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"summarization",
"translation",
"en",
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"arxiv:1805.12471",
"arxiv:1708.00055",
"arxiv:1704.05426",
"arxiv:1606.05250",
"arxiv:1808.09121",
"arxi... | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5"} | {
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# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. ... | null | [
"apache-2.0"
] | [
"c4"
] | [
"en",
"fr",
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"long-context",
"en",
"arxiv:2412.13663",
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] | null | {"architectures": ["ModernBertForMaskedLM"], "model_type": "modernbert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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## 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"
] | 149,655,232 | null | null | [
"ModernBertForMaskedLM",
"modernbert",
"AutoModelForMaskedLM"
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"fill-mask"
] | [
"text"
] | [
"text"
] | [
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"BertPreTokenizer"
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} | 1,827,869 | 32,073,683 | False | 2022-03-02T23:29:05Z | 2025-08-29T14:36:22Z | sentence-transformers | 14 | 0 | null | text-ranking | {"parameters": {"I64": 512, "F32": 19164673}, "total": 19165185} | [
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"base_model:quantized:cross-encoder/ms-marco-MiniLM... | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": ["cross-encoder/ms-marco-MiniLM-L12-v2"], "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-ranki... | # 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"
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"en"
] | 19,165,185 | null | null | [
"BertForSequenceClassification",
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"AutoModelForSequenceClassification"
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"text-ranking",
"text-classification"
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"text"
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"text"
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"logits"
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"Split",
"ByteLevel"
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"ByteLevel"
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".gitattributes",
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... | 4a7511c7510a87a493e7f30f51065a84b1a3943f | [
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"text-generation",
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"conversational",
"en",
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"th",
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"endpoints_compati... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_messag... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "de", "fr", "it", "pt", "hi", "es", "th"], "library_name": "transformers", "license": "llama3.2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["facebook", "meta... | ## Model Information
The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic r... | null | [
"llama3.2"
] | null | [
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th"
] | 3,212,749,824 | null | null | [
"AutoModelForCausalLM",
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"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
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621ffdc036468d709f174341 | 634a9186a6003172 | 5f9f736c7dc6fbfa | 28,996 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 8 | distilbert/distilbert-base-cased-distilled-squad | distilbert | null | 218,356 | 25,027,555 | False | 2022-03-02T23:29:04Z | 2024-05-06T13:46:31Z | transformers | 266 | 1 | [{"name": "distilbert-base-cased-distilled-squad", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 79.5998, "name": "Exact Match", "verified": tru... | question-answering | {"parameters": {"F32": 65192450}, "total": 65192450} | [
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"arxiv:1910.09700",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DistilBertForQuestionAnswering"], "model_type": "distilbert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForQuestionAnswering",
"custom_class": null,
"pipeline_tag": "question-answering",
"processor": "AutoTokenizer"
} | {"datasets": ["squad"], "language": "en", "license": "apache-2.0", "metrics": ["squad"], "model-index": [{"name": "distilbert-base-cased-distilled-squad", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "va... | # DistilBERT base cased distilled SQuAD
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental ... | null | [
"apache-2.0"
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"squad"
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"AutoModelForQuestionAnswering",
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] | [
"text"
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"NFC"
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"Split",
"ByteLevel"
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{
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"id": "Qwen/Qwen2.5-14B"
}
],
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} | 1,396,222 | 33,421,148 | False | 2024-09-16T11:56:10Z | 2024-09-25T12:33:04Z | transformers | 325 | 2 | null | text-generation | null | [
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"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/Qwen2.5-14B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-14B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-g... | # Qwen2.5-14B-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/Qwen2.5-14B-Instruct/blob/main/LICENSE"
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"en"
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"AutoModelForCausalLM",
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} | 1,573,304 | 34,979,931 | manual | 2025-03-10T12:09:00Z | 2025-04-04T13:12:40Z | transformers | 894 | 3 | null | text-generation | null | [
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"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"... | null | {"architectures": ["WhisperForConditionalGeneration"], "model_type": "whisper", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForSpeechSeq2Seq",
"custom_class": null,
"pipeline_tag": "automatic-speech-recognition",
"processor": "AutoProcessor"
} | {"language": ["en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", ... | # 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",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"uk",
"el",
"ms",
"cs",
"ro",
"da",
"hu",
"ta",
"no",
"th",
"ur",
"hr",
"bg",
"lt",
"la",
"mi",
"ml",
"cy",
"sk"... | null | null | null | [
"WhisperForConditionalGeneration",
"whisper",
"AutoModelForSpeechSeq2Seq"
] | [
"automatic-speech-recognition"
] | [
"multimodal"
] | [
"audio"
] | [
"text"
] |
66962a41534f204a2b9982fd | 54895f2d8faef315 | e6ffdc5dd5f1fd86 | 256,000 | BPE | 580,604 | true | true | true | true | 249 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
"Fuse"
] | 50 | google/gemma-2-2b | google | null | 536,112 | 24,639,131 | manual | 2024-07-16T08:07:29Z | 2024-08-07T18:25:49Z | transformers | 636 | 5 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00003.safetensors",
"model-00002-of-00003.safetensors",
"model-00003-of-00003.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.... | c5ebcd40d208330abc697524c919956e692655cf | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"arxiv:2009.03300",
"arxiv:1905.07830",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1905.10044",
"arxiv:1907.10641",
"arxiv:1811.00937",
"arxiv:1809.02789",
"arxiv:1911.01547",
"arxiv:1705.03551",
"arxiv:2107.03374",
"arxiv... | null | {"architectures": ["Gemma2ForCausalLM"], "model_type": "gemma2", "tokenizer_config": {"bos_token": "<bos>", "eos_token": "<eos>", "pad_token": "<pad>", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "gemma", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": null, "extra_gated_heading": "Access Gemma on Hugging Face", "ext... | null | null | [
"gemma"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"gemma2",
"Gemma2ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f1802ee | da443ce982f05a4c | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 51 | sentence-transformers/paraphrase-xlm-r-multilingual-v1 | sentence-transformers | null | 94,474 | 24,152,388 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:25:10Z | sentence-transformers | 71 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 278043648}, "total": 278044162} | [
".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... | c2e19453aae46f98a865abfdf173f35f2e119f5a | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["XLMRobertaModel"], "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, "normalized": ... | {
"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-xlm-r-multilingual-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 | 278,044,162 | null | null | [
"XLMRobertaModel",
"AutoModel",
"xlm-roberta"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc036468d709f17434b | 30094a1eabdbef67 | db117e9883c4d0ab | 50,257 | BPE | 50,000 | false | true | true | true | 1 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 4 | openai-community/gpt2-medium | openai-community | null | 684,262 | 26,579,081 | False | 2022-03-02T23:29:04Z | 2024-02-19T12:39:04Z | transformers | 197 | 0 | null | text-generation | {"parameters": {"F32": 379988992}, "total": 379988992} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"generation_config.json",
"generation_config_for_text_generation.json",
"merges.txt",
"model.safetensors",
"onnx/config.json",
"onnx/decoder_model.onnx",
"onnx/decoder_model_merged.onnx",
"onnx/decoder_with_past_model.onnx",
... | 6dcaa7a952f72f9298047fd5137cd6e4f05f41da | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"onnx",
"safetensors",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "tokenizer_config": {}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # GPT-2 Medium
## Model Details
**Model Description:** GPT-2 Medium is the **355M parameter** version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective.
- **Developed by:** OpenAI, see [as... | null | [
"mit"
] | null | [
"en"
] | 379,988,992 | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
62862f3ce0ba4d3805ce3c7a | 7a4cc4a6090329ff | ab8f40e5a89244c4 | 250,680 | BPE | 250,434 | false | true | true | true | 4 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 31 | bigscience/bloom-560m | bigscience | null | 190,475 | 23,368,014 | False | 2022-05-19T11:51:24Z | 2023-09-26T09:16:49Z | transformers | 371 | 0 | null | text-generation | {"parameters": {"F16": 559214592}, "total": 559214592} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"onnx/config.json",
"onnx/decoder_model.onnx",
"onnx/decoder_model.onnx_data",
"onnx/decoder_model_merged.onnx",
"onnx/decoder_model_merged.onnx_data",
"onnx/decoder_with_past_model.onnx",
"on... | ac2ae5fab2ce3f9f40dc79b5ca9f637430d24971 | [
"transformers",
"pytorch",
"jax",
"onnx",
"safetensors",
"bloom",
"text-generation",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",... | null | {"architectures": ["BloomForCausalLM"], "model_type": "bloom", "tokenizer_config": {"unk_token": "<unk>", "eos_token": "</s>", "bos_token": "<s>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "... | <h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" al... | null | [
"bigscience-bloom-rail-1.0"
] | null | [
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"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",
... | 559,214,592 | null | null | [
"AutoModelForCausalLM",
"BloomForCausalLM",
"bloom"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17def1 | 492a9f4f584c75aa | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 5 | microsoft/infoxlm-large | microsoft | null | 116,847 | 23,309,151 | False | 2022-03-02T23:29:05Z | 2021-08-04T11:43:05Z | transformers | 13 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"tokenizer.json"
] | d616d637f0720deda963cebbfc630657d2b7d3ae | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2007.07834",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["XLMRobertaForMaskedLM"], "model_type": "xlm-roberta"} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | null | # InfoXLM
**InfoXLM** (NAACL 2021, [paper](https://arxiv.org/pdf/2007.07834.pdf), [repo](https://github.com/microsoft/unilm/tree/master/infoxlm), [model](https://huggingface.co/microsoft/infoxlm-base)) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.
**MD5**
```
05b95b7d9774... | null | null | null | null | null | null | null | [
"AutoModelForMaskedLM",
"xlm-roberta",
"XLMRobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
669778078e2484972c50bedd | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill | opensearch-project | null | 1,145,053 | 32,279,988 | False | 2024-07-17T07:51:35Z | 2025-06-30T06:34:14Z | sentence-transformers | 19 | 0 | null | feature-extraction | {"parameters": {"F32": 66985530}, "total": 66985530} | [
".gitattributes",
"README.md",
"config.json",
"config_sentence_transformers.json",
"document_1_SpladePooling/config.json",
"idf.json",
"model.safetensors",
"modules.json",
"pytorch_model.bin",
"query_0_SparseStaticEmbedding/config.json",
"query_0_SparseStaticEmbedding/model.safetensors",
"quer... | 8921a26c78b8559d6604eb1f5c0b74c079bee38f | [
"sentence-transformers",
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"learned sparse",
"opensearch",
"transformers",
"retrieval",
"passage-retrieval",
"document-expansion",
"bag-of-words",
"sparse-encoder",
"sparse",
"asymmetric",
"inference-free",
"splade",
"feature-extra... | null | {"architectures": ["DistilBertForMaskedLM"], "model_type": "distilbert", "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": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "feature-extraction", "tags": ["learned sparse", "opensearch", "transformers",... | null | null | [
"apache-2.0"
] | null | [
"en"
] | 66,985,530 | null | null | [
"distilbert",
"AutoModelForMaskedLM",
"DistilBertForMaskedLM"
] | [
"fill-mask",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
66268dc9cd54f5fffb4e2e24 | f5e7f943c396c76b | ddde944c7b357600 | 32,011 | BPE | 61,249 | true | false | true | true | 14 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 52 | microsoft/Phi-3-mini-4k-instruct | microsoft | null | 747,020 | 27,675,955 | False | 2024-04-22T16:18:17Z | 2025-12-10T20:22:18Z | transformers | 1,403 | 3 | null | text-generation | {"parameters": {"BF16": 3821079552}, "total": 3821079552} | [
".gitattributes",
"CODE_OF_CONDUCT.md",
"LICENSE",
"NOTICE.md",
"README.md",
"SECURITY.md",
"added_tokens.json",
"config.json",
"configuration_phi3.py",
"data_summary_card.md",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetens... | f39ac1d28e925b323eae81227eaba4464caced4e | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"nlp",
"code",
"conversational",
"custom_code",
"en",
"fr",
"license:mit",
"eval-results",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Phi3ForCausalLM"], "auto_map": {"AutoConfig": "configuration_phi3.Phi3Config", "AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"}, "model_type": "phi3", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{'<|system|>\n' ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "fr"], "library_name": null, "license": "mit", "license_name": null, "license_link": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "ta... | 🎉 **Phi-3.5**: [[mini-instruct]](https://huggingface.co/microsoft/Phi-3.5-mini-instruct); [[MoE-instruct]](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) ; [[vision-instruct]](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)
## Model Summary
The Phi-3-Mini-4K-Instruct is a 3.8B parameters, lightweig... | null | [
"mit",
"https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE"
] | null | [
"en",
"fr"
] | 3,821,079,552 | null | null | [
"AutoModelForCausalLM",
"phi3",
"Phi3ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc036468d709f174351 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | FacebookAI/roberta-large-mnli | FacebookAI | null | 326,638 | 23,521,392 | False | 2022-03-02T23:29:04Z | 2024-02-19T12:47:11Z | transformers | 210 | 0 | null | text-classification | {"parameters": {"F32": 356412419}, "total": 356412419} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 2a8f12d27941090092df78e4ba6f0928eb5eac98 | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"roberta",
"text-classification",
"autogenerated-modelcard",
"en",
"dataset:multi_nli",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:1806.02847",
"arxiv:1804.07461",
"arxiv:1704.05426",
"arxiv:1508.05326"... | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["multi_nli", "wikipedia", "bookcorpus"], "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": ["autogenerated-modelcard"]} | # roberta-large-mnli
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation-results)
- [Environmental Impact](#en... | null | [
"mit"
] | [
"multi_nli",
"wikipedia",
"bookcorpus"
] | [
"en"
] | 356,412,419 | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
665ee74789b4fd787a568664 | 34093184d10f76a9 | 35b001f3a491f9f9 | 151,646 | BPE | 151,387 | true | true | true | true | 3 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 53 | Qwen/Qwen2-7B-Instruct | Qwen | {
"models": [
{
"_id": "665f181fb9e194a20c5e8d44",
"id": "Qwen/Qwen2-7B"
}
],
"relation": "finetune"
} | 391,031 | 22,702,374 | False | 2024-06-04T10:07:03Z | 2024-08-21T10:29:04Z | transformers | 684 | 0 | null | text-generation | {"parameters": {"BF16": 7615616512}, "total": 7615616512} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
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"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"tokenizer.json",
"... | f2826a00ceef68f0f2b946d945ecc0477ce4450c | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"arxiv:2309.00071",
"base_model:Qwen/Qwen2-7B",
"base_model:finetune:Qwen/Qwen2-7B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] +... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2-7B", "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"]} | # Qwen2-7B-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 7B Qwen2 mod... | null | [
"apache-2.0"
] | null | [
"en"
] | 7,615,616,512 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17bab8 | 382b5a4eb98f0296 | d965e4a9140ecf72 | 21,128 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
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"BertPreTokenizer"
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] | 33 | hfl/chinese-roberta-wwm-ext | hfl | null | 131,858 | 22,640,266 | False | 2022-03-02T23:29:05Z | 2022-03-01T09:13:56Z | transformers | 400 | 2 | null | fill-mask | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 5c58d0b8ec1d9014354d691c538661bf00bfdb44 | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["zh"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["bert"]} | # Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)*... | null | [
"apache-2.0"
] | null | [
"zh"
] | null | null | null | [
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"bert",
"BertForMaskedLM"
] | [
"fill-mask"
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"text"
] | [
"text"
] | [
"logits"
] |
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"Metaspace"
] | [
"Replace",
"ByteFallback",
"Fuse",
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] | 35 | mistralai/Mistral-7B-v0.1 | mistralai | null | 454,618 | 23,222,227 | False | 2023-09-20T13:03:50Z | 2025-07-24T16:44:02Z | transformers | 4,057 | 3 | null | text-generation | {"parameters": {"BF16": 7241732096}, "total": 7241732096} | [
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] | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": null, "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForCausalLM",
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"en"
] | 7,241,732,096 | null | null | [
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"text"
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"text"
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68913539bd3d0a833438591d | 441f36ff7235b58e | 985b920c8f6b14f1 | 200,019 | BPE | 446,189 | false | true | true | true | 21 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 54 | openai/gpt-oss-20b | openai | null | 6,734,875 | 54,662,871 | False | 2025-08-04T22:33:29Z | 2025-08-26T17:25:47Z | transformers | 4,485 | 15 | null | text-generation | {"parameters": {"BF16": 1804459584, "U8": 19707494400}, "total": 21511953984} | [
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"original/c... | 6cee5e81ee83917806bbde320786a8fb61efebee | [
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
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"arxiv:2508.10925",
"license:apache-2.0",
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"endpoints_compatible",
"8-bit",
"mxfp4",
"deploy:azure",
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] | null | {"architectures": ["GptOssForCausalLM"], "model_type": "gpt_oss", "quantization_config": {"quant_method": "mxfp4"}, "tokenizer_config": {"bos_token": "<|startoftext|>", "eos_token": "<|return|>", "pad_token": "<|endoftext|>"}, "chat_template_jinja": "{#-\n In addition to the normal inputs of `messages` and `tools`, th... | {
"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": "text-generation", "tags": ["vllm"]} | <p align="center">
<img alt="gpt-oss-20b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-20b.svg">
</p>
<p align="center">
<a href="https://gpt-oss.com"><strong>Try gpt-oss</strong></a> ·
<a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Guides</strong></a> ·
<a href="https:/... | null | [
"apache-2.0"
] | null | null | 21,511,953,984 | null | null | [
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"GptOssForCausalLM",
"gpt_oss"
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"text"
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"BertNormalizer"
] | [
"BertPreTokenizer"
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"WordPiece"
] | 0 | cross-encoder/ms-marco-TinyBERT-L2-v2 | cross-encoder | {
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{
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"id": "nreimers/BERT-Tiny_L-2_H-128_A-2"
}
],
"relation": "quantized"
} | 485,031 | 22,944,825 | False | 2022-03-02T23:29:05Z | 2025-08-29T14:37:18Z | sentence-transformers | 43 | 0 | null | text-ranking | {"parameters": {"I64": 512, "F32": 4386049}, "total": 4386561} | [
".gitattributes",
"README.md",
"config.json",
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"onnx/model_qint8_avx512_vnni.onn... | 81d1926f67cb8eee2c2be17ca9f793c7c3bd20cc | [
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"base_model:quantized:nreimers/BERT-Tiny_L-2_H-128_A-2"... | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": ["nreimers/BERT-Tiny_L-2_H-128_A-2"], "datasets": ["sentence-transformers/msmarco"], "eval_results": null, "language": ["en"], "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-ranking",... | # Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a... | null | [
"apache-2.0"
] | [
"sentence-transformers/msmarco"
] | [
"en"
] | 4,386,561 | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
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"text"
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621ffdc036468d709f17433b | 634a9186a6003172 | 5f9f736c7dc6fbfa | 28,996 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 8 | google-bert/bert-large-cased | google-bert | null | 64,749 | 20,999,716 | False | 2022-03-02T23:29:04Z | 2024-02-19T11:06:20Z | transformers | 36 | 0 | null | fill-mask | {"parameters": {"F32": 334661958}, "total": 334661958} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"pytorch_model.bin",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt",
"whole-word-masking.tar.gz"
] | 06fa25dc1b6e034e91607fd67c247051ccaeff65 | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["bookcorpus", "wikipedia"], "eval_results": null, "language": "en", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # BERT large model (cased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is cased: it makes a difference
between engl... | null | [
"apache-2.0"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | 334,661,958 | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
63314d954db0a767bbe88341 | dda72719c593cc4c | 539cc58b8fdaf772 | 51,864 | BPE | 50,000 | false | true | true | true | 1,608 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 55 | openai/whisper-base.en | openai | null | 77,829 | 20,553,709 | False | 2022-09-26T06:58:29Z | 2024-01-22T17:55:08Z | transformers | 40 | 0 | [{"name": "whisper-base.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 | {"parameters": {"F32": 72593408}, "total": 72593408} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"generation_config.json",
"merges.txt",
"model.safetensors",
"normalizer.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer... | 911407f4214e0e1d82085af863093ec0b66f9cd6 | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"whisper",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"en",
"arxiv:2212.04356",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["WhisperForConditionalGeneration"], "model_type": "whisper", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": ... | {
"auto_model": "AutoModelForSpeechSeq2Seq",
"custom_class": null,
"pipeline_tag": "automatic-speech-recognition",
"processor": "AutoProcessor"
} | {"language": ["en"], "license": "apache-2.0", "pipeline_tag": "automatic-speech-recognition", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispee... | # Whisper
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours
of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need
for fine-tuning.
Whisper was proposed in the paper [Robust Speec... | null | [
"apache-2.0"
] | null | [
"en"
] | 72,593,408 | null | null | [
"WhisperForConditionalGeneration",
"whisper",
"AutoModelForSpeechSeq2Seq"
] | [
"automatic-speech-recognition"
] | [
"multimodal"
] | [
"audio"
] | [
"text"
] |
621ffdc136468d709f17dc94 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | martin-ha/toxic-comment-model | martin-ha | null | 31,735 | 21,491,286 | False | 2022-03-02T23:29:05Z | 2022-05-06T02:24:31Z | transformers | 67 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 9842c08b35a4687e7b211187d676986c8c96256d | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ## Model description
This model is a fine-tuned version of the [DistilBERT model](https://huggingface.co/transformers/model_doc/distilbert.html) to classify toxic comments.
## How to use
You can use the model with the following code.
```python
from transformers import AutoModelForSequenceClassification, AutoTokeniz... | null | null | null | [
"en"
] | null | null | null | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17b0e2 | ceaca5674104ea33 | c70f101c299b3fe1 | 31,006 | WordPiece | null | true | true | true | true | 9 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 56 | finiteautomata/beto-sentiment-analysis | finiteautomata | null | 853,962 | 23,122,660 | False | 2022-03-02T23:29:05Z | 2023-02-25T14:23:57Z | transformers | 35 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 9384c7f339a6d62c9e2e1d686f225f92e3a3353b | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"sentiment-analysis",
"es",
"arxiv:2106.09462",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["es"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["sentiment-analysis"]} | # Sentiment Analysis in Spanish
## beto-sentiment-analysis
**NOTE: this model will be removed soon -- use [pysentimiento/robertuito-sentiment-analysis](https://huggingface.co/pysentimiento/robertuito-sentiment-analysis) instead**
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/pysent... | null | null | null | [
"es"
] | null | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66cee7970454d6467b65060e | 7967e1fae39d98e0 | 75b5e47a4b00191e | 151,655 | BPE | 151,387 | true | true | true | true | 12 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 57 | Qwen/Qwen2-VL-2B-Instruct | Qwen | {
"models": [
{
"_id": "66d95a65e83856057c631778",
"id": "Qwen/Qwen2-VL-2B"
}
],
"relation": "finetune"
} | 2,359,643 | 30,238,810 | False | 2024-08-28T09:02:15Z | 2025-01-12T01:59:29Z | transformers | 497 | 1 | null | image-text-to-text | null | [
".gitattributes",
"LICENSE",
"README.md",
"chat_template.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"preprocessor_config.json",
"tokenizer.json",
"tokenizer_config.json"... | 895c3a49bc3fa70a340399125c650a463535e71c | [
"transformers",
"safetensors",
"qwen2_vl",
"image-text-to-text",
"multimodal",
"conversational",
"en",
"arxiv:2409.12191",
"arxiv:2308.12966",
"base_model:Qwen/Qwen2-VL-2B",
"base_model:finetune:Qwen/Qwen2-VL-2B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
... | null | {"architectures": ["Qwen2VLForConditionalGeneration"], "model_type": "qwen2_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 helpfu... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": ["Qwen/Qwen2-VL-2B"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["multimodal"]} | # Qwen2-VL-2B-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
We're excited to unveil **Qwen2-VL**, the la... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"AutoModelForImageTextToText",
"qwen2_vl",
"Qwen2VLForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
621ffdc136468d709f181375 | 8a8c94e1a59f5186 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 11 | vennify/t5-base-grammar-correction | vennify | null | 109,037 | 19,513,475 | False | 2022-03-02T23:29:05Z | 2022-01-14T16:35:23Z | transformers | 183 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | 9e4a09d21dca1072a69302df9261289d03c3ed78 | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"grammar",
"en",
"dataset:jfleg",
"arxiv:1702.04066",
"license:cc-by-nc-sa-4.0",
"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": ["jfleg"], "eval_results": null, "language": "en", "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": ["grammar", "text2text-generation"]} | # T5 Grammar Correction
This model generates a revised version of inputted text with the goal of containing fewer grammatical errors.
It was trained with [Happy Transformer](https://github.com/EricFillion/happy-transformer)
using a dataset called [JFLEG](https://arxiv.org/abs/1702.04066). Here's a [full article](htt... | null | [
"cc-by-nc-sa-4.0"
] | [
"jfleg"
] | [
"en"
] | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
650015a612c1442d9930ad41 | 0becab10e3734e80 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 58 | BAAI/bge-reranker-large | BAAI | null | 813,841 | 22,878,119 | False | 2023-09-12T07:39:18Z | 2024-05-11T13:39:02Z | transformers | 454 | 1 | [{"name": "bge-reranker-base", "results": [{"task": {"type": "Reranking"}, "dataset": {"type": "C-MTEB/CMedQAv1-reranking", "name": "MTEB CMedQAv1", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "map", "value": 81.27206722525007, "verified": false}, {"type": "mrr", "value": 84.14238095... | feature-extraction | {"parameters": {"I64": 514, "F32": 559891457}, "total": 559891971} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"onnx/model.onnx",
"onnx/model.onnx_data",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 55611d7bca2a7133960a6d3b71e083071bbfc312 | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"xlm-roberta",
"text-classification",
"mteb",
"feature-extraction",
"en",
"zh",
"arxiv:2401.03462",
"arxiv:2312.15503",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"text-embeddings-infer... | null | {"architectures": ["XLMRobertaForSequenceClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": "... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"language": ["en", "zh"], "license": "mit", "pipeline_tag": "feature-extraction", "tags": ["mteb"], "model-index": [{"name": "bge-reranker-base", "results": [{"task": {"type": "Reranking"}, "dataset": {"name": "MTEB CMedQAv1", "type": "C-MTEB/CMedQAv1-reranking", "config": "default", "split": "test", "revision": "None... | **We have updated the [new reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker), supporting larger lengths, more languages, and achieving better performance.**
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
... | null | [
"mit"
] | null | [
"en",
"zh"
] | 559,891,971 | null | null | [
"AutoModelForSequenceClassification",
"XLMRobertaForSequenceClassification",
"xlm-roberta"
] | [
"feature-extraction",
"text-classification"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc036468d709f17436a | c217221ff1d632ed | c23356a9adc44e29 | 32,000 | Unigram | null | true | true | true | true | 9 | Sequence | Sequence | Metaspace | [
"Replace",
"Replace",
"NFKD",
"StripAccents",
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 59 | xlnet/xlnet-base-cased | xlnet | null | 527,654 | 21,059,561 | False | 2022-03-02T23:29:04Z | 2023-01-24T14:50:31Z | transformers | 81 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"generation_config_for_text_generation.json",
"pytorch_model.bin",
"rust_model.ot",
"spiece.model",
"tf_model.h5",
"tokenizer.json"
] | ceaa69c7bc5e512b5007106a7ccbb7daf24b2c79 | [
"transformers",
"pytorch",
"tf",
"rust",
"xlnet",
"text-generation",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1906.08237",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["XLNetLMHeadModel"], "model_type": "xlnet"} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["bookcorpus", "wikipedia"], "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # XLNet (base-sized model)
XLNet model pre-trained on English language. It was introduced in the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al. and first released in [this repository](https://github.com/zihangdai/xlnet/).
Disclaimer:... | null | [
"mit"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"XLNetLMHeadModel",
"xlnet"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
62c7fc5d5bde502a1927e753 | 7903ca758156b287 | ffae8233e53a99e4 | 256,204 | BPE | 514,098 | true | true | true | true | 207 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 60 | facebook/nllb-200-distilled-600M | facebook | null | 926,215 | 21,426,934 | False | 2022-07-08T09:43:57Z | 2024-02-14T17:18:36Z | transformers | 874 | 2 | null | translation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | f8d333a098d19b4fd9a8b18f94170487ad3f821d | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"nllb",
"translation",
"ace",
"acm",
"acq",
"aeb",
"af",
"ajp",
"ak",
"als",
"am",
"apc",
"ar",
"ars",
"ary",
"arz",
"as",
"ast",
"awa",
"ayr",
"azb",
"azj",
"ba",
"bm",
"ban",
"be",
"bem",
"b... | null | {"architectures": ["M2M100ForConditionalGeneration"], "model_type": "m2m_100", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": "<pad>", "... | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["flores-200"], "eval_results": null, "language": ["ace", "acm", "acq", "aeb", "af", "ajp", "ak", "als", "am", "apc", "ar", "ars", "ary", "arz", "as", "ast", "awa", "ayr", "azb", "azj", "ba", "bm", "ban", "be", "bem", "bn", "bho", "bjn", "bo", "bs", "bug", "bg", "ca", "ceb", "cs", "cjk"... | # NLLB-200
This is the model card of NLLB-200's distilled 600M variant.
Here are the [metrics](https://tinyurl.com/nllb200densedst600mmetrics) for that particular checkpoint.
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algori... | null | [
"cc-by-nc-4.0"
] | [
"flores-200"
] | [
"ace",
"acm",
"acq",
"aeb",
"af",
"ajp",
"ak",
"als",
"am",
"apc",
"ar",
"ars",
"ary",
"arz",
"as",
"ast",
"awa",
"ayr",
"azb",
"azj",
"ba",
"bm",
"ban",
"be",
"bem",
"bn",
"bho",
"bjn",
"bo",
"bs",
"bug",
"bg",
"ca",
"ceb",
"cs",
"cjk",
"ckb",... | null | null | [
"bleu",
"spbleu",
"chrf++"
] | [
"AutoModelForSeq2SeqLM",
"m2m_100",
"M2M100ForConditionalGeneration"
] | [
"text2text-generation",
"translation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f180298 | 5ef9eb7677e33416 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 19 | sentence-transformers/all-roberta-large-v1 | sentence-transformers | null | 661,247 | 22,177,273 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:35:32Z | sentence-transformers | 66 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 355359744}, "total": 355360258} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"data_config.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",
"o... | cf74d8acd4f198de950bf004b262e6accfed5d2c | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"openvino",
"roberta",
"fill-mask",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
... | null | {"architectures": ["RobertaForMaskedLM"], "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": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "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": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # all-roberta-large-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformer... | null | [
"apache-2.0"
] | null | [
"en"
] | 355,360,258 | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask",
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6695cd12321386ed51d7bc22 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | meta-llama/Llama-3.1-405B | meta-llama | null | 386,304 | 20,313,172 | manual | 2024-07-16T01:29:54Z | 2024-09-25T17:01:49Z | transformers | 965 | 1 | null | text-generation | {"parameters": {"BF16": 405853388800}, "total": 405853388800} | [
".gitattributes",
"LICENSE",
"README.md",
"USE_POLICY.md",
"config.json",
"generation_config.json",
"model-00001-of-00191.safetensors",
"model-00002-of-00191.safetensors",
"model-00003-of-00191.safetensors",
"model-00004-of-00191.safetensors",
"model-00005-of-00191.safetensors",
"model-00006-o... | b906e4dc842aa489c962f9db26554dcfdde901fe | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"license:llama3.1",
"eval-results",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|end_of_text|>"}} | {
"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": ["facebook", "meta... | null | null | [
"llama3.1"
] | null | [
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th"
] | 405,853,388,800 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65d45c6c492611d68f3084a2 | 84d05c6a5fead2ab | 893a35bd62abcf4d | 32,002 | BPE | 58,980 | true | false | true | true | 5 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 61 | llava-hf/llava-v1.6-mistral-7b-hf | llava-hf | null | 630,755 | 20,550,360 | False | 2024-02-20T08:01:48Z | 2025-12-22T09:41:22Z | transformers | 304 | 0 | null | image-text-to-text | {"parameters": {"F16": 7566747648}, "total": 7566747648} | [
".gitattributes",
"README.md",
"added_tokens.json",
"chat_template.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",
"pr... | 2424fdd47412fccc66d91719126b420e9fbd7065 | [
"transformers",
"safetensors",
"llava_next",
"image-text-to-text",
"vision",
"conversational",
"en",
"arxiv:2310.03744",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlavaNextForConditionalGeneration"], "model_type": "llava_next", "processor_config": {"chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{ '<<SYS>>\n' + message['content'][0]['text'] + '\n<</SYS>>\n\n' }}{% elif message['role'] == 'user' %}{{ '[INST] ' }}{# Render al... | {
"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": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["vision", "image-text-to-text"], "inference": true} | # LLaVa-Next, leveraging [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) as LLM
The LLaVA-NeXT model was proposed in [LLaVA-NeXT: Improved reasoning, OCR, and world knowledge](https://llava-vl.github.io/blog/2024-01-30-llava-next/) by Haotian Liu, Chunyuan Li, Yuheng Li,... | null | [
"apache-2.0"
] | null | [
"en"
] | 7,566,747,648 | null | null | [
"AutoModelForImageTextToText",
"LlavaNextForConditionalGeneration",
"llava_next"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
66cee7d175a88154443a4056 | 7967e1fae39d98e0 | 75b5e47a4b00191e | 151,655 | BPE | 151,387 | true | true | true | true | 12 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 57 | Qwen/Qwen2-VL-7B-Instruct | Qwen | {
"models": [
{
"_id": "66d95a7ee8dd73ce1ff235b6",
"id": "Qwen/Qwen2-VL-7B"
}
],
"relation": "finetune"
} | 1,479,051 | 25,722,072 | False | 2024-08-28T09:03:13Z | 2025-02-06T05:18:08Z | transformers | 1,270 | 0 | null | image-text-to-text | {"parameters": {"BF16": 8291375616}, "total": 8291375616} | [
".gitattributes",
"LICENSE",
"README.md",
"chat_template.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00005.safetensors",
"model-00002-of-00005.safetensors",
"model-00003-of-00005.safetensors",
"model-00004-of-00005.safetensors",
"model-00005-of-00005.safetenso... | eed13092ef92e448dd6875b2a00151bd3f7db0ac | [
"transformers",
"safetensors",
"qwen2_vl",
"image-text-to-text",
"multimodal",
"conversational",
"en",
"arxiv:2409.12191",
"arxiv:2308.12966",
"base_model:Qwen/Qwen2-VL-7B",
"base_model:finetune:Qwen/Qwen2-VL-7B",
"license:apache-2.0",
"eval-results",
"text-generation-inference",
"endpoi... | null | {"architectures": ["Qwen2VLForConditionalGeneration"], "model_type": "qwen2_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 helpfu... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": ["Qwen/Qwen2-VL-7B"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["multimodal"], "new_version": "Qwen/Qw... | # Qwen2-VL-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
We're excited to unveil **Qwen2-VL**, the la... | null | [
"apache-2.0"
] | null | [
"en"
] | 8,291,375,616 | null | null | [
"AutoModelForImageTextToText",
"qwen2_vl",
"Qwen2VLForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
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"BertNormalizer"
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"BertPreTokenizer"
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"WordPiece"
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"t... | eb6b20cd65fcbdf7a2bc4ebac97908b3b21da981 | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"nomic_bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"transformers",
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"custom_code",
"en",
"arxiv:2402.01613",
"license:apache-2.0",
"model-index",
"text-embeddings-inference",
"endpoints_compa... | null | {"architectures": ["NomicBertModel"], "auto_map": {"AutoConfig": "nomic-ai/nomic-bert-2048--configuration_hf_nomic_bert.NomicBertConfig", "AutoModel": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertModel", "AutoModelForMaskedLM": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertForPreTraining"}, "... | {
"auto_model": "AutoModel",
"custom_class": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertModel",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"language": ["en"], "library_name": "sentence-transformers", "license": "apache-2.0", "pipeline_tag": "sentence-similarity", "tags": ["feature-extraction", "sentence-similarity", "mteb", "transformers", "transformers.js"], "new_version": "nomic-ai/nomic-embed-text-v1.5", "model-index": [{"name": "epoch_0_model", "resu... | # nomic-embed-text-v1: A Reproducible Long Context (8192) Text Embedder
[Blog](https://www.nomic.ai/blog/posts/nomic-embed-text-v1) | [Technical Report](https://arxiv.org/abs/2402.01613) | [AWS SageMaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-tpqidcj54zawi) | [Atlas Embedding and Unstructured Da... | null | [
"apache-2.0"
] | null | [
"en"
] | 136,731,648 | null | null | [
"AutoModel",
"NomicBertModel",
"nomic_bert",
"nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f181aae | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | yikuan8/Clinical-Longformer | yikuan8 | null | 451,968 | 18,617,154 | False | 2022-03-02T23:29:05Z | 2023-01-24T20:58:27Z | transformers | 69 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 5e2ebe8f6e98d4751eaf71a8514e0edbe73989a2 | [
"transformers",
"pytorch",
"longformer",
"fill-mask",
"clinical",
"en",
"arxiv:2201.11838",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LongformerForMaskedLM"], "model_type": "longformer", "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": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["longformer", "clinical"]} | <span style="font-size:larger;">**Clinical-Longformer**</span> is a clinical knowledge enriched version of Longformer that was further pre-trained using MIMIC-III clinical notes. It allows up to 4,096 tokens as the model input. Clinical-Longformer consistently out-performs ClinicalBERT across 10 baseline dataset for at... | null | null | null | [
"en"
] | null | null | null | [
"longformer",
"LongformerForMaskedLM",
"AutoModelForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17e9b5 | 193d5960a8efbbf2 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 63 | obi/deid_roberta_i2b2 | obi | null | 545,098 | 19,976,148 | False | 2022-03-02T23:29:05Z | 2025-02-23T03:36:26Z | transformers | 38 | 0 | null | token-classification | {"parameters": {"I64": 514, "F32": 354356269}, "total": 354356783} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 78f2152eb93ddd817290ce8dbe46f1a6685e09fc | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"token-classification",
"deidentification",
"medical notes",
"ehr",
"phi",
"en",
"dataset:I2B2",
"arxiv:1907.11692",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForTokenClassification"], "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": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["I2B2"], "eval_results": null, "language": ["en"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["F1", "Recall", "Precision"], "model_name": null, "pipeline_tag": null, "tags": ["deidentification", "medical notes", "ehr", "phi"], "thumb... | # Model Description
* A RoBERTa [[Liu et al., 2019]](https://arxiv.org/pdf/1907.11692.pdf) model fine-tuned for de-identification of medical notes.
* Sequence Labeling (token classification): The model was trained to predict protected health information (PHI/PII) entities (spans). A list of protected health informatio... | null | [
"mit"
] | [
"I2B2"
] | [
"en"
] | 354,356,783 | null | [
"F1",
"Recall",
"Precision"
] | [
"roberta",
"AutoModelForTokenClassification",
"RobertaForTokenClassification"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
64fff58369219ce3e48e1a1a | 382b5a4eb98f0296 | d965e4a9140ecf72 | 21,128 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 33 | BAAI/bge-large-zh-v1.5 | BAAI | null | 628,227 | 20,295,579 | False | 2023-09-12T05:22:11Z | 2024-04-02T14:00:04Z | sentence-transformers | 616 | 1 | null | feature-extraction | null | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 79e7739b6ab944e86d6171e44d24c997fc1e0116 | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"zh",
"arxiv:2401.03462",
"arxiv:2312.15503",
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"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"deploy... | 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": ["zh"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"]} | <h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Conta... | null | [
"mit"
] | null | [
"zh"
] | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
62517677aae41fe76930b91c | c88b36417e2a82ee | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 64 | kredor/punctuate-all | kredor | null | 591,875 | 20,140,148 | False | 2022-04-09T12:05:11Z | 2024-04-26T05:37:58Z | transformers | 25 | 0 | null | token-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | 0fe37019de3f5e4fbd83289fd94e07fa588e47df | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:wmt/europarl",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["XLMRobertaForTokenClassification"], "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": fals... | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["wmt/europarl"], "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["f1", "recall", "precision"], "model_name": null, "pipeline_tag": null, "tags": null} | This is based on [Oliver Guhr's work](https://huggingface.co/oliverguhr/fullstop-punctuation-multilang-large). The difference is that it is a finetuned xlm-roberta-base instead of an xlm-roberta-large and on twelve languages instead of four. The languages are: English, German, French, Spanish, Bulgarian, Italian, Polis... | null | [
"mit"
] | [
"wmt/europarl"
] | null | null | null | [
"f1",
"recall",
"precision"
] | [
"XLMRobertaForTokenClassification",
"AutoModelForTokenClassification",
"xlm-roberta"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
63dbda13057a688a88d0ed2a | 191e06b04083279c | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 26 | alimazhar-110/website_classification | alimazhar-110 | null | 463 | 17,115,038 | False | 2023-02-02T15:43:15Z | 2023-02-03T07:17:44Z | transformers | 38 | 0 | [{"name": "website_classification", "results": []}] | text-classification | null | [
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".gitignore",
"README.md",
"config.json",
"pytorch_model.bin",
"runs/Feb02_15-43-05_22b072ae40ca/1675352602.2127542/events.out.tfevents.1675352602.22b072ae40ca.112.1",
"runs/Feb02_15-43-05_22b072ae40ca/events.out.tfevents.1675352602.22b072ae40ca.112.0",
"runs/Feb03_06-52-12_efe35bf9c... | fe00d751072043616700d68bfdaf80b4ab487f62 | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["accuracy", "f1", "precision", "recall"], "model_name": "website_classification", "pipeline_tag": null, "tags": ["generated_from_trainer"]} | <!-- 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. -->
# website_classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unc... | null | [
"apache-2.0"
] | null | null | null | null | [
"accuracy",
"f1",
"precision",
"recall"
] | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17ba9b | 382b5a4eb98f0296 | d965e4a9140ecf72 | 21,128 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 33 | hfl/chinese-bert-wwm-ext | hfl | null | 14,426 | 17,231,763 | False | 2022-03-02T23:29:05Z | 2021-05-19T19:06:39Z | transformers | 190 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
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"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 2a995a880017c60e4683869e817130d8af548486 | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["zh"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Ya... | null | [
"apache-2.0"
] | null | [
"zh"
] | null | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
632dca502aabd87df295b7b9 | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | neulab/codebert-python | neulab | null | 95,381 | 17,496,758 | False | 2022-09-23T15:01:36Z | 2025-06-12T10:03:03Z | transformers | 26 | 0 | null | fill-mask | {"parameters": {"I64": 514, "F32": 124697433}, "total": 124697947} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | c4ff459e2a91d002d9bb4ab8cc2b7ee7125ef213 | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"arxiv:2302.05527",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "cls_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstri... | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | null | This is a `microsoft/codebert-base-mlm` model, trained for 1,000,000 steps (with `batch_size=32`) on **Python** code from the `codeparrot/github-code-clean` dataset, on the masked-language-modeling task.
It is intended to be used in CodeBERTScore: [https://github.com/neulab/code-bert-score](https://github.com/neulab/... | null | null | null | null | 124,697,947 | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
683f05dc98de733bf4c59f3d | 6b8b206af73b863d | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
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<p align="center">
<img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/logo_qwen3.png" width="400"/>
<p>
## Highlights
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building up... | null | [
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## Model Details
### Model Description
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} | {"language": ["en"], "library_name": "transformers", "license": "apache-2.0", "tags": ["sentence-transformers", "gte", "mteb", "transformers.js", "sentence-similarity"], "model-index": [{"name": "gte-base-en-v1.5", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassifica... | <!-- **English** | [中文](./README_zh.md) -->
# gte-base-en-v1.5
We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance.
The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/new... | null | [
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# sentence-transformers/distilbert-base-nli-mean-tokens
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MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts.
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The Grounding DINO model was proposed in [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://arxiv.org/abs/2303.05499) by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu,... | null | [
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"special_tokens_map.json",
"tokenizer.json",
"... | a5068878eee0dd41fb6806aa08157582ce9e1acd | [
"transformers",
"safetensors",
"llava_llama",
"text-generation",
"visual-question-answering",
"dataset:Lin-Chen/ShareGPT4V",
"dataset:ShareGPT4Video/ShareGPT4Video",
"arxiv:2406.04325",
"license:apache-2.0",
"region:us"
] | null | {"architectures": ["LlavaLlamaForCausalLM"], "model_type": "llava_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'] | ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": ["Lin-Chen/ShareGPT4V", "ShareGPT4Video/ShareGPT4Video"], "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": "visual-question-answering", "tags": null, "i... | <br>
<br>
# sharegpt4video-8b Model Card
## Model details
**Model type:**
sharegpt4video-8b is an open-source video chatbot trained by fine-tuning the entire model on open-source [video instruction data](https://huggingface.co/datasets/ShareGPT4Video/ShareGPT4Video). The training process takes around 5 hour on 32xA1... | null | [
"apache-2.0"
] | [
"Lin-Chen/ShareGPT4V",
"ShareGPT4Video/ShareGPT4Video"
] | null | null | null | null | [
"AutoModelForCausalLM",
"LlavaLlamaForCausalLM",
"llava_llama"
] | [
"visual-question-answering",
"text-generation"
] | [
"text",
"multimodal"
] | [
"text",
"image"
] | [
"logits",
"text"
] |
6354723706d707b33247ecaf | 820723d44f714bed | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 73 | michelleli99/emotion_text_classifier | michelleli99 | null | 13,779 | 16,272,115 | False | 2022-10-22T22:44:07Z | 2023-05-03T00:39:47Z | transformers | 148 | 0 | null | text-classification | null | [
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"tokenizer_config.json",
"training_args.bin",
"vocab.json"
] | dc4df5597fcda82589511c3900fedbe1c0ffec82 | [
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"roberta",
"text-classification",
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"sentiment",
"emotion",
"twitter",
"reddit",
"en",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | 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": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["distilroberta", "sentiment", "emotion", "twitter", "reddit"], "widget": [{"text": "Oh my ... | null | null | null | null | [
"en"
] | null | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc036468d709f17434c | 30094a1eabdbef67 | db117e9883c4d0ab | 50,257 | BPE | 50,000 | false | true | true | true | 1 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 4 | openai-community/gpt2-xl | openai-community | null | 218,521 | 15,944,717 | False | 2022-03-02T23:29:04Z | 2024-02-19T12:39:12Z | transformers | 376 | 0 | null | text-generation | {"parameters": {"F32": 1607942848}, "total": 1607942848} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"generation_config.json",
"generation_config_for_text_generation.json",
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"rust_model.ot",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 15ea56dee5df4983c59b2538573817e1667135e2 | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "tokenizer_config": {}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # GPT-2 XL
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact... | null | [
"mit"
] | null | [
"en"
] | 1,607,942,848 | null | null | [
"GPT2LMHeadModel",
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] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
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656eedec8bb9f4f8d95d6376 | b49e169598db4d83 | 0cdc2927ce4cf866 | 32,002 | BPE | 61,249 | true | false | true | true | 5 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 74 | llava-hf/llava-1.5-7b-hf | llava-hf | null | 3,556,657 | 24,312,556 | False | 2023-12-05T09:31:24Z | 2025-06-06T08:42:20Z | transformers | 351 | 2 | null | image-text-to-text | {"parameters": {"F16": 7063427072}, "total": 7063427072} | [
".gitattributes",
"README.md",
"added_tokens.json",
"chat_template.jinja",
"chat_template.json",
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"generation_config.json",
"model-00001-of-00003.safetensors",
"model-00002-of-00003.safetensors",
"model-00003-of-00003.safetensors",
"model.safetensors.index.json",
"preprocessor_co... | b234b804b114d9e37bb655e11cbbb5f5e971b7a9 | [
"transformers",
"safetensors",
"llava",
"image-text-to-text",
"vision",
"conversational",
"en",
"dataset:liuhaotian/LLaVA-Instruct-150K",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlavaForConditionalGeneration"], "model_type": "llava", "processor_config": {"chat_template": "{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalt... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": ["liuhaotian/LLaVA-Instruct-150K"], "eval_results": null, "language": ["en"], "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["vision", "image-text-to-text"], "arxi... | # LLaVA Model Card
Below is the model card of Llava model 7b, which is copied from the original Llava model card that you can find [here](https://huggingface.co/liuhaotian/llava-v1.5-13b).
Check out... | null | [
"llama2"
] | [
"liuhaotian/LLaVA-Instruct-150K"
] | [
"en"
] | 7,063,427,072 | null | null | [
"AutoModelForImageTextToText",
"LlavaForConditionalGeneration",
"llava"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
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"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 51 | sentence-transformers/stsb-xlm-r-multilingual | sentence-transformers | null | 120,782 | 16,215,107 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:22:22Z | sentence-transformers | 53 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 278043648}, "total": 278044162} | [
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"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
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"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/m... | a9a41e8dd95580d588c64113c4dcfca5ec6682e8 | [
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"onnx",
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"openvino",
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"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["XLMRobertaModel"], "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, "normalized": ... | {
"auto_model": "AutoModel",
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"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/stsb-xlm-r-multilingual
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 yo... | null | [
"apache-2.0"
] | null | null | 278,044,162 | null | null | [
"XLMRobertaModel",
"AutoModel",
"xlm-roberta"
] | [
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"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
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] |
621ffdc036468d709f174355 | 8a8c94e1a59f5186 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 11 | google-t5/t5-3b | google-t5 | null | 533,672 | 18,445,256 | False | 2022-03-02T23:29:04Z | 2024-01-29T15:44:49Z | transformers | 52 | 0 | null | translation | {"parameters": {"F32": 2851599360}, "total": 2851599360} | [
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"config.json",
"model.safetensors",
"pytorch_model.bin",
"spiece.model",
"tf_model.h5",
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] | bed96aab9ee46012a5046386105ee5fd0ac572f0 | [
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"t5",
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"translation",
"en",
"fr",
"ro",
"de",
"multilingual",
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"arxiv:1805.12471",
"arxiv:1708.00055",
"arxiv:1704.05426",
"arxiv:1606.05250",
"arxiv:1808.09121",
"arxiv:1810.12885",... | null | {"architectures": ["T5WithLMHeadModel"], "model_type": "t5"} | {
"auto_model": "AutoModelWithLMHead",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["c4"], "eval_results": null, "language": ["en", "fr", "ro", "de", "multilingual"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["summarization", "translation"]} | # Model Card for T5-3B
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bi... | null | [
"apache-2.0"
] | [
"c4"
] | [
"en",
"fr",
"ro",
"de",
"multilingual"
] | 2,851,599,360 | null | null | [
"t5",
"T5WithLMHeadModel",
"AutoModelWithLMHead"
] | [
"translation",
"summarization",
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 75 | deepseek-ai/DeepSeek-R1-Distill-Qwen-32B | deepseek-ai | null | 965,513 | 23,041,108 | False | 2025-01-20T09:19:00Z | 2025-02-24T03:31:29Z | transformers | 1,526 | 2 | null | text-generation | {"parameters": {"BF16": 32763876352}, "total": 32763876352} | [
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"... | 711ad2ea6aa40cfca18895e8aca02ab92df1a746 | [
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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... | {
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"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # DeepSeek-R1
<!-- 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 | [
"mit"
] | null | null | 32,763,876,352 | null | null | [
"AutoModelForCausalLM",
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"qwen2"
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"text"
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"text"
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"Metaspace"
] | [
"Replace",
"ByteFallback",
"Fuse",
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] | 35 | mistralai/Mistral-7B-Instruct-v0.1 | mistralai | {
"models": [
{
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"id": "mistralai/Mistral-7B-v0.1"
}
],
"relation": "finetune"
} | 379,396 | 16,419,678 | False | 2023-09-27T14:31:52Z | 2025-07-24T16:44:46Z | transformers | 1,828 | 1 | null | text-generation | null | [
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"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
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} | {"base_model": "mistralai/Mistral-7B-v0.1", "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": ["finetuned", "mistral-common"], "inference": fals... | null | null | [
"apache-2.0"
] | null | null | null | null | null | [
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63314c44fece0490c3c94e60 | 61736c54febdeba6 | aa8af6ff55c14796 | 51,865 | BPE | 50,000 | false | true | true | true | 1,608 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 16 | openai/whisper-medium | openai | null | 601,301 | 17,245,663 | False | 2022-09-26T06:52:52Z | 2024-02-29T10:57:42Z | transformers | 282 | 0 | [{"name": "whisper-medium", "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", "value... | automatic-speech-recognition | null | [
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"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
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"audio",
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"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
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"fi",
"vi",
"he",
"... | null | {"architectures": ["WhisperForConditionalGeneration"], "model_type": "whisper", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForSpeechSeq2Seq",
"custom_class": null,
"pipeline_tag": "automatic-speech-recognition",
"processor": "AutoProcessor"
} | {"language": ["en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", ... | # 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 | [
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"bg",
"lt",
"la",
"mi",
"ml",
"cy",
"sk"... | null | null | null | [
"WhisperForConditionalGeneration",
"whisper",
"AutoModelForSpeechSeq2Seq"
] | [
"automatic-speech-recognition"
] | [
"multimodal"
] | [
"audio"
] | [
"text"
] |
66944f1fe0c5c2e493a804f5 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | meta-llama/Llama-3.1-8B | meta-llama | null | 1,387,690 | 20,616,230 | manual | 2024-07-14T22:20:15Z | 2024-10-16T22:00:37Z | transformers | 2,128 | 16 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
".gitattributes",
"LICENSE",
"README.md",
"USE_POLICY.md",
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"en",
"de",
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"es",
"th",
"arxiv:2204.05149",
"license:llama3.1",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|end_of_text|>"}} | {
"auto_model": "AutoModelForCausalLM",
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} | {"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": ["facebook", "meta... | null | null | [
"llama3.1"
] | null | [
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th"
] | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17cbd2 | b6a49271ca7583d0 | dc945455af795596 | 32,000 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 76 | jhgan/ko-sroberta-multitask | jhgan | null | 834,039 | 16,467,922 | False | 2022-03-02T23:29:05Z | 2022-06-13T16:34:48Z | sentence-transformers | 143 | 0 | null | sentence-similarity | null | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"eval/similarity_evaluation_sts-dev_results.csv",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"similarity_evaluation_sts-test_results.csv",
"special_tokens_map.json",... | ab957ae6a91e99c4cad36d52063a2a9cf1bf4419 | [
"sentence-transformers",
"pytorch",
"tf",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"ko",
"arxiv:2004.03289",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaModel"], "model_type": "roberta", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "bos_token": "[CLS]", "eos_token": "[SEP]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "ko", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "tr... | # ko-sroberta-multitask
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes ea... | null | null | null | [
"ko"
] | null | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
66e81c0ecfd2f50991047e92 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-0.5B-Instruct | Qwen | {
"models": [
{
"_id": "66e6cfeb6998c3d86c14e163",
"id": "Qwen/Qwen2.5-0.5B"
}
],
"relation": "finetune"
} | 5,593,599 | 32,645,708 | False | 2024-09-16T11:52:46Z | 2024-09-25T12:32:56Z | transformers | 487 | 2 | null | text-generation | {"parameters": {"BF16": 494032768}, "total": 494032768} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 7ae557604adf67be50417f59c2c2f167def9a775 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:finetune:Qwen/Qwen2.5-0.5B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- 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/Qwen2.5-0.5B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text... | # Qwen2.5-0.5B-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 kn... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | 494,032,768 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
680f0c5b4e1c1da6491f3b9d | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen3-4B-Base | Qwen | null | 961,921 | 15,887,391 | False | 2025-04-28T05:04:27Z | 2025-07-26T03:45:37Z | transformers | 83 | 1 | null | text-generation | {"parameters": {"BF16": 4022468096}, "total": 4022468096} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
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"model-00003-of-00003.safetensors",
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"tokenizer.json",
"tokenizer_config.json",
"vocab.json"... | 906bfd4b4dc7f14ee4320094d8b41684abff8539 | [
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"conversational",
"arxiv:2505.09388",
"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": 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} | null | null | [
"apache-2.0"
] | null | null | 4,022,468,096 | null | null | [
"AutoModelForCausalLM",
"Qwen3ForCausalLM",
"qwen3"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65ba36c403bb6d3836afbee3 | c1b502033637b918 | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 77 | tasksource/deberta-small-long-nli | tasksource | {
"models": [
{
"_id": "621ffdc136468d709f17dee6",
"id": "microsoft/deberta-v3-small"
}
],
"relation": "finetune"
} | 30,619 | 13,365,097 | False | 2024-01-31T12:02:12Z | 2024-08-28T15:06:37Z | transformers | 49 | 0 | null | zero-shot-classification | {"parameters": {"F32": 141897219}, "total": 141897219} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json"
] | 9a77395d4d3751be9e2a69c4ae318491d9b3fffb | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"deberta-v3-small",
"deberta-v3",
"deberta",
"nli",
"natural-language-inference",
"multitask",
"multi-task",
"pipeline",
"extreme-multi-task",
"extreme-mtl",
"tasksource",
"zero-shot",
"rlhf",
"zero-sh... | 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-small", "datasets": ["nyu-mll/glue", "aps/super_glue", "facebook/anli", "tasksource/babi_nli", "sick", "snli", "scitail", "hans", "alisawuffles/WANLI", "tasksource/recast", "sileod/probability_words_nli", "joey234/nan-nli", "pietrolesci/nli_fever", "pietrolesci/breaking_nli", "pietr... | # Model Card for DeBERTa-v3-small-tasksource-nli
[DeBERTa-v3-small](https://hf.co/microsoft/deberta-v3-small) with context length of 1680 tokens fine-tuned on tasksource for 250k steps. I oversampled long NLI tasks (ConTRoL, doc-nli).
Training data include HelpSteer v1/v2, logical reasoning tasks (FOLIO, FOL-nli, Log... | null | [
"apache-2.0"
] | [
"nyu-mll/glue",
"aps/super_glue",
"facebook/anli",
"tasksource/babi_nli",
"sick",
"snli",
"scitail",
"hans",
"alisawuffles/WANLI",
"tasksource/recast",
"sileod/probability_words_nli",
"joey234/nan-nli",
"pietrolesci/nli_fever",
"pietrolesci/breaking_nli",
"pietrolesci/conj_nli",
"pietr... | [
"en"
] | 141,897,219 | null | [
"accuracy"
] | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
] | [
"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
638f09dbc4444c6ca86f3917 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | microsoft/git-base | microsoft | null | 12,057 | 13,257,863 | False | 2022-12-06T09:22:35Z | 2023-04-24T09:52:15Z | transformers | 106 | 0 | null | image-to-text | {"parameters": {"I64": 1221, "F32": 176619066}, "total": 176620287} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 1f7fe8444292beb4a259e3a5b6eba440cd5999d4 | [
"transformers",
"pytorch",
"safetensors",
"git",
"image-text-to-text",
"vision",
"image-to-text",
"image-captioning",
"en",
"arxiv:2205.14100",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["GitForCausalLM"], "model_type": "git", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "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": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": "microsoft/git-base", "pipeline_tag": "image-to-text", "tags": ["vision", "image-to-text", "image-captioning"]} | # GIT (GenerativeImage2Text), base-sized
GIT (short for GenerativeImage2Text) model, base-sized version. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [this repository](https://github.com/micro... | null | [
"mit"
] | null | [
"en"
] | 176,620,287 | null | null | [
"AutoModelForImageTextToText",
"GitForCausalLM",
"git"
] | [
"image-text-to-text",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
621ffdc036468d709f174333 | 2af4952b80b9ccb9 | 8a455ce24d6490bf | 30,000 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 78 | google-bert/bert-base-german-cased | google-bert | null | 464,276 | 15,322,556 | False | 2022-03-02T23:29:04Z | 2024-02-19T11:03:41Z | transformers | 82 | 0 | null | fill-mask | {"parameters": {"F32": 109705010}, "total": 109705010} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"onnx/added_tokens.json",
"onnx/config.json",
"onnx/generation_config.json",
"onnx/model.onnx",
"onnx/special_tokens_map.json",
"onnx/tokenizer.json",
"onnx/tokenizer_config.json",
"onnx/vocab.txt",
"p... | 0b061b4f7ce140d10dce6b6bd8a77da8ec80931b | [
"transformers",
"pytorch",
"tf",
"jax",
"onnx",
"safetensors",
"bert",
"fill-mask",
"exbert",
"de",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "de", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["exbert"], "thumbnail": "https://static.tildacdn.com/tild6438-3730-4164-b266-613634323466... | <a href="https://huggingface.co/exbert/?model=bert-base-german-cased">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
# German BERT
## Overview
**Language model:** bert-base-cased
**La... | null | [
"mit"
] | null | [
"de"
] | 109,705,010 | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
64c1cfeb2ae1946c29711f43 | 6949732424a8d297 | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 42 | stabilityai/StableBeluga-7B | stabilityai | null | 957 | 13,159,635 | False | 2023-07-27T02:01:15Z | 2023-08-29T20:21:36Z | transformers | 128 | 0 | null | text-generation | {"parameters": {"F32": 2048, "F16": 6738415616}, "total": 6738417664} | [
".gitattributes",
"LICENSE.txt",
"LLAMA 2 LICENSE/LICENSE.txt",
"LLAMA 2 LICENSE/Responsible-Use-Guide.pdf",
"LLAMA 2 LICENSE/USE_POLICY.md",
"Notice",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetenso... | 5cc044e873924415d43837cda6165074af3ee0da | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:conceptofmind/cot_submix_original",
"dataset:conceptofmind/flan2021_submix_original",
"dataset:conceptofmind/t0_submix_original",
"dataset:conceptofmind/niv2_submix_original",
"arxiv:2307.09288",
"arxiv:2306.02707",
"tex... | 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": ["conceptofmind/cot_submix_original", "conceptofmind/flan2021_submix_original", "conceptofmind/t0_submix_original", "conceptofmind/niv2_submix_original"], "eval_results": null, "language": ["en"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metric... | # Stable Beluga 7B
Use [Stable Chat (Research Preview)](https://chat.stability.ai/chat) to test Stability AI's best language models for free
## Model Description
`Stable Beluga 7B` is a Llama2 7B model finetuned on an Orca style Dataset
## Usage
Start chatting with `Stable Beluga 7B` using the following code snipp... | null | null | [
"conceptofmind/cot_submix_original",
"conceptofmind/flan2021_submix_original",
"conceptofmind/t0_submix_original",
"conceptofmind/niv2_submix_original"
] | [
"en"
] | 6,738,417,664 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17a149 | b8db556f1f02dec9 | 7f171e98bdd0da5f | 83,828 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 79 | cointegrated/rubert-tiny2 | cointegrated | null | 1,135,308 | 17,129,341 | False | 2022-03-02T23:29:05Z | 2025-12-30T08:33:24Z | sentence-transformers | 166 | 3 | null | sentence-similarity | {"parameters": {"I64": 2048, "F32": 29376502}, "total": 29378550} | [
".gitattributes",
".gitignore",
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"tokenizer_config.json",
"vocab.txt"
] | e8ed3b0c8bbf4fb6984c3de043bf7d2f4e5969ae | [
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"pretraining",
"russian",
"fill-mask",
"embeddings",
"masked-lm",
"tiny",
"feature-extraction",
"sentence-similarity",
"transformers",
"ru",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForPreTraining"], "model_type": "bert", "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": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ru"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["russian", "fill-mask", "pretraining", "embeddings", "masked-lm", "tin... | This is an updated version of [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny): a small Russian BERT-based encoder with high-quality sentence embeddings. This [post in Russian](https://habr.com/ru/post/669674/) gives more details.
The differences from the previous version include:
- a larger... | null | [
"mit"
] | null | [
"ru"
] | 29,378,550 | null | null | [
"bert",
"AutoModelForPreTraining",
"BertForPreTraining"
] | [
"fill-mask",
"sentence-similarity",
"feature-extraction",
"pretraining"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6352c0d3507b679c3c5f41f4 | 2d0341bd3a73bae6 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 32 | google/flan-t5-xxl | google | null | 18,855 | 13,000,659 | False | 2022-10-21T15:54:59Z | 2023-07-27T11:42:14Z | transformers | 1,279 | 1 | null | null | {"parameters": {"F32": 11266928640}, "total": 11266928640} | [
".gitattributes",
"README.md",
"config.json",
"flax_model-00001-of-00005.msgpack",
"flax_model-00002-of-00005.msgpack",
"flax_model-00003-of-00005.msgpack",
"flax_model-00004-of-00005.msgpack",
"flax_model-00005-of-00005.msgpack",
"flax_model.msgpack.index.json",
"generation_config.json",
"model... | ae7c9136adc7555eeccc78cdd960dfd60fb346ce | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"t5",
"text2text-generation",
"en",
"fr",
"ro",
"de",
"multilingual",
"dataset:svakulenk0/qrecc",
"dataset:taskmaster2",
"dataset:djaym7/wiki_dialog",
"dataset:deepmind/code_contests",
"dataset:lambada",
"dataset:gsm8k",
"dat... | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["svakulenk0/qrecc", "taskmaster2", "djaym7/wiki_dialog", "deepmind/code_contests", "lambada", "gsm8k", "aqua_rat", "esnli", "quasc", "qed"], "eval_results": null, "language": ["en", "fr", "ro", "de", "multilingual"], "library_name": null, "license": "apache-2.0", "license_name": null, ... | # Model Card for FLAN-T5 XXL
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan2_architecture.jpg"
alt="drawing" width="600"/>
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Bias, Risk... | null | [
"apache-2.0"
] | [
"svakulenk0/qrecc",
"taskmaster2",
"djaym7/wiki_dialog",
"deepmind/code_contests",
"lambada",
"gsm8k",
"aqua_rat",
"esnli",
"quasc",
"qed"
] | [
"en",
"fr",
"ro",
"de",
"multilingual"
] | 11,266,928,640 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
62571853cf42812891a7184e | 191e06b04083279c | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 26 | Seethal/sentiment_analysis_generic_dataset | Seethal | null | 270 | 12,837,431 | False | 2022-04-13T18:37:07Z | 2022-04-19T06:26:33Z | transformers | 23 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | 26edc13094473094204e42245018bee141699c8c | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "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"
} | null | ## BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and English.
## Model description
BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashi... | null | null | null | null | null | null | null | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6510db76f141bc34f95198cb | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | IDEA-Research/grounding-dino-tiny | IDEA-Research | null | 604,900 | 16,447,578 | False | 2023-09-25T00:59:34Z | 2024-05-12T09:03:39Z | transformers | 97 | 0 | null | zero-shot-object-detection | {"parameters": {"I64": 28812, "F32": 172249090}, "total": 172277902} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | a2bb814dd30d776dcf7e30523b00659f4f141c71 | [
"transformers",
"pytorch",
"safetensors",
"grounding-dino",
"zero-shot-object-detection",
"vision",
"arxiv:2303.05499",
"license:apache-2.0",
"region:us"
] | null | {"architectures": ["GroundingDinoForObjectDetection"], "model_type": "grounding-dino", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForZeroShotObjectDetection",
"custom_class": null,
"pipeline_tag": "zero-shot-object-detection",
"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": "zero-shot-object-detection", "tags": ["vision"], "inference": false} | # Grounding DINO model (tiny variant)
The Grounding DINO model was proposed in [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://arxiv.org/abs/2303.05499) by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu,... | null | [
"apache-2.0"
] | null | null | 172,277,902 | null | null | [
"AutoModelForZeroShotObjectDetection",
"grounding-dino",
"GroundingDinoForObjectDetection"
] | [
"zero-shot-object-detection"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"image"
] |
621ffdc136468d709f1802ab | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | sentence-transformers/distilbert-base-nli-stsb-mean-tokens | sentence-transformers | null | 57,846 | 13,065,058 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:33:22Z | sentence-transformers | 11 | 0 | null | sentence-similarity | {"parameters": {"F32": 66362880}, "total": 66362880} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
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"config_sentence_transformers.json",
"model.safetensors",
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"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/m... | 84fc3c34014de8c85cde3fece74ab56fbe9d4fce | [
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"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
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] | null | {"architectures": ["DistilBertModel"], "model_type": "distilbert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | **⚠️ 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/distilbert-base-nli-stsb-mean-tokens
This is a [s... | null | [
"apache-2.0"
] | null | null | 66,362,880 | null | null | [
"AutoModel",
"distilbert",
"DistilBertModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f17ade9 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | facebook/dpr-ctx_encoder-single-nq-base | facebook | null | 407,887 | 14,006,397 | False | 2022-03-02T23:29:05Z | 2022-12-21T15:16:53Z | transformers | 25 | 0 | null | null | null | [
".DS_Store",
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | bb21a3c2b1656d60c6a8e920283bc40dabddadb8 | [
"transformers",
"pytorch",
"tf",
"dpr",
"en",
"dataset:nq_open",
"arxiv:2004.04906",
"arxiv:1702.08734",
"arxiv:1910.09700",
"license:cc-by-nc-4.0",
"region:us"
] | null | {"architectures": ["DPRContextEncoder"], "model_type": "dpr", "tokenizer_config": {}} | {
"auto_model": "DPRContextEncoder",
"custom_class": null,
"pipeline_tag": null,
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["nq_open"], "eval_results": null, "language": "en", "library_name": null, "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["dpr"], "inference": false} | # `dpr-ctx_encoder-single-nq-base`
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation-results)
- [Environment... | null | [
"cc-by-nc-4.0"
] | [
"nq_open"
] | [
"en"
] | null | null | null | [
"DPRContextEncoder",
"dpr"
] | [
null
] | null | null | null |
621ffdc136468d709f1814a1 | a5cf9094a13a7ef2 | f766a15c782f2300 | 66,050 | BPE | 64,000 | false | true | true | true | 1 | null | WhitespaceSplit | BPEDecoder | null | [
"WhitespaceSplit"
] | [
"BPEDecoder"
] | 80 | vinai/bertweet-base | vinai | null | 197,364 | 13,589,154 | False | 2022-03-02T23:29:05Z | 2024-08-20T03:58:08Z | transformers | 41 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"__init__.py",
"bpe.codes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"tf_model.h5",
"tokenization_bertweet.py",
"tokenization_bertweet_fast.py",
"tokenizer.json",
"vocab.txt"
] | b349c1243407b0dcffeabb2337497477286e27ab | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta"} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"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} | # <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets
BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) pre-training procedure.... | null | [
"mit"
] | null | null | null | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc036468d709f174345 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | distilbert/distilbert-base-uncased-distilled-squad | distilbert | null | 45,817 | 12,925,416 | False | 2022-03-02T23:29:04Z | 2024-05-06T13:46:39Z | transformers | 119 | 0 | null | question-answering | {"parameters": {"F32": 66364418}, "total": 66364418} | [
".gitattributes",
"384-8bits.tflite",
"384-fp16.tflite",
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"transformers",
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"safetensors",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DistilBertForQuestionAnswering"], "model_type": "distilbert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForQuestionAnswering",
"custom_class": null,
"pipeline_tag": "question-answering",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["squad"], "eval_results": null, "language": "en", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"text": "Which name is also used to describe the Amazon rain... | # DistilBERT base uncased distilled SQuAD
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmenta... | null | [
"apache-2.0"
] | [
"squad"
] | [
"en"
] | 66,364,418 | null | null | [
"AutoModelForQuestionAnswering",
"distilbert",
"DistilBertForQuestionAnswering"
] | [
"question-answering"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63526d7c7e4cc3135fd0f17c | 2d0341bd3a73bae6 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 32 | google/flan-t5-small | google | null | 578,708 | 14,936,788 | False | 2022-10-21T09:59:24Z | 2023-10-10T18:01:54Z | transformers | 469 | 1 | null | null | {"parameters": {"F32": 76961152}, "total": 76961152} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"generation_config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json"
] | 0fc9ddf78a1e988dac52e2dac162b0ede4fd74ab | [
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"dataset:taskmaster2",
"dataset:djaym7/wiki_dialog",
"dataset:deepmind/code_contests",
"dataset:lambada",
"dataset:gsm8k",
"dat... | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["svakulenk0/qrecc", "taskmaster2", "djaym7/wiki_dialog", "deepmind/code_contests", "lambada", "gsm8k", "aqua_rat", "esnli", "quasc", "qed"], "eval_results": null, "language": ["en", "fr", "ro", "de", "multilingual"], "library_name": null, "license": "apache-2.0", "license_name": null, ... | # Model Card for FLAN-T5 small
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan2_architecture.jpg"
alt="drawing" width="600"/>
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Bias, Ri... | null | [
"apache-2.0"
] | [
"svakulenk0/qrecc",
"taskmaster2",
"djaym7/wiki_dialog",
"deepmind/code_contests",
"lambada",
"gsm8k",
"aqua_rat",
"esnli",
"quasc",
"qed"
] | [
"en",
"fr",
"ro",
"de",
"multilingual"
] | 76,961,152 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
621ffdc136468d709f1802ff | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | sentence-transformers/stsb-roberta-base | sentence-transformers | null | 324,694 | 14,038,537 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:22:45Z | sentence-transformers | 1 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 124645632}, "total": 124646146} | [
".gitattributes",
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"README.md",
"config.json",
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"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.... | 32d471df2968a46d1fe447d66a9275e8e63fcf12 | [
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"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",... | **⚠️ 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/stsb-roberta-base
This is a [sentence-transformer... | null | [
"apache-2.0"
] | null | null | 124,646,146 | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6446b0089cb2a8f4a4ef5937 | 3a732ab237c338af | c21f0239d1cf0f86 | 65,024 | BPE | 64,784 | false | true | false | true | 12 | null | Sequence | ByteLevel | null | [
"Punctuation",
"ByteLevel",
"Digits",
"Split"
] | [
"ByteLevel"
] | 45 | tiiuae/falcon-7b | tiiuae | null | 151,767 | 12,972,988 | False | 2023-04-24T16:36:24Z | 2024-10-12T13:20:18Z | transformers | 1,099 | 0 | null | text-generation | {"parameters": {"BF16": 7217189760}, "total": 7217189760} | [
".gitattributes",
"README.md",
"config.json",
"configuration_falcon.py",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"modeling_falcon.py",
"pytorch_model-00001-of-00002.bin",
"pytorch_model-00002-of-00002.bin",... | ec89142b67d748a1865ea4451372db8313ada0d8 | [
"transformers",
"pytorch",
"safetensors",
"falcon",
"text-generation",
"custom_code",
"en",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2205.14135",
"arxiv:1911.02150",
"arxiv:2101.00027",
"arxiv:2005.14165",
"arxiv:2104.09864",
"arxiv:2306.01116",
"license:apache-2.0",
"text-generation... | null | {"architectures": ["FalconForCausalLM"], "auto_map": {"AutoConfig": "configuration_falcon.FalconConfig", "AutoModel": "modeling_falcon.FalconModel", "AutoModelForSequenceClassification": "modeling_falcon.FalconForSequenceClassification", "AutoModelForTokenClassification": "modeling_falcon.FalconForTokenClassification",... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["tiiuae/falcon-refinedweb"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "inference": false, "new_version": "tiiuae/falcon-1... | # 🚀 Falcon-7B
**Falcon-7B is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. It is made available under the Apache 2.0 license.**
*Paper coming soon* 😊.
... | null | [
"apache-2.0"
] | [
"tiiuae/falcon-refinedweb"
] | [
"en"
] | 7,217,189,760 | null | null | [
"AutoModelForCausalLM",
"falcon",
"FalconForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17a330 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | dandelin/vilt-b32-mlm | dandelin | null | 14,705 | 14,062,745 | False | 2022-03-02T23:29:05Z | 2022-07-06T12:18:37Z | transformers | 13 | 1 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 9507e9c3da12076e10f272e942569dc5190edc1c | [
"transformers",
"pytorch",
"vilt",
"fill-mask",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["ViltForMaskedLM"], "model_type": "vilt", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "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} | # Vision-and-Language Transformer (ViLT), pre-trained only
Vision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). 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 first... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"AutoModelForMaskedLM",
"vilt",
"ViltForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f1802e4 | 62ad3ad2b18d7994 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 81 | sentence-transformers/paraphrase-MiniLM-L3-v2 | sentence-transformers | null | 1,297,080 | 17,148,436 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:26:46Z | sentence-transformers | 29 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 17389824}, "total": 17390336} | [
".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... | 4ca70771034acceecb2e72475f72050fcdde4ddc | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:s2orc",
"dataset:ms_marco",
"dataset:wiki_atomic_edits",
"dataset:snli",
"dat... | 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": ["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-MiniLM-L3-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 yo... | 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 | 17,390,336 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
64ce027759503263d9b1d9f2 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | BAAI/bge-small-en | BAAI | null | 375,421 | 13,347,653 | False | 2023-08-05T08:04:07Z | 2023-12-13T03:53:21Z | transformers | 89 | 0 | [{"name": "bge-small-en", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 74.3432... | feature-extraction | {"parameters": {"I64": 512, "F32": 33360000}, "total": 33360512} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 2275a7bdee235e9b4f01fa73aa60d3311983cfea | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"mteb",
"sentence transformers",
"en",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"language": ["en"], "license": "mit", "tags": ["mteb", "sentence transformers"], "model-index": [{"name": "bge-small-en", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "en", "split": "test", "revision... | **Recommend switching to newest [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-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=#frequ... | null | [
"mit"
] | null | [
"en"
] | 33,360,512 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
63e1291d28f32df66563f006 | 8e4e571045f91525 | 0b1ca067fdcf74b8 | 50,266 | BPE | 50,000 | false | true | true | true | 3 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 82 | Salesforce/blip2-opt-2.7b | Salesforce | null | 567,758 | 14,924,181 | False | 2023-02-06T16:21:49Z | 2025-02-03T06:27:50Z | transformers | 436 | 0 | null | image-text-to-text | {"parameters": {"F32": 3744761856}, "total": 3744761856} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"preprocessor_config.json",
"processor_config.json",
"pytorch_model-00001-of-00002.... | 59a1ef6c1e5117b3f65523d1c6066825bcf315e3 | [
"transformers",
"pytorch",
"safetensors",
"blip-2",
"visual-question-answering",
"vision",
"image-to-text",
"image-captioning",
"image-text-to-text",
"en",
"arxiv:2301.12597",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Blip2ForConditionalGeneration"], "model_type": "blip-2", "tokenizer_config": {"bos_token": "</s>", "eos_token": "</s>", "pad_token": "<pad>", "unk_token": "</s>"}} | {
"auto_model": "AutoModelForVisualQuestionAnswering",
"custom_class": null,
"pipeline_tag": "visual-question-answering",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["vision", "image-to-text", "image-captioning", "visual-question-answering... | # BLIP-2, OPT-2.7b, pre-trained only
BLIP-2 model, leveraging [OPT-2.7b](https://huggingface.co/facebook/opt-2.7b) (a large language model with 2.7 billion parameters).
It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.... | null | [
"mit"
] | null | [
"en"
] | 3,744,761,856 | null | null | [
"Blip2ForConditionalGeneration",
"AutoModelForVisualQuestionAnswering",
"blip-2"
] | [
"image-text-to-text",
"visual-question-answering",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text",
"logits"
] |
678e12d6308aaf716869ea0e | dae53fc866d79bd6 | 1b9fd2d5dbbc6e0d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 83 | deepseek-ai/DeepSeek-R1-Distill-Llama-8B | deepseek-ai | null | 1,148,981 | 15,861,986 | False | 2025-01-20T09:09:42Z | 2025-02-24T03:32:07Z | transformers | 842 | 2 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"figures/benchmark.jpg",
"generation_config.json",
"model-00001-of-000002.safetensors",
"model-00002-of-000002.safetensors",
"model.safetensors.index.json",
"tokenizer.json",
"tokenizer_config.json"
] | 6a6f4aa4197940add57724a7707d069478df56b1 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2501.12948",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "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",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # DeepSeek-R1
<!-- 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 | [
"mit"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
678dc6fff905d106be796d8a | a54a9c2e7b95f458 | ef43ab3563390dbf | 128,815 | BPE | 127,741 | true | true | true | true | 818 | Sequence | Sequence | ByteLevel | [] | [
"Split",
"Split",
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 84 | deepseek-ai/DeepSeek-R1 | deepseek-ai | null | 2,203,466 | 17,068,124 | False | 2025-01-20T03:46:07Z | 2025-03-27T04:01:59Z | transformers | 13,107 | 12 | null | text-generation | {"parameters": {"BF16": 3918786560, "F8_E4M3": 680571043840, "F32": 41555600}, "total": 684531386000} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"configuration_deepseek.py",
"figures/benchmark.jpg",
"generation_config.json",
"model-00001-of-000163.safetensors",
"model-00002-of-000163.safetensors",
"model-00003-of-000163.safetensors",
"model-00004-of-000163.safetensors",
"model-00... | 56d4cbbb4d29f4355bab4b9a39ccb717a14ad5ad | [
"transformers",
"safetensors",
"deepseek_v3",
"text-generation",
"conversational",
"custom_code",
"arxiv:2501.12948",
"license:mit",
"eval-results",
"text-generation-inference",
"endpoints_compatible",
"fp8",
"region:us"
] | null | {"architectures": ["DeepseekV3ForCausalLM"], "auto_map": {"AutoConfig": "configuration_deepseek.DeepseekV3Config", "AutoModel": "modeling_deepseek.DeepseekV3Model", "AutoModelForCausalLM": "modeling_deepseek.DeepseekV3ForCausalLM"}, "model_type": "deepseek_v3", "quantization_config": {"quant_method": "fp8"}, "tokenizer... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # DeepSeek-R1
<!-- 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 | [
"mit"
] | null | null | 684,531,386,000 | null | null | [
"AutoModelForCausalLM",
"deepseek_v3",
"DeepseekV3ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
657a1fff16886e681230c05a | 83bf28f8b6767a2f | ae88e903e2b563a1 | 50,295 | BPE | 50,000 | false | true | true | true | 39 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 85 | microsoft/phi-2 | microsoft | null | 1,735,944 | 18,504,882 | False | 2023-12-13T21:19:59Z | 2025-12-08T11:35:44Z | transformers | 3,435 | 1 | null | text-generation | {"parameters": {"F16": 2779683840}, "total": 2779683840} | [
".gitattributes",
"CODE_OF_CONDUCT.md",
"LICENSE",
"NOTICE.md",
"README.md",
"SECURITY.md",
"added_tokens.json",
"config.json",
"data_summary_card.md",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.j... | 810d367871c1d460086d9f82db8696f2e0a0fcd0 | [
"transformers",
"safetensors",
"phi",
"text-generation",
"nlp",
"code",
"en",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["PhiForCausalLM"], "model_type": "phi", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "mit", "license_name": null, "license_link": "https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["nlp", "code"]} | ## Model Summary
Phi-2 is a Transformer with **2.7 billion** parameters. It was trained using the same data sources as [Phi-1.5](https://huggingface.co/microsoft/phi-1.5), augmented with a new data source that consists of various NLP synthetic texts and filtered websites (for safety and educational value). When assess... | null | [
"mit",
"https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE"
] | null | [
"en"
] | 2,779,683,840 | null | null | [
"AutoModelForCausalLM",
"phi",
"PhiForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
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This is the model card for the EMNLP 2021 paper [WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER](https://aclanthology.org/2021.findings-emnlp.215/). We fine-tuned a multilingual languag... | null | [
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should probably proofread and complete it, then remove this comment. -->
<img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="marg... | null | [
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] | [
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] | [
"en"
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"NFC"
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"ByteLevel"
] | 47 | Qwen/Qwen3-32B | Qwen | null | 3,778,408 | 24,845,577 | False | 2025-04-27T03:52:59Z | 2025-07-26T03:45:22Z | transformers | 673 | 3 | null | text-generation | null | [
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"https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE"
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"BertNormalizer"
] | [
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] | [
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] | 89 | klue/bert-base | klue | null | 43,856 | 11,227,878 | False | 2022-03-02T23:29:05Z | 2023-06-12T12:30:04Z | transformers | 62 | 1 | null | fill-mask | {"parameters": {"I64": 512, "F32": 111243010}, "total": 111243522} | [
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] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": "ko", "library_name": null, "license": "cc-by-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["korean", "klue"], "mask_token": "[MASK]", "widget": [{"text": "\ub300\ud55c\ubb... | # KLUE BERT base
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-... | null | [
"cc-by-sa-4.0"
] | null | [
"ko"
] | 111,243,522 | null | null | [
"AutoModelForMaskedLM",
"bert",
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] | [
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] | [
"text"
] | [
"text"
] | [
"logits"
] |
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"NFC"
] | [
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"ByteLevel"
] | [
"ByteLevel"
] | 75 | deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | deepseek-ai | null | 633,494 | 17,413,391 | False | 2025-01-20T09:04:18Z | 2025-02-24T03:32:35Z | transformers | 1,464 | 7 | null | text-generation | {"parameters": {"BF16": 1777088000}, "total": 1777088000} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"figures/benchmark.jpg",
"generation_config.json",
"model.safetensors",
"tokenizer.json",
"tokenizer_config.json"
] | ad9f0ae0864d7fbcd1cd905e3c6c5b069cc8b562 | [
"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",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # DeepSeek-R1
<!-- 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 | [
"mit"
] | null | null | 1,777,088,000 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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