idx:census_4976b234-9028-4b4b-8a2f-8ac59d636219_269</code> \| <code>This measurement was conducted with 10x 3' v3. Neuron cell type from a 29-year-old male cerebellum, specifically from the Cerebellar Vermis - CBV region, with European self-reported ethnicity, analyzed at the nucleus level.</code> \| <code>This measurement was conducted with 10x 3' v3. Endothelial cells derived from the cerebellum (specifically, cerebellar vermis) of a 42-year-old male, classified under the vascular supercluster term.</code> \| <code>sample_idx:census_4976b234-9028-4b4b-8a2f-8ac59d636219_923</code> \| \| <code>sample_idx:census_44882825-0da1-4547-b721-2c6105d4a9d1_10258</code> \| <code>This measurement was conducted with 10x 5' v1. Cell sample from the tonsil of a 9-year-old female with recurrent tonsillitis, characterized as a centroblast B cell with IGLC2, IGLV7-43, IGLJ3 immunoglobulin genes expressed.</code> \| <code>This measurement was conducted with 10x 5' v1. Centroblast cells derived from a 3-year-old male human tonsil sample, with obstructive sleep apnea and recurrent tonsillitis, undergoing affinity maturation and differentiation into memory or plasma cells.</code> \| <code>sample_idx:census_44882825-0da1-4547-b721-2c6105d4a9d1_9654</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 256 - `learning_rate`: 0.05 - `num_train_epochs`: 4 - `warmup_ratio`: 0.1 - `bf16`: True - `gradient_checkpointing`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 256 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: True - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs \| Epoch \| Step \| Training Loss \| cellxgene pseudo bulk 100k multiplets natural language annotation loss \| cellxgene_pseudo_bulk_100k_multiplets_natural_language_annotation_cell_sentence_1_cosine_accuracy \| \|:------:\|:----:\|:-------------:\|:----------------------------------------------------------------------:\|:-------------------------------------------------------------------------------------------------:\| \| 0.3155 \| 100 \| 4.3348 \| 21.3762 \| 0.5055 \| \| 0.6309 \| 200 \| 3.3159 \| 21.8799 \| 0.5079 \| \| 0.9464 \| 300 \| 2.9731 \| 22.2929 \| 0.5059 \| \| 1.2618 \| 400 \| 2.8069 \| 20.0968 \| 0.5046 \| \| 1.5773 \| 500 \| 2.7025 \| 19.3518 \| 0.5092 \| \| 1.8927 \| 600 \| 2.6328 \| 18.7736 \| 0.5114 \| \| 2.2082 \| 700 \| 2.5721 \| 19.3342 \| 0.5138 \| \| 2.5237 \| 800 \| 2.4768 \| 18.3519 \| 0.5119 \| \| 2.8391 \| 900 \| 2.4534 \| 18.4348 \| 0.5136 \| \| 3.1546 \| 1000 \| 2.4333 \| 18.1047 \| 0.5130 \| \| 3.4700 \| 1100 \| 2.4221 \| 17.8854 \| 0.5188 \| \| 3.7855 \| 1200 \| 2.4177 \| 17.6722 \| 0.5187 \| ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 5.0.0 - Transformers: 4.55.0.dev0 - PyTorch: 2.5.1+cu121 - Accelerate: 1.9.0 - Datasets: 2.19.1 - Tokenizers: 0.21.4 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755700402	lisaozill03	2025-08-20T14:58:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T14:58:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755700304	quantumxnode	2025-08-20T14:58:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T14:58:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
cullenrigby/Llama-3.1-8B-Q6_K-GGUF	cullenrigby	2025-08-20T14:58:00Z	0	0	transformers	[ "transformers", "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo", "text-generation", "en", "de", "fr", "it", "pt", "hi", "es", "th", "base_model:meta-llama/Llama-3.1-8B", "base_model:quantized:meta-llama/Llama-3.1-8B", "license:llama3.1", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T14:57:29Z	--- language: - en - de - fr - it - pt - hi - es - th pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 - llama-cpp - gguf-my-repo license: llama3.1 extra_gated_prompt: "### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\nLlama 3.1 Version\ \ Release Date: July 23, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview.\n\ \"Licensee\" or \"you\" means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf), of\ \ the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\"Llama 3.1\"\ \ means the foundational large language models and software and algorithms, including\ \ machine-learning model code, trained model weights, inference-enabling code, training-enabling\ \ code, fine-tuning enabling code and other elements of the foregoing distributed\ \ by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means,\ \ collectively, Meta’s proprietary Llama 3.1 and Documentation (and any portion\ \ thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms\ \ Ireland Limited (if you are located in or, if you are an entity, your principal\ \ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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Generating or facilitating false online engagement, including fake reviews\ \ and other means of fake online engagement\n4. Fail to appropriately disclose to\ \ end users any known dangers of your AI system\nPlease report any violation of\ \ this Policy, software “bug,” or other problems that could lead to a violation\ \ of this Policy through one of the following means:\n * Reporting issues with\ \ the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)\n\ \ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\ \ * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting\ \ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: LlamaUseReport@meta.com" extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location ? By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit library_name: transformers base_model: meta-llama/Llama-3.1-8B --- # cullenrigby/Llama-3.1-8B-Q6_K-GGUF This model was converted to GGUF format from [`meta-llama/Llama-3.1-8B`](https://huggingface.co/meta-llama/Llama-3.1-8B) 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/meta-llama/Llama-3.1-8B) 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 cullenrigby/Llama-3.1-8B-Q6_K-GGUF --hf-file llama-3.1-8b-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo cullenrigby/Llama-3.1-8B-Q6_K-GGUF --hf-file llama-3.1-8b-q6_k.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 cullenrigby/Llama-3.1-8B-Q6_K-GGUF --hf-file llama-3.1-8b-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo cullenrigby/Llama-3.1-8B-Q6_K-GGUF --hf-file llama-3.1-8b-q6_k.gguf -c 2048 ```
AnonymousCS/xlmr_immigration_combo14_0	AnonymousCS	2025-08-20T14:57:37Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T13:59:51Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo14_0 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. --> # xlmr_immigration_combo14_0 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2669 - Accuracy: 0.9049 - 1-f1: 0.8560 - 1-recall: 0.8494 - 1-precision: 0.8627 - Balanced Acc: 0.8910 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.6288 \| 1.0 \| 25 \| 0.6068 \| 0.6671 \| 0.0 \| 0.0 \| 0.0 \| 0.5 \| \| 0.4561 \| 2.0 \| 50 \| 0.4201 \| 0.8560 \| 0.7255 \| 0.5714 \| 0.9933 \| 0.7848 \| \| 0.2346 \| 3.0 \| 75 \| 0.2805 \| 0.9036 \| 0.8380 \| 0.7490 \| 0.9510 \| 0.8649 \| \| 0.1801 \| 4.0 \| 100 \| 0.2428 \| 0.9152 \| 0.8648 \| 0.8147 \| 0.9214 \| 0.8900 \| \| 0.2359 \| 5.0 \| 125 \| 0.2488 \| 0.9139 \| 0.8646 \| 0.8263 \| 0.9068 \| 0.8919 \| \| 0.1115 \| 6.0 \| 150 \| 0.2669 \| 0.9049 \| 0.8560 \| 0.8494 \| 0.8627 \| 0.8910 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
pobiiiiiii/blockassist-bc-ravenous_yapping_ferret_1755701779	pobiiiiiii	2025-08-20T14:56:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous yapping ferret", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T14:56:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous yapping ferret --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
wasabuko/blockassist-bc-noisy_zealous_macaw_1755699198	wasabuko	2025-08-20T14:54:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "noisy zealous macaw", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T14:51:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - noisy zealous macaw --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
4everStudent/sft-mat-qwen3-4B-081925-merged	4everStudent	2025-08-20T14:54:27Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-20T14:53:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
mradermacher/MathTutor-7B-H_v0.0.1-GGUF	mradermacher	2025-08-20T14:54:13Z	48	0	transformers	[ "transformers", "gguf", "en", "base_model:Sandesh2027/MathTutor-7B-H_v0.0.1", "base_model:quantized:Sandesh2027/MathTutor-7B-H_v0.0.1", "endpoints_compatible", "region:us", "conversational" ]	null	2025-07-10T00:02:23Z	--- base_model: Sandesh2027/MathTutor-7B-H_v0.0.1 language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Sandesh2027/MathTutor-7B-H_v0.0.1 <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#MathTutor-7B-H_v0.0.1-GGUF).* 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/MathTutor-7B-H_v0.0.1-GGUF/resolve/main/MathTutor-7B-H_v0.0.1.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MathTutor-7B-H_v0.0.1-GGUF/resolve/main/MathTutor-7B-H_v0.0.1.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MathTutor-7B-H_v0.0.1-GGUF/resolve/main/MathTutor-7B-H_v0.0.1.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MathTutor-7B-H_v0.0.1-GGUF/resolve/main/MathTutor-7B-H_v0.0.1.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MathTutor-7B-H_v0.0.1-GGUF/resolve/main/MathTutor-7B-H_v0.0.1.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MathTutor-7B-H_v0.0.1-GGUF/resolve/main/MathTutor-7B-H_v0.0.1.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MathTutor-7B-H_v0.0.1-GGUF/resolve/main/MathTutor-7B-H_v0.0.1.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MathTutor-7B-H_v0.0.1-GGUF/resolve/main/MathTutor-7B-H_v0.0.1.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MathTutor-7B-H_v0.0.1-GGUF/resolve/main/MathTutor-7B-H_v0.0.1.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MathTutor-7B-H_v0.0.1-GGUF/resolve/main/MathTutor-7B-H_v0.0.1.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MathTutor-7B-H_v0.0.1-GGUF/resolve/main/MathTutor-7B-H_v0.0.1.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/MathTutor-7B-H_v0.0.1-GGUF/resolve/main/MathTutor-7B-H_v0.0.1.f16.gguf) \| f16 \| 15.3 \| 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 -->
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755699992	ihsanridzi	2025-08-20T14:53:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T14:53:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Vasya777/blockassist-bc-lumbering_enormous_sloth_1755701545	Vasya777	2025-08-20T14:53:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lumbering enormous sloth", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T14:53:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lumbering enormous sloth --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
liukevin666/blockassist-bc-yawning_striped_cassowary_1755701430	liukevin666	2025-08-20T14:53:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T14:51:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
aleebaster/blockassist-bc-sly_eager_boar_1755700020	aleebaster	2025-08-20T14:52:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly eager boar", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T14:52:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sly eager boar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
thanobidex/blockassist-bc-colorful_shiny_hare_1755699960	thanobidex	2025-08-20T14:52:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful shiny hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T14:52:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - colorful shiny hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Infamoushugger/Mortubrux	Infamoushugger	2025-08-20T14:51:29Z	0	0	null	[ "region:us" ]	null	2025-08-20T14:34:27Z	https://arcenciel.io/models/11690
chainway9/blockassist-bc-untamed_quick_eel_1755699778	chainway9	2025-08-20T14:51:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed quick eel", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T14:51:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - untamed quick eel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
luckycanucky/llama3-unaligned	luckycanucky	2025-08-20T14:51:12Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "unsloth", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-20T13:08:58Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
roeker/blockassist-bc-quick_wiry_owl_1755701220	roeker	2025-08-20T14:48:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T14:47:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
pobiiiiiii/blockassist-bc-ravenous_yapping_ferret_1755701233	pobiiiiiii	2025-08-20T14:47:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous yapping ferret", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T14:47:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous yapping ferret --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mang3dd/blockassist-bc-tangled_slithering_alligator_1755699680	mang3dd	2025-08-20T14:47:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T14:47:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
abishekcodes/bert-new-ner	abishekcodes	2025-08-20T14:47:04Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-08-19T19:42:53Z	--- library_name: transformers license: apache-2.0 base_model: google-bert/bert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: bert-new-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-new-ner This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0246 - Precision: 0.9645 - Recall: 0.9682 - F1: 0.9664 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| 0.0227 \| 1.0 \| 1002 \| 0.0263 \| 0.9540 \| 0.9614 \| 0.9577 \| \| 0.0125 \| 2.0 \| 2004 \| 0.0237 \| 0.9554 \| 0.9720 \| 0.9637 \| \| 0.0064 \| 3.0 \| 3006 \| 0.0246 \| 0.9645 \| 0.9682 \| 0.9664 \| ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.4
liukevin666/blockassist-bc-yawning_striped_cassowary_1755701071	liukevin666	2025-08-20T14:46:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T14:45:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

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