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"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 101 | sentence-transformers/msmarco-bert-base-dot-v5 | sentence-transformers | null | 582,832 | 7,932,938 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:20:47Z | sentence-transformers | 20 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 109482240}, "total": 109482752} | [
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"1_Pooling/config.json",
"README.md",
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"onnx/model_qint8_arm64.onnx",
"onnx/m... | dbf04e3911e5b11c887fe563d3f2479f81c4415f | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"arxiv:1908.10084",
"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": ["en"], "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", "sent... | # msmarco-bert-base-dot-v5
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500K (query, answer) pairs from the [MS MARCO dataset](https://github.com/microsoft/MSMARCO-P... | null | null | null | [
"en"
] | 109,482,752 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
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"NFC"
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"Split",
"ByteLevel"
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"ByteLevel"
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"models": [
{
"_id": "66e6d09952865ca1e41c6566",
"id": "Qwen/Qwen2.5-32B"
}
],
"relation": "finetune"
} | 4,098,183 | 19,629,113 | False | 2024-09-17T04:17:55Z | 2024-09-25T12:33:09Z | transformers | 341 | 3 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00017.safetensors",
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"model-00005-of-00017.safetensors",
"model-00006-of-0... | 5ede1c97bbab6ce5cda5812749b4c0bdf79b18dd | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
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"arxiv:2309.00071",
"arxiv:2407.10671",
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"base_model:finetune:Qwen/Qwen2.5-32B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azu... | 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/Qwen2.5-32B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-32B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-g... | # Qwen2.5-32B-Instruct
## 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 kno... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-32B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17a608 | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | deepset/tinyroberta-squad2 | deepset | null | 124,394 | 6,134,129 | False | 2022-03-02T23:29:05Z | 2024-09-26T09:12:41Z | transformers | 113 | 0 | [{"name": "deepset/tinyroberta-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 78.8627, "name": "Exact Match", "verified": true, "ver... | question-answering | {"parameters": {"I64": 514, "F32": 81529346}, "total": 81529860} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 12b287c9df677e28b07f0a023850dba68c997dbf | [
"transformers",
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"safetensors",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"arxiv:1909.10351",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForQuestionAnswering"], "model_type": "roberta", "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": "AutoModelForQuestionAnswering",
"custom_class": null,
"pipeline_tag": "question-answering",
"processor": "AutoTokenizer"
} | {"datasets": ["squad_v2"], "language": "en", "license": "cc-by-4.0", "model-index": [{"name": "deepset/tinyroberta-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [{"t... | # tinyroberta for Extractive QA
This is the *distilled* version of the [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) model. This model has a comparable prediction quality and runs at twice the speed of the base model.
## Overview
**Language model:** tinyroberta-squad2
**Language:... | null | [
"cc-by-4.0"
] | [
"squad_v2"
] | [
"en"
] | 81,529,860 | null | null | [
"roberta",
"AutoModelForQuestionAnswering",
"RobertaForQuestionAnswering"
] | [
"question-answering"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6250d5ec62900d56e38573c1 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | facebook/flava-full | facebook | null | 21,057 | 8,102,713 | False | 2022-04-09T00:40:12Z | 2024-02-22T17:51:43Z | transformers | 43 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 51e2b5fac49169959b01da7f3708d8e0d8d7304a | [
"transformers",
"pytorch",
"flava",
"pretraining",
"arxiv:2112.04482",
"arxiv:2108.10904",
"license:bsd-3-clause",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["FlavaForPreTraining"], "model_type": "flava", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForPreTraining",
"custom_class": null,
"pipeline_tag": "pretraining",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "bsd-3-clause", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ## Model Card: FLAVA
## Model Details
FLAVA model was developed by the researchers at FAIR to understand if a single model can work across different modalities with a unified architecture. The model was pretrained solely using publicly available multimodal datasets containing 70M image-text pairs in total and thus fu... | null | [
"bsd-3-clause"
] | null | null | null | null | null | [
"flava",
"FlavaForPreTraining",
"AutoModelForPreTraining"
] | [
"pretraining"
] | null | null | null |
621ffdc136468d709f1802e8 | 08d5e624483310cd | f87d7bfce4fd9617 | 30,000 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Replace",
"Replace",
"NFKD",
"StripAccents",
"Lowercase",
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 132 | sentence-transformers/paraphrase-albert-small-v2 | sentence-transformers | null | 273,627 | 6,548,397 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:26:11Z | sentence-transformers | 10 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 11683584}, "total": 11684096} | [
".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... | 9d490b476eb5291c7885bb6d4961318740493cf2 | [
"sentence-transformers",
"pytorch",
"tf",
"rust",
"onnx",
"safetensors",
"openvino",
"albert",
"feature-extraction",
"sentence-similarity",
"transformers",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:s2orc",
"dataset:ms_marco",
"dataset:wiki_atomic_edits",
"dataset:s... | null | {"architectures": ["AlbertModel"], "model_type": "albert", "tokenizer_config": {"bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": true, "rstrip": false, "normalized": tru... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["flax-sentence-embeddings/stackexchange_xml", "s2orc", "ms_marco", "wiki_atomic_edits", "snli", "multi_nli", "embedding-data/altlex", "embedding-data/simple-wiki", "embedding-data/flickr30k-captions", "embedding-data/coco_captions", "embedding-data/sentence-compression", "embedding-dat... | # sentence-transformers/paraphrase-albert-small-v2
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 model becomes easy when... | null | [
"apache-2.0"
] | [
"flax-sentence-embeddings/stackexchange_xml",
"s2orc",
"ms_marco",
"wiki_atomic_edits",
"snli",
"multi_nli",
"embedding-data/altlex",
"embedding-data/simple-wiki",
"embedding-data/flickr30k-captions",
"embedding-data/coco_captions",
"embedding-data/sentence-compression",
"embedding-data/QQP",
... | null | 11,684,096 | null | null | [
"AlbertModel",
"albert",
"AutoModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f1802de | f02914cc3fa57434 | 84fb34b2e1636fc5 | 30,527 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 41 | sentence-transformers/nli-mpnet-base-v2 | sentence-transformers | null | 162,069 | 6,259,614 | False | 2022-03-02T23:29:05Z | 2025-08-19T10:22:53Z | sentence-transformers | 15 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 109486464}, "total": 109486978} | [
".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... | c2f4dd9a1dc4337c28cfbd650433f761bb304c50 | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"text-embeddings-inference",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["MPNetModel"], "model_type": "mpnet", "tokenizer_config": {"bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, ... | {
"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/nli-mpnet-base-v2
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 model becomes easy when you have... | null | [
"apache-2.0"
] | null | null | 109,486,978 | null | null | [
"MPNetModel",
"AutoModel",
"mpnet"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
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"NFC"
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"ByteLevel"
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"ByteLevel"
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".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | e93a9faa9c77e5d09219f6c868bfc7a1bd65593c | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/the_pile_deduplicated",
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"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"depl... | 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/the_pile_deduplicated"], "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/the_pile_deduplicated"
] | [
"en"
] | 95,592,496 | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"NFC"
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"Split",
"ByteLevel"
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"ByteLevel"
] | 47 | Qwen/Qwen3-14B | Qwen | {
"models": [
{
"_id": "680f0cffb790c8c34a30bdf2",
"id": "Qwen/Qwen3-14B-Base"
}
],
"relation": "finetune"
} | 2,988,628 | 12,410,404 | False | 2025-04-27T03:42:45Z | 2025-07-26T03:49:23Z | transformers | 382 | 2 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00008.safetensors",
"model-00002-of-00008.safetensors",
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"model-00004-of-00008.safetensors",
"model-00005-of-00008.safetensors",
"model-00006-of-0... | 40c069824f4251a91eefaf281ebe4c544efd3e18 | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2309.00071",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-14B-Base",
"base_model:finetune:Qwen/Qwen3-14B-Base",
"license:apache-2.0",
"text-generation-inference",
"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/Qwen3-14B-Base"], "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generatio... | null | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"Qwen3ForCausalLM",
"qwen3"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63314d6d49a956391544c94d | dda72719c593cc4c | 539cc58b8fdaf772 | 51,864 | BPE | 50,000 | false | true | true | true | 1,608 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 55 | openai/whisper-tiny.en | openai | null | 113,061 | 5,821,774 | False | 2022-09-26T06:57:49Z | 2024-01-22T17:55:12Z | transformers | 112 | 0 | [{"name": "whisper-tiny.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", "valu... | automatic-speech-recognition | null | [
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"license:apache-2.0",
"model-index",
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] | 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"
] | null | null | null | [
"WhisperForConditionalGeneration",
"whisper",
"AutoModelForSpeechSeq2Seq"
] | [
"automatic-speech-recognition"
] | [
"multimodal"
] | [
"audio"
] | [
"text"
] |
642b5e60f3d829a868c7ab00 | 84892a81d307796d | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 133 | huggyllama/llama-7b | huggyllama | null | 311,313 | 6,424,287 | False | 2023-04-03T23:16:48Z | 2024-07-02T15:46:56Z | transformers | 356 | 0 | null | text-generation | {"parameters": {"F16": 6738415616, "F32": 2048}, "total": 6738417664} | [
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"pytorch_model-00002-of-00002.bin",
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"sp... | 4782ad278652c7c71b72204d462d6d01eaaf7549 | [
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"text-generation",
"conversational",
"license:other",
"text-generation-inference",
"endpoints_compatible",
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] | 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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"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "other", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | This contains the weights for the LLaMA-7b model. This model is under a non-commercial license (see the LICENSE file).
You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/vi... | null | [
"other"
] | null | null | 6,738,417,664 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
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"text-generation"
] | [
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"text"
] | [
"text"
] |
621ffdc136468d709f1787c7 | ae849e4e01717c5e | 9e14ca63c90db3f4 | 32,000 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 134 | akdeniz27/bert-base-turkish-cased-ner | akdeniz27 | null | 238,099 | 5,878,994 | False | 2022-03-02T23:29:05Z | 2024-06-18T09:42:03Z | transformers | 25 | 0 | null | token-classification | {"parameters": {"I64": 512, "F32": 110032135}, "total": 110032647} | [
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"README.md",
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"model.onnx",
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"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 99995f7d2be4b3a28c74f0d36ee97f8c04ee0571 | [
"transformers",
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"token-classification",
"tr",
"doi:10.57967/hf/0949",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForTokenClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "tr", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"text": "Mustafa Kemal Atat\u00fcrk 19 May\u0131s 1919'da Samsun'a \u00e... | # Turkish Named Entity Recognition (NER) Model
This model is the fine-tuned model of "dbmdz/bert-base-turkish-cased"
using a reviewed version of well known Turkish NER dataset
(https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt).
# Fine-tuning parameters:
```
task = "ner"
model_checkpoint = "dbmdz/... | null | [
"mit"
] | null | [
"tr"
] | 110,032,647 | null | null | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
67c35b9bb236f0d365bf29d3 | 46323c0f61df476d | 1fe366e13272caa0 | 262,145 | BPE | 514,906 | true | true | true | true | 6,415 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
"Fuse"
] | 49 | google/gemma-3-27b-it | google | {
"models": [
{
"_id": "67c35b8b87a7f49a82593992",
"id": "google/gemma-3-27b-pt"
}
],
"relation": "finetune"
} | 1,078,578 | 12,381,516 | manual | 2025-03-01T19:10:19Z | 2025-03-21T20:29:02Z | transformers | 1,935 | 9 | null | image-text-to-text | {"parameters": {"BF16": 27432406640}, "total": 27432406640} | [
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"arxi... | null | {"architectures": ["Gemma3ForConditionalGeneration"], "model_type": "gemma3", "processor_config": {"chat_template": "{{ bos_token }}\n{%- if messages[0]['role'] == 'system' -%}\n {%- if messages[0]['content'] is string -%}\n {%- set first_user_prefix = messages[0]['content'] + '\n\n' -%}\n {%- else -%}\n ... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": "google/gemma-3-27b-pt", "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 Gemma ... | null | null | [
"gemma"
] | null | null | 27,432,406,640 | null | null | [
"AutoModelForImageTextToText",
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"gemma3"
] | [
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] | [
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] | [
"text",
"image"
] | [
"text"
] |
621ffdc136468d709f17a32e | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | dandelin/vilt-b32-finetuned-vqa | dandelin | null | 92,695 | 5,681,969 | False | 2022-03-02T23:29:05Z | 2022-08-02T13:03:04Z | transformers | 419 | 0 | null | visual-question-answering | null | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
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"vocab.txt"
] | d0a1f6ab88522427a7ae76ceb6e1e1e7b68a1d08 | [
"transformers",
"pytorch",
"vilt",
"visual-question-answering",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["ViltForVisualQuestionAnswering"], "model_type": "vilt", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "ViltForVisualQuestionAnswering",
"custom_class": null,
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"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["visual-question-answering"], "widget": [{"text": "What's the animal doing?", "src... | # Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2
Vision-and-Language Transformer (ViLT) model fine-tuned on [VQAv2](https://visualqa.org/). It was introduced in the paper [ViLT: Vision-and-Language Transformer
Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and f... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"ViltForVisualQuestionAnswering",
"vilt"
] | [
null,
"visual-question-answering"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
621ffdc136468d709f17a213 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | cross-encoder/ms-marco-electra-base | cross-encoder | {
"models": [
{
"_id": "621ffdc136468d709f17b61d",
"id": "google/electra-base-discriminator"
}
],
"relation": "quantized"
} | 32,499 | 5,394,496 | False | 2022-03-02T23:29:05Z | 2025-08-29T14:35:07Z | sentence-transformers | 7 | 0 | null | text-ranking | {"parameters": {"I64": 512, "F32": 109483009}, "total": 109483521} | [
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... | 8b9301757513f8a695920c334c3a9f365ebeb357 | [
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"base_model:google/electra-base-discriminator",
"base_model:quantized:google/electra-base-discriminator",
... | null | {"architectures": ["ElectraForSequenceClassification"], "model_type": "electra", "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": ["google/electra-base-discriminator"], "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"
] | 109,483,521 | null | null | [
"ElectraForSequenceClassification",
"AutoModelForSequenceClassification",
"electra"
] | [
"text-ranking",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
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"Replace",
"Replace",
"NFKD",
"StripAccents",
"Lowercase",
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 132 | GPTCache/paraphrase-albert-small-v2 | GPTCache | null | 123,250 | 6,605,495 | False | 2023-05-09T03:40:39Z | 2023-05-09T03:51:00Z | transformers | 2 | 0 | null | feature-extraction | null | [
".gitattributes",
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"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | 5fb246187b5489d59ce0db167e739192759defab | [
"transformers",
"albert",
"feature-extraction",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["AlbertModel"], "model_type": "albert", "tokenizer_config": {"bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": true, "rstrip": false, "normalized": tru... | {
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} | {"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} | null | null | [
"mit"
] | null | null | null | null | null | [
"AlbertModel",
"albert",
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] | [
"feature-extraction"
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"multimodal"
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"text"
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"embeddings"
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653ff324a3839006573a2d87 | 52bad4bf7b21579a | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 39 | laion/larger_clap_general | laion | null | 725,098 | 9,860,594 | False | 2023-10-30T18:17:08Z | 2023-10-31T19:56:46Z | transformers | 48 | 0 | null | feature-extraction | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | ada0c23a36c4e8582805bb38fec3905903f18b41 | [
"transformers",
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"clap",
"feature-extraction",
"arxiv:2211.06687",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["ClapModel"], "model_type": "clap", "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": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # Model
## TL;DR
CLAP is to audio what CLIP is to image. This is an improved CLAP checkpoint, specifically trained on general audio, music and speech.
## Description
CLAP (Contrastive Language-Audio Pretraining) is a neural network trained on a variety of (audio, text) pairs. It can be instructed in to predict the ... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"clap",
"AutoModel",
"ClapModel"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
67dd8463d7019776f5fc5b23 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-VL-32B-Instruct | Qwen | null | 100,464 | 8,418,405 | False | 2025-03-21T15:23:15Z | 2025-04-14T10:55:03Z | transformers | 477 | 1 | null | image-text-to-text | {"parameters": {"BF16": 32763876352, "F32": 688841984}, "total": 33452718336} | [
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] | 8faed761d45a263340a0528343f099c05c9a4323 | [
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] | 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 a helpful assistant.' ... | {
"auto_model": "AutoModelForCausalLM",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-1.5B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": nu... | # Qwen2.5-1.5B
## 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 knowledge**... | null | [
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"feature-extraction",
"robotics",
"vla",
"image-text-to-text",
"multimodal",
"pretraining",
"custom_code",
"en",
"arxiv:2406.09246",
"license:mit",
"region:us"
] | null | {"architectures": ["OpenVLAForActionPrediction"], "auto_map": {"AutoConfig": "configuration_prismatic.OpenVLAConfig", "AutoModelForVision2Seq": "modeling_prismatic.OpenVLAForActionPrediction"}, "model_type": "openvla", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<PAD>", "unk_token": "<un... | {
"auto_model": "AutoModelForVision2Seq",
"custom_class": "modeling_prismatic.OpenVLAForActionPrediction",
"pipeline_tag": "feature-extraction",
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} | {"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": "robotics", "tags": ["robotics", "vla", "image-text-to-text", "multimodal", "pretraining"]} | # OpenVLA 7B
OpenVLA 7B (`openvla-7b`) is an open vision-language-action model trained on 970K robot manipulation episodes from the [Open X-Embodiment](https://robotics-transformer-x.github.io/) dataset.
The model takes language instructions and camera images as input and generates robot actions. It supports controll... | null | [
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] | null | [
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] | 7,541,237,184 | null | null | [
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"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
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} | null | null | null | null | null | null | 3,106,032 | null | null | [
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}
],
"relation": "finetune"
} | 66,790 | 5,617,716 | manual | 2024-02-13T01:07:30Z | 2024-08-14T08:36:20Z | transformers | 1,241 | 1 | null | text-generation | null | [
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} | {"base_model": "google/gemma-7b", "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": [], "widget": [{"messages": [{"role": "user", "content": "How doe... | null | null | [
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6517857470694414233ae947 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
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"BertPreTokenizer"
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"WordPiece"
] | 0 | jackhhao/jailbreak-classifier | jackhhao | null | 3,010 | 5,041,513 | False | 2023-09-30T02:18:28Z | 2024-04-05T01:02:15Z | transformers | 22 | 0 | null | text-classification | null | [
".gitattributes",
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"config.json",
"pytorch_model.bin",
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] | 771aa6f1391933e7cba0b21f0f17750c7a74a901 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"jailbreak",
"security",
"moderation",
"prompt-injection",
"en",
"dataset:Open-Orca/OpenOrca",
"dataset:jackhhao/jailbreak-classification",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 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": null, "datasets": ["Open-Orca/OpenOrca", "jackhhao/jailbreak-classification"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": null, "pipeline_tag": "text-classification",... | # Jailbreak Classifier
Classifies prompts as jailbreaks or benign. This is a fine-tune checkpoint of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [jailbreak-classification](https://huggingface.co/datasets/jackhhao/jailbreak-classification) dataset.
## Training Details
### Training Data
Fine-... | null | [
"apache-2.0"
] | [
"Open-Orca/OpenOrca",
"jackhhao/jailbreak-classification"
] | [
"en"
] | null | null | [
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] | [
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] | 137 | MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 | MoritzLaurer | null | 218,716 | 5,440,278 | False | 2022-08-22T16:59:35Z | 2024-04-11T13:49:19Z | transformers | 351 | 0 | [{"name": "DeBERTa-v3-base-xnli-multilingual-nli-2mil7", "results": [{"task": {"type": "text-classification", "name": "Natural Language Inference"}, "dataset": {"name": "MultiNLI-matched", "type": "multi_nli", "split": "validation_matched"}, "metrics": [{"type": "accuracy", "value": "0,857", "verified": false}]}, {"tas... | zero-shot-classification | {"parameters": {"I64": 512, "F16": 278811651}, "total": 278812163} | [
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"multilingual",
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"hi",
"id",
"it",
"tr",
"ru",
"bn",
"ur",
"mr",
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"vi",
"fa",
"pl",
... | 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"
} | {"datasets": ["MoritzLaurer/multilingual-NLI-26lang-2mil7", "xnli", "multi_nli", "facebook/anli", "fever", "lingnli", "alisawuffles/WANLI"], "language": ["multilingual", "zh", "ja", "ar", "ko", "de", "fr", "es", "pt", "hi", "id", "it", "tr", "ru", "bn", "ur", "mr", "ta", "vi", "fa", "pl", "uk", "nl", "sv", "he", "sw", ... | # Model card for mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
## Model description
This multilingual model can perform natural language inference (NLI) on 100 languages and is therefore also suitable for multilingual zero-shot classification. The underlying mDeBERTa-v3-base model was pre-trained by Microsoft on the [... | null | [
"mit"
] | [
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"facebook/anli",
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] | 278,812,163 | null | [
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"pipeline_tag": "text2text-generation",
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} | null | null | null | null | null | null | null | null | null | [
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"models": [
{
"_id": "621ffdc136468d709f17dee4",
"id": "microsoft/deberta-v3-base"
}
],
"relation": "quantized"
} | 104,498 | 5,157,836 | False | 2023-11-25T08:09:08Z | 2025-05-30T10:27:02Z | transformers | 104 | 2 | [{"name": "deberta-v3-base-prompt-injection", "results": []}] | text-classification | {"parameters": {"F32": 184423682}, "total": 184423682} | [
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"onnx/added_tokens.json",
"onnx/config.json",
"onnx/model.onnx",
"onnx/model_optimized.onnx",
"onnx/special_tokens_map.json",
"onnx/sp... | 447ec1ac62f47c9be5a0bc65e8d52569b37315a6 | [
"transformers",
"onnx",
"safetensors",
"deberta-v2",
"text-classification",
"prompt-injection",
"injection",
"security",
"generated_from_trainer",
"en",
"dataset:Lakera/gandalf_ignore_instructions",
"dataset:rubend18/ChatGPT-Jailbreak-Prompts",
"dataset:imoxto/prompt_injection_cleaned_datase... | 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": "microsoft/deberta-v3-base", "datasets": ["Lakera/gandalf_ignore_instructions", "rubend18/ChatGPT-Jailbreak-Prompts", "imoxto/prompt_injection_cleaned_dataset-v2", "hackaprompt/hackaprompt-dataset", "fka/awesome-chatgpt-prompts", "teven/prompted_examples", "Dahoas/synthetic-hh-rlhf-prompts", "Dahoas/hh_p... | # Model Card for deberta-v3-base-prompt-injection
**There is a newer version of the model - [protectai/deberta-v3-base-prompt-injection-v2](https://huggingface.co/protectai/deberta-v3-base-prompt-injection-v2).**
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/debert... | null | [
"apache-2.0"
] | [
"Lakera/gandalf_ignore_instructions",
"rubend18/ChatGPT-Jailbreak-Prompts",
"imoxto/prompt_injection_cleaned_dataset-v2",
"hackaprompt/hackaprompt-dataset",
"fka/awesome-chatgpt-prompts",
"teven/prompted_examples",
"Dahoas/synthetic-hh-rlhf-prompts",
"Dahoas/hh_prompt_format",
"MohamedRashad/ChatGPT... | [
"en"
] | 184,423,682 | null | [
"accuracy",
"recall",
"precision",
"f1"
] | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f1802e3 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | sentence-transformers/paraphrase-MiniLM-L12-v2 | sentence-transformers | null | 221,626 | 5,315,514 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:26:58Z | sentence-transformers | 6 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 33360000}, "total": 33360512} | [
".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... | 2b9058918c3d228389532ccf813578658e4af706 | [
"sentence-transformers",
"pytorch",
"tf",
"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/paraphrase-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when y... | null | [
"apache-2.0"
] | null | null | 33,360,512 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f17def8 | 492a9f4f584c75aa | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 5 | microsoft/layoutxlm-base | microsoft | null | 8,470 | 4,929,635 | False | 2022-03-02T23:29:05Z | 2022-09-16T03:41:38Z | transformers | 73 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.4.13.0.json",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"tokenizer.json"
] | 8e04ebc4d3ba0013cf943b697c0aedf19b06472a | [
"transformers",
"pytorch",
"layoutlmv2",
"arxiv:2104.08836",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | {"model_type": "layoutlmv2"} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "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": null} | # LayoutXLM
**Multimodal (text + layout/format + image) pre-training for document AI**
LayoutXLM is a multilingual variant of LayoutLMv2.
The documentation of this model in the Transformers library can be found [here](https://huggingface.co/docs/transformers/model_doc/layoutxlm).
[Microsoft Document AI](https://www.... | null | [
"cc-by-nc-sa-4.0"
] | null | null | null | null | null | [
"layoutlmv2",
"AutoModel"
] | [
null
] | null | null | null |
6797079422990ae89b5aff86 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-VL-72B-Instruct | Qwen | null | 105,817 | 5,731,646 | False | 2025-01-27T04:12:04Z | 2025-06-06T02:48:50Z | transformers | 601 | 2 | null | image-text-to-text | {"parameters": {"BF16": 73410777344}, "total": 73410777344} | [
".gitattributes",
"LICENSE",
"README.md",
"chat_template.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00038.safetensors",
"model-00002-of-00038.safetensors",
"model-00003-of-00038.safetensors",
"model-00004-of-00038.safetensors",
"model-00005-of-00038.safetenso... | 89c86200743eec961a297729e7990e8f2ddbc4c5 | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"multimodal",
"conversational",
"en",
"arxiv:2309.00071",
"arxiv:2409.12191",
"arxiv:2308.12966",
"license:other",
"eval-results",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen2_5_VLForConditionalGeneration"], "model_type": "qwen2_5_vl", "processor_config": {"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a h... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": "qwen", "license_link": "https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text",... | # Qwen2.5-VL-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
In the past five months since Qwen2-VL’s ... | null | [
"other",
"qwen",
"https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | 73,410,777,344 | null | null | [
"qwen2_5_vl",
"AutoModelForImageTextToText",
"Qwen2_5_VLForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
666343a112668a91856a7040 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | llamafactory/tiny-random-Llama-3 | llamafactory | null | 535,427 | 7,341,090 | False | 2024-06-07T17:30:09Z | 2025-02-11T16:15:07Z | transformers | 3 | 0 | null | text-generation | {"parameters": {"F16": 4112464}, "total": 4112464} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"value_head.safetensors"
] | bf2a2e3bf199ad2ee96f02a3c00246c608db22a8 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "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 + '<|e... | {
"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": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "inference": false} | A tiny version of https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct | null | [
"apache-2.0"
] | null | null | 4,112,464 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
62862fbd504d37700308a82e | 7a4cc4a6090329ff | ab8f40e5a89244c4 | 250,680 | BPE | 250,434 | false | true | true | true | 4 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 31 | bigscience/bloom | bigscience | null | 7,485 | 4,829,937 | False | 2022-05-19T11:53:33Z | 2023-07-28T17:50:20Z | transformers | 4,988 | 1 | [{"name": "bloom", "results": [{"task": {"type": "text-generation"}, "dataset": {"type": "openai_humaneval", "name": "humaneval"}, "metrics": [{"name": "pass@1", "type": "pass@1", "value": 0.15542682926829265, "verified": false}, {"name": "pass@10", "type": "pass@10", "value": 0.3278356276947017, "verified": false}, {"... | text-generation | {"parameters": {"BF16": 176247271424}, "total": 176247271424} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors.index.json",
"model_00001-of-00072.safetensors",
"model_00002-of-00072.safetensors",
"model_00003-of-00072.safetensors",
"model_00004-of-00072.safetensors",
"model_00005-of-00072.safetensors",
"model_00006-of-00072.safetensors",
"m... | 053d9cd9fbe814e091294f67fcfedb3397b954bb | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bloom",
"text-generation",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
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"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
... | 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"
} | {"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", "vi", "wo", "xh", "yo", "zh", "zu"], "license": "bigscience-b... | <img src="https://cdn-uploads.huggingface.co/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
BigScience Large Open-science Open-access Multilingual Language Model
Version 1.3 / 6 July 2022
Current Check... | null | [
"bigscience-bloom-rail-1.0"
] | null | [
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
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"mr",
"ne",
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"pt",
"rn",
"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
... | 176,247,271,424 | null | null | [
"AutoModelForCausalLM",
"BloomForCausalLM",
"bloom"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f1802f1 | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | sentence-transformers/roberta-base-nli-mean-tokens | sentence-transformers | null | 3,854 | 4,800,195 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:36:41Z | sentence-transformers | 0 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 124645632}, "total": 124646146} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"merges.txt",
"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.... | 84386c833d43bdea66b52f3fd4c1f77b21ba92da | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaModel"], "model_type": "roberta", "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": "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",... | **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/roberta-base-nli-mean-tokens
This is a [sentence-... | null | [
"apache-2.0"
] | null | null | 124,646,146 | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
669a2f8537a353887d527fc0 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4 | hugging-quants | null | 194,605 | 6,085,626 | False | 2024-07-19T09:19:01Z | 2024-08-07T07:29:21Z | transformers | 89 | 0 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"USE_POLICY.md",
"config.json",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | db1f81ad4b8c7e39777509fac66c652eb0a52f91 | [
"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-3.1-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) which is the BF16 half-precision official version released by Meta AI.
## Model Information
The Meta Llama 3.1 collecti... | null | [
"llama3.1"
] | null | [
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6361975719e7e4bf45752658 | c97a6495c5454551 | f26f356cd728caa8 | 49,408 | BPE | 48,894 | true | true | true | true | 2 | Sequence | Sequence | ByteLevel | [
"NFC",
"Replace",
"Lowercase"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 2 | timm/vit_base_patch16_clip_224.openai | timm | null | 139,405 | 5,487,772 | False | 2022-11-01T22:01:59Z | 2025-01-21T21:59:22Z | timm | 11 | 0 | null | image-feature-extraction | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"open_clip_config.json",
"open_clip_model.safetensors",
"open_clip_pytorch_model.bin",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 977e3dd0ec55ab8da155f2fbeb6b5f54948b6e3d | [
"timm",
"pytorch",
"open_clip",
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"image-feature-extraction",
"vision",
"transformers",
"arxiv:2103.00020",
"arxiv:1908.04913",
"license:apache-2.0",
"region:us"
] | null | {"tokenizer_config": {"bos_token": "<|startoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "timm", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["image-feature-extraction", "timm", "vision", "transformers"]} | # CLIP (OpenAI model for timm)
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was no... | null | [
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... | null | {"architectures": ["DistilBertForMaskedLM"], "model_type": "distilbert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search,... | null | null | [
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} | 250,210 | 5,685,845 | False | 2024-10-31T13:41:35Z | 2025-09-22T20:44:35Z | transformers | 182 | 0 | null | text-generation | null | [
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"o... | a10cc1512eabd3dde888204e902eca88bddb4951 | [
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... | 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",
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"pipeline_tag": "text-generation",
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} | {"base_model": ["HuggingFaceTB/SmolLM2-360M"], "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 | [
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} | {"datasets": ["bigcode/the-stack"], "language": ["code"], "license": "bigcode-openrail-m", "pipeline_tag": "text-generation", "programming_language": ["Java", "JavaScript", "Python"], "inference": true, "widget": [{"text": "def print_hello_world():", "example_title": "Hello world", "group": "Python"}], "model-index": [... | # SantaCoder
Play with the model on the [SantaCoder Space Demo](https://huggingface.co/spaces/bigcode/santacoder-demo).
# Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training]... | null | [
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"text-embeddings-inference",
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"deplo... | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {}} | {
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"pipeline_tag": "text-classification",
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} | {"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 Base 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-specificatio... | null | [
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} | {"language": "en", "license": "mit", "tags": ["vision", "video-classification"], "model-index": [{"name": "nielsr/xclip-base-patch32", "results": [{"task": {"type": "video-classification"}, "dataset": {"name": "Kinetics 400", "type": "kinetics-400"}, "metrics": [{"type": "top-1 accuracy", "value": 80.4, "verified": fal... | # X-CLIP (base-sized model)
X-CLIP model (base-sized, patch resolution of 32) trained fully-supervised on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et ... | null | [
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"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": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["chat"]} | # Qwen2-1.5B-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 1.5B Qwen2... | null | [
"apache-2.0"
] | null | [
"en"
] | 1,543,714,304 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67bcf3bca03bde20d15377c6 | 8b2ea89605281e7b | 0d1b0351a03fb052 | 200,029 | BPE | 199,742 | false | true | false | true | 14 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 140 | microsoft/Phi-4-multimodal-instruct | microsoft | null | 302,749 | 6,051,074 | False | 2025-02-24T22:33:32Z | 2025-12-10T20:18:10Z | transformers | 1,575 | 1 | null | automatic-speech-recognition | {"parameters": {"BF16": 5574460384}, "total": 5574460384} | [
".gitattributes",
"CODE_OF_CONDUCT.md",
"LICENSE",
"README.md",
"SECURITY.md",
"SUPPORT.md",
"added_tokens.json",
"config.json",
"configuration_phi4mm.py",
"data_summary_card.md",
"examples/what_is_shown_in_this_image.wav",
"examples/what_is_the_traffic_sign_in_the_image.wav",
"figures/audio... | 93f923e1a7727d1c4f446756212d9d3e8fcc5d81 | [
"transformers",
"safetensors",
"phi4mm",
"text-generation",
"nlp",
"code",
"audio",
"automatic-speech-recognition",
"speech-summarization",
"speech-translation",
"visual-question-answering",
"phi-4-multimodal",
"phi",
"phi-4-mini",
"custom_code",
"multilingual",
"ar",
"zh",
"cs",... | null | {"architectures": ["Phi4MMForCausalLM"], "auto_map": {"AutoConfig": "configuration_phi4mm.Phi4MMConfig", "AutoModelForCausalLM": "modeling_phi4mm.Phi4MMForCausalLM", "AutoTokenizer": "Xenova/gpt-4o"}, "model_type": "phi4mm", "tokenizer_config": {"bos_token": "<|endoftext|>", "chat_template": "{% for message in messages... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_phi4mm.Phi4MMForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual", "ar", "zh", "cs", "da", "nl", "en", "fi", "fr", "de", "he", "hu", "it", "ja", "ko", "no", "pl", "pt", "ru", "es", "sv", "th", "tr", "uk"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": "ht... | null | null | [
"mit",
"https://huggingface.co/microsoft/Phi-4-multimodal-instruct/resolve/main/LICENSE"
] | null | [
"multilingual",
"ar",
"zh",
"cs",
"da",
"nl",
"en",
"fi",
"fr",
"de",
"he",
"hu",
"it",
"ja",
"ko",
"no",
"pl",
"pt",
"ru",
"es",
"sv",
"th",
"tr",
"uk"
] | 5,574,460,384 | null | null | [
"Phi4MMForCausalLM",
"AutoModelForCausalLM",
"phi4mm",
"modeling_phi4mm.Phi4MMForCausalLM"
] | [
"automatic-speech-recognition",
"visual-question-answering",
"text-generation"
] | [
"text",
"multimodal"
] | [
"text",
"audio",
"image"
] | [
"text",
"logits"
] |
6582bf1e3fc227c16d78b1c2 | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | intfloat/e5-mistral-7b-instruct | intfloat | null | 349,652 | 5,467,608 | False | 2023-12-20T10:17:02Z | 2024-04-23T08:03:51Z | sentence-transformers | 563 | 1 | [{"name": "e5-mistral-7b-instruct", "results": [{"task": {"type": "STS"}, "dataset": {"type": "C-MTEB/AFQMC", "name": "MTEB AFQMC", "config": "default", "split": "validation", "revision": "None"}, "metrics": [{"type": "cos_sim_pearson", "value": 37.863226091673866, "verified": false}, {"type": "cos_sim_spearman", "valu... | feature-extraction | {"parameters": {"F16": 7110660096}, "total": 7110660096} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"added_tokens.json",
"config.json",
"config_sentence_transformers.json",
"lora/adapter_config.json",
"lora/adapter_model.bin",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"module... | 07163b72af1488142a360786df853f237b1a3ca1 | [
"sentence-transformers",
"pytorch",
"safetensors",
"mistral",
"feature-extraction",
"mteb",
"transformers",
"en",
"arxiv:2401.00368",
"arxiv:2104.08663",
"arxiv:2210.07316",
"arxiv:2212.03533",
"license:mit",
"model-index",
"eval-results",
"text-embeddings-inference",
"endpoints_comp... | null | {"architectures": ["MistralModel"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "</s>", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"language": ["en"], "license": "mit", "tags": ["mteb", "sentence-transformers", "transformers"], "model-index": [{"name": "e5-mistral-7b-instruct", "results": [{"task": {"type": "STS"}, "dataset": {"name": "MTEB AFQMC", "type": "C-MTEB/AFQMC", "config": "default", "split": "validation", "revision": "None"}, "metrics":... | ## E5-mistral-7b-instruct
[Improving Text Embeddings with Large Language Models](https://arxiv.org/pdf/2401.00368.pdf). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
This model has 32 layers and the embedding size is 4096.
## Usage
Below is an example to encode queries and... | null | [
"mit"
] | null | [
"en"
] | 7,110,660,096 | null | null | [
"AutoModel",
"mistral",
"MistralModel"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
65d743298bfb1f0cd1280774 | 63369c77377903b0 | d625f89b795a2eae | 69,382 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Strip",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 141 | Tochka-AI/ruRoPEBert-e5-base-2k | Tochka-AI | null | 2,917 | 4,515,015 | False | 2024-02-22T12:50:49Z | 2024-03-13T11:17:16Z | transformers | 11 | 0 | null | feature-extraction | {"parameters": {"F32": 139003654}, "total": 139003654} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"modeling_rope_bert.py",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 2cb99471f875086cb684708d195d6e7db13f6644 | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"feature-extraction",
"custom_code",
"ru",
"dataset:uonlp/CulturaX",
"arxiv:2309.09400",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RoPEBertForMaskedLM"], "auto_map": {"AutoConfig": "modeling_rope_bert.RoPEBertConfig", "AutoModel": "modeling_rope_bert.RoPEBertModel", "AutoModelForMaskedLM": "modeling_rope_bert.RoPEBertForMaskedLM", "AutoModelForSequenceClassification": "modeling_rope_bert.RoPEBertForSequenceClassification"}, "mo... | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": "modeling_rope_bert.RoPEBertForMaskedLM",
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["uonlp/CulturaX"], "eval_results": null, "language": ["ru"], "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "feature-extraction", "tags": null} | # ruRoPEBert Sentence Model for Russian language
This is an encoder model from **Tochka AI** based on the **RoPEBert** architecture, using the cloning method described in [our article on Habr](https://habr.com/ru/companies/tochka/articles/797561/).
[CulturaX](https://huggingface.co/papers/2309.09400) dataset was used... | null | null | [
"uonlp/CulturaX"
] | [
"ru"
] | 139,003,654 | null | null | [
"modeling_rope_bert.RoPEBertForMaskedLM",
"AutoModelForMaskedLM",
"RoPEBertForMaskedLM",
"bert"
] | [
"fill-mask",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
64039de6923fe79058a667f4 | f56392a3c3b561ef | f26f356cd728caa8 | 49,408 | BPE | 48,894 | true | true | true | true | 2 | Sequence | Sequence | ByteLevel | [
"NFC",
"Replace",
"Lowercase"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 7 | vinid/plip | vinid | null | 374,986 | 5,429,256 | False | 2023-03-04T19:37:10Z | 2023-03-31T02:46:21Z | transformers | 55 | 0 | null | zero-shot-image-classification | null | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 67ade53ddd32195868f422585f72698ef5d15094 | [
"transformers",
"pytorch",
"clip",
"zero-shot-image-classification",
"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"
} | null | ## Model Use (from [CLIP Model Card](https://huggingface.co/openai/clip-vit-large-patch14))
### Intended Use
The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it ... | null | null | null | null | null | null | null | [
"AutoModelForZeroShotImageClassification",
"CLIPModel",
"clip"
] | [
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
6759b5e9a9cfaefaabc087f1 | 5499fef3506cd876 | cf1efbe71ef28fc9 | 50,368 | BPE | 50,009 | true | true | true | true | 116 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 48 | answerdotai/ModernBERT-large | answerdotai | null | 301,125 | 5,214,673 | False | 2024-12-11T15:55:21Z | 2025-01-15T20:11:27Z | transformers | 463 | 2 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"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",
"onnx/model_quantized.onnx",
"onnx/model_uint8.onnx",
"pytorch_model.bin",
"special_t... | 45bb4654a4d5aaff24dd11d4781fa46d39bf8c13 | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"modernbert",
"fill-mask",
"masked-lm",
"long-context",
"en",
"arxiv:2412.13663",
"license:apache-2.0",
"deploy:azure",
"region:us"
] | null | {"architectures": ["ModernBertForMaskedLM"], "model_type": "modernbert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"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": "fill-mask", "tags": ["fill-mask", "masked-lm", "long-context", "modernbert"], "infer... | # ModernBERT
## Table of Contents
1. [Model Summary](#model-summary)
2. [Usage](#Usage)
3. [Evaluation](#Evaluation)
4. [Limitations](#limitations)
5. [Training](#training)
6. [License](#license)
7. [Citation](#citation)
## Model Summary
ModernBERT is a modernized bidirectional encoder-only Transformer model (BERT-s... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"ModernBertForMaskedLM",
"modernbert",
"AutoModelForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
669e14059a4bf63e080cca8f | c6de6331832e687a | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 142 | meta-llama/Llama-Guard-3-8B | meta-llama | {
"models": [
{
"_id": "66944f1fe0c5c2e493a804f5",
"id": "meta-llama/Llama-3.1-8B"
}
],
"relation": "finetune"
} | 99,708 | 4,831,998 | manual | 2024-07-22T08:10:45Z | 2024-10-11T14:52:47Z | transformers | 283 | 2 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
".gitattributes",
"LICENSE",
"README.md",
"USE_POLICY.md",
"config.json",
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"llama_guard_3_figure.png",
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"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.ind... | 7327bd9f6efbbe6101dc6cc4736302b3cbb6e425 | [
"transformers",
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"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"conversational",
"en",
"arxiv:2407.21783",
"arxiv:2312.06674",
"arxiv:2204.05862",
"arxiv:2308.01263",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% if messages|length % 2 == 0 %}{% set role = 'Agent' %}{% else %}{% set role = 'User' %}{% endif %}{{ \" <|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nTask: Check if t... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Meta-Llama-3.1-8B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["facebook", "meta", "pytorch", "llama", "ll... | null | null | [
"llama3.1"
] | null | [
"en"
] | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6580b3e398aa9fcdd25a1976 | 15b5d73dd8ce17a3 | 830fa437e6f588f3 | 50,000 | BPE | 49,721 | true | true | true | true | 23 | NFKC | Sequence | ByteLevel | [
"NFKC"
] | [
"Split",
"Digits",
"Split",
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 143 | vikp/texify | vikp | null | 151,012 | 5,227,334 | False | 2023-12-18T21:04:35Z | 2024-01-03T05:32:08Z | transformers | 15 | 0 | null | image-text-to-text | {"parameters": {"I64": 12500, "F16": 312445752}, "total": 312458252} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"model.safetensors",
"preprocessor_config.json",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | ce49c1fe10842e78b8be61f9e762b85ac952807d | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["VisionEncoderDecoderModel"], "model_type": "vision-encoder-decoder", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"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": "cc-by-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | OCR equation images and text to latex. See [texify](https://github.com/VikParuchuri/texify). | null | [
"cc-by-sa-4.0"
] | null | null | 312,458,252 | null | null | [
"AutoModelForImageTextToText",
"vision-encoder-decoder",
"VisionEncoderDecoderModel"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
647865dead83f3939b48d5c6 | d69c589accf1778c | 9cede39e4b3a7ae8 | 50,344 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 144 | fxmarty/pix2struct-tiny-random | fxmarty | null | 2,022 | 4,454,527 | False | 2023-06-01T09:33:18Z | 2023-06-01T09:47:36Z | transformers | 2 | 0 | null | image-to-text | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | 69e8f58784a2e53c39275550624a4541cfebefd4 | [
"transformers",
"pytorch",
"pix2struct",
"image-text-to-text",
"image-to-text",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Pix2StructForConditionalGeneration"], "model_type": "pix2struct", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"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": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-to-text", "tags": null} | null | [
"mit"
] | null | null | null | null | null | [
"AutoModelForImageTextToText",
"Pix2StructForConditionalGeneration",
"pix2struct"
] | [
"image-text-to-text",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] | |
64a8eb078a1a9187c2dddef1 | cbf239f725f44dff | 7bbb3a8066088a40 | 32,000 | BPE | 64,812 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 145 | Maykeye/TinyLLama-v0 | Maykeye | null | 129,946 | 4,721,131 | False | 2023-07-08T04:50:15Z | 2025-03-05T12:38:35Z | transformers | 43 | 0 | null | text-generation | {"parameters": {"BF16": 4621392}, "total": 4621392} | [
".gitattributes",
"README.md",
"backup/do_backup.py",
"backup/step-1-10300.bin",
"config.json",
"demo.py",
"generation_config.json",
"model.onnx",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json",
"train.ipy... | 298338802ab94432b917bcce11382aa151aee50f | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
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* Full training process is included in the notebook train.ipynb. Recreating it as simple as downloading
TinyStoriesV2-GPT4-train.txt and TinyStoriesV2-GPT4-valid.txt in the same folder with the notebook and running
the cells... | null | [
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"endpoints_c... | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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Модель на основе DistilBERT для обнаружения попыток обхода фильтров (jailbreak) в текстах.
## 📚 Детали модели
- **Архитектура**: DistilBERT
- **Задача**: Классификация текста (обнаружение jailbreak)
- **Входные данные**: Текстовые строки
- **Выходные данные**: Метка кла... | null | [
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We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb
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It is intended to be used in CodeBERTScore: [https://github.com/neulab/code-bert-score](https://github.com/neulab/co... | null | null | null | null | null | null | null | [
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1. [Introduction](#introduction)
2. [Using PhoBERT with `transformers`](#transformers)
- [Installation](#install2)
- [Pre-trained models](#models2)
- [Example usage](#usage2)
3. [Using PhoBERT with `fairseq`](#fairseq)
4. [Notes](#vncorenlp)
# <a name="introduction"></a> PhoBERT: Pre-trained ... | null | [
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[](https://github.com/marqo-ai/marqo-FashionCLIP)
Marqo-FashionSigLIP is a multimodal embedding model that provides up to [57% improvement in MRR and recall](https://www.marqo.ai/blog/search-model-for-fashion) over [fashi... | null | [
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This is the [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) fine-tuned to predict the logical relationship between two short texts: entailment, contradiction, or neutral.
## Usage
How to run the model for NLI:
```python
# !pip install t... | null | null | [
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"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | d8c2e37b439f4fc47c649dc8b90cdcd3a4e0c80e | [
"transformers",
"pytorch",
"LanguageBindImage",
"zero-shot-image-classification",
"arxiv:2310.01852",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["CLIPModel"], "model_type": "LanguageBindImage", "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 | [
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] | [
"logits"
] |
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"Replace",
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] | 42 | fxmarty/tiny-llama-fast-tokenizer | fxmarty | null | 17,993 | 4,298,091 | False | 2023-04-17T07:52:39Z | 2024-10-01T11:10:27Z | transformers | 11 | 0 | null | text-generation | null | [
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] | 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":... | {
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} | null | Note: this model has random weights and is useful only for testing purposes. | null | null | null | null | null | null | null | [
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] | 151 | deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct | deepseek-ai | null | 319,323 | 5,373,292 | False | 2024-06-14T06:23:33Z | 2024-07-03T05:16:11Z | transformers | 570 | 5 | null | text-generation | {"parameters": {"BF16": 15706484224}, "total": 15706484224} | [
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"auto_model": "AutoModelForCausalLM",
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<!-- 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-V2" />
</div>
<hr>
<div align="center" style="line-... | null | [
"other",
"deepseek-license",
"LICENSE"
] | null | null | 15,706,484,224 | null | null | [
"AutoModelForCausalLM",
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"deepseek_v2"
] | [
"text-generation"
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"text"
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"Replace"
] | null | [
"Replace",
"ByteFallback",
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] | 106 | google/gemma-2b-it | google | null | 55,931 | 4,508,998 | manual | 2024-02-08T13:23:59Z | 2024-09-27T12:19:02Z | transformers | 862 | 0 | null | text-generation | null | [
".gitattributes",
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... | {"total": 2506434560, "architecture": "gemma", "context_length": 8192, "bos_token": "<bos>", "eos_token": "<eos>"} | {"architectures": ["GemmaForCausalLM"], "model_type": "gemma", "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 %... | {
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"gemma"
] | null | null | null | 2,506,434,560 | null | [
"AutoModelForCausalLM",
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"text-generation"
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678e14e3ccefc7605ae5d807 | d1b17ca2d3ca5ffe | 83d37c6e8ee56b77 | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 75 | deepseek-ai/DeepSeek-R1-Distill-Qwen-14B | deepseek-ai | null | 567,723 | 6,242,713 | False | 2025-01-20T09:18:27Z | 2025-02-24T03:31:45Z | transformers | 615 | 5 | null | text-generation | {"parameters": {"BF16": 14770033664}, "total": 14770033664} | [
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"token... | 1df8507178afcc1bef68cd8c393f61a886323761 | [
"transformers",
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"text-generation",
"conversational",
"arxiv:2501.12948",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<\uff5cbegin\u2581of\u2581sentence\uff5c>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<\uff5cend\u258... | {
"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": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # DeepSeek-R1
<!-- 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="center... | null | [
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67c35be6eae05d8f94fae4c2 | 46323c0f61df476d | 1fe366e13272caa0 | 262,145 | BPE | 514,906 | true | true | true | true | 6,415 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
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],
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} | 2,534,085 | 12,192,318 | manual | 2025-03-01T19:11:34Z | 2025-03-21T20:28:56Z | transformers | 696 | 6 | null | image-text-to-text | null | [
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"arxi... | null | {"architectures": ["Gemma3ForConditionalGeneration"], "model_type": "gemma3", "processor_config": {"chat_template": "{{ bos_token }}\n{%- if messages[0]['role'] == 'system' -%}\n {%- if messages[0]['content'] is string -%}\n {%- set first_user_prefix = messages[0]['content'] + '\n\n' -%}\n {%- else -%}\n ... | {
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"gemma"
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] | 152 | jinaai/jina-clip-v1 | jinaai | null | 70,150 | 4,529,138 | False | 2024-05-21T13:52:49Z | 2025-05-20T10:39:21Z | transformers | 257 | 0 | null | feature-extraction | {"parameters": {"F32": 222672128}, "total": 222672128} | [
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639863ef8f4da57c2069fd83 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
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] | [
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] | 0 | Salesforce/blip-vqa-capfilt-large | Salesforce | null | 17,612 | 4,181,555 | False | 2022-12-13T11:37:19Z | 2025-02-03T06:42:26Z | transformers | 53 | 0 | null | visual-question-answering | null | [
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] | 270352c30d7166e585cd686c3f2250e04bb509da | [
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"tf",
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"arxiv:2201.12086",
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] | null | {"architectures": ["BlipForQuestionAnswering"], "model_type": "blip", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForVisualQuestionAnswering",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "bsd-3-clause", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "visual-question-answering", "tags": ["visual-question-answering"], "inference": false, "langua... | # BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Model card for BLIP trained on visual question answering - large architecture (with ViT large backbone).
| ![BLIP.gif](https://cdn-uploads.huggingface.co/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12c... | null | [
"bsd-3-clause"
] | null | null | null | null | null | [
"blip",
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] | [
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"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | OpenGVLab/InternVL3_5-241B-A28B-Instruct | OpenGVLab | {
"models": [
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}
],
"relation": "finetune"
} | 52 | 4,071,033 | False | 2025-08-25T16:38:34Z | 2025-08-29T17:57:02Z | transformers | 15 | 0 | null | image-text-to-text | {"parameters": {"BF16": 240699370368}, "total": 240699370368} | [
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"arxiv:2312.14238",
"arxiv:2404.16821",
"arxiv:2412.05271",
"arxiv:2411.1... | 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_5-241B-A28B-Pretrained"], "datasets": ["OpenGVLab/MMPR-v1.2", "OpenGVLab/MMPR-Tiny"], "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeli... | # InternVL3_5-241B-A28B-Instruct
[\[📂 GitHub\]](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://hu... | null | [
"apache-2.0"
] | [
"OpenGVLab/MMPR-v1.2",
"OpenGVLab/MMPR-Tiny"
] | [
"multilingual"
] | 240,699,370,368 | null | null | [
"AutoModel",
"internvl_chat",
"InternVLChatModel",
"modeling_internvl_chat.InternVLChatModel"
] | [
"image-text-to-text",
"feature-extraction"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text",
"embeddings"
] |
655f77e548695217d53a1e97 | 2b80a37c48945822 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 153 | vikp/layout_segmenter | vikp | null | 1,564 | 4,368,931 | False | 2023-11-23T16:03:49Z | 2023-12-22T05:54:58Z | transformers | 15 | 0 | null | token-classification | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 48f04ef8f77a822afabfd0e14ad996414a028a91 | [
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LayoutLMv3ForTokenClassification"], "model_type": "layoutlmv3", "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": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoProcessor"
} | null | Segments pdf page layout into blocks. Based on layoutlmv3.
Used in [marker](https://github.com/VikParuchuri/marker). | null | null | null | null | null | null | null | [
"AutoModelForTokenClassification",
"LayoutLMv3ForTokenClassification",
"layoutlmv3"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
67449256db3726cf00881dbb | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | trl-internal-testing/tiny-LlamaForCausalLM-3.2 | trl-internal-testing | null | 172,035 | 4,764,735 | False | 2024-11-25T15:05:58Z | 2025-12-19T19:10:34Z | transformers | 1 | 0 | null | text-generation | {"parameters": {"BF16": 2054056}, "total": 2054056} | [
".gitattributes",
"README.md",
"chat_template.jinja",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | a96553d37fcbb870c7da4d56fa9466189d2f81d4 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"conversational",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|eot_id|>"}, "chat_template_jinja": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}... | {
"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": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["trl"]} | # Tiny LlamaForCausalLM
This is a minimal model built for unit tests in the [TRL](https://github.com/huggingface/trl) library. | null | null | null | null | 2,054,056 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63e6e0965c3664766ed8c064 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | BridgeTower/bridgetower-large-itm-mlm-itc | BridgeTower | null | 131,542 | 4,460,316 | False | 2023-02-11T00:25:58Z | 2023-03-08T22:33:21Z | transformers | 12 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"tokenizer.json",
"vocab.json"
] | 0e15d648ae27b0a8cf94b61ddc731ddca76f6ee2 | [
"transformers",
"pytorch",
"bridgetower",
"gaudi",
"en",
"dataset:conceptual_captions",
"dataset:conceptual_12m",
"dataset:sbu_captions",
"dataset:visual_genome",
"dataset:mscoco_captions",
"arxiv:2206.08657",
"arxiv:1504.00325",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": ["conceptual_captions", "conceptual_12m", "sbu_captions", "visual_genome", "mscoco_captions"], "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": [... | # BridgeTower large-itm-mlm-itc model
The BridgeTower model was proposed in "BridgeTower: Building Bridges Between Encoders in Vision-Language Representative Learning" by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
The model was pretrained on English language using masked language mode... | null | [
"mit"
] | [
"conceptual_captions",
"conceptual_12m",
"sbu_captions",
"visual_genome",
"mscoco_captions"
] | [
"en"
] | null | null | null | [
"AutoModel"
] | [
null
] | null | null | null |
646bc2435d68f5c15a2f1a4d | d3f34e62810cdb97 | 91785edaf5f6a173 | 32,003 | BPE | 61,249 | true | false | true | true | 6 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 154 | Salesforce/instructblip-vicuna-7b | Salesforce | null | 12,331 | 4,087,965 | False | 2023-05-22T19:28:03Z | 2025-02-03T06:44:19Z | transformers | 99 | 0 | null | image-text-to-text | {"parameters": {"F32": 7913726720}, "total": 7913726720} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"preprocessor_config.json",... | 19103d0c5b5263c8a7891012e08573439fb6607f | [
"transformers",
"pytorch",
"safetensors",
"instructblip",
"image-text-to-text",
"vision",
"image-captioning",
"en",
"arxiv:2305.06500",
"license:other",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["InstructBlipForConditionalGeneration"], "model_type": "instructblip", "tokenizer_config": {"bos_token": "</s>", "eos_token": "</s>", "pad_token": "[PAD]", "unk_token": "</s>", "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": "en", "library_name": null, "license": "other", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["vision", "image-captioning"]} | # InstructBLIP model
InstructBLIP model using [Vicuna-7b](https://github.com/lm-sys/FastChat#model-weights) as language model. InstructBLIP was introduced in the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Dai et al.
Disclaimer: The... | null | [
"other"
] | null | [
"en"
] | 7,913,726,720 | null | null | [
"instructblip",
"InstructBlipForConditionalGeneration",
"AutoModelForImageTextToText"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
65c6213e0831983147a0a3a7 | a9ba6522a506ea59 | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 155 | blaze999/Medical-NER | blaze999 | {
"models": [
{
"_id": "621ffdc136468d709f17dee4",
"id": "microsoft/deberta-v3-base"
}
],
"relation": "finetune"
} | 14,905 | 4,037,032 | False | 2024-02-09T12:57:34Z | 2024-04-08T06:15:22Z | transformers | 225 | 0 | [{"name": "deberta-med-ner-2", "results": []}] | token-classification | {"parameters": {"F32": 183895379}, "total": 183895379} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | a9d5061193e969de80b24225f926cb224caac1ce | [
"transformers",
"safetensors",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"medical",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["DebertaV2ForTokenClassification"], "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": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": "microsoft/deberta-v3-base", "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": "deberta-med-ner-2", "pipeline_tag": "token-classification", "tags": ["generated_from_trainer", "medical"... | <!-- 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. -->
# deberta-med-ner-2
This model is a fine-tuned version of [DeBERTa](https://huggingface.co/microsoft/deberta-v3-base) on the Pub... | null | [
"mit"
] | null | null | 183,895,379 | null | null | [
"DebertaV2ForTokenClassification",
"AutoModelForTokenClassification",
"deberta-v2"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6515ff5b7f18cec973af0828 | 5385e084548307e9 | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 156 | TheBloke/Mistral-7B-Instruct-v0.1-GPTQ | TheBloke | {
"models": [
{
"_id": "65143cd8e31c0e2e3df713e5",
"id": "mistralai/Mistral-7B-Instruct-v0.1"
}
],
"relation": "quantized"
} | 320 | 3,957,394 | False | 2023-09-28T22:34:03Z | 2023-09-29T20:48:48Z | transformers | 84 | 0 | null | text-generation | {"parameters": {"I32": 6979321856, "F16": 263786496}, "total": 7243108352} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"quantize_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 6ae1e4ae2cfbaf107c705ed722ec243b4f88014d | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"conversational",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:quantized:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"text-generation-inference",
"4-bit",
"gptq",
"deploy:azure",
"regio... | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": "<s>", "chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversa... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "mistralai/Mistral-7B-Instruct-v0.1", "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": "Mistral 7B Instruct v0.1", "pipeline_tag": "text-generation", "tags": ["finetuned"], "... | <!-- 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 | [
"apache-2.0"
] | null | null | 7,243,108,352 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65a4ce6d9f0a34c17376fa20 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | Qdrant/bge-small-en-v1.5-onnx-Q | Qdrant | null | 367,143 | 5,329,498 | False | 2024-01-15T06:19:25Z | 2024-07-15T12:50:23Z | transformers | 2 | 0 | null | sentence-similarity | null | [
".gitattributes",
"README.md",
"config.json",
"model_optimized.onnx",
"ort_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 52398278842ec682c6f32300af41344b1c0b0bb2 | [
"transformers",
"onnx",
"bert",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"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": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": null} | Quantized ONNX port of [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) for text classification and similarity searches.
### Usage
Here's an example of performing inference using the model with [FastEmbed](https://github.com/qdrant/fastembed).
```py
from fastembed import TextEmbedding
documen... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
64993af29c04c6314f954df8 | ffdab586917c0510 | fe86ee69d0e07a4f | 4,096 | BPE | 4,087 | false | true | true | false | 5 | null | Whitespace | null | null | [
"Whitespace"
] | null | 157 | zhihan1996/DNABERT-2-117M | zhihan1996 | null | 79,857 | 4,317,710 | False | 2023-06-26T07:14:58Z | 2025-06-30T21:44:21Z | transformers | 93 | 2 | null | null | null | [
".gitattributes",
"LICENSE",
"README.md",
"bert_layers.py",
"bert_padding.py",
"config.json",
"configuration_bert.py",
"flash_attn_triton.py",
"generation_config.json",
"pytorch_model.bin",
"tokenizer.json",
"tokenizer_config.json"
] | 7bce263b15377fc15361f52cfab88f8b586abda0 | [
"transformers",
"pytorch",
"biology",
"medical",
"genomics",
"custom_code",
"arxiv:2306.15006",
"endpoints_compatible",
"region:us"
] | null | {"auto_map": {"AutoConfig": "configuration_bert.BertConfig", "AutoModel": "bert_layers.BertModel", "AutoModelForMaskedLM": "bert_layers.BertForMaskedLM", "AutoModelForSequenceClassification": "bert_layers.BertForSequenceClassification"}, "tokenizer_config": {"unk_token": "[UNK]", "cls_token": "[CLS]", "sep_token": "[SE... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": ["matthews_correlation", "f1"], "model_name": null, "pipeline_tag": null, "tags": ["biology", "medical", "genomics"]} | null | null | null | null | null | null | null | [
"matthews_correlation",
"f1"
] | [
"AutoModel"
] | [
null
] | null | null | null |
662824c0bfeb2ea792d69f33 | 3f287b04362fff24 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 158 | Orenguteng/Llama-3-8B-Lexi-Uncensored | Orenguteng | null | 1,624 | 3,877,133 | False | 2024-04-23T21:14:40Z | 2024-05-27T06:16:40Z | transformers | 273 | 0 | [{"name": "Llama-3-8B-Lexi-Uncensored", "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": 59.56, "na... | text-generation | {"parameters": {"F32": 805306368, "BF16": 7224954880}, "total": 8030261248} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"... | ff95e3bfcd6142759ce82099b58bc7a789ac241b | [
"transformers",
"safetensors",
"llama",
"text-generation",
"uncensored",
"llama3",
"instruct",
"open",
"conversational",
"license:llama3",
"model-index",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "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 + '<|e... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"license": "llama3", "tags": ["uncensored", "llama3", "instruct", "open"], "model-index": [{"name": "Llama-3-8B-Lexi-Uncensored", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": ... | 
This model is based on Llama-3-8b-Instruct, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](https://llama.meta.com/llama3/license/)
Lexi is uncensored, which makes the model compliant. ... | null | [
"llama3"
] | null | null | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
629e473a46b4826be2c81e93 | dccbb7b59eac010e | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 159 | MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli | MoritzLaurer | null | 41,950 | 4,396,211 | False | 2022-06-06T18:28:10Z | 2024-04-11T13:49:10Z | transformers | 120 | 1 | [{"name": "DeBERTa-v3-large-mnli-fever-anli-ling-wanli", "results": [{"task": {"type": "text-classification", "name": "Natural Language Inference"}, "dataset": {"name": "MultiNLI-matched", "type": "multi_nli", "split": "validation_matched"}, "metrics": [{"type": "accuracy", "value": "0,912", "verified": false}]}, {"tas... | zero-shot-classification | {"parameters": {"I64": 512, "F16": 435064835}, "total": 435065347} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"onnx/model.onnx",
"onnx/model_quantized.onnx",
"pytorch_model.bin",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json"
] | b3546ea6b0346eb6f8d5d68b13c7dc6d0376b3d7 | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"deberta-v2",
"text-classification",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:facebook/anli",
"dataset:fever",
"dataset:lingnli",
"dataset:alisawuffles/WANLI",
"arxiv:2104.07179",
"arxiv:2111.09543",
"license:mit"... | null | {"architectures": ["DebertaV2ForSequenceClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "eos_token": "[SEP]", "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"
} | {"datasets": ["multi_nli", "facebook/anli", "fever", "lingnli", "alisawuffles/WANLI"], "language": ["en"], "license": "mit", "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification", "tags": ["text-classification", "zero-shot-classification"], "model-index": [{"name": "DeBERTa-v3-large-mnli-fever-anli-ling-w... | # DeBERTa-v3-large-mnli-fever-anli-ling-wanli
## Model description
This model was fine-tuned on the [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), Adversarial-NLI ([ANLI](https://huggingface.co/datasets/anli)),... | null | [
"mit"
] | [
"multi_nli",
"facebook/anli",
"fever",
"lingnli",
"alisawuffles/WANLI"
] | [
"en"
] | 435,065,347 | null | [
"accuracy"
] | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
] | [
"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
64ce0266749587dbe0ebffb6 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | BAAI/bge-base-en | BAAI | null | 329,101 | 5,274,993 | False | 2023-08-05T08:03:50Z | 2024-04-17T13:00:18Z | transformers | 61 | 0 | [{"name": "bge-base-en", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 75.73134... | feature-extraction | {"parameters": {"I64": 512, "F32": 109482240}, "total": 109482752} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"onnx/config.json",
"onnx/model.onnx",
"onnx/special_tokens_map.json",
"onnx/tokenizer.json",
"onnx/tokenizer_config.json",
"onnx/vocab.txt",
"py... | b737bf5dcc6ee8bdc530531266b4804a5d77b5d8 | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"mteb",
"en",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"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"
} | {"language": ["en"], "license": "mit", "tags": ["mteb"], "model-index": [{"name": "bge-base-en", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9f... | **Recommend switching to newest [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5), which has more reasonable similarity distribution and same method of usage.**
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequen... | null | [
"mit"
] | null | [
"en"
] | 109,482,752 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
6470e3161f0e7ee7fb1e887d | ba8202ef2c1107f0 | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 160 | JackFram/llama-160m | JackFram | null | 55,231 | 4,030,120 | False | 2023-05-26T16:49:26Z | 2024-01-04T09:26:17Z | transformers | 37 | 0 | null | text-generation | {"parameters": {"F32": 162417792}, "total": 162417792} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"optimizer.pt",
"pytorch_model.bin",
"rng_state.pth",
"rng_state_0.pth",
"rng_state_1.pth",
"rng_state_2.pth",
"rng_state_3.pth",
"scheduler.pt",
"special_tokens_map.json",
"tokenizer.json",
"t... | aca9b687d1425f863dcf5de9a4c96e3fe36266dd | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:wikipedia",
"arxiv:2305.09781",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"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"
} | {"base_model": null, "datasets": ["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": "text-generation", "tags": null} | ## Model description
This is a LLaMA-like model with only 160M parameters trained on Wikipedia and part of the C4-en and C4-realnewslike datasets.
No evaluation has been conducted yet, so use it with care.
The model is mainly developed as a base Small Speculative Model in the [SpecInfer](https://arxiv.org/abs/2305.0... | null | [
"apache-2.0"
] | [
"wikipedia"
] | [
"en"
] | 162,417,792 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6887274f2626398dc2e9f540 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Qwen/Qwen3-30B-A3B-Instruct-2507 | Qwen | null | 1,004,466 | 9,618,078 | False | 2025-07-28T07:31:27Z | 2025-09-17T06:56:43Z | transformers | 791 | 2 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"config_1m.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00016.safetensors",
"model-00002-of-00016.safetensors",
"model-00003-of-00016.safetensors",
"model-00004-of-00016.safetensors",
"model-00005-of-00016.safetensors",... | 0d7cf23991f47feeb3a57ecb4c9cee8ea4a17bfe | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"arxiv:2402.17463",
"arxiv:2407.02490",
"arxiv:2501.15383",
"arxiv:2404.06654",
"arxiv:2505.09388",
"license:apache-2.0",
"eval-results",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "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... | {
"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": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation... | # Qwen3-30B-A3B-Instruct-2507
<a href="https://chat.qwen.ai/?model=Qwen3-30B-A3B-2507" 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>
## Highlights
We introduce the... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507/blob/main/LICENSE"
] | null | null | null | null | null | [
"qwen3_moe",
"AutoModelForCausalLM",
"Qwen3MoeForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65d8af504f7ca8579bf7182c | c201230c641b14d0 | 5cc89648c8003a49 | 256,000 | BPE | 580,604 | true | false | true | true | 4 | Replace | null | Sequence | [
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse"
] | 161 | fxmarty/tiny-random-GemmaForCausalLM | fxmarty | null | 6,929 | 3,850,493 | False | 2024-02-23T14:44:32Z | 2024-02-23T17:06:41Z | transformers | 0 | 0 | null | text-generation | {"parameters": {"F32": 8193824}, "total": 8193824} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | ca53c1ebb8b142110b71662d702e4923e5426cb4 | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"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": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | This one with a custom `config.head_dim` as allowed by the architecture (see 7b model). | null | [
"mit"
] | null | null | 8,193,824 | null | null | [
"AutoModelForCausalLM",
"gemma",
"GemmaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17e8fe | e68b0b111c49dac2 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 28 | nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large | nreimers | null | 10,439 | 3,850,270 | False | 2022-03-02T23:29:05Z | 2021-06-20T19:03:16Z | transformers | 18 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | d828558d1a570cbbb5e62a8dbf85c8f18bf7982a | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["XLMRobertaForMaskedLM"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normali... | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | null | # Multilingual MiniLMv2
This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm) | null | null | null | null | null | null | null | [
"AutoModelForMaskedLM",
"xlm-roberta",
"XLMRobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62860399d238eeca5885cc82 | c124163b72739cca | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 162 | fabiochiu/t5-base-tag-generation | fabiochiu | null | 91,495 | 4,224,220 | False | 2022-05-19T08:45:13Z | 2023-08-03T07:55:12Z | transformers | 53 | 0 | [{"name": "t5-base-tag-generation", "results": []}] | null | {"parameters": {"F32": 222903552}, "total": 222903552} | [
".gitattributes",
".gitignore",
"README.md",
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"model.safetensors",
"pytorch_model.bin",
"runs/May19_10-53-10_d4c4df668d47/1652957726.3562257/events.out.tfevents.1652957726.d4c4df668d47.79.1",
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"runs/May... | 601db4c2d05bb45f6094c1a7dcf26d60b0f2a0b8 | [
"transformers",
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"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": "t5-base-tag-generation", "pipeline_tag": null, "tags": ["generated_from_trainer"], "widget": [{"text": "Python is a high... | # Model description
This model is [t5-base](https://huggingface.co/t5-base) fine-tuned on the [190k Medium Articles](https://www.kaggle.com/datasets/fabiochiusano/medium-articles) dataset for predicting article tags using the article textual content as input. While usually formulated as a multi-label classification pr... | null | [
"apache-2.0"
] | null | null | 222,903,552 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
621ffdc136468d709f17a225 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | cross-encoder/stsb-roberta-base | cross-encoder | {
"models": [
{
"_id": "621ffdc036468d709f174350",
"id": "FacebookAI/roberta-base"
}
],
"relation": "quantized"
} | 146,835 | 4,779,588 | False | 2022-03-02T23:29:05Z | 2025-04-11T08:18:13Z | sentence-transformers | 5 | 0 | null | text-ranking | {"parameters": {"I64": 514, "F32": 124646401}, "total": 124646915} | [
".gitattributes",
"CECorrelationEvaluator_sts-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",
"on... | d576534b67143e2c70ee9966d7fdbf5835728d13 | [
"sentence-transformers",
"pytorch",
"jax",
"onnx",
"safetensors",
"openvino",
"roberta",
"text-classification",
"transformers",
"text-ranking",
"en",
"dataset:sentence-transformers/stsb",
"base_model:FacebookAI/roberta-base",
"base_model:quantized:FacebookAI/roberta-base",
"license:apach... | 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": ["FacebookAI/roberta-base"], "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": ["t... | null | null | [
"apache-2.0"
] | [
"sentence-transformers/stsb"
] | [
"en"
] | 124,646,915 | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-ranking",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62ff7b86ce3c00cd2ca28a15 | 4496c5c12cbe29e0 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 163 | biu-nlp/f-coref | biu-nlp | null | 302,382 | 4,725,734 | False | 2022-08-19T12:01:10Z | 2022-11-28T11:35:52Z | transformers | 19 | 0 | [{"name": "biu-nlp/f-coref", "results": [{"task": {"type": "coreference-resolution", "name": "coreference-resolution"}, "dataset": {"name": "ontonotes", "type": "coreference"}, "metrics": [{"name": "Avg. F1", "type": "CoNLL", "value": 78.5, "verified": false}]}]}] | null | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | e91dfff12879495d882ba9460d0c5d5dd44ade59 | [
"transformers",
"pytorch",
"roberta",
"fast",
"coreference-resolution",
"en",
"dataset:multi_news",
"dataset:ontonotes",
"arxiv:2209.04280",
"arxiv:2205.12644",
"arxiv:1907.10529",
"arxiv:2101.00434",
"arxiv:2109.04127",
"license:mit",
"model-index",
"endpoints_compatible",
"region:u... | null | {"architectures": ["FastCorefModel"], "model_type": "roberta", "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": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": "AutoTokenizer"
} | {"datasets": ["multi_news", "ontonotes"], "language": ["en"], "license": "mit", "metrics": ["CoNLL"], "tags": ["fast", "coreference-resolution"], "task_categories": ["coreference-resolution"], "model-index": [{"name": "biu-nlp/f-coref", "results": [{"task": {"type": "coreference-resolution", "name": "coreference-resolu... | ## F-Coref: Fast, Accurate and Easy to Use Coreference Resolution
[F-Coref](https://arxiv.org/abs/2209.04280) allows to process 2.8K OntoNotes documents in 25 seconds on a V100 GPU (compared to 6 minutes for the [LingMess](https://arxiv.org/abs/2205.12644) model, and to 12 minutes of the popular AllenNLP coreference m... | null | [
"mit"
] | [
"multi_news",
"ontonotes"
] | [
"en"
] | null | null | [
"CoNLL"
] | [
"roberta",
"AutoModel",
"FastCorefModel"
] | [
null
] | null | null | null |
623d82b1c29adf5ef616da7e | 5b8a6156b5b6e013 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 37 | ml6team/keyphrase-extraction-distilbert-inspec | ml6team | null | 794 | 3,797,242 | False | 2022-03-25T08:52:01Z | 2023-05-06T08:45:37Z | transformers | 30 | 0 | [{"name": "DeDeckerThomas/keyphrase-extraction-distilbert-inspec", "results": [{"task": {"type": "keyphrase-extraction", "name": "Keyphrase Extraction"}, "dataset": {"type": "midas/inspec", "name": "inspec"}, "metrics": [{"type": "F1 (Seqeval)", "value": 0.509, "name": "F1 (Seqeval)", "verified": false}, {"type": "F1@M... | token-classification | null | [
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"training_args.bin",
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"transformers",
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"token-classification",
"keyphrase-extraction",
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"dataset:midas/inspec",
"arxiv:2112.08547",
"license:mit",
"model-index",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["DistilBertForTokenClassification"], "model_type": "distilbert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"datasets": ["midas/inspec"], "language": "en", "license": "mit", "metrics": ["seqeval"], "tags": ["keyphrase-extraction"], "widget": [{"text": "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content... | # 🔑 Keyphrase Extraction Model: distilbert-inspec
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first... | null | [
"mit"
] | [
"midas/inspec"
] | [
"en"
] | null | null | [
"seqeval"
] | [
"AutoModelForTokenClassification",
"distilbert",
"DistilBertForTokenClassification"
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"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
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66e9866925a2de6873aa5a9b | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
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"ByteLevel"
] | 15 | Qwen/Qwen2.5-Coder-7B-Instruct | Qwen | {
"models": [
{
"_id": "66e81d2494457ccb513ef9c9",
"id": "Qwen/Qwen2.5-Coder-7B"
}
],
"relation": "finetune"
} | 2,581,921 | 10,028,052 | False | 2024-09-17T13:38:49Z | 2025-01-12T02:03:41Z | transformers | 672 | 3 | null | text-generation | {"parameters": {"BF16": 7615616512}, "total": 7615616512} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
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"model.safetensors.index.json",
"tokenizer.json",
"... | c03e6d358207e414f1eca0bb1891e29f1db0e242 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"codeqwen",
"chat",
"qwen",
"qwen-coder",
"conversational",
"en",
"arxiv:2409.12186",
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"base_model:Qwen/Qwen2.5-Coder-7B",
"base_model:finetune:Qwen/Qwen2.5-Coder-7B",
"license:a... | 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-Coder-7B"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_t... | # Qwen2.5-Coder-7B-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-Coder is the latest series of C... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | 7,615,616,512 | null | null | [
"AutoModelForCausalLM",
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] | [
"text"
] | [
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"ByteLevel"
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"ByteLevel"
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"en",
"dataset:squad_v2",
"license:mit",
"model-index",
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"deploy:azure",
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] | null | {"architectures": ["RobertaForQuestionAnswering"], "model_type": "roberta", "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": "AutoModelForQuestionAnswering",
"custom_class": null,
"pipeline_tag": "question-answering",
"processor": "AutoTokenizer"
} | {"datasets": ["squad_v2"], "language": "en", "license": "mit", "tags": ["exbert"], "thumbnail": "https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg", "model-index": [{"name": "deepset/roberta-base-squad2-distilled", "results": [{"task": {"type": "question-answerin... | # roberta-base distilled for Extractive QA
## Overview
**Language model:** deepset/roberta-base-squad2-distilled
**Language:** English
**Training data:** SQuAD 2.0 training set
**Eval data:** SQuAD 2.0 dev set
**Code:** See [an example extractive QA pipeline built with Haystack](https://haystack.deepset.a... | null | [
"mit"
] | [
"squad_v2"
] | [
"en"
] | 124,057,092 | null | null | [
"roberta",
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] | [
"question-answering"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f1786f4 | 1b7abe0d4b349f41 | 32c99d7cd67fcc7e | 30,873 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 164 | ahmedrachid/FinancialBERT-Sentiment-Analysis | ahmedrachid | null | 29,623 | 3,936,548 | False | 2022-03-02T23:29:05Z | 2022-02-07T14:58:57Z | transformers | 96 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
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] | 656931965473ec085d195680bd62687b140c038f | [
"transformers",
"pytorch",
"bert",
"text-classification",
"financial-sentiment-analysis",
"sentiment-analysis",
"en",
"dataset:financial_phrasebank",
"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": ["financial_phrasebank"], "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": ["financial-sentiment-analysis", "sentiment-analysis"], "widget": [{"te... | ### FinancialBERT for Sentiment Analysis
[*FinancialBERT*](https://huggingface.co/ahmedrachid/FinancialBERT) is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that financial practitioners and researchers can bene... | null | null | [
"financial_phrasebank"
] | [
"en"
] | null | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65cd9a883f97d5bd5d5a2796 | 3bc066976c19613d | fe86ee69d0e07a4f | 4,096 | BPE | 4,087 | false | true | true | false | 5 | null | Whitespace | null | null | [
"Whitespace"
] | null | 165 | zhihan1996/DNABERT-S | zhihan1996 | null | 738,522 | 9,722,250 | False | 2024-02-15T05:00:56Z | 2025-06-30T21:44:45Z | transformers | 8 | 0 | null | feature-extraction | null | [
".gitattributes",
"README.md",
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"bert_padding.py",
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"configuration_bert.py",
"flash_attn_triton.py",
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"special_tokens_map.json",
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] | 00e47f96cdea35e4b6f5df89e5419cbe47d490c6 | [
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"feature-extraction",
"biology",
"genomics",
"custom_code",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertModel"], "auto_map": {"AutoConfig": "configuration_bert.BertConfig", "AutoModel": "bert_layers.BertModel", "AutoModelForMaskedLM": "bert_layers.BertForMaskedLM", "AutoModelForSequenceClassification": "bert_layers.BertForSequenceClassification"}, "model_type": "bert", "tokenizer_config": {"cls_to... | {
"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": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["biology", "genomics"]} | null | null | [
"apache-2.0"
] | null | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
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"feature-extraction"
] | [
"multimodal"
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"text"
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"embeddings"
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62823cb83486d6b46b53e2b3 | 94b7beae315eb68a | befceb9191a2d18d | 50,358 | BPE | 50,000 | true | true | true | true | 102 | NFKC | ByteLevel | ByteLevel | [
"NFKC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 166 | paust/pko-t5-large | paust | null | 133 | 3,721,091 | False | 2022-05-16T11:59:52Z | 2023-06-28T17:03:42Z | transformers | 21 | 0 | null | null | {"parameters": {"F32": 820485120}, "total": 820485120} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 42e43de43587bb8888c49d109d00b772ae469264 | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"ko",
"arxiv:2105.09680",
"license:cc-by-4.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "unk_token": "<pad>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "ko", "library_name": null, "license": "cc-by-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # pko-t5-large
[Source Code](https://github.com/paust-team/pko-t5)
pko-t5 는 한국어 전용 데이터로 학습한 [t5 v1.1 모델](https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/released_checkpoints.md)입니다.
한국어를 tokenize 하기 위해서 sentencepiece 대신 OOV 가 없는 BBPE 를 사용했으며 한국어 데이터 (... | null | [
"cc-by-4.0"
] | null | [
"ko"
] | 820,485,120 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
621ffdc136468d709f1802c7 | 2f04b0c5e9fb2ab9 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 101 | sentence-transformers/msmarco-distilbert-cos-v5 | sentence-transformers | null | 70,503 | 3,994,117 | False | 2022-03-02T23:29:05Z | 2024-11-05T17:05:46Z | sentence-transformers | 10 | 0 | null | sentence-similarity | {"parameters": {"F32": 66362880}, "total": 66362880} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
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"onnx/model_O1.onnx",
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"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/m... | c598d92e340e735b82e5b73d7db135209b57b4b0 | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"arxiv:1908.10084",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"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": ["en"], "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", "sent... | # msmarco-distilbert-cos-v5
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500k (query, answer) pairs from the [MS MARCO Passages dataset](https://github.com/microsoft... | null | null | null | [
"en"
] | 66,362,880 | null | null | [
"AutoModel",
"distilbert",
"DistilBertModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f1802e9 | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | sentence-transformers/paraphrase-distilroberta-base-v1 | sentence-transformers | null | 78,848 | 4,248,045 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:25:59Z | sentence-transformers | 7 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 82118400}, "total": 82118914} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"flax_model.msgpack",
"merges.txt",
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"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
... | 48bffbbd27bf028ecdd0cd55abb51236ec12ef1b | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"onnx",
"safetensors",
"openvino",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaModel"], "model_type": "roberta", "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, "normalized": t... | {
"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/paraphrase-distilroberta-base-v1
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 model becomes eas... | null | [
"apache-2.0"
] | null | null | 82,118,914 | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
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"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 53 | fxmarty/tiny-dummy-qwen2 | fxmarty | null | 6,767 | 3,735,572 | False | 2024-03-19T11:07:14Z | 2024-03-20T07:18:24Z | transformers | 1 | 0 | null | text-generation | {"parameters": {"F32": 1217480}, "total": 1217480} | [
".gitattributes",
"README.md",
"added_tokens.json",
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"merges.txt",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | d2d85e2f738eb7f6241e052696d4636d8ca27ece | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"license:mit",
"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 %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}... | {
"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} | null | null | [
"mit"
] | null | null | 1,217,480 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64b6da63dbbd1f2cdb63dd2c | 02217ffa5907f57a | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 130 | NousResearch/Llama-2-7b-hf | NousResearch | null | 146,350 | 4,450,589 | False | 2023-07-18T18:30:59Z | 2024-06-03T19:23:18Z | transformers | 172 | 0 | null | text-generation | {"parameters": {"F32": 2048, "F16": 6738415616}, "total": 6738417664} | [
".gitattributes",
"LICENSE.txt",
"README.md",
"Responsible-Use-Guide.pdf",
"USE_POLICY.md",
"config.json",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"pytorch_model-00001-of-00003.bin",
"pytorch_model-00002-... | 8efe6c9b93655b934e27bd9981e3ec13e55aee9d | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"llama-2",
"en",
"text-generation-inference",
"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"
} | {"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": "text-generation", "tags": ["facebook", "meta", "pytorch", "llama", "llama-2"], "extra_gated_heading": ... | # **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## M... | null | null | null | [
"en"
] | 6,738,417,664 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
660a88f8777c77a2e8717a99 | bfe694f64f4a18f5 | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 22 | MoritzLaurer/deberta-v3-large-zeroshot-v2.0 | MoritzLaurer | {
"models": [
{
"_id": "621ffdc136468d709f17dee5",
"id": "microsoft/deberta-v3-large"
}
],
"relation": "quantized"
} | 295,795 | 5,201,059 | False | 2024-04-01T10:14:16Z | 2024-04-11T13:42:28Z | transformers | 125 | 1 | null | zero-shot-classification | {"parameters": {"F16": 435063810}, "total": 435063810} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"onnx/added_tokens.json",
"onnx/config.json",
"onnx/model.onnx",
"onnx/special_tokens_map.json",
"onnx/spm.model",
"onnx/tokenizer.json",
"onnx/tokenizer_config.json",
"special_tokens_map.json",
"spm.mo... | cf44676c28ba7312e5c5f8f8d2c22b3e0c9cdae2 | [
"transformers",
"onnx",
"safetensors",
"deberta-v2",
"text-classification",
"zero-shot-classification",
"en",
"arxiv:2312.17543",
"base_model:microsoft/deberta-v3-large",
"base_model:quantized:microsoft/deberta-v3-large",
"license:mit",
"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": "microsoft/deberta-v3-large", "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": "zero-shot-classification", "tags": ["text-classification", "zero-sh... | # Model description: deberta-v3-large-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 cl... | null | [
"mit"
] | null | [
"en"
] | 435,063,810 | null | null | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
] | [
"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
680da7c9aa6c98cb395660ac | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Qwen/Qwen3-30B-A3B | Qwen | {
"models": [
{
"_id": "680f0c1ce7b853214d784a83",
"id": "Qwen/Qwen3-30B-A3B-Base"
}
],
"relation": "finetune"
} | 1,465,903 | 7,605,079 | False | 2025-04-27T03:43:05Z | 2025-07-26T03:45:17Z | transformers | 869 | 1 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00016.safetensors",
"model-00002-of-00016.safetensors",
"model-00003-of-00016.safetensors",
"model-00004-of-00016.safetensors",
"model-00005-of-00016.safetensors",
"model-00006-of-0... | ad44e777bcd18fa416d9da3bd8f70d33ebb85d39 | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"arxiv:2309.00071",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-30B-A3B-Base",
"base_model:finetune:Qwen/Qwen3-30B-A3B-Base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "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... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Qwen/Qwen3-30B-A3B-Base"], "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-g... | null | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE"
] | null | null | null | null | null | [
"qwen3_moe",
"AutoModelForCausalLM",
"Qwen3MoeForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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