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For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
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<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
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This is the 4th iteration English supervised-fine-tuning (SFT) model of
the [Open-Assistant](https://github.com/LAION-AI/Open-Assistant) project.
It is based on a Pythia 12B that was fine-tuned on human demonstrations
of assistant conversations collected through the
[https://open-... | null | [
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## Introduction
Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
* 8 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B and 72B dense models, and an MoE... | null | [
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## Model description
[CodeT5+](https://github.com/salesforce/CodeT5/tree/main/CodeT5+) is a new family of open code large language models
with an encoder-decoder architecture that can flexibly operate in different modes (i.e. _encoder-only_, _decoder-only_,
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This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base)
## Tra... | null | [
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"base_model:finetune:Qwen/Qwen2-0.5B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] +... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2-0.5B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["chat"]} | # Qwen2-0.5B-Instruct
## Introduction
Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 0.5B Qwen2... | null | [
"apache-2.0"
] | null | [
"en"
] | 494,032,768 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
673f92e080c7c8e3327fd6a4 | 7b66071fbb0d7bdc | 7d900c9c71170270 | 257,153 | BPE | 580,604 | true | true | true | true | 1,370 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
"Fuse"
] | 171 | google/paligemma2-3b-pt-224 | google | null | 13,324 | 3,915,690 | manual | 2024-11-21T20:06:56Z | 2024-12-05T12:42:34Z | transformers | 164 | 0 | null | image-text-to-text | null | [
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] | 96eeb174da13ca1a2b247e4d0867436296c36420 | [
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"arxiv:2209.04372",
"arxiv:2103.01913",
"arxiv:1908.04913",
... | null | {"architectures": ["PaliGemmaForConditionalGeneration"], "model_type": "paligemma", "tokenizer_config": {"bos_token": "<bos>", "eos_token": "<eos>", "pad_token": "<pad>", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "gemma", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": null, "extra_gated_heading": "Access PaliGemma on Hugging Face... | null | null | [
"gemma"
] | null | null | null | null | null | [
"PaliGemmaForConditionalGeneration",
"AutoModelForImageTextToText",
"paligemma"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
67f7f46c3d01987d02efd7ec | b647996d6543f730 | 7ab62ce4e32550df | 151,674 | BPE | 151,387 | true | true | true | true | 31 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 172 | OpenGVLab/InternVL3-14B | OpenGVLab | {
"models": [
{
"_id": "67ff92f1d09d777763ef3c59",
"id": "OpenGVLab/InternVL3-14B-Instruct"
}
],
"relation": "finetune"
} | 46,270 | 4,536,201 | False | 2025-04-10T16:40:12Z | 2025-09-11T12:50:33Z | transformers | 79 | 0 | null | image-text-to-text | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"configuration_intern_vit.py",
"configuration_internvl_chat.py",
"conversation.py",
"examples/image1.jpg",
"examples/image2.jpg",
"examples/red-panda.mp4",
"generation_config.json",
"merges.txt",
"model-00001-of-00007.safeten... | 419aa10d2db7da6c64382ad3124f79a60cec42aa | [
"transformers",
"safetensors",
"internvl_chat",
"feature-extraction",
"internvl",
"custom_code",
"image-text-to-text",
"conversational",
"multilingual",
"dataset:OpenGVLab/MMPR-v1.2",
"arxiv:2312.14238",
"arxiv:2404.16821",
"arxiv:2412.05271",
"arxiv:2411.10442",
"arxiv:2504.10479",
"a... | null | {"architectures": ["InternVLChatModel"], "auto_map": {"AutoConfig": "configuration_internvl_chat.InternVLChatConfig", "AutoModel": "modeling_internvl_chat.InternVLChatModel", "AutoModelForCausalLM": "modeling_internvl_chat.InternVLChatModel"}, "model_type": "internvl_chat", "tokenizer_config": {"bos_token": null, "chat... | {
"auto_model": "AutoModel",
"custom_class": "modeling_internvl_chat.InternVLChatModel",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"base_model": ["OpenGVLab/InternVL3-14B-Instruct"], "datasets": ["OpenGVLab/MMPR-v1.2"], "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": "apache-2.0", "license_name": "qwen", "license_link": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE", "metrics": ... | # InternVL3-14B
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271) [\[📜 InternVL2.5-MPO\]](https://huggingface.co/pape... | null | [
"apache-2.0",
"qwen",
"https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE"
] | [
"OpenGVLab/MMPR-v1.2"
] | [
"multilingual"
] | null | null | null | [
"AutoModel",
"internvl_chat",
"InternVLChatModel",
"modeling_internvl_chat.InternVLChatModel"
] | [
"image-text-to-text",
"feature-extraction"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text",
"embeddings"
] |
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"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp | McGill-NLP | null | 6,253 | 3,627,815 | False | 2024-04-04T02:59:33Z | 2024-05-21T22:01:47Z | transformers | 12 | 0 | null | sentence-similarity | null | [
".gitattributes",
"README.md",
"adapter_config.json",
"adapter_model.safetensors",
"attn_mask_utils.py",
"config.json",
"modeling_mistral_encoder.py",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | e76f9757923897a0c5204b3075f1062f484d033b | [
"transformers",
"safetensors",
"mistral",
"feature-extraction",
"text-embedding",
"embeddings",
"information-retrieval",
"beir",
"text-classification",
"language-model",
"text-clustering",
"text-semantic-similarity",
"text-evaluation",
"text-reranking",
"sentence-similarity",
"Sentence... | null | {"architectures": ["MistralEncoderModel"], "auto_map": {"AutoModel": "McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp--modeling_mistral_encoder.MistralEncoderModel"}, "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == ... | {
"auto_model": "AutoModel",
"custom_class": "McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp--modeling_mistral_encoder.MistralEncoderModel",
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["text-embedding", "embeddings", "information-retrieval", "be... | # LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine... | null | [
"mit"
] | null | [
"en"
] | null | null | null | [
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"mistral",
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] | [
"sentence-similarity",
"feature-extraction",
"text-classification"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
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"ByteLevel"
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"ByteLevel"
] | 128 | DeepChem/ChemBERTa-77M-MTR | DeepChem | null | 134,678 | 4,032,097 | False | 2022-03-02T23:29:04Z | 2022-01-20T17:55:55Z | transformers | 23 | 0 | null | null | null | [
".gitattributes",
"added_tokens.json",
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] | 66b895cab8adebea0cb59a8effa66b2020f204ca | [
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"auto_model": "RobertaForRegression",
"custom_class": null,
"pipeline_tag": null,
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} | null | null | null | null | null | null | null | null | null | [
"roberta",
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null
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621ffdc136468d709f17a4d6 | 4c362f85d6703fb6 | b15dbf58509420e7 | 31,002 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 173 | dccuchile/bert-base-spanish-wwm-cased | dccuchile | null | 40,638 | 3,717,804 | False | 2022-03-02T23:29:05Z | 2024-01-18T01:47:12Z | transformers | 75 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | c4d86612f51b4f46759c8390d1798c2febe71b93 | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"masked-lm",
"es",
"arxiv:1904.09077",
"arxiv:1906.01502",
"arxiv:1812.10464",
"arxiv:1901.07291",
"arxiv:1904.02099",
"arxiv:1906.01569",
"arxiv:1908.11828",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["es"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["masked-lm"]} | # BETO: Spanish BERT
BETO is a [BERT model](https://github.com/google-research/bert) trained on a [big Spanish corpus](https://github.com/josecannete/spanish-corpora). BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you find Tensorflow and Pytorch checkpoints for the... | null | null | null | [
"es"
] | null | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc036468d709f174328 | a3d54d6c1d5c62b4 | f87d7bfce4fd9617 | 30,000 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Replace",
"Replace",
"NFKD",
"StripAccents",
"Lowercase",
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 12 | albert/albert-base-v1 | albert | null | 135,447 | 3,734,672 | False | 2022-03-02T23:29:04Z | 2024-02-19T10:57:35Z | transformers | 12 | 0 | null | fill-mask | {"parameters": {"F32": 11842272}, "total": 11842272} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"spiece.model",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"with-prefix-tf_model.h5"
] | 082438ba120d36b97b9288772a41144e941705b9 | [
"transformers",
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["AlbertForMaskedLM"], "model_type": "albert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["bookcorpus", "wikipedia"], "eval_results": null, "language": "en", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["exbert"]} | # ALBERT Base v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make ... | null | [
"apache-2.0"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | 11,842,272 | null | null | [
"albert",
"AutoModelForMaskedLM",
"AlbertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
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"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 174 | shibing624/text2vec-base-multilingual | shibing624 | null | 109,469 | 4,035,728 | False | 2023-06-22T06:28:12Z | 2024-07-31T09:01:11Z | sentence-transformers | 58 | 0 | [{"name": "text2vec-base-multilingual", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "v... | sentence-similarity | {"parameters": {"I64": 512, "F32": 117653760}, "total": 117654272} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"eval_results.txt",
"model.safetensors",
"modules.json",
"onnx/config.json",
"onnx/model.onnx",
"onnx/special_tokens_map.json",
"onnx/tokenizer.json",
"onnx/tokenizer_config.json",
"onnx/unigram.json",
"pytorch_model.bi... | 6633dc49e554de7105458f8f2e96445c6598e9d1 | [
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"pytorch",
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"safetensors",
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"feature-extraction",
"sentence-similarity",
"transformers",
"text2vec",
"mteb",
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"en",
"de",
"fr",
"it",
"nl",
"pt",
"pl",
"ru",
"dataset:shibing624/nli-zh-all",
"license:apache-2.0",
"model-index",
... | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": "<pad>", "sep_token": "</s>", "unk... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"datasets": ["shibing624/nli-zh-all"], "language": ["zh", "en", "de", "fr", "it", "nl", "pt", "pl", "ru"], "library_name": "sentence-transformers", "license": "apache-2.0", "metrics": ["spearmanr"], "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "t... | # shibing624/text2vec-base-multilingual
This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-multilingual.
It maps sentences to a 384 dimensional dense vector space and can be used for tasks
like sentence embeddings, text matching or semantic search.
- training dataset: https://huggingface.co/datasets... | null | [
"apache-2.0"
] | [
"shibing624/nli-zh-all"
] | [
"zh",
"en",
"de",
"fr",
"it",
"nl",
"pt",
"pl",
"ru"
] | 117,654,272 | null | [
"spearmanr"
] | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f1802bc | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | sentence-transformers/msmarco-MiniLM-L12-v3 | sentence-transformers | null | 2,331 | 3,439,744 | False | 2022-03-02T23:29:05Z | 2025-03-06T12:09:05Z | sentence-transformers | 25 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 33360000}, "total": 33360512} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
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"config_sentence_transformers.json",
"flax_model.msgpack",
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"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint... | 04138389795d8715e1298fc51c0dc7d18d9670cd | [
"sentence-transformers",
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"tf",
"jax",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/msmarco-MiniLM-L12-v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you ... | null | [
"apache-2.0"
] | null | null | 33,360,512 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
669fd9253d5dd8967444de17 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
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] | [
"ByteLevel"
] | 18 | unsloth/Meta-Llama-3.1-8B-Instruct | unsloth | {
"models": [
{
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"id": "meta-llama/Llama-3.1-8B-Instruct"
}
],
"relation": "finetune"
} | 364,398 | 4,790,518 | False | 2024-07-23T16:24:05Z | 2025-02-15T10:24:46Z | transformers | 94 | 0 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
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"... | a2856192dd7c25b842431f39c179a6c2c2f627d1 | [
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"safetensors",
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"text-generation",
"llama-3",
"meta",
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"unsloth",
"conversational",
"en",
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"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"text-generation-inference",
"endpoints_... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_messag... | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Llama-3.1-8B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["llama-3", "llama", "meta", "facebook", "u... | # Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/D... | null | [
"llama3.1"
] | null | [
"en"
] | 8,030,261,248 | null | null | [
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] | [
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] | [
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] | [
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] | [
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] |
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"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
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] | 175 | Xenova/tiny-random-Phi3ForCausalLM | Xenova | null | 5,777 | 3,447,600 | False | 2024-05-15T21:17:51Z | 2024-05-15T21:39:48Z | transformers | 0 | 0 | null | text-generation | {"parameters": {"F32": 2072736}, "total": 2072736} | [
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"onnx/mod... | bac53a980e9402513e0a5f8433656b4f845c1b9b | [
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"onnx",
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"text-generation",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["Phi3ForCausalLM"], "model_type": "phi3", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assis... | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": []} | # Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Code used to generate the model:
```py
from transformers import Phi3Config, Phi3ForCausalLM, AutoTokenizer
model = Phi3ForCausalLM(Phi3Config(
hidden_size=32,
intermediate_size=64,
num_attention_heads=4,
num_hidden_l... | null | null | null | null | 2,072,736 | null | null | [
"AutoModelForCausalLM",
"phi3",
"Phi3ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"NFD"
] | [
"Whitespace"
] | null | 176 | TurkuNLP/eccobert-base-cased-v1 | TurkuNLP | null | 17 | 3,385,291 | False | 2022-04-09T19:11:30Z | 2024-11-18T13:29:22Z | transformers | 0 | 0 | null | null | {"parameters": {"I64": 512, "F32": 125085010}, "total": 125085522} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"tokenizer.json"
] | a7ac54595acf05beac50261938a04bc1157c03e7 | [
"transformers",
"pytorch",
"safetensors",
"bert",
"pretraining",
"en",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForPreTraining"], "model_type": "bert"} | {
"auto_model": "AutoModelForPreTraining",
"custom_class": null,
"pipeline_tag": "pretraining",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # ECCO-BERT base model (cased)
A pretrained BERT model trained exclusively on the ECCO (Eighteenth Century Collections Online) dataset of digitized documents published during the 18th century in the United Kingdom. The model is equivalent in size to [bert-base-cased](https://huggingface.co/bert-base-cased). The model ... | null | null | null | [
"en"
] | 125,085,522 | null | null | [
"bert",
"AutoModelForPreTraining",
"BertForPreTraining"
] | [
"pretraining"
] | null | null | null |
621ffdc036468d709f17436b | c217221ff1d632ed | c23356a9adc44e29 | 32,000 | Unigram | null | true | true | true | true | 9 | Sequence | Sequence | Metaspace | [
"Replace",
"Replace",
"NFKD",
"StripAccents",
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 59 | xlnet/xlnet-large-cased | xlnet | null | 2,219 | 3,465,489 | False | 2022-03-02T23:29:04Z | 2023-01-24T14:50:34Z | transformers | 24 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"generation_config_for_text_generation.json",
"pytorch_model.bin",
"spiece.model",
"tf_model.h5",
"tokenizer.json"
] | 37658b4f179eaf971d127d16bfbd9ca676a93034 | [
"transformers",
"pytorch",
"tf",
"xlnet",
"text-generation",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1906.08237",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["XLNetLMHeadModel"], "model_type": "xlnet"} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["bookcorpus", "wikipedia"], "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # XLNet (large-sized model)
XLNet model pre-trained on English language. It was introduced in the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al. and first released in [this repository](https://github.com/zihangdai/xlnet/).
Disclaimer... | null | [
"mit"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"XLNetLMHeadModel",
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"ByteLevel"
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] | 86 | EleutherAI/pythia-14m-deduped | EleutherAI | null | 19,470 | 3,648,585 | False | 2023-07-19T13:57:54Z | 2026-02-12T04:16:13Z | transformers | 29 | 1 | null | text-generation | {"parameters": {"F16": 14067736, "BOOL": 25165824}, "total": 39233560} | [
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"arxiv:2201.07311",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"reg... | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
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"models": [
{
"_id": "621ffdc036468d709f176f71",
"id": "ProsusAI/finbert"
}
],
"relation": "finetune"
} | 12,034 | 3,400,663 | False | 2023-04-13T17:52:32Z | 2024-12-15T01:58:13Z | transformers | 49 | 0 | [{"name": "CryptoBERT", "results": []}] | text-classification | {"parameters": {"I64": 512, "F32": 109483778}, "total": 109484290} | [
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"pytorch_model.bin",
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] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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should probably proofread and complete it, then remove this comment. -->
# CryptoBERT
This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on the Custom Crypto... | null | null | null | [
"en"
] | 109,484,290 | null | null | [
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] | null | [
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"on... | 2b12c2c0088918e76151fd5937b7bba986ef1f98 | [
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"base_model:FacebookAI/roberta-large",
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"license:apa... | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
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} | {"base_model": ["FacebookAI/roberta-large"], "datasets": ["sentence-transformers/stsb"], "eval_results": null, "language": ["en"], "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-ranking", "tags": ["... | null | null | [
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] | 178 | nvidia/Llama-3.1-Nemotron-Nano-VL-8B-V1 | nvidia | null | 1,829,408 | 9,043,960 | False | 2025-06-03T00:04:22Z | 2025-12-04T17:42:32Z | transformers | 176 | 0 | null | image-text-to-text | null | [
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"nvidia",
"VLM",
"llama3.1",
"image-text-to-text",
"license:other",
"endpoints_compatible",
"region:us"
] | null | {} | {
"auto_model": "AutoModel",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "other", "license_name": "nvidia-open-model-license", "license_link": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/", "metrics": null, "model_name": null, "... | # Llama-3.1-Nemotron-Nano-VL-8B-V1
## Model Overview
### Description
Llama Nemotron Nano VL is a leading document intelligence vision language model (VLMs) that enables the ability to query and summarize images from the physical or virtual world. Llama Nemotron Nano VL is deployable in the data center, cloud and at... | null | [
"other",
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"https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/"
] | null | null | null | null | null | [
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null
] | [
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"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ko"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "... | # snunlp/KR-SBERT-V40K-klueNLI-augSTS
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this mo... | null | null | null | [
"ko"
] | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
66e98aaab04516d7642d1692 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-72B-Instruct-AWQ | Qwen | {
"models": [
{
"_id": "66e81cefd1b1391042d0e47e",
"id": "Qwen/Qwen2.5-72B-Instruct"
}
],
"relation": "quantized"
} | 472,248 | 5,957,049 | False | 2024-09-17T13:56:58Z | 2024-10-09T12:27:10Z | transformers | 75 | 0 | null | text-generation | {"parameters": {"I32": 70464307200, "F16": 2493554688}, "total": 72957861888} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00011.safetensors",
"model-00002-of-00011.safetensors",
"model-00003-of-00011.safetensors",
"model-00004-of-00011.safetensors",
"model-00005-of-00011.safetensors",
"model-00006-of-0... | 698703eae6604af048a3d2f509995dc302088217 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"arxiv:2309.00071",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-72B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-72B-Instruct",
"license:other",
"text-generation-inference",
"endpoints_compatible",... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"bits": 4, "quant_method": "awq"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-72B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": "qwen", "license_link": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct-AWQ/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag... | # Qwen2.5-72B-Instruct-AWQ
## Introduction
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly **more... | null | [
"other",
"qwen",
"https://huggingface.co/Qwen/Qwen2.5-72B-Instruct-AWQ/blob/main/LICENSE"
] | null | [
"en"
] | 72,957,861,888 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
663c9156a1d4ea334e856156 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | Qdrant/all_miniLM_L6_v2_with_attentions | Qdrant | null | 345,632 | 4,841,486 | False | 2024-05-09T09:03:18Z | 2025-06-09T14:12:57Z | transformers | 13 | 0 | null | sentence-similarity | null | [
".gitattributes",
"README.md",
"config.json",
"model.onnx",
"special_tokens_map.json",
"stopwords.txt",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 9695632d760ba609397b32f56fb5b1b870cf92a5 | [
"transformers",
"onnx",
"bert",
"feature-extraction",
"sentence-similarity",
"en",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": null} | ONNX port of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) adjusted to return attention weights.
This model is intended to be used for [BM42 searches](https://qdrant.tech/articles/bm42/).
### Usage
Here's an example of performing inference using the model wi... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
63314de55102481d8dcde831 | dda72719c593cc4c | 539cc58b8fdaf772 | 51,864 | BPE | 50,000 | false | true | true | true | 1,608 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 55 | openai/whisper-small.en | openai | null | 77,232 | 3,557,509 | False | 2022-09-26T06:59:49Z | 2024-01-22T17:55:26Z | transformers | 55 | 0 | [{"name": "whisper-small.en", "results": [{"task": {"name": "Automatic Speech Recognition", "type": "automatic-speech-recognition"}, "dataset": {"name": "LibriSpeech (clean)", "type": "librispeech_asr", "config": "clean", "split": "test", "args": {"language": "en"}}, "metrics": [{"name": "Test WER", "type": "wer", "val... | automatic-speech-recognition | {"parameters": {"F32": 241734144}, "total": 241734144} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"generation_config.json",
"merges.txt",
"model.safetensors",
"normalizer.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer... | e8727524f962ee844a7319d92be39ac1bd25655a | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"whisper",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"en",
"arxiv:2212.04356",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["WhisperForConditionalGeneration"], "model_type": "whisper", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": ... | {
"auto_model": "AutoModelForSpeechSeq2Seq",
"custom_class": null,
"pipeline_tag": "automatic-speech-recognition",
"processor": "AutoProcessor"
} | {"language": ["en"], "license": "apache-2.0", "pipeline_tag": "automatic-speech-recognition", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispee... | # Whisper
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours
of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need
for fine-tuning.
Whisper was proposed in the paper [Robust Speec... | null | [
"apache-2.0"
] | null | [
"en"
] | 241,734,144 | null | null | [
"WhisperForConditionalGeneration",
"whisper",
"AutoModelForSpeechSeq2Seq"
] | [
"automatic-speech-recognition"
] | [
"multimodal"
] | [
"audio"
] | [
"text"
] |
669a4947a8b62d05156b01a9 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4 | hugging-quants | null | 83,802 | 3,771,859 | False | 2024-07-19T11:08:55Z | 2024-08-07T07:16:54Z | transformers | 108 | 0 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"USE_POLICY.md",
"config.json",
"generation_config.json",
"model-00001-of-00009.safetensors",
"model-00002-of-00009.safetensors",
"model-00003-of-00009.safetensors",
"model-00004-of-00009.safetensors",
"model-00005-of-00009.safetensors",
"model-00006-o... | 2123003760781134cfc31124aa6560a45b491fdf | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3.1",
"meta",
"autoawq",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"license:llama3.1",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"deploy:azure",
"region:us... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "awq"}, "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_u... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "de", "fr", "it", "pt", "hi", "es", "th"], "library_name": "transformers", "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["llama-3.1", "met... | > [!IMPORTANT]
> This repository is a community-driven quantized version of the original model [`meta-llama/Meta-Llama-3.1-70B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct) which is the FP16 half-precision official version released by Meta AI.
## Model Information
The Meta Llama 3.1 collec... | null | [
"llama3.1"
] | null | [
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63264b5fff539edeea9491f6 | 2012b52fa41ec860 | db117e9883c4d0ab | 50,257 | BPE | 50,000 | false | true | true | true | 1 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 113 | Gustavosta/MagicPrompt-Stable-Diffusion | Gustavosta | null | 8,181 | 3,294,988 | False | 2022-09-17T22:34:07Z | 2023-07-09T22:10:48Z | transformers | 737 | 0 | null | text-generation | {"parameters": {"F32": 124439808, "U8": 12582912}, "total": 137022720} | [
".gitattributes",
"README.md",
"config.json",
"coreml/text-generation/float16_model.mlpackage/Data/com.apple.CoreML/model.mlmodel",
"coreml/text-generation/float16_model.mlpackage/Data/com.apple.CoreML/weights/weight.bin",
"coreml/text-generation/float16_model.mlpackage/Manifest.json",
"coreml/text-gene... | c2dfdbff1007791b5952aff9c02e622a0461f914 | [
"transformers",
"pytorch",
"coreml",
"safetensors",
"gpt2",
"text-generation",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # MagicPrompt - Stable Diffusion
This is a model from the MagicPrompt series of models, which are [GPT-2](https://huggingface.co/gpt2) models intended to generate prompt texts for imaging AIs, in this case: [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion).
## 🖼️ Here's an example:
<img src="https... | null | [
"mit"
] | null | null | 137,022,720 | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67a435b8763a1c7dc2339052 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | unsloth/Qwen2.5-7B-Instruct-unsloth-bnb-4bit | unsloth | {
"models": [
{
"_id": "66e81cbcd683a3f4e5291bbf",
"id": "Qwen/Qwen2.5-7B-Instruct"
}
],
"relation": "quantized"
} | 94,597 | 3,614,280 | False | 2025-02-06T04:08:24Z | 2025-04-28T04:16:29Z | transformers | 2 | 0 | null | text-generation | {"parameters": {"F32": 379584, "BF16": 2167477760, "U8": 5618452694}, "total": 7786310038} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
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"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json... | b849156629a5c8d6e9f95ed2e4ec8bda3507938e | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"qwen",
"conversational",
"zho",
"eng",
"fra",
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"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:quantized:Qwen... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-7B-Instruct", "datasets": null, "eval_results": null, "language": ["zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": ... | <div>
<p style="margin-bottom: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/qwen-25-66fe4c08fb9ada518e8a0d3f">our collection</a> for versions of Qwen2.5 including 4-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em>Unsloth's <a href="https://unsloth.ai/blog/dynamic-... | null | [
"apache-2.0"
] | null | [
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara"
] | 7,786,310,038 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6623ee97853f526291c9f6f9 | 1ca11202486a1852 | a0b6201332bae429 | 128,257 | BPE | 280,147 | false | true | true | true | 257 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 180 | lmms-lab/llama3-llava-next-8b | lmms-lab | null | 2,783 | 3,252,831 | False | 2024-04-20T16:34:31Z | 2024-08-17T08:47:11Z | transformers | 106 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
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"model-00002-of-00004.safetensors",
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"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"preprocessor_config.json",
"special_tokens_map.... | 6d6dcb8d7948364bebef9c11aba6b0e6c8835391 | [
"transformers",
"safetensors",
"llava",
"text-generation",
"conversational",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlavaLlamaForCausalLM"], "model_type": "llava", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim +... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoProcessor"
} | null | # LLaVA Model Card
## Model Details
Model type: LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture.
Base LLM: meta-llama/Meta-Llama-3-8B-Instruct
### Model Description
**Repository:** h... | null | null | null | null | null | null | null | [
"AutoModelForCausalLM",
"LlavaLlamaForCausalLM",
"llava"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc036468d709f174c63 | 756ca39f69dfbe3f | 2501e8c4d334e1d7 | 50,272 | BPE | 50,000 | false | true | true | true | 12 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 181 | Babelscape/rebel-large | Babelscape | null | 253,188 | 11,649,328 | False | 2022-03-02T23:29:04Z | 2023-06-20T10:17:00Z | transformers | 235 | 1 | [{"name": "REBEL", "results": [{"task": {"name": "Relation Extraction", "type": "Relation-Extraction"}, "dataset": {"name": "CoNLL04", "type": "CoNLL04"}, "metrics": [{"name": "RE+ Macro F1", "type": "re+ macro f1", "value": 76.65, "verified": false}]}, {"task": {"name": "Relation Extraction", "type": "Relation-Extract... | null | {"parameters": {"F32": 406348896}, "total": 406348896} | [
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"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 44eb6cb4585df284ce6c4d6a7013f83fe473c052 | [
"transformers",
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"dataset:Babelscape/rebel-dataset",
"license:cc-by-nc-sa-4.0",
"model-index",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["BartForConditionalGeneration"], "model_type": "bart", "tokenizer_config": {"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "n... | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"datasets": ["Babelscape/rebel-dataset"], "language": ["en"], "license": "cc-by-nc-sa-4.0", "tags": ["seq2seq", "relation-extraction"], "widget": [{"text": "Punta Cana is a resort town in the municipality of Higuey, in La Altagracia Province, the eastern most province of the Dominican Republic"}], "model-index": [{"na... | [](https://paperswithcode.com/sota/relation-extraction-on-nyt?p=rebel-relation-extraction-by-end-to-end)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithco... | null | [
"cc-by-nc-sa-4.0"
] | [
"Babelscape/rebel-dataset"
] | [
"en"
] | 406,348,896 | null | null | [
"bart",
"BartForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
64c442845d0ab485fbe8121d | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | Moritz-Pfeifer/CentralBankRoBERTa-sentiment-classifier | Moritz-Pfeifer | null | 1,117 | 3,220,524 | False | 2023-07-28T22:34:44Z | 2024-11-15T05:35:14Z | transformers | 7 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"optimizer.pt",
"pytorch_model.bin",
"scheduler.pt",
"tokenizer.json",
"trainer_state.json",
"training_args.bin"
] | 5055d180f238771264d90d6cd22dc6dc479bd000 | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"finance",
"en",
"dataset:Moritz-Pfeifer/CentralBankCommunication",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta"} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["Moritz-Pfeifer/CentralBankCommunication"], "eval_results": null, "language": ["en"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-classification", "tags": ["finance"], "widget": [{"text":... | <div style="display: flex; align-items: center; gap: 10px;">
<a href="https://doi.org/10.1016/j.jfds.2023.100114">
<img src="https://img.shields.io/badge/Paper_Page-j.jfds.2023.100114-green" alt="Paper Page">
</a>
<a href="https://github.com/Moritz-Pfeifer/CentralBankRoBERTa">
<img src="https://img.shield... | null | [
"mit"
] | [
"Moritz-Pfeifer/CentralBankCommunication"
] | [
"en"
] | null | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65e09668f91fca2e4dba3c91 | c52f6cc93730d4e8 | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 120 | mixedbread-ai/mxbai-rerank-base-v1 | mixedbread-ai | null | 1,193,058 | 5,420,918 | False | 2024-02-29T14:36:24Z | 2025-04-02T14:40:30Z | transformers | 46 | 0 | null | text-ranking | {"parameters": {"F16": 184422913}, "total": 184422913} | [
".gitattributes",
"LICENSE",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"onnx/model.onnx",
"onnx/model_quantized.onnx",
"onnx/quantize_config.json",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json"
] | 800f24c113213a187e65bde9db00c15a2bb12738 | [
"transformers",
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"deberta-v2",
"text-classification",
"reranker",
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"sentence-transformers",
"text-ranking",
"en",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["DebertaV2ForSequenceClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-ranking", "tags": ["reranker", "transformers.js", "sentence-transformers"]} | null | null | [
"apache-2.0"
] | null | [
"en"
] | 184,422,913 | null | null | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
] | [
"text-ranking",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
655c096a0c27b3fb707d3db0 | c97a6495c5454551 | f26f356cd728caa8 | 49,408 | BPE | 48,894 | true | true | true | true | 2 | Sequence | Sequence | ByteLevel | [
"NFC",
"Replace",
"Lowercase"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 2 | LanguageBind/LanguageBind_Video_merge | LanguageBind | null | 23,953 | 3,263,625 | False | 2023-11-21T01:35:38Z | 2024-02-01T06:55:09Z | transformers | 5 | 0 | null | zero-shot-image-classification | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | efc40ec6ba6b2081276c11e7e19b24f08a099e79 | [
"transformers",
"pytorch",
"LanguageBindVideo",
"zero-shot-image-classification",
"arxiv:2310.01852",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["CLIPModel"], "model_type": "LanguageBindVideo", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<|startoftext|>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "no... | {
"auto_model": "AutoModelForZeroShotImageClassification",
"custom_class": null,
"pipeline_tag": "zero-shot-image-classification",
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | <p align="center">
<img src="https://s11.ax1x.com/2024/02/01/pFMDAm9.png" width="250" style="margin-bottom: 0.2;"/>
<p>
<h2 align="center"> <a href="https://arxiv.org/pdf/2310.01852.pdf">【ICLR 2024 🔥】LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment</a></h2>
<h5 ... | null | [
"mit"
] | null | null | null | null | null | [
"AutoModelForZeroShotImageClassification",
"CLIPModel",
"LanguageBindVideo"
] | [
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
621ffdc136468d709f17ba9d | 382b5a4eb98f0296 | d965e4a9140ecf72 | 21,128 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 33 | hfl/chinese-electra-180g-base-discriminator | hfl | null | 267 | 3,143,340 | False | 2022-03-02T23:29:05Z | 2021-03-03T01:26:14Z | transformers | 13 | 0 | null | null | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 693c1a7e58307777ad4cdf6b80b47c777c028572 | [
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"model_type": "electra", "tokenizer_config": {}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["zh"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accel... | null | [
"apache-2.0"
] | null | [
"zh"
] | null | null | null | [
"AutoModel",
"electra"
] | [
null
] | null | null | null |
621ffdc136468d709f17e584 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | nbroad/ESG-BERT | nbroad | null | 1,363 | 3,134,156 | False | 2022-03-02T23:29:05Z | 2023-04-26T04:50:33Z | transformers | 70 | 0 | null | text-classification | {"parameters": {"I64": 512, "F32": 109502234}, "total": 109502746} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 494c87a1d2bff7bc98cd75a3b40db1265a8567bd | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"en",
"arxiv:1910.09700",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"text": "In fiscal year 2019, we reduced our comprehensive carbon footp... | # Model Card for ESG-BERT
Domain Specific BERT Model for Text Mining in Sustainable Investing
# Model Details
## Model Description
- **Developed by:** [Mukut Mukherjee](https://www.linkedin.com/in/mukutm/), [Charan Pothireddi](https://www.linkedin.com/in/sree-charan-pothireddi-6a0a3587/) and [Parabole.ai]... | null | null | null | [
"en"
] | 109,502,746 | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc036468d709f17433d | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | google-bert/bert-large-uncased-whole-word-masking | google-bert | null | 7,624 | 3,167,220 | False | 2022-03-02T23:29:04Z | 2024-02-19T11:08:36Z | transformers | 22 | 0 | null | fill-mask | {"parameters": {"F32": 336226108}, "total": 336226108} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"pytorch_model.bin",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | bf1420893378c390773c9452c3602fcee89f9241 | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["bookcorpus", "wikipedia"], "eval_results": null, "language": "en", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # BERT large model (uncased) whole word masking
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not... | null | [
"apache-2.0"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | 336,226,108 | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17a210 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | cross-encoder/ms-marco-TinyBERT-L2 | cross-encoder | {
"models": [
{
"_id": "621ffdc136468d709f17e8f1",
"id": "nreimers/BERT-Tiny_L-2_H-128_A-2"
}
],
"relation": "quantized"
} | 27,225 | 3,274,498 | False | 2022-03-02T23:29:05Z | 2025-08-29T14:35:54Z | sentence-transformers | 21 | 0 | null | text-ranking | {"parameters": {"I64": 512, "F32": 4386049}, "total": 4386561} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/model_qint8_avx512.onnx",
"onnx/model_qint8_avx512_vnni.onn... | c9187181f395bd1e7907e7764adc9f3aa6afb26a | [
"sentence-transformers",
"pytorch",
"jax",
"onnx",
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"openvino",
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"text-classification",
"transformers",
"text-ranking",
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"base_model:nreimers/BERT-Tiny_L-2_H-128_A-2",
"base_model:quantized:nreimers/BERT-Tiny_L-2_H-128_A-2"... | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": ["nreimers/BERT-Tiny_L-2_H-128_A-2"], "datasets": ["sentence-transformers/msmarco"], "eval_results": null, "language": ["en"], "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-ranking",... | # Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a... | null | [
"apache-2.0"
] | [
"sentence-transformers/msmarco"
] | [
"en"
] | 4,386,561 | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-ranking",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc036468d709f174334 | 0c501b4f47463b4a | ad4b234fa1a3ca7b | 31,102 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 182 | google-bert/bert-base-german-dbmdz-cased | google-bert | null | 77 | 3,106,560 | False | 2022-03-02T23:29:04Z | 2024-02-19T11:03:54Z | transformers | 0 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | f14984d5ec25548212323f69adc0f39dd47336ea | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "de", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | This model is the same as [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased). See the [dbmdz/bert-base-german-cased model card](https://huggingface.co/dbmdz/bert-base-german-cased) for details on the model. | null | [
"mit"
] | null | [
"de"
] | null | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
64898274ee39dded81644503 | 2092a9ca51a9724d | c678ba3e3a4740c3 | 57,532 | Unigram | null | true | true | true | true | 15 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 183 | fxmarty/tiny-doc-qa-vision-encoder-decoder | fxmarty | null | 2,133 | 3,108,616 | False | 2023-06-14T09:03:48Z | 2023-10-17T09:09:37Z | transformers | 16 | 0 | null | document-question-answering | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | b4710bf6f394ad1770c5428e785a7789a278caf9 | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"document-question-answering",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["VisionEncoderDecoderModel"], "model_type": "vision-encoder-decoder", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": ... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "document-question-answering", "tags": null} | null | null | [
"mit"
] | null | null | null | null | null | [
"AutoModelForImageTextToText",
"vision-encoder-decoder",
"VisionEncoderDecoderModel"
] | [
"image-text-to-text",
"document-question-answering"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
63b9bd5500d568f2e64fb16d | 6ad670d2742d0236 | 5f06a539f7067498 | 50,400 | BPE | 50,000 | false | true | true | true | 144 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 112 | PygmalionAI/pygmalion-6b | PygmalionAI | null | 2,064 | 3,079,874 | False | 2023-01-07T18:43:33Z | 2023-01-13T17:53:49Z | transformers | 753 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"pytorch_model-00001-of-00002.bin",
"pytorch_model-00002-of-00002.bin",
"pytorch_model.bin.index.json",
"runs/events.out.tfevents.1672953635.uft-2.1037250.0",
"runs/events.out.tfevents.1672954410.uft-2.1044935.0",
"... | 2a0d74449c8fbf0378194e95f64aa92e16297294 | [
"transformers",
"pytorch",
"tensorboard",
"gptj",
"text-generation",
"text generation",
"conversational",
"en",
"license:creativeml-openrail-m",
"region:us"
] | null | {"architectures": ["GPTJForCausalLM"], "model_type": "gptj", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized"... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "creativeml-openrail-m", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["text generation", "conversational"], "thumbnail": null, "inference":... | # Pygmalion 6B
## Model description
Pymalion 6B is a proof-of-concept dialogue model based on EleutherAI's [GPT-J-6B](https://huggingface.co/EleutherAI/gpt-j-6B).
**Warning:** This model is **NOT** suitable for use by minors. It **will** output X-rated content under certain circumstances.
## Training data
The fine... | null | [
"creativeml-openrail-m"
] | null | [
"en"
] | null | null | null | [
"gptj",
"AutoModelForCausalLM",
"GPTJForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
675353905c3556c8ec99be8a | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | kosbu/Llama-3.3-70B-Instruct-AWQ | kosbu | {
"models": [
{
"_id": "6745f28f9333dfcc06268b1e",
"id": "meta-llama/Llama-3.3-70B-Instruct"
}
],
"relation": "quantized"
} | 483,452 | 5,120,813 | False | 2024-12-06T19:42:08Z | 2024-12-07T06:10:11Z | transformers | 10 | 0 | null | text-generation | {"parameters": {"I32": 68451041280, "F16": 2102665216}, "total": 70553706496} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00009.safetensors",
"model-00002-of-00009.safetensors",
"model-00003-of-00009.safetensors",
"model-00004-of-00009.safetensors",
"model-00005-of-00009.safetensors",
"model-00006-of-00009.safetensors",
"model-0... | ee59475f940b222132f7445947375973f3820483 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"llama-3",
"awq",
"conversational",
"en",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"de",
"base_model:meta-llama/Llama-3.3-70B-Instruct",
"base_model:quantized:meta-llama/Llama-3.3-70B-Instruct",
"license:... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "awq"}, "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_u... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["meta-llama/Llama-3.3-70B-Instruct"], "datasets": null, "eval_results": null, "language": ["en", "fr", "it", "pt", "hi", "es", "th", "de"], "library_name": "transformers", "license": "llama3.3", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-genera... | # Llama-3.3-70B-Instruct AWQ 4-Bit Quantized Version
This repository provides the AWQ 4-bit quantized version of [`meta-llama/Llama-3.3-70B-Instruct`](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct), originally developed by Meta AI. | null | [
"llama3.3"
] | null | [
"en",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"de"
] | 70,553,706,496 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6529ff4162a48851895da120 | 8d76fe7b80771b69 | 3f5617f282b50422 | 30,521 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 184 | naufalihsan/indonesian-sbert-large | naufalihsan | null | 10,340 | 3,104,749 | False | 2023-10-14T02:38:57Z | 2023-10-22T04:00:55Z | sentence-transformers | 8 | 0 | null | sentence-similarity | null | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"eval/similarity_evaluation_sts-dev_results.csv",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"similarity_evaluation_sts-test_results.csv",
"special_tokens_map.json",... | a5cbfbd8f6fa9ecac98896a8e14ea7b879a41e2d | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "tr... | # {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | null | null | null | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
65ae9d8c1dac7b5be47ef381 | 34093184d10f76a9 | 35b001f3a491f9f9 | 151,646 | BPE | 151,387 | true | true | true | true | 3 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 53 | Qwen/Qwen1.5-1.8B | Qwen | null | 25,036 | 3,115,083 | False | 2024-01-22T16:53:32Z | 2024-04-05T10:39:41Z | transformers | 58 | 0 | null | text-generation | {"parameters": {"BF16": 1836828672}, "total": 1836828672} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 7846de7ed421727b318d6605a0bfab659da2c067 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"pretrained",
"conversational",
"en",
"arxiv:2309.16609",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "other", "license_name": "tongyi-qianwen-research", "license_link": "https://huggingface.co/Qwen/Qwen1.5-1.8B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tag... | # Qwen1.5-1.8B
## Introduction
Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
* 8 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B and 72B dense models, and an MoE... | null | [
"other",
"tongyi-qianwen-research",
"https://huggingface.co/Qwen/Qwen1.5-1.8B/blob/main/LICENSE"
] | null | [
"en"
] | 1,836,828,672 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64b76532a5018e3c7c97b10e | 6949732424a8d297 | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 42 | TinyPixel/Llama-2-7B-bf16-sharded | TinyPixel | null | 683 | 3,018,939 | False | 2023-07-19T04:23:14Z | 2023-12-18T08:34:55Z | transformers | 74 | 0 | null | text-generation | {"parameters": {"F32": 2048, "BF16": 6738415616}, "total": 6738417664} | [
".gitattributes",
"config.json",
"generation_config.json",
"model-00001-of-00014.safetensors",
"model-00002-of-00014.safetensors",
"model-00003-of-00014.safetensors",
"model-00004-of-00014.safetensors",
"model-00005-of-00014.safetensors",
"model-00006-of-00014.safetensors",
"model-00007-of-00014.s... | 3f5d08bf8c31192686e3e88d0b9d2cdeff4115e4 | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": true, "rstrip":... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | 6,738,417,664 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67f7f455f5630353dc3a9a4b | b647996d6543f730 | 7ab62ce4e32550df | 151,674 | BPE | 151,387 | true | true | true | true | 31 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 172 | OpenGVLab/InternVL3-78B | OpenGVLab | {
"models": [
{
"_id": "67ff92b1076c67c2283306a3",
"id": "OpenGVLab/InternVL3-78B-Instruct"
}
],
"relation": "finetune"
} | 40,799 | 6,202,442 | False | 2025-04-10T16:39:49Z | 2025-09-11T12:51:29Z | transformers | 233 | 0 | null | image-text-to-text | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"configuration_intern_vit.py",
"configuration_internvl_chat.py",
"conversation.py",
"examples/image1.jpg",
"examples/image2.jpg",
"examples/red-panda.mp4",
"generation_config.json",
"merges.txt",
"model-00001-of-00033.safeten... | 4a42b163cc0e222e97b10b562eee98da6bf26077 | [
"transformers",
"safetensors",
"internvl_chat",
"feature-extraction",
"internvl",
"custom_code",
"image-text-to-text",
"conversational",
"multilingual",
"dataset:OpenGVLab/MMPR-v1.2",
"arxiv:2312.14238",
"arxiv:2404.16821",
"arxiv:2412.05271",
"arxiv:2411.10442",
"arxiv:2504.10479",
"a... | null | {"architectures": ["InternVLChatModel"], "auto_map": {"AutoConfig": "configuration_internvl_chat.InternVLChatConfig", "AutoModel": "modeling_internvl_chat.InternVLChatModel", "AutoModelForCausalLM": "modeling_internvl_chat.InternVLChatModel"}, "model_type": "internvl_chat", "tokenizer_config": {"bos_token": null, "chat... | {
"auto_model": "AutoModel",
"custom_class": "modeling_internvl_chat.InternVLChatModel",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"base_model": ["OpenGVLab/InternVL3-78B-Instruct"], "datasets": ["OpenGVLab/MMPR-v1.2"], "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": "other", "license_name": "qwen", "license_link": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE", "metrics": null,... | # InternVL3-78B
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271) [\[📜 InternVL2.5-MPO\]](https://huggingface.co/pape... | null | [
"other",
"qwen",
"https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE"
] | [
"OpenGVLab/MMPR-v1.2"
] | [
"multilingual"
] | null | null | null | [
"AutoModel",
"internvl_chat",
"InternVLChatModel",
"modeling_internvl_chat.InternVLChatModel"
] | [
"image-text-to-text",
"feature-extraction"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text",
"embeddings"
] |
621ffdc136468d709f17bafc | 5914e4a1a2e6b800 | b15dbf58509420e7 | 31,002 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 185 | hiiamsid/sentence_similarity_spanish_es | hiiamsid | null | 228,659 | 3,669,975 | False | 2022-03-02T23:29:05Z | 2024-06-20T05:46:33Z | sentence-transformers | 53 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 109850880}, "total": 109851392} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"eval/readme.md",
"eval/similarity_evaluation_sts-dev_results.csv",
"model.safetensors",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"similarity_evaluation_stsb-mul... | 66ab46adac3910bb6ea6085b962a25e49513b981 | [
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"es",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["es"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-simila... | # hiiamsid/sentence_similarity_spanish_es
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using thi... | null | [
"apache-2.0"
] | null | [
"es"
] | 109,851,392 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f17a21c | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | cross-encoder/nli-distilroberta-base | cross-encoder | {
"models": [
{
"_id": "621ffdc036468d709f174349",
"id": "distilbert/distilroberta-base"
}
],
"relation": "quantized"
} | 20,077 | 3,055,843 | False | 2022-03-02T23:29:05Z | 2025-04-11T09:02:20Z | sentence-transformers | 26 | 0 | null | zero-shot-classification | {"parameters": {"I64": 514, "F32": 82120707}, "total": 82121221} | [
".gitattributes",
"CESoftmaxAccuracyEvaluator_AllNLI-dev_results.csv",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"model.safetensors",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx"... | b14d131f9d32668a5e6a982729b57ff6ed5dfcbd | [
"sentence-transformers",
"pytorch",
"jax",
"onnx",
"safetensors",
"openvino",
"roberta",
"text-classification",
"transformers",
"zero-shot-classification",
"en",
"dataset:nyu-mll/multi_nli",
"dataset:stanfordnlp/snli",
"base_model:distilbert/distilroberta-base",
"base_model:quantized:dis... | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": ["distilbert/distilroberta-base"], "datasets": ["nyu-mll/multi_nli", "stanfordnlp/snli"], "eval_results": null, "language": "en", "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": null, "pipeline_tag": "zer... | null | null | [
"apache-2.0"
] | [
"nyu-mll/multi_nli",
"stanfordnlp/snli"
] | [
"en"
] | 82,121,221 | null | [
"accuracy"
] | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66e81cefd1b1391042d0e47e | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-72B-Instruct | Qwen | {
"models": [
{
"_id": "66e6d0a6126f9577f878c2e9",
"id": "Qwen/Qwen2.5-72B"
}
],
"relation": "finetune"
} | 683,222 | 5,494,315 | False | 2024-09-16T11:56:31Z | 2025-01-12T02:07:38Z | transformers | 922 | 2 | null | text-generation | {"parameters": {"BF16": 72706203648}, "total": 72706203648} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00037.safetensors",
"model-00002-of-00037.safetensors",
"model-00003-of-00037.safetensors",
"model-00004-of-00037.safetensors",
"model-00005-of-00037.safetensors",
"model-00006-of-0... | 495f39366efef23836d0cfae4fbe635880d2be31 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"arxiv:2309.00071",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-72B",
"base_model:finetune:Qwen/Qwen2.5-72B",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Aliba... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-72B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": "qwen", "license_link": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-gene... | # Qwen2.5-72B-Instruct
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
Qwen2.5 is the latest series of Qwen large l... | null | [
"other",
"qwen",
"https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | 72,706,203,648 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64d3ca7289c71fbc8b9cd0b6 | e23b6bdbe047bb7e | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | h2oai/h2ogpt-4096-llama2-7b-chat | h2oai | null | 1,179 | 2,996,841 | False | 2023-08-09T17:18:42Z | 2023-08-24T18:35:05Z | transformers | 15 | 0 | null | text-generation | {"parameters": {"F16": 6738417664}, "total": 6738417664} | [
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": false, "rstrip... | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["facebook", "meta", "pytorch", "llama", "llama-2", "h2ogpt"], "inferenc... | h2oGPT clone of [Meta's Llama 2 7B Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf).
Try it live on our [h2oGPT demo](https://gpt.h2o.ai) with side-by-side LLM comparisons and private document chat!
See how it compares to other models on our [LLM Leaderboard](https://evalgpt.ai/)!
See more at [H2O.ai](htt... | null | [
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"en"
] | 6,738,417,664 | null | null | [
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6623f2c68fee5ba324a5fecc | a9ba6522a506ea59 | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
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"Metaspace"
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"models": [
{
"_id": "621ffdc136468d709f17dee4",
"id": "microsoft/deberta-v3-base"
}
],
"relation": "quantized"
} | 436,329 | 4,305,262 | False | 2024-04-20T16:52:22Z | 2024-05-28T07:07:49Z | transformers | 104 | 3 | [{"name": "deberta-v3-base-prompt-injection-v2", "results": []}] | text-classification | {"parameters": {"F32": 184423682}, "total": 184423682} | [
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... | null | {"architectures": ["DebertaV2ForSequenceClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
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} | {"base_model": "microsoft/deberta-v3-base", "datasets": ["natolambert/xstest-v2-copy", "VMware/open-instruct", "alespalla/chatbot_instruction_prompts", "HuggingFaceH4/grok-conversation-harmless", "Harelix/Prompt-Injection-Mixed-Techniques-2024", "OpenSafetyLab/Salad-Data", "jackhhao/jailbreak-classification"], "eval_re... | # Model Card for deberta-v3-base-prompt-injection-v2
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) specifically developed to detect and classify prompt injection attacks which can manipulate language models into producing unintended outputs.
## Int... | null | [
"apache-2.0"
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"alespalla/chatbot_instruction_prompts",
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"Harelix/Prompt-Injection-Mixed-Techniques-2024",
"OpenSafetyLab/Salad-Data",
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"en"
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64fd4022e0dc35986bd9d402 | 83bf28f8b6767a2f | ae88e903e2b563a1 | 50,295 | BPE | 50,000 | false | true | true | true | 39 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 85 | microsoft/phi-1_5 | microsoft | null | 105,058 | 3,376,387 | False | 2023-09-10T04:03:46Z | 2025-11-24T16:58:09Z | transformers | 1,355 | 1 | null | text-generation | {"parameters": {"F16": 1418270720}, "total": 1418270720} | [
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"merges.txt",
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"vo... | 77aa61eeac94fbf33d492b9f2744c98b42d5b5eb | [
"transformers",
"safetensors",
"phi",
"text-generation",
"nlp",
"code",
"en",
"arxiv:2309.05463",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
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] | null | {"architectures": ["PhiForCausalLM"], "model_type": "phi", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "mit", "license_name": null, "license_link": "https://huggingface.co/microsoft/phi-1_5/resolve/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["nlp", "code"]} | ## Model Summary
The language model Phi-1.5 is a Transformer with **1.3 billion** parameters. It was trained using the same data sources as [phi-1](https://huggingface.co/microsoft/phi-1), augmented with a new data source that consists of various NLP synthetic texts. When assessed against benchmarks testing common sen... | null | [
"mit",
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] | null | [
"en"
] | 1,418,270,720 | null | null | [
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621ffdc036468d709f1753d7 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | Elron/bleurt-tiny-512 | Elron | null | 52,031 | 3,157,036 | False | 2022-03-02T23:29:04Z | 2022-11-26T15:13:43Z | transformers | 4 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
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] | 82430b75718a0647fc49b9216ccdd9f0b30dfa72 | [
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"bert",
"text-classification",
"arxiv:1910.09700",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["text-classification", "bert"]} | # Model Card for bleurt-tiny-512
# Model Details
## Model Description
Pytorch version of the original BLEURT models from ACL paper
- **Developed by:** Elron Bandel, Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research
- **Shared by [Optional]:** Elron Bandel
- **Model type:** Text Classificatio... | null | null | null | null | null | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
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621ffdc036468d709f1744de | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | ActivationAI/distilbert-base-uncased-finetuned-emotion | ActivationAI | null | 14 | 2,930,916 | False | 2022-03-02T23:29:04Z | 2022-03-02T03:40:08Z | transformers | 1 | 0 | [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"name": "Accuracy", "type": "accuracy", "value": 0.928, "verified": false}, {"name": "F1", "type"... | text-classification | null | [
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"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"datasets": ["emotion"], "license": "apache-2.0", "metrics": ["accuracy", "f1"], "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | <!-- 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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | null | [
"apache-2.0"
] | [
"emotion"
] | null | null | null | [
"accuracy",
"f1"
] | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62afdb861f7044b25f1b629a | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | ElKulako/cryptobert | ElKulako | null | 160,342 | 3,766,389 | False | 2022-06-20T02:29:26Z | 2025-05-26T11:52:00Z | transformers | 186 | 1 | null | text-classification | {"parameters": {"I64": 514, "F32": 124647939}, "total": 124648453} | [
".gitattributes",
"README.md",
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"merges.txt",
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"pytorch_model.bin",
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] | 9e37c910fe87727cb842a9ac55c6388256fe0f15 | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"cryptocurrency",
"crypto",
"BERT",
"sentiment classification",
"NLP",
"bitcoin",
"ethereum",
"shib",
"social media",
"sentiment analysis",
"cryptocurrency sentiment analysis",
"en",
"dataset:ElKulako/stock... | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "cls_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized"... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["ElKulako/stocktwits-crypto"], "eval_results": null, "language": ["en"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["cryptocurrency", "crypto", "BERT", "sentiment classification... | For academic reference, cite the following paper: https://ieeexplore.ieee.org/document/10223689
# CryptoBERT
CryptoBERT is a pre-trained NLP model to analyse the language and sentiments of cryptocurrency-related social media posts and messages. It was built by further training the [vinai's bertweet-base](https://huggi... | null | [
"mit"
] | [
"ElKulako/stocktwits-crypto"
] | [
"en"
] | 124,648,453 | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
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660bd08f9ca5bf23dfb708a9 | 6950482313cc6dd9 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 68 | MoritzLaurer/bge-m3-zeroshot-v2.0 | MoritzLaurer | {
"models": [
{
"_id": "65b62fc2fec79c744516d3ee",
"id": "BAAI/bge-m3-retromae"
}
],
"relation": "quantized"
} | 53,856 | 3,185,516 | False | 2024-04-02T09:31:59Z | 2024-04-22T11:09:02Z | transformers | 61 | 1 | null | zero-shot-classification | {"parameters": {"F16": 567756802}, "total": 567756802} | [
".gitattributes",
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"sentencepiece... | 9abf1c8aaeb82a2447809c20753ed0b106b76652 | [
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"multilingual",
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"base_model:quantized:BAAI/bge-m3-retromae",
"license:mit",
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] | null | {"architectures": ["XLMRobertaForSequenceClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "BAAI/bge-m3-retromae", "datasets": null, "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "zero-shot-classification", "tags": ["text-classification", "zer... | # Model description: bge-m3-zeroshot-v2.0
## zeroshot-v2.0 series of models
Models in this series are designed for efficient zeroshot classification with the Hugging Face pipeline.
These models can do classification without training data and run on both GPUs and CPUs.
An overview of the latest zeroshot classifiers ... | null | [
"mit"
] | null | [
"multilingual"
] | 567,756,802 | null | null | [
"AutoModelForSequenceClassification",
"XLMRobertaForSequenceClassification",
"xlm-roberta"
] | [
"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
63e2682e1c0d033a0fa1efbd | 2fdb1eebc803cc77 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 2 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 186 | Salesforce/blip2-opt-2.7b-coco | Salesforce | null | 372,370 | 4,567,387 | False | 2023-02-07T15:03:10Z | 2025-02-03T06:40:11Z | transformers | 11 | 0 | null | image-to-text | {"parameters": {"F32": 3873967616}, "total": 3873967616} | [
".gitattributes",
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... | cda95b9319b722f79c9451a3d8ff92eea02048dc | [
"transformers",
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"vision",
"image-to-text",
"image-captioning",
"en",
"arxiv:2301.12597",
"license:mit",
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] | null | {"architectures": ["Blip2ForConditionalGeneration"], "model_type": "blip-2", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": ... | {
"auto_model": "AutoModelForVisualQuestionAnswering",
"custom_class": null,
"pipeline_tag": "visual-question-answering",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-to-text", "tags": ["vision", "image-to-text", "image-captioning", "visual-question-answering"], "... | # BLIP-2, OPT-2.7b, fine-tuned on COCO
BLIP-2 model, leveraging [OPT-2.7b](https://huggingface.co/facebook/opt-2.7b) (a large language model with 2.7 billion parameters).
It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxi... | null | [
"mit"
] | null | [
"en"
] | 3,873,967,616 | null | null | [
"Blip2ForConditionalGeneration",
"AutoModelForVisualQuestionAnswering",
"blip-2"
] | [
"visual-question-answering",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits",
"text"
] |
63314e6a4bd187e9ce03f707 | dda72719c593cc4c | 539cc58b8fdaf772 | 51,864 | BPE | 50,000 | false | true | true | true | 1,608 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 55 | openai/whisper-medium.en | openai | null | 39,341 | 3,117,480 | False | 2022-09-26T07:02:02Z | 2024-01-22T17:55:36Z | transformers | 57 | 0 | [{"name": "whisper-medium.en", "results": [{"task": {"name": "Automatic Speech Recognition", "type": "automatic-speech-recognition"}, "dataset": {"name": "LibriSpeech (clean)", "type": "librispeech_asr", "config": "clean", "split": "test", "args": {"language": "en"}}, "metrics": [{"name": "Test WER", "type": "wer", "va... | automatic-speech-recognition | {"parameters": {"F32": 763856896}, "total": 763856896} | [
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"tf_model.h5",
"tokenizer.json",
"tokenizer... | 2e98eb6279edf5095af0c8dedb36bdec0acd172b | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"whisper",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"en",
"arxiv:2212.04356",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["WhisperForConditionalGeneration"], "model_type": "whisper", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": ... | {
"auto_model": "AutoModelForSpeechSeq2Seq",
"custom_class": null,
"pipeline_tag": "automatic-speech-recognition",
"processor": "AutoProcessor"
} | {"language": ["en"], "license": "apache-2.0", "pipeline_tag": "automatic-speech-recognition", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispee... | # Whisper
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours
of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need
for fine-tuning.
Whisper was proposed in the paper [Robust Speec... | null | [
"apache-2.0"
] | null | [
"en"
] | 763,856,896 | null | null | [
"WhisperForConditionalGeneration",
"whisper",
"AutoModelForSpeechSeq2Seq"
] | [
"automatic-speech-recognition"
] | [
"multimodal"
] | [
"audio"
] | [
"text"
] |
6836db82a3626cb7b5343be8 | a54a9c2e7b95f458 | ef43ab3563390dbf | 128,815 | BPE | 127,741 | true | true | true | true | 818 | Sequence | Sequence | ByteLevel | [] | [
"Split",
"Split",
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 84 | deepseek-ai/DeepSeek-R1-0528 | deepseek-ai | null | 814,464 | 5,949,060 | False | 2025-05-28T09:46:42Z | 2025-05-29T11:37:44Z | transformers | 2,410 | 3 | null | text-generation | {"parameters": {"BF16": 3918786560, "F8_E4M3": 680571043840, "F32": 41555600}, "total": 684531386000} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"configuration_deepseek.py",
"figures/benchmark.png",
"generation_config.json",
"model-00001-of-000163.safetensors",
"model-00002-of-000163.safetensors",
"model-00003-of-000163.safetensors",
"model-00004-of-000163.safetensors",
"model-00... | 4236a6af538feda4548eca9ab308586007567f52 | [
"transformers",
"safetensors",
"deepseek_v3",
"text-generation",
"conversational",
"custom_code",
"arxiv:2501.12948",
"license:mit",
"eval-results",
"text-generation-inference",
"endpoints_compatible",
"fp8",
"region:us"
] | null | {"architectures": ["DeepseekV3ForCausalLM"], "auto_map": {"AutoConfig": "configuration_deepseek.DeepseekV3Config", "AutoModel": "modeling_deepseek.DeepseekV3Model", "AutoModelForCausalLM": "modeling_deepseek.DeepseekV3ForCausalLM"}, "model_type": "deepseek_v3", "quantization_config": {"quant_method": "fp8"}, "tokenizer... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # DeepSeek-R1-0528
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
</div>
<hr>
<div... | null | [
"mit"
] | null | null | 684,531,386,000 | null | null | [
"AutoModelForCausalLM",
"deepseek_v3",
"DeepseekV3ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63e3f73a27da6c56511c6ccc | 58967ba68263f0aa | 2f8d5857ae5fa330 | 50,277 | BPE | 50,009 | true | true | true | true | 25 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 86 | EleutherAI/pythia-160m | EleutherAI | null | 2,551,849 | 7,715,300 | False | 2023-02-08T19:25:46Z | 2023-07-09T15:52:09Z | transformers | 39 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 50f5173d932e8e61f858120bcb800b97af589f46 | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/pile",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"reg... | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["EleutherAI/pile"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["pytorch", "causal-lm", "pythia"]} | The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and ... | null | [
"apache-2.0"
] | [
"EleutherAI/pile"
] | [
"en"
] | null | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
648896c641ce22b04beaaa13 | f56392a3c3b561ef | f26f356cd728caa8 | 49,408 | BPE | 48,894 | true | true | true | true | 2 | Sequence | Sequence | ByteLevel | [
"NFC",
"Replace",
"Lowercase"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 7 | flaviagiammarino/pubmed-clip-vit-base-patch32 | flaviagiammarino | null | 20,418 | 3,015,011 | False | 2023-06-13T16:18:14Z | 2023-12-28T12:36:18Z | transformers | 23 | 0 | null | zero-shot-image-classification | null | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"preprocessor_config.json",
"pytorch_model.bin",
"scripts/flax_example.py",
"scripts/input.jpeg",
"scripts/output.jpeg",
"scripts/pt_example.py",
"scripts/pt_model.py",
"scripts/tf_example.py",
"special_token... | 26c0c67f6da303ad2a38909130bd35744ea93517 | [
"transformers",
"pytorch",
"tf",
"jax",
"clip",
"zero-shot-image-classification",
"medical",
"vision",
"en",
"arxiv:2112.13906",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["CLIPModel"], "model_type": "clip", "tokenizer_config": {"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|startoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "... | {
"auto_model": "AutoModelForZeroShotImageClassification",
"custom_class": null,
"pipeline_tag": "zero-shot-image-classification",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["medical", "vision"], "widget": [{"src": "https://huggingface.co/flaviagiammarino/pubme... | # Model Card for PubMedCLIP
PubMedCLIP is a fine-tuned version of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for the medical domain.
## Model Description
PubMedCLIP was trained on the [Radiology Objects in COntext (ROCO)](https://github.com/razorx89/roco-dataset) dataset, a large-scale multimodal... | null | [
"mit"
] | null | [
"en"
] | null | null | null | [
"AutoModelForZeroShotImageClassification",
"CLIPModel",
"clip"
] | [
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
621ffdc136468d709f180625 | ddacdaddb2d34550 | 0602b91d20d6a709 | 51,200 | BPE | 42,185 | true | true | false | true | 375 | Sequence | Sequence | Metaspace | [
"NFKC",
"BertNormalizer"
] | [
"Metaspace"
] | [
"Metaspace"
] | 187 | skt/kogpt2-base-v2 | skt | null | 633,385 | 4,466,461 | False | 2022-03-02T23:29:05Z | 2021-09-23T16:29:28Z | transformers | 61 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"tokenizer.json"
] | d0c0df48bf2b2c9350dd855021a5b216f560c0c7 | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"ko",
"license:cc-by-nc-sa-4.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2"} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "ko", "library_name": null, "license": "cc-by-nc-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["gpt2"]} | For more details: https://github.com/SKT-AI/KoGPT2 | null | [
"cc-by-nc-sa-4.0"
] | null | [
"ko"
] | null | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f1802ad | 71140a663949f258 | 8a01c02d4d0a401c | 119,547 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 9 | sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking | sentence-transformers | null | 5,563 | 2,909,985 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:37:33Z | sentence-transformers | 9 | 0 | null | sentence-similarity | {"parameters": {"F32": 134734080}, "total": 134734080} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/m... | 32df3a23644e4247e9006aa7eaeb82be8ee7b3f8 | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["DistilBertModel"], "model_type": "distilbert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this mod... | null | [
"apache-2.0"
] | null | null | 134,734,080 | null | null | [
"AutoModel",
"distilbert",
"DistilBertModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
658d815deaba17684e9feb04 | e23b6bdbe047bb7e | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T | TinyLlama | null | 50,492 | 3,079,388 | False | 2023-12-28T14:08:29Z | 2024-09-27T22:46:12Z | transformers | 186 | 0 | [{"name": "TinyLlama-1.1B-intermediate-step-1431k-3T", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "val... | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 59f6f375b26bde864a6ca194a9a3044570490064 | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"license:apache-2.0",
"model-index",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": false, "rstrip... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"datasets": ["cerebras/SlimPajama-627B", "bigcode/starcoderdata"], "language": ["en"], "license": "apache-2.0", "model-index": [{"name": "TinyLlama-1.1B-intermediate-step-1431k-3T", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "ty... | <div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-0... | null | [
"apache-2.0"
] | [
"cerebras/SlimPajama-627B",
"bigcode/starcoderdata"
] | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64b6ce072edcb4a1aa820cbe | 6949732424a8d297 | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 42 | TheBloke/Llama-2-7B-Chat-GPTQ | TheBloke | {
"models": [
{
"_id": "64b02a230c8415cd4a159fff",
"id": "meta-llama/Llama-2-7b-chat-hf"
}
],
"relation": "quantized"
} | 22,684 | 2,949,601 | False | 2023-07-18T17:38:15Z | 2023-09-27T12:44:48Z | transformers | 267 | 0 | null | text-generation | {"parameters": {"F32": 2048, "I32": 6476005376, "F16": 263770112}, "total": 6739777536} | [
".gitattributes",
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"license:llama2",
"text-generation-inference",
"4-bit",
"gptq... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "cont... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Llama-2-7b-chat-hf", "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": "Llama 2 7B Chat", "pipeline_tag": "text-generation", "tags": ["facebook", "meta", "pytorch", ... | <!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<d... | null | [
"llama2"
] | null | [
"en"
] | 6,739,777,536 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
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] | [
"text"
] | [
"text"
] |
621ffdc036468d709f174343 | 0c501b4f47463b4a | ad4b234fa1a3ca7b | 31,102 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 182 | distilbert/distilbert-base-german-cased | distilbert | null | 275,900 | 4,175,308 | False | 2022-03-02T23:29:04Z | 2024-05-06T13:46:46Z | transformers | 25 | 0 | null | fill-mask | {"parameters": {"F32": 67431550}, "total": 67431550} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 6f96562de9886779ce7d48810dff8f5c4afb6d0b | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"de",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DistilBertForMaskedLM"], "model_type": "distilbert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "de", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | null | null | [
"apache-2.0"
] | null | [
"de"
] | 67,431,550 | null | null | [
"distilbert",
"AutoModelForMaskedLM",
"DistilBertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17defc | be7f2c5798d7f779 | 84fb34b2e1636fc5 | 30,527 | WordPiece | null | true | true | true | true | 6 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 188 | microsoft/mpnet-base | microsoft | null | 148,754 | 3,200,489 | False | 2022-03-02T23:29:05Z | 2024-02-29T09:53:52Z | transformers | 48 | 0 | null | fill-mask | {"parameters": {"F32": 132993790}, "total": 132993790} | [
".gitattributes",
"config.json",
"model.safetensors",
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We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing
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] | null | {"architectures": ["Kosmos2ForConditionalGeneration"], "model_type": "kosmos-2", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
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<a href="https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" target="_blank"><figure><img src="https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" width="384"><figcaption><b>[... | null | [
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"dat... | null | {"architectures": ["MiniCPMO"], "auto_map": {"AutoConfig": "configuration_minicpm.MiniCPMOConfig", "AutoModel": "modeling_minicpmo.MiniCPMO", "AutoModelForCausalLM": "modeling_minicpmo.MiniCPMO"}, "model_type": "minicpmo", "tokenizer_config": {"bos_token": "<|im_start|>", "chat_template": "{%- if tools %}\n {{- '<|i... | {
"auto_model": "AutoModel",
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[GitHub](https://github.com/OpenBMB/MiniCPM-o) | [Online Demo](https://minicpm-omni-webdemo-us.modelbest.cn) | [Technical Blog](https://openbmb.notion.site/MiniCPM-o-2-6-A-GPT-4o-Level-MLLM-for-Vision-Speech-and-Multimodal-Live... | null | [
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} | {"base_model": "meta-llama/Llama-3.3-70B-Instruct", "datasets": null, "eval_results": null, "language": ["en", "fr", "it", "pt", "hi", "es", "th", "de"], "library_name": "transformers", "license": "llama3.3", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ... | # mlx-community/Llama-3.3-70B-Instruct-4bit
The Model [mlx-community/Llama-3.3-70B-Instruct-4bit](https://huggingface.co/mlx-community/Llama-3.3-70B-Instruct-4bit) was
converted to MLX format from [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct)
using mlx-lm version **0.20.... | null | [
"llama3.3"
] | null | [
"en",
"fr",
"it",
"pt",
"hi",
"es",
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] | 11,025,129,472 | null | null | [
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621ffdc136468d709f17da61 | 191e06b04083279c | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
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should probably proofread and complete it, then remove this comment. -->
# distilbert-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) o... | null | [
"apache-2.0"
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],
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} | 2,769 | 2,877,070 | False | 2024-09-20T13:32:03Z | 2024-10-25T11:20:21Z | transformers | 138 | 0 | null | text-generation | {"parameters": {"BF16": 12247802880}, "total": 12247802880} | [
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## Vikhr-Nemo-12B-Instruct-R-21-09-24
### Описание
**Vikhr-Nemo** - это наша флагманская унимодальная LLM (Large Language Model) представляющая из себя улучшенную версию [mistralai/Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) командой **... | null | [
"apache-2.0"
] | [
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"Vikhrmodels/Grounded-RAG-RU-v2"
] | [
"en",
"ru"
] | 12,247,802,880 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
669446eef658d6ee957e2475 | bafc5e0c20be7cd8 | 82d3db801e5d7f68 | 49,152 | BPE | 48,900 | false | true | false | true | 17 | null | Sequence | ByteLevel | null | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 115 | HuggingFaceTB/SmolLM-135M | HuggingFaceTB | null | 184,580 | 4,075,211 | False | 2024-07-14T21:45:18Z | 2024-08-01T20:12:34Z | transformers | 254 | 0 | null | text-generation | {"parameters": {"F32": 134515008}, "total": 134515008} | [
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"onnx/model_int8.onnx",
"onnx/model_q4.onnx",
"onnx/model_quantized.onnx",
"onnx/model_uint8.onnx",
"special_tokens_... | 1d461723eec654e65efdc40cf49301c89c0c92f4 | [
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"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["HuggingFaceTB/smollm-corpus"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # SmolLM
<center>
<img src="https://huggingface.co/datasets/HuggingFaceTB/images/resolve/main/banner_smol.png" alt="SmolLM" width="1100" height="600">
</center>
## Table of Contents
1. [Model Summary](##model-summary)
2. [Limitations](##limitations)
3. [Training](##training)
4. [License](##license)
5. [Citation... | null | [
"apache-2.0"
] | [
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] | [
"en"
] | 134,515,008 | null | null | [
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] | [
"Split"
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"Replace",
"ByteFallback",
"Fuse"
] | 50 | google/gemma-2-27b-it | google | {
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} | 345,422 | 4,910,682 | manual | 2024-06-24T08:05:04Z | 2024-08-27T19:41:54Z | transformers | 560 | 0 | null | text-generation | {"parameters": {"BF16": 27227128320}, "total": 27227128320} | [
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"arxiv:1907.10641",
"arxiv:1811.00937",
"arxiv:1809.02789",
"arxiv:1911.01547",
"arxiv:1705.03551",
"arxiv:2... | null | {"architectures": ["Gemma2ForCausalLM"], "model_type": "gemma2", "tokenizer_config": {"bos_token": "<bos>", "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "google/gemma-2-27b", "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "gemma", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": null, "extra_gated_heading": "Access Gemma on Hug... | null | null | [
"gemma"
] | null | null | 27,227,128,320 | null | null | [
"AutoModelForCausalLM",
"gemma2",
"Gemma2ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6548e839bd9211fee7239add | 5afd7d59d2aa5e01 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 125 | sentence-transformers-testing/stsb-bert-tiny-safetensors | sentence-transformers-testing | null | 808,941 | 4,858,387 | False | 2023-11-06T13:20:57Z | 2024-01-17T11:47:16Z | sentence-transformers | 4 | 0 | null | sentence-similarity | {"parameters": {"F32": 4385920}, "total": 4385920} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | f3cb857cba53019a20df283396bcca179cf051a4 | [
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "senten... | # sentence-transformers-testing/stsb-bert-tiny-safetensors
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transfo... | null | null | null | null | 4,385,920 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
67e125b6c185423c1e404364 | 4d197a7691fb4d20 | 1b54110df6c0848b | 128,815 | BPE | 127,741 | true | true | true | true | 818 | Sequence | Sequence | ByteLevel | [] | [
"Split",
"Split",
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 94 | deepseek-ai/DeepSeek-V3-0324 | deepseek-ai | null | 377,423 | 4,057,700 | False | 2025-03-24T09:28:22Z | 2025-03-27T04:01:53Z | transformers | 3,093 | 3 | null | text-generation | {"parameters": {"BF16": 3918786560, "F8_E4M3": 680571043840, "F32": 41555600}, "total": 684531386000} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"configuration_deepseek.py",
"figures/0324_comparison.png",
"model-00001-of-000163.safetensors",
"model-00002-of-000163.safetensors",
"model-00003-of-000163.safetensors",
"model-00004-of-000163.safetensors",
"model-00005-of-000163.safetens... | e9b33add76883f293d6bf61f6bd89b497e80e335 | [
"transformers",
"safetensors",
"deepseek_v3",
"text-generation",
"conversational",
"custom_code",
"arxiv:2412.19437",
"license:mit",
"eval-results",
"text-generation-inference",
"endpoints_compatible",
"fp8",
"region:us"
] | null | {"architectures": ["DeepseekV3ForCausalLM"], "auto_map": {"AutoConfig": "configuration_deepseek.DeepseekV3Config", "AutoModel": "modeling_deepseek.DeepseekV3Model", "AutoModelForCausalLM": "modeling_deepseek.DeepseekV3ForCausalLM"}, "model_type": "deepseek_v3", "quantization_config": {"quant_method": "fp8"}, "tokenizer... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # DeepSeek-V3-0324
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
</div>
<hr>
<div align="c... | null | [
"mit"
] | null | null | 684,531,386,000 | null | null | [
"AutoModelForCausalLM",
"deepseek_v3",
"DeepseekV3ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63817cb4fb49cd1c12096127 | 2d0341bd3a73bae6 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 32 | pszemraj/flan-t5-large-grammar-synthesis | pszemraj | null | 2,177 | 2,828,585 | False | 2022-11-26T02:40:52Z | 2025-12-29T03:41:10Z | transformers | 93 | 0 | null | null | {"parameters": {"F32": 783150080}, "total": 783150080} | [
".gitattributes",
".gitignore",
"README.md",
"config.json",
"ggml-model-Q6_K.gguf",
"model.safetensors",
"onnx/model.onnx",
"onnx/onnx__MatMul_7105",
"onnx/onnx__MatMul_7115",
"onnx/onnx__MatMul_7116",
"onnx/onnx__MatMul_7119",
"onnx/onnx__MatMul_7120",
"onnx/onnx__MatMul_7121",
"onnx/onnx... | 924f44b5b7403d0727880a72f45714f0a70379a0 | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"gguf",
"t5",
"text2text-generation",
"grammar",
"spelling",
"punctuation",
"error-correction",
"grammar synthesis",
"FLAN",
"dataset:jfleg",
"arxiv:2107.06751",
"doi:10.57967/hf/0138",
"license:cc-by-nc-sa-4.0",
"license:apache-2.0... | {"total": 783150080, "architecture": "t5", "context_length": 512, "eos_token": "</s>"} | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["jfleg"], "eval_results": null, "language": null, "library_name": null, "license": ["cc-by-nc-sa-4.0", "apache-2.0"], "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["grammar", "spelling", "punctuation", "error-correction"... | # grammar-synthesis-large: FLAN-t5
<a href="https://colab.research.google.com/gist/pszemraj/5dc89199a631a9c6cfd7e386011452a0/demo-flan-t5-large-grammar-synthesis.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
A fine-tuned version of [google/flan-t5-large](htt... | null | [
"cc-by-nc-sa-4.0",
"apache-2.0"
] | [
"jfleg"
] | null | 783,150,080 | 783,150,080 | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
68386185bdc46832e6e88e6c | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Qwen/Qwen3-Reranker-0.6B | Qwen | {
"models": [
{
"_id": "680f0da2f99a20ddfc9a9f26",
"id": "Qwen/Qwen3-0.6B-Base"
}
],
"relation": "finetune"
} | 840,595 | 5,752,599 | False | 2025-05-29T13:30:45Z | 2025-06-09T05:59:29Z | transformers | 326 | 3 | null | text-ranking | {"parameters": {"BF16": 595776512}, "total": 595776512} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 6e9e69830b95c52b5fd889b7690dda3329508de3 | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-ranking",
"arxiv:2506.05176",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call o... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Qwen/Qwen3-0.6B-Base"], "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-ranking", "tags": null} | # Qwen3-Reranker-0.6B
<p align="center">
<img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/logo_qwen3.png" width="400"/>
<p>
## Highlights
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upo... | null | [
"apache-2.0"
] | null | null | 595,776,512 | null | null | [
"AutoModelForCausalLM",
"Qwen3ForCausalLM",
"qwen3"
] | [
"text-ranking",
"text-generation"
] | [
"text"
] | [
"text"
] | [
"logits",
"text"
] |
623d84751f63e259e898e709 | 62e5144950c47120 | d965e4a9140ecf72 | 21,128 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 192 | DMetaSoul/sbert-chinese-general-v2 | DMetaSoul | null | 660 | 3,157,820 | False | 2022-03-25T08:59:33Z | 2022-04-04T07:22:23Z | sentence-transformers | 37 | 0 | null | sentence-similarity | null | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 14b486c50cdb0e7cff9792488021c905abfd9a57 | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "tr... | # DMetaSoul/sbert-chinese-general-v2
此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型，在百万级语义相似数据集 [SimCLUE](https://github.com/CLUEbenchmark/SimCLUE) 上进行训练，适用于**通用语义匹配**场景，从效果来看该模型在各种任务上**泛化能力更好**。
注：此模型的[轻量化版本](https://huggingface.co/DMetaSoul/sbert-chinese-general-v2-distill)，也已经开源啦！
... | null | null | null | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc036468d709f174352 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | openai-community/roberta-large-openai-detector | openai-community | null | 7,822 | 2,754,235 | False | 2022-03-02T23:29:04Z | 2024-04-10T09:56:29Z | transformers | 29 | 0 | null | text-classification | {"parameters": {"F32": 356411394}, "total": 356411394} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 38f3e0ccf205e9c4e0b38ae9d75ec948141bf832 | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"text-classification",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1904.09751",
"arxiv:1910.09700",
"arxiv:1908.09203",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["bookcorpus", "wikipedia"], "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["exbert"]} | # RoBERTa Large OpenAI Detector
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specificati... | null | [
"mit"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | 356,411,394 | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62862f667cc3b7bc1b9031ee | 7a4cc4a6090329ff | ab8f40e5a89244c4 | 250,680 | BPE | 250,434 | false | true | true | true | 4 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 31 | bigscience/bloom-1b7 | bigscience | null | 54,633 | 2,907,915 | False | 2022-05-19T11:52:06Z | 2023-05-11T21:17:30Z | transformers | 122 | 0 | null | text-generation | {"parameters": {"F16": 1722408960}, "total": 1722408960} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | cc72a88036c2fb937d65efeacc57a0c2ef5d6fe5 | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bloom",
"text-generation",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
... | null | {"architectures": ["BloomForCausalLM"], "model_type": "bloom", "tokenizer_config": {"unk_token": "<unk>", "eos_token": "</s>", "bos_token": "<s>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "... | <h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" al... | null | [
"bigscience-bloom-rail-1.0"
] | null | [
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
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"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
... | 1,722,408,960 | null | null | [
"AutoModelForCausalLM",
"BloomForCausalLM",
"bloom"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
672388f6e1133cff79766318 | bafc5e0c20be7cd8 | 82d3db801e5d7f68 | 49,152 | BPE | 48,900 | false | true | false | true | 17 | null | Sequence | ByteLevel | null | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 115 | HuggingFaceTB/SmolLM2-135M-Instruct | HuggingFaceTB | {
"models": [
{
"_id": "6722d34c3189f1de8ad8ce22",
"id": "HuggingFaceTB/SmolLM2-135M"
}
],
"relation": "quantized"
} | 934,419 | 5,086,451 | False | 2024-10-31T13:41:10Z | 2025-09-22T20:43:15Z | transformers | 303 | 2 | null | text-generation | {"parameters": {"BF16": 134515008}, "total": 134515008} | [
".gitattributes",
"README.md",
"all_results.json",
"config.json",
"eval_results.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"onnx/model.onnx",
"onnx/model_bnb4.onnx",
"onnx/model_fp16.onnx",
"onnx/model_int8.onnx",
"onnx/model_q4.onnx",
"onnx/model_q4f16.onnx",
"o... | 12fd25f77366fa6b3b4b768ec3050bf629380bac | [
"transformers",
"tensorboard",
"onnx",
"safetensors",
"llama",
"text-generation",
"transformers.js",
"conversational",
"en",
"arxiv:2502.02737",
"base_model:HuggingFaceTB/SmolLM2-135M",
"base_model:quantized:HuggingFaceTB/SmolLM2-135M",
"license:apache-2.0",
"text-generation-inference",
... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|im_start|>", "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>\n'... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["HuggingFaceTB/SmolLM2-135M"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["safetensors", "onnx", "transfo... | # SmolLM2
## Table of Contents
1. [Model Summary](##model-summary)
2. [Limitations](##limitations)
3. [Training](##training)
4. [License](##license)
5. [Citation](##citation)
## Model Summary
Smo... | null | [
"apache-2.0"
] | null | [
"en"
] | 134,515,008 | null | null | [
"AutoModelForCausalLM",
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6696baec8fbd2777a1074e07 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | meta-llama/Llama-3.1-405B-Instruct | meta-llama | {
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{
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}
],
"relation": "finetune"
} | 165,202 | 3,430,633 | manual | 2024-07-16T18:24:44Z | 2024-09-25T17:02:11Z | transformers | 593 | 0 | null | text-generation | {"parameters": {"BF16": 405853388800}, "total": 405853388800} | [
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... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_messag... | {
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} | {"base_model": "meta-llama/Meta-Llama-3.1-405B", "datasets": null, "eval_results": null, "language": ["en", "de", "fr", "it", "pt", "hi", "es", "th"], "library_name": "transformers", "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation"... | null | null | [
"llama3.1"
] | null | [
"en",
"de",
"fr",
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"pt",
"hi",
"es",
"th"
] | 405,853,388,800 | null | null | [
"AutoModelForCausalLM",
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667fbfa0b0ba06a1848a6108 | 34093184d10f76a9 | 35b001f3a491f9f9 | 151,646 | BPE | 151,387 | true | true | true | true | 3 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 53 | Alibaba-NLP/gte-Qwen2-1.5B-instruct | Alibaba-NLP | null | 249,284 | 3,819,339 | False | 2024-06-29T08:02:40Z | 2025-05-28T13:11:05Z | sentence-transformers | 229 | 0 | [{"name": "gte-qwen2-7B-instruct", "results": [{"dataset": {"config": "en", "name": "MTEB AmazonCounterfactualClassification (en)", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205", "split": "test", "type": "mteb/amazon_counterfactual"}, "metrics": [{"type": "accuracy", "value": 83.98507462686567, "verified": fal... | sentence-similarity | {"parameters": {"F32": 1776197120}, "total": 1776197120} | [
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] | null | {"architectures": ["Qwen2ForCausalLM"], "auto_map": {"AutoModel": "modeling_qwen.Qwen2Model", "AutoModelForCausalLM": "modeling_qwen.Qwen2ForCausalLM", "AutoModelForSequenceClassification": "modeling_qwen.Qwen2ForSequenceClassification"}, "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "... | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
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} | {"license": "apache-2.0", "tags": ["mteb", "sentence-transformers", "transformers", "Qwen2", "sentence-similarity"], "model-index": [{"name": "gte-qwen2-7B-instruct", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual"... | ## gte-Qwen2-1.5B-instruct
**gte-Qwen2-1.5B-instruct** is the latest model in the gte (General Text Embedding) model family. The model is built on [Qwen2-1.5B](https://huggingface.co/Qwen/Qwen2-1.5B) LLM model and use the same training data and strategies as the [gte-Qwen2-7B-instruct](https://huggingface.co/Alibaba-N... | null | [
"apache-2.0"
] | null | null | 1,776,197,120 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"sentence-similarity",
"text-generation"
] | [
"text"
] | [
"text"
] | [
"logits",
"text"
] |
6414afedb6e553f665e31c26 | 57f204641b997d42 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 193 | humarin/chatgpt_paraphraser_on_T5_base | humarin | {
"models": [
{
"_id": "621ffdc036468d709f174356",
"id": "google-t5/t5-base"
}
],
"relation": "finetune"
} | 23,628 | 2,956,865 | False | 2023-03-17T18:22:37Z | 2026-03-26T09:55:45Z | transformers | 193 | 0 | null | text-generation | {"parameters": {"F32": 222903552}, "total": 222903552} | [
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"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | d3ab1136f6c3d3d2f8031d07551d2054a5372a50 | [
"transformers",
"pytorch",
"safetensors",
"t5",
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"en",
"dataset:humarin/chatgpt-paraphrases",
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"base_model:finetune:google-t5/t5-base",
"license:openrail",
"text-generation-inference",
"endpoints_compatible",
"deploy:... | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["google-t5/t5-base"], "datasets": ["humarin/chatgpt-paraphrases"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "openrail", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": null, "inference... | This model was trained on our [ChatGPT paraphrase dataset](https://huggingface.co/datasets/humarin/chatgpt-paraphrases).
This dataset is based on the [Quora paraphrase question](https://www.kaggle.com/competitions/quora-question-pairs), texts from the [SQUAD 2.0](https://huggingface.co/datasets/squad_v2) and the [CNN ... | null | [
"openrail"
] | [
"humarin/chatgpt-paraphrases"
] | [
"en"
] | 222,903,552 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65a4f4a83e3a00a9c1385657 | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | mlabonne/NeuralDaredevil-7B | mlabonne | {
"models": [
{
"_id": "6599d41994cadc9cdcf3da9d",
"id": "mlabonne/Daredevil-7B"
}
],
"relation": "finetune"
} | 44 | 2,673,879 | False | 2024-01-15T09:02:32Z | 2024-07-14T09:43:00Z | transformers | 41 | 0 | [{"name": "NeuralDaredevil-7B", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 69.88, "name": "no... | text-generation | {"parameters": {"F16": 7241732096}, "total": 7241732096} | [
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"model.safetensors.index.json",
"special_tokens_map.json",
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"tokenizer.model",
"tokenizer_config.... | adc3b68feddb54317de325958e16c021459a5e28 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
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"lazymergekit",
"dpo",
"rlhf",
"mlabonne/example",
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"base_model:finetune:mlabonne/Daredevil-7B",
"license:cc-by-nc-4.0",
"model-index",
"text-generatio... | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "mlabonne/Daredevil-7B", "license": "cc-by-nc-4.0", "tags": ["merge", "mergekit", "lazymergekit", "dpo", "rlhf", "mlabonne/example"], "model-index": [{"name": "NeuralDaredevil-7B", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (2... | 
# NeuralDaredevil-7B
NeuralDaredevil-7B is a DPO fine-tune of [mlabonne/Daredevil-7B](https://huggingface.co/mlabonne/Daredevil-7B) using the [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) preference dataset and m... | null | [
"cc-by-nc-4.0"
] | null | null | 7,241,732,096 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc036468d709f17432b | a3d54d6c1d5c62b4 | f87d7bfce4fd9617 | 30,000 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Replace",
"Replace",
"NFKD",
"StripAccents",
"Lowercase",
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 12 | albert/albert-large-v2 | albert | null | 2,954 | 2,696,751 | False | 2022-03-02T23:29:04Z | 2024-02-19T10:58:48Z | transformers | 21 | 0 | null | fill-mask | {"parameters": {"F32": 17875424}, "total": 17875424} | [
".gitattributes",
"README.md",
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"model.safetensors",
"pytorch_model.bin",
"spiece.model",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"with-prefix-tf_model.h5"
] | dfed3a5ef4499fb3351c4ebbcf487375d1e942c8 | [
"transformers",
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["AlbertForMaskedLM"], "model_type": "albert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["bookcorpus", "wikipedia"], "eval_results": null, "language": "en", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # ALBERT Large v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make... | null | [
"apache-2.0"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | 17,875,424 | null | null | [
"albert",
"AutoModelForMaskedLM",
"AlbertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
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643cf85ffdb3d500061d24a0 | 84892a81d307796d | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 133 | HuggingFaceH4/tiny-random-LlamaForCausalLM | HuggingFaceH4 | null | 42,755 | 2,751,554 | False | 2023-04-17T07:42:23Z | 2023-04-19T21:39:04Z | transformers | 2 | 0 | null | text-generation | null | [
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"README.md",
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"generation_config.json",
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] | 4306640c58123489a8b89632c01b5ba58dbf349e | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": true, "rstrip":... | {
"auto_model": "AutoModelForCausalLM",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "duplicated_from": "HuggingFaceM4/tiny-random-LlamaForCausalLM"} | null | null | null | null | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
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"text"
] | [
"text"
] | |
64965ff42ec29bb1a76c180e | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | TrustSafeAI/RADAR-Vicuna-7B | TrustSafeAI | null | 33,563 | 2,875,741 | False | 2023-06-24T03:16:04Z | 2023-11-07T17:18:27Z | transformers | 9 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"tokenizer.json",
"vocab.json"
] | 4ff1f23a69a36aa1df47b0933be6279f1b896c9b | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"arxiv:1907.11692",
"arxiv:2307.03838",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta"} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-classification", "tags": null} | <br>
# RADAR Model Card
## Model Details
RADAR-Vicuna-7B is an AI-text detector trained via adversarial learning between the detector and a paraphraser on human-text corpus ([OpenWebText](https://huggingface.co/datasets/Skylion007/openwebtext)) and AI-text corpus generated
based on [OpenWebText](https://huggingface... | null | null | null | null | null | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
67548c423cc32b93ce51b9a3 | 71140a663949f258 | 8a01c02d4d0a401c | 119,547 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 9 | tabularisai/multilingual-sentiment-analysis | tabularisai | {
"models": [
{
"_id": "621ffdc036468d709f174344",
"id": "distilbert/distilbert-base-multilingual-cased"
}
],
"relation": "finetune"
} | 349,301 | 4,179,203 | False | 2024-12-07T17:56:18Z | 2026-03-29T10:14:05Z | transformers | 362 | 0 | null | text-classification | {"parameters": {"F32": 135328517}, "total": 135328517} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 7f0afb5f3f2f2174fb147a42c821a2fccab8d8a4 | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"sentiment-analysis",
"sentiment",
"synthetic data",
"multi-class",
"social-media-analysis",
"customer-feedback",
"product-reviews",
"brand-monitoring",
"multilingual",
"🇪🇺",
"region:eu",
"synthetic",
"en",
"zh",... | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "distilbert/distilbert-base-multilingual-cased", "datasets": ["tabularisai/swahili_sentiment_dataset"], "eval_results": null, "language": ["en", "zh", "es", "hi", "ar", "bn", "pt", "ru", "ja", "de", "ms", "te", "vi", "ko", "fr", "tr", "it", "pl", "uk", "tl", "nl", "gsw", "sw"], "library_name": "transform... | # 🚀 Multilingual Sentiment Classification Model (23 Languages)
<!-- TRY IT HERE: `coming soon`
-->
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/sznxwdqBXj)
# NEWS!
- 2025/8: Major model update +1 new language: **Swahili**! Also,... | null | [
"cc-by-nc-4.0"
] | [
"tabularisai/swahili_sentiment_dataset"
] | [
"en",
"zh",
"es",
"hi",
"ar",
"bn",
"pt",
"ru",
"ja",
"de",
"ms",
"te",
"vi",
"ko",
"fr",
"tr",
"it",
"pl",
"uk",
"tl",
"nl",
"gsw",
"sw"
] | 135,328,517 | null | null | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
645a5ca799a55c57325adde4 | 5ef9eb7677e33416 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 19 | aglazkova/bart_finetuned_keyphrase_extraction | aglazkova | null | 11 | 2,783,539 | False | 2023-05-09T14:45:59Z | 2024-10-24T12:25:11Z | transformers | 14 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model_args.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
] | cbb80befa19a2318e2380081c72d4f78f6a16ae9 | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:midas/krapivin",
"dataset:midas/inspec",
"dataset:midas/kptimes",
"dataset:midas/duc2001",
"arxiv:1910.13461",
"arxiv:2312.10700",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BartForConditionalGeneration"], "model_type": "bart", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["midas/krapivin", "midas/inspec", "midas/kptimes", "midas/duc2001"], "eval_results": null, "language": ["en"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"text":... | # BART fine-tuned for keyphrase generation
<!-- Provide a quick summary of what the model is/does. -->
This is the <a href="https://huggingface.co/facebook/bart-base">bart-base</a> (<a href = "https://arxiv.org/abs/1910.13461">Lewis et al.. 2019</a>) model finetuned for the keyphrase generation task (<a href="https:/... | null | null | [
"midas/krapivin",
"midas/inspec",
"midas/kptimes",
"midas/duc2001"
] | [
"en"
] | null | null | null | [
"bart",
"BartForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
621ffdc136468d709f1802bd | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | sentence-transformers/msmarco-MiniLM-L6-v3 | sentence-transformers | null | 369,164 | 3,886,047 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:17:32Z | sentence-transformers | 15 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 22713216}, "total": 22713728} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"flax_model.msgpack",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint... | fea93b3df3924e5649a4e322c345f951239d2c13 | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/msmarco-MiniLM-L6-v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you h... | null | [
"apache-2.0"
] | null | null | 22,713,728 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
648068c39aafd41918a62b66 | e2ac746b464fbda0 | 7c18fa8b6cc1f58a | 49,156 | BPE | 48,891 | false | true | false | true | 23 | null | Sequence | ByteLevel | null | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 194 | HuggingFaceH4/starchat-beta | HuggingFaceH4 | null | 1,069 | 2,570,736 | False | 2023-06-07T11:23:47Z | 2023-06-09T10:18:22Z | transformers | 263 | 0 | [{"name": "starchat-beta", "results": []}] | text-generation | {"parameters": {"BF16": 15517480960}, "total": 15517480960} | [
".gitattributes",
"README.md",
"added_tokens.json",
"all_results.json",
"config.json",
"dialogue_template.json",
"eval_results.json",
"generation_config.json",
"handler.py",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensor... | b1bcda690655777373f57ea6614eb095ec2c886f | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt_bigcode",
"text-generation",
"generated_from_trainer",
"license:bigcode-openrail-m",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["GPTBigCodeForCausalLM"], "model_type": "gpt_bigcode", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "bigcode-openrail-m", "license_name": null, "license_link": null, "metrics": null, "model_name": "starchat-beta", "pipeline_tag": null, "tags": ["generated_from_trainer"], "widget": [{"text": "How can I write a... | <img src="https://huggingface.co/HuggingFaceH4/starchat-beta/resolve/main/model_logo.png" alt="StarChat Beta Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Model Card for StarChat-β
StarChat is a series of language models that are trained to act as helpful coding assistants. Star... | null | [
"bigcode-openrail-m"
] | null | null | 15,517,480,960 | null | null | [
"gpt_bigcode",
"AutoModelForCausalLM",
"GPTBigCodeForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
62f02987e57174582cd334e3 | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | impira/layoutlm-document-qa | impira | null | 18,628 | 2,790,534 | False | 2022-08-07T21:07:19Z | 2023-03-18T00:54:24Z | transformers | 1,168 | 0 | null | document-question-answering | {"parameters": {"I64": 514, "F32": 127792898}, "total": 127793412} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"pyproject.toml",
"pytorch_model.bin",
"setup.cfg",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | beed3c4d02d86017ebca5bd0fdf210046b907aa6 | [
"transformers",
"pytorch",
"tf",
"safetensors",
"layoutlm",
"document-question-answering",
"pdf",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LayoutLMForQuestionAnswering"], "model_type": "layoutlm", "tokenizer_config": {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
"auto_model": "AutoModelForDocumentQuestionAnswering",
"custom_class": null,
"pipeline_tag": "document-question-answering",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "document-question-answering", "tags": ["layoutlm", "document-question-answering", "pdf"], "widget": [{"... | # LayoutLM for Visual Question Answering
This is a fine-tuned version of the multi-modal [LayoutLM](https://aka.ms/layoutlm) model for the task of question answering on documents. It has been fine-tuned using both the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) and [DocVQA](https://www.docvqa.org/) datasets.
... | null | [
"mit"
] | null | [
"en"
] | 127,793,412 | null | null | [
"AutoModelForDocumentQuestionAnswering",
"layoutlm",
"LayoutLMForQuestionAnswering"
] | [
"document-question-answering"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
67b05c8762fcf619d6df8e22 | 616c061030c03d7b | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 195 | Qwen/Qwen2.5-VL-7B-Instruct-AWQ | Qwen | {
"models": [
{
"_id": "6795ffcd88cd7c0294702a72",
"id": "Qwen/Qwen2.5-VL-7B-Instruct"
}
],
"relation": "quantized"
} | 502,493 | 4,895,849 | False | 2025-02-15T09:21:11Z | 2025-04-06T16:25:10Z | transformers | 99 | 0 | null | image-text-to-text | {"parameters": {"I32": 6525288448, "BF16": 1766749184, "F16": 129024}, "total": 8292166656} | [
".gitattributes",
"LICENSE",
"README.md",
"added_tokens.json",
"chat_template.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"preprocessor_config.json",
"special_tokens_map.... | 536a35794df8831aa814970ee8f89eff577e7718 | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"multimodal",
"conversational",
"en",
"arxiv:2309.00071",
"arxiv:2409.12191",
"arxiv:2308.12966",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-VL-7B-Instruct",
"license:apache-2.0",
"text-gen... | null | {"architectures": ["Qwen2_5_VLForConditionalGeneration"], "model_type": "qwen2_5_vl", "quantization_config": {"bits": 4, "quant_method": "awq"}, "processor_config": {"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and me... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": ["Qwen/Qwen2.5-VL-7B-Instruct"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["multimodal"]} | # Qwen2.5-VL-7B-Instruct-AWQ
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
In the past five months since Qwen2-VL... | null | [
"apache-2.0"
] | null | [
"en"
] | 8,292,166,656 | null | null | [
"qwen2_5_vl",
"AutoModelForImageTextToText",
"Qwen2_5_VLForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
65c5577b1080431ea9e083cd | e2c43495aed6e9fb | e6ffdc5dd5f1fd86 | 256,000 | BPE | 580,604 | true | false | true | true | 217 | Replace | null | Sequence | [
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse"
] | 106 | google/gemma-7b | google | null | 30,631 | 2,821,063 | manual | 2024-02-08T22:36:43Z | 2024-06-27T14:09:40Z | transformers | 3,290 | 4 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"examples/example_fsdp.py",
"examples/example_sft_qlora.py",
"examples/notebook_sft_peft.ipynb",
"gemma-7b.gguf",
"generation_config.json",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
... | ff6768d9368919a1f025a54f9f5aa0ee591730bb | [
"transformers",
"safetensors",
"gguf",
"gemma",
"text-generation",
"arxiv:2305.14314",
"arxiv:2312.11805",
"arxiv:2009.03300",
"arxiv:1905.07830",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1905.10044",
"arxiv:1907.10641",
"arxiv:1811.00937",
"arxiv:1809.02789",
"arxiv:1911.01547",... | {"total": 8538074112, "architecture": "gemma", "context_length": 8192, "bos_token": "<bos>", "eos_token": "<eos>"} | {"architectures": ["GemmaForCausalLM"], "model_type": "gemma", "tokenizer_config": {"bos_token": "<bos>", "eos_token": "<eos>", "pad_token": "<pad>", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "gemma", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "extra_gated_heading": "Access Gemma on Hugging Face", "extra_gated_prom... | null | null | [
"gemma"
] | null | null | null | 8,538,074,112 | null | [
"AutoModelForCausalLM",
"gemma",
"GemmaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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