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classes | has_pre_tokenizer bool 2
classes | has_post_processor bool 2
classes | has_decoder bool 2
classes | num_added_tokens int64 0 479k | normalizer_type large_stringclasses 11
values | pre_tokenizer_type large_stringclasses 10
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values | normalizer_types listlengths 0 11.3k ⌀ | pre_tokenizer_types listlengths 1 35 ⌀ | decoder_types listlengths 0 24 ⌀ | tokenizer_id int64 0 24.8k | id large_stringlengths 7 122 | author stringlengths 2 41 ⌀ | base_models dict | downloads int64 0 206M | downloads_all_time int64 0 2.81B | gated stringclasses 3
values | created_at timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-11-03 14:41:54 | last_modified timestamp[us, tz=UTC]date 2020-12-11 21:34:15 2026-03-29 10:14:05 ⌀ | library_name stringclasses 36
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
67f65cdd156c9664279df2e8 | cb3f886e13ad7505 | 1f1d26e73b02d1f3 | 49,152 | BPE | 48,891 | false | true | false | true | 19 | null | Sequence | ByteLevel | null | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 70 | ibm-granite/granite-3.3-8b-base | ibm-granite | null | 2,136 | 1,592,755 | False | 2025-04-09T11:41:17Z | 2025-05-08T15:02:30Z | transformers | 27 | 0 | null | text-generation | {"parameters": {"BF16": 8170835968}, "total": 8170835968} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
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"... | cfb7adb44a974653cbb2ff883653971c54dba578 | [
"transformers",
"safetensors",
"granite",
"text-generation",
"language",
"granite-3.3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["GraniteForCausalLM"], "model_type": "granite", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["language", "granite-3.3"]} | # Granite-3.3-8B-Base
**Model Summary:**
Granite-3.3-8B-Base is a decoder-only language model with a 128K token context window. It improves upon Granite-3.1-8B-Base by adding support for Fill-in-the-Middle (FIM) using specialized tokens, enabling the model to generate content conditioned on both prefix and suffix. ... | null | [
"apache-2.0"
] | null | null | 8,170,835,968 | null | null | [
"granite",
"AutoModelForCausalLM",
"GraniteForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17aded | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | facebook/dpr-reader-single-nq-base | facebook | null | 944 | 1,573,171 | False | 2022-03-02T23:29:05Z | 2022-12-21T15:19:45Z | transformers | 2 | 0 | null | null | null | [
".DS_Store",
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 38f47a4986084c53447ba92ab0a83076b58d86a8 | [
"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": ["DPRReader"], "model_type": "dpr", "tokenizer_config": {}} | {
"auto_model": "DPRReader",
"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-reader-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)
- [Environmental Impac... | null | [
"cc-by-nc-4.0"
] | [
"nq_open"
] | [
"en"
] | null | null | null | [
"DPRReader",
"dpr"
] | [
null
] | null | null | null |
67aa351607885e396824ffed | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | AIDC-AI/Ovis2-4B | AIDC-AI | null | 71,896 | 1,812,485 | False | 2025-02-10T17:19:18Z | 2025-08-15T15:21:14Z | transformers | 61 | 0 | null | image-text-to-text | {"parameters": {"BF16": 4616106486}, "total": 4616106486} | [
".gitattributes",
"NOTICE",
"README.md",
"added_tokens.json",
"config.json",
"configuration_aimv2.py",
"configuration_ovis.py",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"modeling_aimv2.py",... | 30d3ed6869e44de8e8d986d79fdc772bfa1c66fc | [
"transformers",
"safetensors",
"ovis",
"text-generation",
"MLLM",
"image-text-to-text",
"conversational",
"custom_code",
"en",
"zh",
"dataset:AIDC-AI/Ovis-dataset",
"arxiv:2405.20797",
"license:apache-2.0",
"region:us"
] | null | {"architectures": ["Ovis"], "auto_map": {"AutoConfig": "configuration_ovis.OvisConfig", "AutoModelForCausalLM": "modeling_ovis.Ovis"}, "model_type": "ovis", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_ovis.Ovis",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": ["AIDC-AI/Ovis-dataset"], "eval_results": null, "language": ["en", "zh"], "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": ["MLLM"]} | # Ovis2-4B
<div align="center">
<img src=https://cdn-uploads.huggingface.co/production/uploads/637aebed7ce76c3b834cea37/3IK823BZ8w-mz_QfeYkDn.png width="30%"/>
</div>
<span style="color: #ED7D31; font-size: 22px;">It is recommended to use the latest version: [Ovis2.5](https://huggingface.co/collections/AIDC-AI/ovis2... | null | [
"apache-2.0"
] | [
"AIDC-AI/Ovis-dataset"
] | [
"en",
"zh"
] | 4,616,106,486 | null | null | [
"ovis",
"AutoModelForCausalLM",
"modeling_ovis.Ovis",
"Ovis"
] | [
"image-text-to-text",
"text-generation"
] | [
"text",
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
6243062db23d3e5324f40f05 | c667b81ba860e8b6 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 271 | ml6team/keyphrase-extraction-kbir-inspec | ml6team | null | 47,115 | 1,709,467 | False | 2022-03-29T13:14:21Z | 2023-05-06T08:46:52Z | transformers | 134 | 0 | [{"name": "DeDeckerThomas/keyphrase-extraction-kbir-inspec", "results": [{"task": {"type": "keyphrase-extraction", "name": "Keyphrase Extraction"}, "dataset": {"type": "midas/inspec", "name": "inspec"}, "metrics": [{"type": "F1 (Seqeval)", "value": 0.588, "name": "F1 (Seqeval)", "verified": false}, {"type": "F1@M", "va... | token-classification | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
] | 75baef776a9c6080987f53c3381cfbbdd9e83576 | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"keyphrase-extraction",
"en",
"dataset:midas/inspec",
"arxiv:2112.08547",
"license:mit",
"model-index",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForTokenClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false,... | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"datasets": ["midas/inspec"], "language": "en", "license": "mit", "metrics": ["seqeval"], "tags": ["keyphrase-extraction"], "widget": [{"text": "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content... | # 🔑 Keyphrase Extraction Model: KBIR-inspec
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done ... | null | [
"mit"
] | [
"midas/inspec"
] | [
"en"
] | null | null | [
"seqeval"
] | [
"roberta",
"AutoModelForTokenClassification",
"RobertaForTokenClassification"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
651efd1ec2f92699e2920533 | 34b58db81e77a472 | 664d254b6eeb0893 | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 272 | KoalaAI/Text-Moderation | KoalaAI | null | 22,564 | 1,778,312 | False | 2023-10-05T18:14:54Z | 2025-01-31T17:37:38Z | transformers | 88 | 0 | null | text-classification | {"parameters": {"I64": 512, "F32": 139199241}, "total": 139199753} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"onnx/model.onnx",
"onnx/model_bnb4.onnx",
"onnx/model_fp16.onnx",
"onnx/model_int8.onnx",
"onnx/model_q4.onnx",
"onnx/model_q4f16.onnx",
"onnx/model_quantized.onnx",
"onnx/model_uint8.onnx",
"pytorch_model.bi... | 838ade0edb66dcffc5532d08ff6ed5c899abfb5c | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"deberta",
"text-classification",
"autotrain",
"en",
"dataset:mmathys/openai-moderation-api-evaluation",
"dataset:KoalaAI/Text-Moderation-v2-small",
"license:openrail",
"co2_eq_emissions",
"endpoints_compatible",
"deploy:azure",
"region:u... | null | {"architectures": ["DebertaForSequenceClassification"], "model_type": "deberta", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["mmathys/openai-moderation-api-evaluation", "KoalaAI/Text-Moderation-v2-small"], "eval_results": null, "language": ["en"], "library_name": null, "license": "openrail", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["autot... | # Text Moderation
This model is a text classification model based on Deberta-v3 that predicts whether a text contains text that could be considered offensive.
It is split up in the following labels:
| Category | Label | Definition |
| -------- | ----- | ---------- |
| sexual | `S` | Content meant to arouse sexual ... | null | [
"openrail"
] | [
"mmathys/openai-moderation-api-evaluation",
"KoalaAI/Text-Moderation-v2-small"
] | [
"en"
] | 139,199,753 | null | null | [
"DebertaForSequenceClassification",
"AutoModelForSequenceClassification",
"deberta"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f180299 | 20ea38d22d7c71bc | c5b19732607eac99 | 31,116 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 273 | sentence-transformers/allenai-specter | sentence-transformers | null | 46,209 | 1,728,637 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:35:21Z | sentence-transformers | 28 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 109938432}, "total": 109938944} | [
".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... | 2c68eeca61259b2dd70c3f2628219f925df7031a | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["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... | # allenai-specter
This model is a conversion of the [AllenAI SPECTER](https://github.com/allenai/specter) model to [sentence-transformers](https://www.SBERT.net). It can be used to map the titles & abstracts of scientific publications to a vector space such that similar papers are close.
## Usage (Sentence-Transform... | null | [
"apache-2.0"
] | null | [
"en"
] | 109,938,944 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6564bca986bb2e3c8c0ce42c | ae0d89a0f34e942d | df536fa522daf92b | 256,000 | Unigram | null | true | true | true | true | 1 | Sequence | Metaspace | Metaspace | [
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 274 | google/madlad400-3b-mt | google | null | 352,092 | 3,127,492 | False | 2023-11-27T15:58:33Z | 2023-11-27T15:58:35Z | transformers | 188 | 1 | null | translation | {"parameters": {"F32": 2940374016}, "total": 2940374016} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"model-q2k.gguf",
"model-q3k.gguf",
"model-q4k.gguf",
"model.safetensors",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | fa184c675da0b5c9e1c8694fccd4e12e2d422094 | [
"transformers",
"safetensors",
"gguf",
"t5",
"text2text-generation",
"text-generation-inference",
"translation",
"multilingual",
"en",
"ru",
"es",
"fr",
"de",
"it",
"pt",
"pl",
"nl",
"vi",
"tr",
"sv",
"id",
"ro",
"cs",
"zh",
"hu",
"ja",
"th",
"fi",
"fa",
"uk... | {"total": 2940374016} | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["allenai/MADLAD-400"], "eval_results": null, "language": ["multilingual", "en", "ru", "es", "fr", "de", "it", "pt", "pl", "nl", "vi", "tr", "sv", "id", "ro", "cs", "zh", "hu", "ja", "th", "fi", "fa", "uk", "da", "el", "no", "bg", "sk", "ko", "ar", "lt", "ca", "sl", "he", "et", "lv", "h... | # Model Card for MADLAD-400-3B-MT
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
5. [Training Details](#training-details)
6. [Evaluation](#evaluation)
7. [Environmental Impact](#environmental... | null | [
"apache-2.0"
] | [
"allenai/MADLAD-400"
] | [
"multilingual",
"en",
"ru",
"es",
"fr",
"de",
"it",
"pt",
"pl",
"nl",
"vi",
"tr",
"sv",
"id",
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"cs",
"zh",
"hu",
"ja",
"th",
"fi",
"fa",
"uk",
"da",
"el",
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"bg",
"sk",
"ko",
"ar",
"lt",
"ca",
"sl",
"he",
"et",
"lv",
"hi",
"sq",
"m... | 2,940,374,016 | 2,940,374,016 | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"translation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6699fca19a4bf63e08b36f3b | 56af75392af5b0cd | a3552020819f9ce3 | 128,258 | BPE | 280,147 | false | true | true | true | 258 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 275 | llava-hf/llama3-llava-next-8b-hf | llava-hf | null | 6,908 | 1,585,014 | False | 2024-07-19T05:41:53Z | 2025-01-27T11:26:10Z | transformers | 51 | 0 | null | image-text-to-text | {"parameters": {"F16": 8355276800}, "total": 8355276800} | [
".gitattributes",
"README.md",
"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",
"preprocessor_config.json"... | b041c0d0ea0dd0196d147206c210c8d1752fc2da | [
"transformers",
"safetensors",
"llava_next",
"image-text-to-text",
"vision",
"conversational",
"en",
"license:llama3",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlavaNextForConditionalGeneration"], "model_type": "llava_next", "processor_config": {"chat_template": "{% for message in messages %}{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'}}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto',... | {
"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": "llama3", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["vision", "image-text-to-text"]} | # LLaVa-Next Model Card
The LLaVA-NeXT model was proposed in [LLaVA-NeXT: Stronger LLMs Supercharge Multimodal Capabilities in the Wild
](https://llava-vl.github.io/blog/2024-05-10-llava-next-stronger-llms/) by Bo Li, Kaichen Zhang, Hao Zhang, Dong Guo, Renrui Zhang, Feng Li, Yuanhan Zhang, Ziwei Liu, Chunyuan Li.
The... | null | [
"llama3"
] | null | [
"en"
] | 8,355,276,800 | null | null | [
"AutoModelForImageTextToText",
"LlavaNextForConditionalGeneration",
"llava_next"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
660bea28a9ea8db7b5185c4f | 867a3890c9943bc8 | d831300fa2535eef | 32,005 | Unigram | null | true | true | true | true | 7 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 276 | gguichard/camembert-large-resaved | gguichard | null | 3 | 4 | False | 2024-04-02T11:21:12Z | 2024-04-02T11:23:04Z | transformers | 0 | 0 | null | feature-extraction | null | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 0645c38b4fdd099b9431a8542c28b4bf61935cf5 | [
"transformers",
"pytorch",
"camembert",
"feature-extraction",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["CamembertModel"], "model_type": "camembert", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": "<pad>", "sep_token": "<... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | null | null | null | [
"CamembertModel",
"AutoModel",
"camembert"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
65f696022e71c692f65faf33 | e68b0b111c49dac2 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 28 | microsoft/llmlingua-2-xlm-roberta-large-meetingbank | microsoft | null | 186,990 | 2,034,780 | False | 2024-03-17T07:04:34Z | 2025-01-08T02:12:15Z | transformers | 27 | 0 | null | token-classification | {"parameters": {"F32": 558945282}, "total": 558945282} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | ebaba9b0e874dadd3003ffcff828e4397e568089 | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"arxiv:2403.12968",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["XLMRobertaForTokenClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"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} | # LLMLingua-2-Bert-base-Multilingual-Cased-MeetingBank
This model was introduced in the paper [**LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression** (Pan et al, 2024)](https://arxiv.org/abs/2403.12968). It is a [XLM-RoBERTa (large-sized model)](https://huggingface.co/FacebookAI... | null | [
"mit"
] | null | null | 558,945,282 | null | null | [
"XLMRobertaForTokenClassification",
"AutoModelForTokenClassification",
"xlm-roberta"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
64b5af4b60274cbb296c0f88 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | fmops/distilbert-prompt-injection | fmops | null | 413 | 1,553,611 | False | 2023-07-17T21:14:51Z | 2023-07-28T17:56:32Z | transformers | 7 | 0 | null | text-classification | {"parameters": {"F32": 66955010}, "total": 66955010} | [
".gitattributes",
".gitignore",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"runs/Jul17_21-59-57_3f0f079c503b/events.out.tfevents.1689631208.3f0f079c503b.32686.3",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | c5da1fefd33c98c447b50dd806fe86aeb9e8c26c | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"en",
"de",
"es",
"dataset:deepset/prompt-injections",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"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": ["deepset/prompt-injections"], "eval_results": null, "language": ["en", "de", "es"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": null, "pipeline_tag": null, "tags": null} | null | [
"apache-2.0"
] | [
"deepset/prompt-injections"
] | [
"en",
"de",
"es"
] | 66,955,010 | null | [
"accuracy"
] | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] | |
621ffdc136468d709f179e41 | 492a9f4f584c75aa | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 5 | cardiffnlp/twitter-xlm-roberta-base | cardiffnlp | null | 6,950 | 1,578,738 | False | 2022-03-02T23:29:05Z | 2023-08-31T01:52:58Z | transformers | 18 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"tf_model.h5",
"tokenizer.json"
] | 4c365f1490cb329b52150ad72f922ea467b5f4e6 | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"multilingual",
"arxiv:2104.12250",
"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"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "multilingual", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"text": "\ud83e\udd17\ud83e\udd17\ud83e\udd17<mask>"}, {"text":... | # Twitter-XLM-Roberta-base
This is a XLM-Roberta-base model trained on ~198M multilingual tweets, described and evaluated in the [reference paper](https://arxiv.org/abs/2104.12250). To evaluate this and other LMs on Twitter-specific data, please refer to the [main repository](https://github.com/cardiffnlp/xlm-t). A usa... | null | null | null | [
"multilingual"
] | null | null | null | [
"AutoModelForMaskedLM",
"xlm-roberta",
"XLMRobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
63a15116b5515dccd4256fe9 | 36abdb28dbacd193 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 277 | hkunlp/instructor-xl | hkunlp | null | 16,931 | 2,399,909 | False | 2022-12-20T06:07:18Z | 2023-01-21T06:33:27Z | sentence-transformers | 583 | 0 | [{"name": "final_xl_results", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 85.... | sentence-similarity | null | [
".gitattributes",
"1_Pooling/config.json",
"2_Dense/config.json",
"2_Dense/pytorch_model.bin",
"README.md",
"config.json",
"config_sentence_transformers.json",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"to... | ce48b213095e647a6c3536364b9fa00daf57f436 | [
"sentence-transformers",
"pytorch",
"t5",
"text-embedding",
"embeddings",
"information-retrieval",
"beir",
"text-classification",
"language-model",
"text-clustering",
"text-semantic-similarity",
"text-evaluation",
"prompt-retrieval",
"text-reranking",
"feature-extraction",
"sentence-si... | null | {"architectures": ["T5EncoderModel"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": "AutoTokenizer"
} | {"language": "en", "license": "apache-2.0", "pipeline_tag": "sentence-similarity", "tags": ["text-embedding", "embeddings", "information-retrieval", "beir", "text-classification", "language-model", "text-clustering", "text-semantic-similarity", "text-evaluation", "prompt-retrieval", "text-reranking", "sentence-transfor... | # hkunlp/instructor-xl
We introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, witho... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"t5",
"T5EncoderModel",
"AutoModel"
] | [
null,
"sentence-similarity",
"feature-extraction",
"text-classification"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
66d070f0e62d6bbf5021c8c2 | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | nvidia/NV-Embed-v2 | nvidia | null | 72,477 | 1,937,279 | False | 2024-08-29T13:00:32Z | 2025-07-21T17:48:20Z | transformers | 508 | 2 | [{"name": "NV-Embed-v2", "results": [{"dataset": {"config": "en", "name": "MTEB AmazonCounterfactualClassification (en)", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205", "split": "test", "type": "mteb/amazon_counterfactual"}, "metrics": [{"type": "accuracy", "value": 94.28358208955224, "verified": false}, {"typ... | feature-extraction | {"parameters": {"F16": 7851016192}, "total": 7851016192} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"configuration_nvembed.py",
"instructions.json",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetenso... | 3fa59658547db50a1e8e3346cf057fd0c77ed6ef | [
"transformers",
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"feature-extraction",
"mteb",
"sentence-transformers",
"custom_code",
"en",
"arxiv:2405.17428",
"arxiv:2407.15831",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
] | null | {"architectures": ["NVEmbedModel"], "auto_map": {"AutoConfig": "configuration_nvembed.NVEmbedConfig", "AutoModel": "modeling_nvembed.NVEmbedModel"}, "model_type": "nvembed", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "</s>", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModel",
"custom_class": "modeling_nvembed.NVEmbedModel",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"language": ["en"], "library_name": "transformers", "license": "cc-by-nc-4.0", "tags": ["mteb", "sentence-transformers"], "model-index": [{"name": "NV-Embed-v2", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "c... | null | null | [
"cc-by-nc-4.0"
] | null | [
"en"
] | 7,851,016,192 | null | null | [
"nvembed",
"AutoModel",
"modeling_nvembed.NVEmbedModel",
"NVEmbedModel"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
621ffdc136468d709f1802bf | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | sentence-transformers/msmarco-MiniLM-L6-cos-v5 | sentence-transformers | null | 16,575 | 1,588,000 | False | 2022-03-02T23:29:05Z | 2024-11-05T16:58:14Z | sentence-transformers | 11 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 22713216}, "total": 22713728} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"flax_model.msgpack",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint... | 14ca9be4bbcf1402eac0f43a2e2ccb6e0f994ba3 | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"arxiv:1908.10084",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "sentence-transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sent... | # msmarco-MiniLM-L6-cos-v5
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500k (query, answer) pairs from the [MS MARCO Passages dataset](https://github.com/microsoft/... | null | null | null | [
"en"
] | 22,713,728 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
66f4586641aafc2b30326e7d | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | unsloth/Llama-3.2-1B | unsloth | {
"models": [
{
"_id": "66eaebb25fcdca258b9576cb",
"id": "meta-llama/Llama-3.2-1B"
}
],
"relation": "finetune"
} | 50,674 | 1,716,339 | False | 2024-09-25T18:37:26Z | 2025-06-02T21:08:48Z | transformers | 64 | 0 | null | text-generation | {"parameters": {"BF16": 1235814400}, "total": 1235814400} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 9535bd9b1d1dea6acafbdc4813b728796aeb28da | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3",
"meta",
"facebook",
"unsloth",
"en",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"regi... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|end_of_text|>", "pad_token": "<|finetune_right_pad_id|>", "unk_token": null}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Llama-3.2-1B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "llama3.2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["llama-3", "llama", "meta", "facebook", "unsloth", ... | ## ***See [our collection](https://huggingface.co/collections/unsloth/llama-32-66f46afde4ca573864321a22) for all versions of Llama 3.2 including GGUF, 4-bit and original 16-bit formats.***
# Finetune Llama 3.2, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tesla T4 noteboo... | null | [
"llama3.2"
] | null | [
"en"
] | 1,235,814,400 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
675b14650193e1f84d75c260 | 8e316423c2276392 | c07e6b923a6fe857 | 128,257 | BPE | 280,147 | false | true | true | true | 257 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 278 | Bllossom/llama-3.2-Korean-Bllossom-AICA-5B | Bllossom | null | 246 | 1,535,282 | False | 2024-12-12T16:50:45Z | 2025-03-14T02:23:58Z | transformers | 98 | 0 | null | image-text-to-text | {"parameters": {"BF16": 5198628129}, "total": 5198628129} | [
".gitattributes",
"README.md",
"chat_template.json",
"config.json",
"generation_config.json",
"model-00001-of-00003.safetensors",
"model-00002-of-00003.safetensors",
"model-00003-of-00003.safetensors",
"model.safetensors.index.json",
"preprocessor_config.json",
"special_tokens_map.json",
"toke... | 43f2e69fc52b2cdaebac64989e01997581be10bb | [
"transformers",
"safetensors",
"mllama",
"image-text-to-text",
"conversational",
"arxiv:2412.10151",
"arxiv:2403.11399",
"arxiv:2403.10882",
"license:llama3.2",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["MllamaForConditionalGeneration"], "model_type": "mllama", "processor_config": {"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_message = true %}\n{%- en... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "llama3.2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": []} | <a href="https://github.com/MLP-Lab/Bllossom">
<img src="https://cdn-uploads.huggingface.co/production/uploads/64a90711c05da19ca834f690/a0VE5UCY1HCEhaHtp3mGa.png" alt="image" width="30%" height="30%">
</a>
# Update!
* [2024.12.12] 추가설명: KMMLU, KoBEST, LogicKor 등 벤치 관련 학습/테스트/유사 데이터를 전혀 사용하지 않았습니다. 벤치데이터 증강해가 쓰까서 학습하... | null | [
"llama3.2"
] | null | null | 5,198,628,129 | null | null | [
"AutoModelForImageTextToText",
"MllamaForConditionalGeneration",
"mllama"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
621ffdc136468d709f17ba9c | 382b5a4eb98f0296 | d965e4a9140ecf72 | 21,128 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
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"BertPreTokenizer"
] | [
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] | ab0aa81da273504efc8540aa4d0bbaa3016a1bb5 | [
"transformers",
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"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
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] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
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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": 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 | [
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"Metaspace"
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"models": [
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],
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] | d5f623888f1415cf89b5c208d09cb620694618ee | [
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"text-generation-i... | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": "<s>", "chat_template": "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"co... | {
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We have a Google Colab Tesla T4 notebook for Mistral v3 7b here: https://colab.research.google.com/drive/1_yNCks4BTD5zOnjozppphh5GzMFaMKq_?usp=sharing
For conversational ShareGPT style and using Mistral v3 Instruct: https://colab.research.... | null | [
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] | 7,466,691,910 | null | null | [
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"Split",
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] | null | {"architectures": ["BertForTokenClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
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} | {"datasets": ["lener_br"], "language": ["pt"], "metrics": ["precision", "recall", "f1", "accuracy"], "tags": ["generated_from_trainer"], "widget": [{"text": "Ao Instituto M\u00e9dico Legal da jurisdi\u00e7\u00e3o do acidente ou da resid\u00eancia cumpre fornecer, no prazo de 90 dias, laudo \u00e0 v\u00edtima (art. 5, \... | ## (BERT base) NER model in the legal domain in Portuguese (LeNER-Br)
**ner-bert-base-portuguese-cased-lenerbr** is a NER model (token classification) in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model [pierreguillou/bert-base-cased-pt-lenerbr](https://huggingface.co/pier... | null | null | [
"lener_br"
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"pt"
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"arxiv:2106.09462",
"text-embeddings-inference",
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] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
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} | {"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": ["emotion-analysis"]} | # Emotion Analysis in Spanish
## beto-emotion-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish. Base model is [BETO](https://github.com/dccuchile/beto), a BERT model tr... | null | null | null | [
"es"
] | 109,859,847 | null | null | [
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66862faf1d70ee8feafc4fba | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
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"ByteLevel"
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] | 280 | setu4993/LEALLA-small | setu4993 | null | 75 | 1,501,356 | False | 2023-05-21T08:17:47Z | 2023-10-19T06:22:00Z | transformers | 16 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 68988672}, "total": 68989184} | [
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"pipeline_tag": "feature-extraction",
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} | {"base_model": null, "datasets": ["CommonCrawl", "Wikipedia"], "eval_results": null, "language": ["af", "am", "ar", "as", "az", "be", "bg", "bn", "bo", "bs", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "he", "hi", "hmn", "hr... | # LEALLA-small
## Model description
LEALLA is a collection of lightweight language-agnostic sentence embedding models supporting 109 languages, distilled from [LaBSE](https://ai.googleblog.com/2020/08/language-agnostic-bert-sentence.html). The model is useful for getting multilingual sentence embeddings and for bi-te... | null | [
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"Precompiled"
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"WhitespaceSplit",
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] | [
"Metaspace"
] | 11 | google/long-t5-tglobal-base | google | null | 29,988 | 2,291,611 | False | 2022-04-16T11:05:48Z | 2023-01-24T17:08:42Z | transformers | 49 | 0 | null | null | null | [
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"arxiv:1912.08777",
"arxiv:1910.10683",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["LongT5ForConditionalGeneration"], "model_type": "longt5"} | {
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} | {"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": null, "tags": null} | # LongT5 (transient-global attention, base-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.co... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"AutoModelForSeq2SeqLM",
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"LongT5ForConditionalGeneration"
] | [
"text2text-generation"
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"ByteLevel"
] | [
"ByteLevel"
] | 6 | allenai/longformer-large-4096 | allenai | null | 3,998 | 1,508,894 | False | 2022-03-02T23:29:05Z | 2022-10-03T22:04:49Z | transformers | 16 | 0 | null | null | null | [
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"transformers",
"pytorch",
"tf",
"longformer",
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"region:us"
] | null | {"model_type": "longformer"} | {
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"en"
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null
] | null | null | null |
65b9ca70f055699cacf5935d | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
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"Fuse",
"Strip"
] | 88 | liuhaotian/llava-v1.6-mistral-7b | liuhaotian | null | 11,402 | 1,559,842 | False | 2024-01-31T04:20:00Z | 2024-05-08T12:12:36Z | transformers | 245 | 0 | null | image-text-to-text | {"parameters": {"BF16": 7566223360}, "total": 7566223360} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
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"model-00002-of-00004.safetensors",
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"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"... | f13b6254afb9d96a82e6f568d7a01101923b3ed9 | [
"transformers",
"safetensors",
"llava_mistral",
"text-generation",
"image-text-to-text",
"conversational",
"license:apache-2.0",
"region:us"
] | null | {"architectures": ["LlavaMistralForCausalLM"], "model_type": "llava_mistral", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/ass... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": null
} | {"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": "image-text-to-text", "tags": null, "inference": false} | <br>
<br>
# LLaVA Model Card
## Model details
**Model type:**
LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
Base LLM: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mist... | null | [
"apache-2.0"
] | null | null | 7,566,223,360 | null | null | [
"llava_mistral",
"LlavaMistralForCausalLM",
"AutoModelForCausalLM"
] | [
"image-text-to-text",
"text-generation"
] | [
"text",
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
62c635b90fa6117e0722807a | 7a4cc4a6090329ff | ab8f40e5a89244c4 | 250,680 | BPE | 250,434 | false | true | true | true | 4 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 31 | bigscience/test-bloomd-6b3 | bigscience | null | 40 | 1,479,193 | False | 2022-07-07T01:24:09Z | 2025-06-01T19:08:50Z | transformers | 3 | 0 | null | text-generation | {"parameters": {"F16": 1027620864}, "total": 1027620864} | [
".gitattributes",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | d927d88aad8cb8a430add801a40e31b7cd7061a2 | [
"transformers",
"pytorch",
"safetensors",
"bloom",
"text-generation",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BloomForCausalLM"], "model_type": "bloom", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | 1,027,620,864 | null | null | [
"AutoModelForCausalLM",
"BloomForCausalLM",
"bloom"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6621509c486de1d8db296970 | 6e6a83dc4b556c95 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 111 | NousResearch/Meta-Llama-3-8B-Instruct | NousResearch | null | 40,097 | 1,716,626 | False | 2024-04-18T16:55:56Z | 2024-07-23T04:40:46Z | transformers | 105 | 1 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"USE_POLICY.md",
"config.json",
"generation_config.json",
"model-00001-of-00004.safetensors",
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"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
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"special_tokens_ma... | 53346005fb0ef11d3b6a83b12c895cca40156b6c | [
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"text-generation",
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"meta",
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"llama-3",
"conversational",
"en",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|e... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "other", "license_name": "llama3", "license_link": "LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3"], "extra_gate... | ## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source ch... | null | [
"other",
"llama3",
"LICENSE"
] | null | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6695aa947e75dfb1088880c5 | bafc5e0c20be7cd8 | 82d3db801e5d7f68 | 49,152 | BPE | 48,900 | false | true | false | true | 17 | null | Sequence | ByteLevel | null | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 115 | HuggingFaceTB/SmolLM-135M-Instruct | HuggingFaceTB | {
"models": [
{
"_id": "669446eef658d6ee957e2475",
"id": "HuggingFaceTB/SmolLM-135M"
}
],
"relation": "quantized"
} | 115,379 | 1,950,016 | False | 2024-07-15T23:02:44Z | 2024-09-04T15:37:45Z | transformers | 134 | 0 | null | text-generation | {"parameters": {"BF16": 134515008}, "total": 134515008} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"onnx/model.onnx",
"onnx/model_bnb4.onnx",
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"onnx/model_int8.onnx",
"onnx/model_q4.onnx",
"onnx/model_q4f16.onnx",
"onnx/model_quantized.onnx",
"onnx/model_uint... | fcc320f490e08fdb4b99d935b2c58d40bf35b0d0 | [
"transformers",
"onnx",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"conversational",
"en",
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"dataset:bigcode/self-oss-instruct-sc2-exec-filter-50k",
"dataset:teknium/OpenHermes-2.5",
"dataset:HuggingFaceTB/... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|im_start|>", "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "HuggingFaceTB/SmolLM-135M", "datasets": ["Magpie-Align/Magpie-Pro-300K-Filtered", "bigcode/self-oss-instruct-sc2-exec-filter-50k", "teknium/OpenHermes-2.5", "HuggingFaceTB/everyday-conversations-llama3.1-2k"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2... | # SmolLM-135M-Instruct
<center>
<img src="https://huggingface.co/datasets/HuggingFaceTB/images/resolve/main/banner_smol.png" alt="SmolLM" width="1100" height="600">
</center>
## Model Summary
SmolLM is a series of small language models available in three sizes: 135M, 360M, and 1.7B parameters.
These models ar... | null | [
"apache-2.0"
] | [
"Magpie-Align/Magpie-Pro-300K-Filtered",
"bigcode/self-oss-instruct-sc2-exec-filter-50k",
"teknium/OpenHermes-2.5",
"HuggingFaceTB/everyday-conversations-llama3.1-2k"
] | [
"en"
] | 134,515,008 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64675c923a7c8dda2302a20c | d3db89cbbce8fb02 | 472de96fe2fe7faf | 32,001 | Unigram | null | true | true | true | true | 4 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 281 | unikei/t5-base-split-and-rephrase | unikei | null | 35 | 1,470,170 | False | 2023-05-19T11:25:06Z | 2024-01-26T10:15:16Z | transformers | 18 | 0 | null | null | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | bb244c3c2bdb6b38db6dcbf332eb1b9987118c2e | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"split and rephrase",
"en",
"dataset:wiki_split",
"dataset:web_split",
"license:bigscience-openrail-m",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 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": ["wiki_split", "web_split"], "eval_results": null, "language": ["en"], "library_name": null, "license": "bigscience-openrail-m", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["split and rephrase"], "widget": [{"text": "Cy... | # T5 model for splitting complex sentences to simple sentences in English
Split-and-rephrase is the task of splitting a complex input sentence into shorter sentences while preserving meaning. (Narayan et al., 2017)
E.g.:
```
Cystic Fibrosis (CF) is an autosomal recessive disorder that affects multiple organs,
which i... | null | [
"bigscience-openrail-m"
] | [
"wiki_split",
"web_split"
] | [
"en"
] | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
621ffdc136468d709f17a7b2 | 69d9860d38b5c798 | cf7a052aa6fec7ad | 30,000 | BPE | 15,977 | true | true | false | true | 229 | Sequence | Metaspace | Metaspace | [
"NFKC",
"BertNormalizer"
] | [
"Metaspace"
] | [
"Metaspace"
] | 282 | digit82/kobart-summarization | digit82 | null | 288,666 | 1,982,065 | False | 2022-03-02T23:29:05Z | 2022-03-01T13:48:13Z | transformers | 7 | 0 | null | null | null | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 0887f7ac6e66df93248890f3460299d28bae1ddd | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BartForConditionalGeneration"], "model_type": "bart", "tokenizer_config": {}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | null | null | null | [
"bart",
"BartForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
688b1597e5e83e19d1b3238a | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Qwen/Qwen3-Coder-30B-A3B-Instruct | Qwen | null | 1,134,092 | 5,693,237 | False | 2025-07-31T07:04:55Z | 2025-12-03T08:05:17Z | transformers | 985 | 5 | null | text-generation | {"parameters": {"BF16": 30532122624}, "total": 30532122624} | [
".gitattributes",
"LICENSE",
"README.md",
"chat_template.jinja",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00016.safetensors",
"model-00002-of-00016.safetensors",
"model-00003-of-00016.safetensors",
"model-00004-of-00016.safetensors",
"model-00005-of-00016.safetens... | b2cff646eb4bb1d68355c01b18ae02e7cf42d120 | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"arxiv:2505.09388",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "tokenizer_config": {"bos_token": null, "chat_template": "{% macro render_extra_keys(json_dict, handled_keys) %}\n {%- if json_dict is mapping %}\n {%- for json_key in json_dict if json_key not in handled_keys %}\n {%- if json_di... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generatio... | # Qwen3-Coder-30B-A3B-Instruct
<a href="https://chat.qwen.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>
## Highlights
**Qwen3-Coder** is available in multiple... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE"
] | null | null | 30,532,122,624 | null | null | [
"qwen3_moe",
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] | [
"text-generation"
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"text"
] | [
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66a0c74d48385720631daa5b | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | NousResearch/Meta-Llama-3.1-8B-Instruct | NousResearch | null | 201,886 | 2,402,511 | False | 2024-07-24T09:20:13Z | 2024-07-24T09:21:20Z | transformers | 39 | 0 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
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"deploy:azure"... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|e... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "de", "fr", "it", "pt", "hi", "es", "th"], "library_name": null, "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["facebook", "meta", "pytorc... | ## Model Information
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue us... | null | [
"llama3.1"
] | null | [
"en",
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648f607f8b579a84b7a653d6 | 0efb0128c4459141 | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 283 | TheBloke/vicuna-7B-v1.3-GPTQ | TheBloke | null | 62 | 1,449,436 | False | 2023-06-18T19:52:31Z | 2023-08-21T07:38:35Z | transformers | 16 | 0 | null | text-generation | {"parameters": {"F32": 1361920, "I32": 6476005376, "F16": 262410240}, "total": 6739777536} | [
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<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<d... | null | [
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68384003444a0e00ec791df0 | bbcc565188dcd24d | a7b4167b9e661fe0 | 151,671 | BPE | 151,387 | true | true | true | true | 28 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 284 | deepseek-ai/DeepSeek-R1-0528-Qwen3-8B | deepseek-ai | null | 149,988 | 2,459,924 | False | 2025-05-29T11:07:47Z | 2025-05-29T13:13:34Z | transformers | 1,042 | 3 | null | text-generation | {"parameters": {"BF16": 8190735360}, "total": 8190735360} | [
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"text-generation",
"conversational",
"arxiv:2501.12948",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "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-R1-0528
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
</div>
<hr>
<div... | null | [
"mit"
] | null | null | 8,190,735,360 | null | null | [
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"text"
] | [
"text"
] |
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"ByteLevel"
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"ByteLevel"
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<img src="assets/EXAONE_Symbol+BI_3d.png", width="300", style="margin: 40 auto;">
<br>
# EXAONE-3.5-7.8B-Instruct
## Introduction
We introduce EXAONE 3.5, a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released ... | null | [
"other",
"exaone",
"LICENSE"
] | null | [
"en",
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"text"
] | [
"text"
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... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|im_start|>", "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>\n'... | {
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## Table of Contents
1. [Model Summary](#model-summary)
2. [Evaluation](#evaluation)
3. [Examples](#examples)
4. [Limitations](#limitations)
5. [Training](#training)
6. [License](#license... | null | [
"apache-2.0"
] | null | [
"en"
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"mit"
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] | 582159a2dfe3e712a8d47ae83dec95ae3bde8e7e | [
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interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and ... | null | [
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"Split",
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"ByteLevel"
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<img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https... | null | [
"other",
"deepseek",
"LICENSE"
] | null | null | 6,740,512,768 | null | null | [
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67d111137be76de1a407165b | 4f91da2c156973cc | 0a1438da55eb2e11 | 102,400 | BPE | 101,782 | true | true | true | true | 362 | NFKC | Sequence | ByteLevel | [
"NFKC"
] | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
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"models": [
{
"_id": "674c61edbeb24e8a6d31b49c",
"id": "LGAI-EXAONE/EXAONE-3.5-32B-Instruct"
}
],
"relation": "finetune"
} | 5,290 | 1,446,203 | False | 2025-03-12T04:44:03Z | 2026-02-06T06:27:56Z | transformers | 299 | 0 | null | text-generation | {"parameters": {"BF16": 32003200000}, "total": 32003200000} | [
".gitattributes",
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"merges.txt",
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"model-00002-of-00014.safetensors",
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"en",
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"base_model:finetune:LGAI-EXAONE/EXAONE-3.5-32B-Instruct",
"license:other",
"region:us"
] | null | {"architectures": ["ExaoneForCausalLM"], "auto_map": {"AutoConfig": "configuration_exaone.ExaoneConfig", "AutoModelForCausalLM": "modeling_exaone.ExaoneForCausalLM", "AutoModelForSequenceClassification": "modeling_exaone.ExaoneForSequenceClassification"}, "model_type": "exaone", "tokenizer_config": {"bos_token": "[BOS]... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_exaone.ExaoneForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": "LGAI-EXAONE/EXAONE-3.5-32B-Instruct", "datasets": null, "eval_results": null, "language": ["en", "ko"], "library_name": "transformers", "license": "other", "license_name": "exaone", "license_link": "LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["lg-ai", "exao... | <p align="center">
<img src="assets/EXAONE_Symbol+BI_3d.png", width="300", style="margin: 40 auto;">
<br>
# EXAONE-Deep-32B
## Introduction
We introduce EXAONE Deep, which exhibits superior capabilities in various reasoning tasks including math and coding benchmarks, ranging from 2.4B to 32B parameters developed and... | null | [
"other",
"exaone",
"LICENSE"
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"en",
"ko"
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"text-classification",
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This is a minimal model built for unit tests in the [TRL](https://github.com/huggingface/trl) library. | null | null | null | null | 2,437,792 | null | null | [
"Qwen2ForSequenceClassification",
"AutoModelForSequenceClassification",
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"Split"
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} | {"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",
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"Gemma2ForCausalLM"
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"text-generation"
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"text"
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"text"
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67adba1e3bdd71325eb74d27 | 616c061030c03d7b | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 195 | Qwen/Qwen2.5-VL-3B-Instruct-AWQ | Qwen | {
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{
"_id": "6795ff8f7534713f94918fc5",
"id": "Qwen/Qwen2.5-VL-3B-Instruct"
}
],
"relation": "quantized"
} | 116,118 | 1,783,173 | False | 2025-02-13T09:23:42Z | 2025-04-06T16:24:35Z | transformers | 61 | 0 | null | image-text-to-text | {"parameters": {"I32": 2774532096, "BF16": 979998720, "F16": 92160}, "total": 3754622976} | [
".gitattributes",
"LICENSE",
"README.md",
"added_tokens.json",
"chat_template.json",
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"model.safetensors",
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] | e7b623934290c5a4da0ee3c6e1e57bfb6b5abbf2 | [
"transformers",
"safetensors",
"qwen2_5_vl",
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"en",
"arxiv:2309.00071",
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"e... | null | {"architectures": ["Qwen2_5_VLForConditionalGeneration"], "model_type": "qwen2_5_vl", "quantization_config": {"bits": 4, "quant_method": "awq"}, "processor_config": {"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and me... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
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} | {"base_model": ["Qwen/Qwen2.5-VL-3B-Instruct"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": null, "license_name": "qwen-research", "license_link": "https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct-AWQ/blob/main/LICENSE", "metrics": null, "model_name": null, "... | # Qwen2.5-VL-3B-Instruct-AWQ
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
In the past five months since Qwen2-VL... | null | [
"qwen-research",
"https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct-AWQ/blob/main/LICENSE"
] | null | [
"en"
] | 3,754,622,976 | null | null | [
"qwen2_5_vl",
"AutoModelForImageTextToText",
"Qwen2_5_VLForConditionalGeneration"
] | [
"image-text-to-text"
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"image"
] | [
"text"
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6425227becb4ee8275edea23 | 912d0fd47df9648d | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 287 | cardiffnlp/twitter-roberta-base-hate-latest | cardiffnlp | null | 3,855 | 1,445,467 | False | 2023-03-30T05:47:39Z | 2025-02-24T11:09:44Z | transformers | 18 | 0 | [{"name": "twitter-roberta-base-hate-latest", "results": []}] | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | cc56585908cbda6d04ba2e1234d911fd1578c9ab | [
"transformers",
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"tf",
"roberta",
"text-classification",
"en",
"license:cc-by-4.0",
"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",
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"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": [], "language": ["en"], "library_name": null, "license": "cc-by-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": "twitter-roberta-base-hate-latest", "pipeline_tag": "text-classification", "tags": null} | # cardiffnlp/twitter-roberta-base-hate-latest
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2022-154m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m) for binary hate-speech classification.
A combination of 13 different hate-speech datasets in the English language were used ... | null | [
"cc-by-4.0"
] | null | [
"en"
] | null | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
67acc0f1b150b8272b721c3b | 2abad10fe0a2a552 | 93e9567de7a781a1 | 49,280 | BPE | 48,900 | false | true | false | true | 145 | null | Sequence | ByteLevel | null | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 288 | docling-project/SmolDocling-256M-preview | docling-project | {
"models": [
{
"_id": "678a79e88b0ce22ae8998bf2",
"id": "HuggingFaceTB/SmolVLM-256M-Instruct"
}
],
"relation": "quantized"
} | 64,677 | 1,811,152 | False | 2025-02-12T15:40:33Z | 2025-09-17T13:36:13Z | transformers | 1,610 | 0 | null | image-text-to-text | null | [
".gitattributes",
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"added_tokens.json",
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"onnx/decoder_model_merged_bnb4.onnx",
"onnx/dec... | ce51f56c4ebe36e0b1c3a55f67b261ba22a50bf8 | [
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"en",
"dataset:ds4sd/SynthCodeNet",
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"dataset:HuggingFaceM4/DoclingMatix",
"arxiv:2503.11576",
"arxiv:2305.03393",
"base_model:HuggingFa... | null | {"architectures": ["Idefics3ForConditionalGeneration"], "model_type": "idefics3", "processor_config": {"chat_template": "<|im_start|>{% for message in messages %}{{message['role'] | capitalize}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if... | {
"auto_model": "AutoModelForImageTextToText",
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} | {"base_model": ["HuggingFaceTB/SmolVLM-256M-Instruct"], "datasets": ["ds4sd/SynthCodeNet", "ds4sd/SynthFormulaNet", "ds4sd/SynthChartNet", "HuggingFaceM4/DoclingMatix"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "cdla-permissive-2.0", "license_name": null, "license_link": null... | null | null | [
"cdla-permissive-2.0"
] | [
"ds4sd/SynthCodeNet",
"ds4sd/SynthFormulaNet",
"ds4sd/SynthChartNet",
"HuggingFaceM4/DoclingMatix"
] | [
"en"
] | null | null | null | [
"AutoModelForImageTextToText",
"Idefics3ForConditionalGeneration",
"idefics3"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
66f45ba3940e8a6c7f596132 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | unsloth/Llama-3.2-3B-Instruct-bnb-4bit | unsloth | {
"models": [
{
"_id": "66eaef786865fea1324edb5d",
"id": "meta-llama/Llama-3.2-3B-Instruct"
}
],
"relation": "quantized"
} | 53,964 | 1,672,173 | False | 2024-09-25T18:51:15Z | 2025-06-02T21:42:45Z | transformers | 33 | 0 | null | text-generation | {"parameters": {"F32": 225344, "BF16": 394177536, "U8": 2906720136}, "total": 3301123016} | [
".gitattributes",
"README.md",
"chat_template.jinja",
"config.json",
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"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | bb1d317a108579fb40e646af8924a5e7ec5604b1 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3",
"meta",
"facebook",
"unsloth",
"conversational",
"en",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:quantized:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"text-generation-inference",
"endpoints... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|eot_id|>", "pad_token": "<|finetune_right_pad_id|>", "unk_token": null, "chat_templa... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Llama-3.2-3B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "llama3.2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["llama-3", "llama", "meta", "facebook", "u... | ## ***See [our collection](https://huggingface.co/collections/unsloth/llama-32-66f46afde4ca573864321a22) for all versions of Llama 3.2 including GGUF, 4-bit and original 16-bit formats.***
# Finetune Llama 3.2, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tesla T4 noteboo... | null | [
"llama3.2"
] | null | [
"en"
] | 3,301,123,016 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6332e082d9fa8a0601600f61 | ccbf883ee7996937 | d965e4a9140ecf72 | 21,128 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 289 | shibing624/bart4csc-base-chinese | shibing624 | null | 148 | 1,422,042 | False | 2022-09-27T11:37:38Z | 2024-02-19T08:42:48Z | transformers | 29 | 0 | null | text-generation | {"parameters": {"F32": 116277896}, "total": 116277896} | [
".gitattributes",
"README.md",
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"model.safetensors",
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"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | 6bce2c5d39d52f542a720bfc2c612623401a2380 | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"zh",
"Text2Text-Generation",
"dataset:shibing624/CSC",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BartForConditionalGeneration"], "model_type": "bart", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["shibing624/CSC"], "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": "text2text-generation", "tags": ["bart", "pytorch", "zh", "Text2Text-Generation"]... | # Bart for Chinese Spelling Correction(bart4csc) Model
BART中文拼写纠错模型
`bart4csc-base-chinese` evaluate SIGHAN2015 test data：
Sentence Level: acc:0.6845, precision:0.6984, recall:0.6354, f1:0.6654
case:
|input_text|pred|
|:-- |:--- |
|辰导中引述她的话说：核子间题的解决之道系于克什米尔纷争。|报导中引述她的话说：核子问题的解决之道系于克什米尔纷争。|
|报导并末说明事故发生的原因。|报导并未说明事故发... | null | [
"apache-2.0"
] | [
"shibing624/CSC"
] | [
"zh"
] | 116,277,896 | null | null | [
"bart",
"BartForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65a284581051c2b0db502684 | e23b6bdbe047bb7e | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | Felladrin/Llama-68M-Chat-v1 | Felladrin | {
"models": [
{
"_id": "64b7485375b23e68c538d7d0",
"id": "JackFram/llama-68m"
}
],
"relation": "finetune"
} | 1,666 | 1,429,374 | False | 2024-01-13T12:38:48Z | 2024-03-04T12:28:10Z | transformers | 13 | 0 | [{"name": "Llama-68M-Chat-v1", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 23.29, "name": "nor... | text-generation | {"parameters": {"F32": 68030208}, "total": 68030208} | [
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"README.md",
"config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 180d584580aa5cf33558d2bce51f1d125e20c7c7 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:THUDM/webglm-qa",
"dataset:databricks/databricks-dolly-15k",
"dataset:cognitivecomputations/wizard_vicuna_70k_unfiltered",
"dataset:totally-not-an-llm/EverythingLM-data-V3",
"dataset:Amod/mental_health_co... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "JackFram/llama-68m", "datasets": ["THUDM/webglm-qa", "databricks/databricks-dolly-15k", "cognitivecomputations/wizard_vicuna_70k_unfiltered", "totally-not-an-llm/EverythingLM-data-V3", "Amod/mental_health_counseling_conversations", "sablo/oasst2_curated", "starfishmedical/webGPT_x_dolly", "Open-Orca/Ope... | # A Llama Chat Model of 68M Parameters
- Base model: [JackFram/llama-68m](https://huggingface.co/JackFram/llama-68m)
- Datasets:
- [THUDM/webglm-qa](https://huggingface.co/datasets/THUDM/webglm-qa)
- [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
- [cognitiveco... | null | [
"apache-2.0"
] | [
"THUDM/webglm-qa",
"databricks/databricks-dolly-15k",
"cognitivecomputations/wizard_vicuna_70k_unfiltered",
"totally-not-an-llm/EverythingLM-data-V3",
"Amod/mental_health_counseling_conversations",
"sablo/oasst2_curated",
"starfishmedical/webGPT_x_dolly",
"Open-Orca/OpenOrca",
"mlabonne/chatml_dpo_p... | [
"en"
] | 68,030,208 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17ae07 | 34805e59899648ad | 080ea6fa4b958367 | 250,027 | Unigram | null | true | true | true | true | 30 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 239 | facebook/mbart-large-en-ro | facebook | null | 127,011 | 1,693,743 | False | 2022-03-02T23:29:05Z | 2023-09-11T13:45:59Z | transformers | 2 | 0 | null | translation | {"parameters": {"F16": 611101867}, "total": 611101867} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"sentence.bpe.model",
"sentencepiece.bpe.model",
"tf_model.h5",
"tokenizer.json"
] | 3f6705eea8aef516fdbdbf3ec20f63a6a522a6ae | [
"transformers",
"pytorch",
"tf",
"safetensors",
"mbart",
"translation",
"en",
"ro",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"model_type": "mbart"} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "ro"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["translation"]} | ### mbart-large-en-ro
This is mbart-large-cc25, finetuned on wmt_en_ro.
It scores BLEU 28.1 without post processing and BLEU 38 with postprocessing. Instructions in `romanian_postprocessing.md`
Original Code: https://github.com/pytorch/fairseq/tree/master/examples/mbart
Docs: https://huggingface.co/transformers/mas... | null | [
"mit"
] | null | [
"en",
"ro"
] | 611,101,867 | null | null | [
"AutoModel",
"mbart"
] | [
null,
"translation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17ed05 | 6a792251ace4fa20 | ceb5045914a9e9c7 | 40,000 | BPE | 39,739 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 250 | pdelobelle/robbert-v2-dutch-ner | pdelobelle | null | 1,468 | 1,426,393 | False | 2022-03-02T23:29:05Z | 2022-08-01T14:49:07Z | transformers | 4 | 0 | null | token-classification | null | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | cbe384104e418a84b62a102e1aa61f75857ed20e | [
"transformers",
"pytorch",
"jax",
"roberta",
"token-classification",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"nl",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForTokenClassification"], "model_type": "roberta", "tokenizer_config": {}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "nl", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT"], "thumbnail": "https://github.com/iPieter/RobBE... | <p align="center">
<img src="https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo_with_name.png" alt="RobBERT: A Dutch RoBERTa-based Language Model" width="75%">
</p>
# RobBERT: Dutch RoBERTa-based Language Model.
[RobBERT](https://github.com/iPieter/RobBERT) is the state-of-the-art Dutch BERT model. ... | null | [
"mit"
] | null | [
"nl"
] | null | null | null | [
"roberta",
"AutoModelForTokenClassification",
"RobertaForTokenClassification"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
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635b680e4b17a81906b039d2 | f1caa81f168f8d5d | 8177c03aff392891 | 62,000 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 290 | NlpHUST/ner-vietnamese-electra-base | NlpHUST | {
"models": [
{
"_id": "621ffdc036468d709f176d44",
"id": "NlpHUST/electra-base-vn"
}
],
"relation": "finetune"
} | 3,480 | 1,451,461 | False | 2022-10-28T05:26:38Z | 2024-11-08T14:25:46Z | transformers | 22 | 0 | [{"name": "ner-vietnamese-electra-base", "results": []}] | token-classification | {"parameters": {"I64": 512, "F32": 133073673}, "total": 133074185} | [
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] | null | {"architectures": ["ElectraForTokenClassification"], "model_type": "electra", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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"processor": "AutoTokenizer"
} | {"base_model": "NlpHUST/electra-base-vn", "datasets": null, "eval_results": [], "language": ["vi"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": "ner-vietnamese-electra-base", "pipeline_tag": null, "tags": ["named-entity-recognition"], "widget": [{"te... | <!-- 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. -->
# vietnamese-ner
This model is a fine-tuned version of [NlpHUST/electra-base-vn](https://huggingface.co/NlpHUST/electra-base-vn) on... | null | null | null | [
"vi"
] | 133,074,185 | null | null | [
"ElectraForTokenClassification",
"AutoModelForTokenClassification",
"electra"
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"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
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66598d3cc3196fbd0f1d0efa | 34093184d10f76a9 | 35b001f3a491f9f9 | 151,646 | BPE | 151,387 | true | true | true | true | 3 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 53 | Qwen/Qwen2-1.5B | Qwen | null | 132,649 | 2,061,238 | False | 2024-05-31T08:41:32Z | 2024-06-06T14:36:18Z | transformers | 100 | 0 | null | text-generation | {"parameters": {"BF16": 1543714304}, "total": 1543714304} | [
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"generation_config.json",
"merges.txt",
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"transformers",
"safetensors",
"qwen2",
"text-generation",
"pretrained",
"conversational",
"en",
"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",
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"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["pretrained"]} | # Qwen2-1.5B
## 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 1.5B Qwen2 base language model.
Comp... | null | [
"apache-2.0"
] | null | [
"en"
] | 1,543,714,304 | null | null | [
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"Qwen2ForCausalLM",
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"text"
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638d1bf45869d78eb7e64cae | e13e27192dde8df0 | 7ce118731980e5c6 | 29,794 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 291 | lucas-leme/FinBERT-PT-BR | lucas-leme | null | 185,012 | 2,220,023 | False | 2022-12-04T22:15:16Z | 2024-02-13T15:20:33Z | transformers | 29 | 0 | null | text-classification | null | [
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"config.json",
"pytorch_model.bin",
"sentiment_index_and_economy.png",
"sentiment_inflation.png",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
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] | c3bf75c85f9b1b0dd257d688822d64390420936c | [
"transformers",
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"bert",
"text-classification",
"pt",
"license:apache-2.0",
"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": "pt", "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": "O futuro de DI caiu 20 bps nesta manh\u00e3", "example_t... | # FinBERT-PT-BR : Financial BERT PT BR
FinBERT-PT-BR is a pre-trained NLP model to analyze sentiment of Brazilian Portuguese financial texts.
The model was trained in two main stages: language modeling and sentiment modeling. In the first stage, a language model was trained with more than 1.4 million texts of financi... | null | [
"apache-2.0"
] | null | [
"pt"
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"BertNormalizer"
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"BertPreTokenizer"
] | [
"WordPiece"
] | 8 | google-bert/bert-base-cased-finetuned-mrpc | google-bert | null | 29,574 | 1,562,573 | False | 2022-03-02T23:29:04Z | 2024-02-19T11:03:21Z | transformers | 3 | 0 | null | fill-mask | {"parameters": {"F32": 108311810}, "total": 108311810} | [
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"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
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} | null | null | null | null | null | null | 108,311,810 | null | null | [
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"WordPiece"
] | 37 | moussaKam/frugalscore_tiny_bert-base_bert-score | moussaKam | null | 1,418 | 1,474,865 | False | 2022-03-02T23:29:05Z | 2022-02-01T10:50:21Z | transformers | 0 | 0 | null | text-classification | null | [
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"auto_model": "AutoModelForSequenceClassification",
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"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | null | ----------------------------------------------|-------------|----------------|------------|
| [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore |
| [moussaKam/frugalscore_small_bert-base_bert-score](ht... | null | null | null | null | null | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
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65cecf05e5413a96b080f285 | 0becab10e3734e80 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 58 | EmbeddedLLM/bge-reranker-base-onnx-o3-cpu | EmbeddedLLM | null | 21 | 1,403,675 | False | 2024-02-16T02:57:09Z | 2024-02-16T07:40:43Z | transformers | 4 | 0 | null | sentence-similarity | null | [
".gitattributes",
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"config.json",
"model.onnx",
"ort_config.json",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
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] | bdf3628a6fc12af4e2e8357bc295c96e5f8fe37e | [
"transformers",
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"text-classification",
"sentence-similarity",
"en",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["XLMRobertaForSequenceClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": 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": "sentence-similarity", "tags": ["sentence-similarity"]} | # ONNX Conversion of [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base)
- ONNX model for CPU with O3 optimisation
## Usage
```python
from itertools import product
import torch.nn.functional as F
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoToken... | null | [
"mit"
] | null | [
"en"
] | null | null | null | [
"AutoModelForSequenceClassification",
"XLMRobertaForSequenceClassification",
"xlm-roberta"
] | [
"sentence-similarity",
"text-classification"
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] | [
"text"
] | [
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621ffdc136468d709f17a0ce | ee291bff3d5dc545 | 483fa5044ce5bdc8 | 50,500 | BPE | 50,000 | false | true | true | true | 240 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 292 | climatebert/distilroberta-base-climate-detector | climatebert | null | 5,194 | 1,416,458 | False | 2022-03-02T23:29:05Z | 2023-06-20T18:52:03Z | transformers | 17 | 0 | null | text-classification | {"parameters": {"I64": 514, "F32": 82300418}, "total": 82300932} | [
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"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 2c3bc660d45a59e31b35f5d3e365ee4f59fdf76c | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"en",
"dataset:climatebert/climate_detection",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "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": fa... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["climatebert/climate_detection"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": null, "pipeline_tag": null, "tags": null} | # Model Card for distilroberta-base-climate-detector
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for detecting climate-related paragraphs.
Using the [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) langua... | null | [
"apache-2.0"
] | [
"climatebert/climate_detection"
] | [
"en"
] | 82,300,932 | null | [
"accuracy"
] | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6791d154ab6d231d22e1cd64 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | unsloth/Llama-3.2-3B-Instruct-unsloth-bnb-4bit | unsloth | {
"models": [
{
"_id": "66eaef786865fea1324edb5d",
"id": "meta-llama/Llama-3.2-3B-Instruct"
}
],
"relation": "quantized"
} | 95,123 | 1,829,749 | False | 2025-01-23T05:19:16Z | 2025-06-02T21:41:30Z | transformers | 10 | 0 | null | text-generation | {"parameters": {"F32": 219920, "BF16": 469675008, "U8": 2828862332}, "total": 3298757260} | [
".gitattributes",
"README.md",
"chat_template.jinja",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 19846d3f624f3eb96f3bdd275620c6bc7e21e1f8 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3",
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"facebook",
"unsloth",
"conversational",
"en",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:quantized:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"text-generation-inference",
"endpoints... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|eot_id|>", "pad_token": "<|finetune_right_pad_id|>", "unk_token": null, "chat_templa... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Llama-3.2-3B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "llama3.2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["llama-3", "llama", "meta", "facebook", "u... | <div>
<p style="margin-bottom: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/llama-32-66f46afde4ca573864321a22">our collection</a> for versions of Llama 3.2 including GGUF & 4-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em>Unsloth's <a href="https://unsloth.ai/blog/dyn... | null | [
"llama3.2"
] | null | [
"en"
] | 3,298,757,260 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6332bbc914204ffa8922cd14 | 7a4cc4a6090329ff | ab8f40e5a89244c4 | 250,680 | BPE | 250,434 | false | true | true | true | 4 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 31 | bigscience/bloomz-7b1 | bigscience | null | 2,864 | 1,430,292 | False | 2022-09-27T09:00:57Z | 2024-02-22T09:05:04Z | transformers | 147 | 0 | [{"name": "bloomz-7b1", "results": [{"task": {"type": "Coreference resolution"}, "dataset": {"type": "winogrande", "name": "Winogrande XL (xl)", "config": "xl", "split": "validation", "revision": "a80f460359d1e9a67c006011c94de42a8759430c"}, "metrics": [{"type": "Accuracy", "value": 55.8, "verified": false}]}, {"task": ... | text-generation | {"parameters": {"F16": 7069016064}, "total": 7069016064} | [
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"logs/logs/main_log.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | fa72440b228d7f42a42ec3681cf09735ad14822f | [
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... | null | {"architectures": ["BloomForCausalLM"], "model_type": "bloom", "tokenizer_config": {"unk_token": "<unk>", "eos_token": "</s>", "bos_token": "<s>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"datasets": ["bigscience/xP3"], "language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh",... | 
# Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
7. [Citation](#citation)
# Model Summary
> We present BLOOMZ & mT0, a ... | null | [
"bigscience-bloom-rail-1.0"
] | [
"bigscience/xP3"
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"ta",
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"tn",
"ts",
"tum",
"tw",
... | 7,069,016,064 | null | null | [
"AutoModelForCausalLM",
"BloomForCausalLM",
"bloom"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6324070dc2d8cfcd809a62c3 | 4b721ed590fede9b | 354a478a1efa401a | 49,408 | BPE | 49,041 | true | true | true | true | 2 | Sequence | Sequence | ByteLevel | [
"NFC",
"Replace",
"Lowercase"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 293 | Bingsu/clip-vit-base-patch32-ko | Bingsu | null | 2,208 | 1,404,331 | False | 2022-09-16T05:18:05Z | 2022-11-08T11:02:10Z | transformers | 7 | 0 | null | zero-shot-image-classification | {"parameters": {"F32": 151277312, "I64": 127}, "total": 151277439} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 844c6cd1d75e142331ffd451ad8a878191869322 | [
"transformers",
"pytorch",
"tf",
"safetensors",
"clip",
"zero-shot-image-classification",
"ko",
"arxiv:2004.09813",
"doi:10.57967/hf/1615",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["CLIPModel"], "model_type": "clip", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<|startoftext|>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized": tr... | {
"auto_model": "AutoModelForZeroShotImageClassification",
"custom_class": null,
"pipeline_tag": "zero-shot-image-classification",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "ko", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/mai... | # clip-vit-base-patch32-ko
Korean CLIP model trained by [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813)
[Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813)로 학습된 한국어 CLIP 모델입니다.
훈련 코드:... | null | [
"mit"
] | null | [
"ko"
] | 151,277,439 | null | null | [
"AutoModelForZeroShotImageClassification",
"CLIPModel",
"clip"
] | [
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
62c422e81f45e8bdb2b12d60 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | naver/efficient-splade-VI-BT-large-query | naver | null | 67,942 | 2,700,274 | False | 2022-07-05T11:39:20Z | 2025-06-30T15:27:13Z | transformers | 18 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"config_sentence_transformers.json",
"document_0_MLMTransformer/config.json",
"document_0_MLMTransformer/model.safetensors",
"document_0_MLMTransformer/sentence_bert_config.json",
"document_0_MLMTransformer/special_tokens_map.json",
"document_0_MLMTransf... | 966075e1ffd29cc567b3900ab0e7760402b4a8e7 | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"splade",
"query-expansion",
"document-expansion",
"bag-of-words",
"passage-retrieval",
"knowledge-distillation",
"document encoder",
"en",
"dataset:ms_marco",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"deploy:az... | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["ms_marco"], "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": ["splade", "query-expansion", "document-expansion", "bag-of-words", "p... | null | null | [
"cc-by-nc-sa-4.0"
] | [
"ms_marco"
] | [
"en"
] | null | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
68119787183c408107a5aeef | d902613821ac8766 | 3a65d0d018eee5a3 | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 269 | Qwen/Qwen2.5-Omni-3B | Qwen | null | 433,972 | 2,841,837 | False | 2025-04-30T03:22:47Z | 2025-04-30T04:01:19Z | transformers | 330 | 1 | null | any-to-any | null | [
".gitattributes",
"LICENSE",
"README.md",
"added_tokens.json",
"chat_template.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00003.safetensors",
"model-00002-of-00003.safetensors",
"model-00003-of-00003.safetensors",
"model.safetensors.index.json",
"preprocesso... | f75b40e3da2003cdd6e1829b1f420ca70797c34e | [
"transformers",
"safetensors",
"qwen2_5_omni",
"multimodal",
"any-to-any",
"en",
"arxiv:2503.20215",
"license:other",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen2_5OmniModel"], "model_type": "qwen2_5_omni", "processor_config": {"chat_template": "{% set audio_count = namespace(value=0) %}{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": "qwen-research", "license_link": "LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "any-to-any", "tags": ["multimodal"]} | null | null | [
"other",
"qwen-research",
"LICENSE"
] | null | [
"en"
] | null | null | null | [
"Qwen2_5OmniModel",
"AutoModel",
"qwen2_5_omni"
] | [
null,
"any-to-any"
] | [
"multimodal"
] | [
"any"
] | [
"any"
] |
623db43ddcc4c1fcf8211edf | 8a8c94e1a59f5186 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 11 | snrspeaks/KeyPhraseTransformer | snrspeaks | null | 191 | 1,389,878 | False | 2022-03-25T12:23:25Z | 2022-03-25T13:05:44Z | transformers | 9 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | 4a31635920d6d0fcaf8d13eb9069cb898e3c1523 | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | null | null | [
"mit"
] | null | null | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
650c013ca27ba14323fa45e2 | 15b5d73dd8ce17a3 | 830fa437e6f588f3 | 50,000 | BPE | 49,721 | true | true | true | true | 23 | NFKC | Sequence | ByteLevel | [
"NFKC"
] | [
"Split",
"Digits",
"Split",
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 143 | facebook/nougat-base | facebook | null | 420,068 | 2,030,661 | False | 2023-09-21T08:39:24Z | 2023-11-20T09:26:49Z | transformers | 188 | 0 | null | image-to-text | {"parameters": {"I64": 48020, "F32": 348687992}, "total": 348736012} | [
".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"
] | abfecedbb34367c820e233f710fdc7f54e6ab249 | [
"transformers",
"pytorch",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"vision",
"nougat",
"image-to-text",
"arxiv:2308.13418",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["VisionEncoderDecoderModel"], "model_type": "vision-encoder-decoder", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-to-text", "tags": ["vision", "nougat"]} | # Nougat model, base-sized version
Nougat model trained on PDF-to-markdown. It was introduced in the paper [Nougat: Neural Optical Understanding for Academic Documents](https://arxiv.org/abs/2308.13418) by Blecher et al. and first released in [this repository](https://github.com/facebookresearch/nougat/tree/main).
D... | null | [
"cc-by-nc-4.0"
] | null | null | 348,736,012 | null | null | [
"AutoModelForImageTextToText",
"vision-encoder-decoder",
"VisionEncoderDecoderModel"
] | [
"image-text-to-text",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
624a913593d46cf4a08fd2e6 | b8649c822cf58ce8 | dc945455af795596 | 32,000 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 294 | bespin-global/klue-sroberta-base-continue-learning-by-mnr | bespin-global | null | 9,558 | 1,436,352 | False | 2022-04-04T06:33:25Z | 2024-04-30T15:07:00Z | sentence-transformers | 31 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 110618112}, "total": 110618626} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"eval/similarity_evaluation_sts-dev_results.csv",
"model.safetensors",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"similarity_evaluation_sts-test_results.csv",
"sp... | 5823912a84a5cd7a29413d73f38360a982cba93d | [
"sentence-transformers",
"pytorch",
"safetensors",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"ko",
"dataset:klue",
"license:cc-by-4.0",
"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": ["klue"], "eval_results": null, "language": ["ko"], "library_name": null, "license": "cc-by-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-sim... | # bespin-global/klue-sroberta-base-continue-learning-by-mnr
해당 모델은 KLUE/NLI, KLUE/STS 데이터셋을 활용하였으며, sentence-transformers의 공식 문서 내 소개된 [continue-learning](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark_continue_training.py) 방법을 통해 아래와 같이 학습되었습니다.
1. NLI 데이터셋을 통해... | null | [
"cc-by-4.0"
] | [
"klue"
] | [
"ko"
] | 110,618,626 | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
66a0e9ce714c5fe89bb48093 | c6de6331832e687a | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 142 | hugging-quants/Meta-Llama-3.1-8B-Instruct-GPTQ-INT4 | hugging-quants | null | 8,744 | 1,442,805 | False | 2024-07-24T11:47:26Z | 2024-08-07T07:26:19Z | transformers | 42 | 0 | null | text-generation | {"parameters": {"I32": 6979321856, "F16": 1052315648}, "total": 8031637504} | [
".gitattributes",
"LICENSE",
"README.md",
"USE_POLICY.md",
"config.json",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"quantize_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.j... | f184d2b18e2b7c257af79aa0bc53ef3ee505caa2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3.1",
"meta",
"autogptq",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"license:llama3.1",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"deploy:azure",
"region:... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "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_... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "de", "fr", "it", "pt", "hi", "es", "th"], "library_name": "transformers", "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["llama-3.1", "met... | > [!IMPORTANT]
> This repository is a community-driven quantized version of the original model [`meta-llama/Meta-Llama-3.1-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) which is the FP16 half-precision official version released by Meta AI.
## Model Information
The Meta Llama 3.1 collecti... | null | [
"llama3.1"
] | null | [
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th"
] | 8,031,637,504 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66eaa052d25d383ddb9a6c5f | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-Coder-1.5B | Qwen | {
"models": [
{
"_id": "66e6d032663c452ad2400439",
"id": "Qwen/Qwen2.5-1.5B"
}
],
"relation": "finetune"
} | 353,170 | 3,983,656 | False | 2024-09-18T09:41:38Z | 2024-11-18T12:53:05Z | transformers | 87 | 1 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | df3ce67c0e24480f20468b6ef2894622d69eb73b | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"qwen",
"qwen-coder",
"codeqwen",
"conversational",
"en",
"arxiv:2409.12186",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"license:apache-2.0",
"text-generation-inference... | 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/Qwen2.5-1.5B"], "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-Coder-1.5B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-... | # Qwen2.5-Coder-1.5B
## Introduction
Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings ... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B/blob/main/LICENSE"
] | null | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
662768ae8ea014ccff02e9e8 | c1b718d8c9016487 | c81b23e1dcb12302 | 50,280 | BPE | 50,009 | true | true | true | true | 28 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 295 | katuni4ka/tiny-random-olmo-hf | katuni4ka | null | 4,014 | 1,395,581 | False | 2024-04-23T07:52:14Z | 2024-04-23T07:52:43Z | transformers | 0 | 0 | null | text-generation | {"parameters": {"F32": 6537728}, "total": 6537728} | [
".gitattributes",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 8f05f41b70736efb2a6e2b15d051eea02a34f052 | [
"transformers",
"safetensors",
"olmo",
"text-generation",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["OlmoForCausalLM"], "model_type": "olmo", "tokenizer_config": {"bos_token": null, "eos_token": "<|endoftext|>", "pad_token": "<|padding|>", "unk_token": null}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | 6,537,728 | null | null | [
"AutoModelForCausalLM",
"OlmoForCausalLM",
"olmo"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67b3956047978df64c438127 | 5913c555399afcb0 | e6ffdc5dd5f1fd86 | 256,000 | BPE | 580,604 | true | true | true | true | 249 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
"Fuse"
] | 147 | google/siglip2-base-patch16-224 | google | null | 565,896 | 3,390,471 | False | 2025-02-17T20:00:32Z | 2025-02-21T04:03:35Z | transformers | 90 | 0 | null | zero-shot-image-classification | null | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"preprocessor_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 75de2d55ec2d0b4efc50b3e9ad70dba96a7b2fa2 | [
"transformers",
"safetensors",
"siglip",
"vision",
"zero-shot-image-classification",
"arxiv:2502.14786",
"arxiv:2303.15343",
"arxiv:2209.06794",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"model_type": "siglip", "tokenizer_config": {"bos_token": "<bos>", "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Convers... | {
"auto_model": "AutoModel",
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} | {"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": "zero-shot-image-classification", "tags": ["vision"], "widget": [{"src": "https://huggi... | null | null | [
"apache-2.0"
] | null | null | null | null | null | [
"AutoModel",
"siglip"
] | [
null,
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
62ff9c6c1e8382dec7d7a3eb | 911e922daa12795e | b6b7d241f1acf16d | 40,030 | Unigram | null | true | true | true | true | 1 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 296 | vinai/bartpho-syllable-base | vinai | null | 147 | 1,364,783 | False | 2022-08-19T14:21:32Z | 2024-08-20T03:57:20Z | transformers | 1 | 0 | null | feature-extraction | null | [
".gitattributes",
"README.md",
"__init__.py",
"config.json",
"dict.txt",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"tokenization_bartpho.py",
"tokenization_bartpho_fast.py",
"tokenizer.json"
] | 83024e88c51296d684ef1eb07a0f7b713baf8e4b | [
"transformers",
"pytorch",
"mbart",
"feature-extraction",
"arxiv:2109.09701",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MBartModel"], "model_type": "mbart"} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # <a name="introduction"></a> BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
The pre-trained model `vinai/bartpho-syllable-base` is the "base" variant of `BARTpho-syllable`, which uses the "base" architecture and pre-training scheme of the sequence-to-sequence denoising model [BART](https://github.co... | null | [
"mit"
] | null | null | null | null | null | [
"AutoModel",
"mbart",
"MBartModel"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
621ffdc136468d709f17d2aa | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | knkarthick/MEETING_SUMMARY | knkarthick | null | 55,366 | 1,675,540 | False | 2022-03-02T23:29:05Z | 2023-03-27T15:08:14Z | transformers | 196 | 0 | [{"name": "MEETING_SUMMARY", "results": [{"task": {"type": "abstractive-text-summarization", "name": "Abstractive Text Summarization"}, "dataset": {"name": "samsum", "type": "samsum"}, "metrics": [{"type": "rouge-1", "value": 53.8795, "name": "Validation ROGUE-1", "verified": false}, {"type": "rouge-2", "value": 28.497... | summarization | {"parameters": {"F32": 406340696}, "total": 406340696} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
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"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | e34cab124a21fabf9a4eede2ab869dd24b93dd7b | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bart",
"text2text-generation",
"seq2seq",
"summarization",
"en",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BartForConditionalGeneration"], "model_type": "bart", "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": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"language": "en", "license": "apache-2.0", "metrics": ["rouge"], "tags": ["bart", "seq2seq", "summarization"], "widget": [{"text": "Hi, I'm David and I'm supposed to be an industrial designer. Um, I just got the project announcement about what the project is. Designing a remote control. That's about it, didn't get any... | Model obtained by Fine Tuning 'facebook/bart-large-xsum' using AMI Meeting Corpus, SAMSUM Dataset, DIALOGSUM Dataset, XSUM Dataset!
## Usage
# Example 1
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
text = '''The tower is 324 metres (1,063 ft) tal... | null | [
"apache-2.0"
] | null | [
"en"
] | 406,340,696 | null | [
"rouge"
] | [
"bart",
"BartForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"summarization"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"Split",
"Split",
"Split",
"Split",
"Split",
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 298 | deepseek-ai/deepseek-llm-7b-chat | deepseek-ai | null | 302,955 | 1,885,079 | False | 2023-11-29T03:19:32Z | 2023-11-29T11:41:18Z | transformers | 217 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model-00001-of-00002.bin",
"pytorch_model-00002-of-00002.bin",
"pytorch_model.bin.index.json",
"tokenizer.json",
"tokenizer_config.json"
] | afbda8b347ec881666061fa67447046fc5164ec8 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"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": null, "license": "other", "license_name": "deepseek", "license_link": "LICENSE", "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | <p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discor... | null | [
"other",
"deepseek",
"LICENSE"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65ec2e5dada92a05d317b3ae | e23b6bdbe047bb7e | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | TinyLlama/TinyLlama_v1.1 | TinyLlama | null | 18,884 | 1,468,209 | False | 2024-03-09T09:39:41Z | 2024-06-07T01:23:32Z | transformers | 111 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"overview.png",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | ff3c701f2424c7625fdefb9dd470f45ef18b02d6 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:cerebras/SlimPajama-627B",
"arxiv:2401.02385",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": false, "rstrip... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["cerebras/SlimPajama-627B"], "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} | # TinyLlama-1.1B-v1.1
- **Codebase:** [github.com/jzhang38/TinyLlama](https://github.com/jzhang38/TinyLlama)
- **Technical Report:** [arxiv.org/pdf/2401.02385](https://arxiv.org/pdf/2401.02385)
<div align="center">
<img src="https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-240k-503b/resolve/main/TinyL... | null | [
"apache-2.0"
] | [
"cerebras/SlimPajama-627B"
] | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6322c1ca2300ff55f925497d | 8bc6b61a7b6c29c2 | 4c6e091e8dffb5aa | 30,003 | BPE | 29,740 | false | true | true | true | 7 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 299 | EleutherAI/polyglot-ko-1.3b | EleutherAI | null | 3,420 | 1,391,208 | False | 2022-09-15T06:10:18Z | 2023-06-07T05:03:00Z | transformers | 92 | 0 | null | text-generation | {"parameters": {"F32": 768, "F16": 1331810304, "BOOL": 100663296}, "total": 1432474368} | [
".gitattributes",
"README.md",
"config.json",
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"model-00002-of-00003.safetensors",
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"model.safetensors.index.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_confi... | 557e162cf6e944fdbae05bab2e45d066a125eacb | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"ko",
"arxiv:2104.09864",
"arxiv:2204.04541",
"arxiv:2306.02254",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ko"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["pytorch", "causal-lm"]} | # Polyglot-Ko-1.3B
## Model Description
Polyglot-Ko is a series of large-scale Korean autoregressive language models made by the EleutherAI polyglot team.
| Hyperparameter | Value |
|... | null | [
"apache-2.0"
] | null | [
"ko"
] | 1,432,474,368 | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
651edb24a2ef3eb14d257224 | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | AIDA-UPM/star | AIDA-UPM | null | 923 | 1,356,820 | False | 2023-10-05T15:49:56Z | 2024-03-15T09:57:25Z | transformers | 7 | 0 | null | feature-extraction | null | [
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | e2a08d0b3cf2dc78548e131045faec351ac46b33 | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2310.11081",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaModel"], "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": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "feature-extraction", "tags": null} | # Style Transformer for Authorship Representations - STAR
This is the repository for the [Style Transformer for Authorship Representations (STAR)](https://arxiv.org/abs/2310.11081) model. We present the weights of our model here.
Also check out our [github repo for STAR](https://github.com/jahuerta92/star) for replica... | null | null | null | null | null | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
648e0eeb8cbb07656041d775 | 67de189ee70711f1 | 830fa437e6f588f3 | 50,000 | BPE | 49,721 | true | true | false | true | 23 | NFKC | Sequence | ByteLevel | [
"NFKC"
] | [
"Split",
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"Split",
"Split",
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] | [
"ByteLevel"
] | 300 | TheBloke/galactica-30B-evol-instruct-70K-GPTQ | TheBloke | null | 22 | 1,353,637 | False | 2023-06-17T19:52:11Z | 2023-08-21T08:01:38Z | transformers | 11 | 0 | null | text-generation | {"parameters": {"I32": 29595009024, "BF16": 732884992, "F16": 3096576}, "total": 30330990592} | [
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"galactica",
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"arxiv:2304.12244",
"license:other",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | null | {"architectures": ["OPTForCausalLM"], "model_type": "opt", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["WizardLM/WizardLM_evol_instruct_70k"], "eval_results": null, "language": null, "library_name": "transformers", "license": "other", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["galactica", "wizardlm", "alp... | <!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<d... | null | [
"other"
] | [
"WizardLM/WizardLM_evol_instruct_70k"
] | null | 30,330,990,592 | null | null | [
"OPTForCausalLM",
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"opt"
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"text-generation"
] | [
"text"
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"text"
] | [
"text"
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621ffdc136468d709f180296 | 715c95117d4d9069 | 84fb34b2e1636fc5 | 30,527 | WordPiece | null | true | true | true | true | 6 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 3 | sentence-transformers/all-mpnet-base-v1 | sentence-transformers | null | 20,768 | 1,444,277 | False | 2022-03-02T23:29:05Z | 2025-08-19T10:16:12Z | sentence-transformers | 12 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 109486464}, "total": 109486978} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"data_config.json",
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"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_... | 3958fb5c143d426dfff5754c2b62b3d6bd45e287 | [
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"onnx",
"safetensors",
"openvino",
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"fill-mask",
"feature-extraction",
"sentence-similarity",
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"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.093... | null | {"architectures": ["MPNetForMaskedLM"], "model_type": "mpnet", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "[UNK]", "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-mpnet-base-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](h... | null | [
"apache-2.0"
] | null | [
"en"
] | 109,486,978 | null | null | [
"MPNetForMaskedLM",
"AutoModelForMaskedLM",
"mpnet"
] | [
"fill-mask",
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc036468d709f1769d4 | 128f4d40fdb1ae99 | 82ae3e9dca39a0cc | 32,005 | BPE | 66,688 | true | true | true | true | 7 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 301 | MilaNLProc/feel-it-italian-emotion | MilaNLProc | null | 86,347 | 1,641,780 | False | 2022-03-02T23:29:04Z | 2022-08-15T20:36:13Z | transformers | 18 | 0 | null | text-classification | null | [
".gitattributes",
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"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"trainer_state.json",
"training_args.bin"
] | 6efdabf62230414aeba764986b4ae317ce7c5c47 | [
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"deploy:azure",
"region:us"
] | null | {"architectures": ["CamembertForSequenceClassification"], "model_type": "camembert", "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": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "it", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["sentiment", "emotion", "Italian"]} | # FEEL-IT: Emotion and Sentiment Classification for the Italian Language
## FEEL-IT Python Package
You can find the package that uses this model for emotion and sentiment classification **[here](https://github.com/MilaNLProc/feel-it)** it is meant to be a very simple interface over HuggingFace models.
## License
Us... | null | null | null | [
"it"
] | null | null | null | [
"AutoModelForSequenceClassification",
"camembert",
"CamembertForSequenceClassification"
] | [
"text-classification"
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"text"
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"text"
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"logits"
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"reg... | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": ["EleutherAI/pile"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["pytorch", "causal-lm", "pythia"]} | The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and ... | null | [
"apache-2.0"
] | [
"EleutherAI/pile"
] | [
"en"
] | 11,997,067,840 | null | null | [
"gpt_neox",
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"GPTNeoXForCausalLM"
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"text-generation"
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"ByteLevel"
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] | null | {"architectures": ["Florence2ForConditionalGeneration"], "auto_map": {"AutoConfig": "configuration_florence2.Florence2Config", "AutoModelForCausalLM": "modeling_florence2.Florence2ForConditionalGeneration"}, "model_type": "florence2", "tokenizer_config": {}} | {
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"mit",
"https://huggingface.co/microsoft/Florence-2-large-ft/resolve/main/LICENSE"
] | null | null | 770,430,041 | null | null | [
"florence2",
"AutoModelForImageTextToText",
"Florence2ForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
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"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 5 | 51la5/roberta-large-NER | 51la5 | null | 18,869 | 1,445,939 | False | 2022-10-17T08:25:02Z | 2022-10-17T08:36:02Z | transformers | 52 | 0 | null | token-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"rust_model.ot",
"sentencepiece.bpe.model",
"tokenizer.json"
] | a1e14ce81283022007d6229f98d6325dda413454 | [
"transformers",
"pytorch",
"rust",
"xlm-roberta",
"token-classification",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
... | null | {"architectures": ["XLMRobertaForTokenClassification"], "model_type": "xlm-roberta"} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "j... | # xlm-roberta-large-finetuned-conll03-english
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#te... | null | null | null | [
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"XLMRobertaForTokenClassification",
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"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
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".gitattributes",
"1_Pooling/config.json",
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"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",... | 82d44689be9cf3c6c6a6f77cc3171c93282873a1 | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:cc-by-nc-3.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "cc-by-nc-3.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-simila... | # pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb
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. It has been trained over the SNLI, MNLI, SCINLI, SCITAIL, MEDNLI... | null | [
"cc-by-nc-3.0"
] | null | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
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"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 14 | BAAI/bge-small-zh-v1.5 | BAAI | null | 1,482,404 | 4,129,254 | False | 2023-09-12T05:22:29Z | 2023-10-12T03:35:59Z | transformers | 104 | 2 | null | feature-extraction | null | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
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"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 7999e1d3359715c523056ef9478215996d62a620 | [
"transformers",
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"feature-extraction",
"zh",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["zh"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | <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"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
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"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | TheBloke/CodeLlama-34B-Instruct-GPTQ | TheBloke | null | 136 | 1,337,369 | False | 2023-08-25T07:43:45Z | 2023-09-27T12:46:12Z | transformers | 74 | 0 | null | text-generation | {"parameters": {"I32": 33218887680, "F16": 528474112}, "total": 33747361792} | [
".gitattributes",
"LICENSE",
"LICENSE.txt",
"Notice",
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"config.json",
"configuration_llama.py",
"generation_config.json",
"model.safetensors",
"modeling_llama.py",
"quantize_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"toke... | 0a2785d47fb7330388e0487e0526bbc53d87c156 | [
"transformers",
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"text-generation",
"llama-2",
"custom_code",
"code",
"arxiv:2308.12950",
"license:llama2",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "auto_map": {"AutoConfig": "configuration_llama.LlamaConfig", "AutoModel": "modeling_llama.LlamaModel", "AutoModelForCausalLM": "modeling_llama.LlamaForCausalLM", "AutoModelForSequenceClassification": "modeling_llama.LlamaForSequenceClassification"}, "quant... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "codellama/CodeLlama-34b-instruct-hf", "datasets": null, "eval_results": null, "language": ["code"], "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": "CodeLlama 34B Instruct", "pipeline_tag": "text-generation", "tags": ["llama-2"], "inf... | <!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<d... | null | [
"llama2"
] | null | [
"code"
] | 33,747,361,792 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
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"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-7B-Instruct-AWQ | Qwen | {
"models": [
{
"_id": "66e81cbcd683a3f4e5291bbf",
"id": "Qwen/Qwen2.5-7B-Instruct"
}
],
"relation": "quantized"
} | 797,194 | 3,430,205 | False | 2024-09-17T13:55:09Z | 2024-10-09T12:26:45Z | transformers | 39 | 0 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | b25037543e9394b818fdfca67ab2a00ecc7dd641 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"arxiv:2309.00071",
"arxiv:2407.10671",
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"base_model:quantized:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatibl... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"bits": 4, "quant_method": "awq"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-7B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-AWQ/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_ta... | # Qwen2.5-7B-Instruct-AWQ
## Introduction
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly **more ... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-AWQ/blob/main/LICENSE"
] | null | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"ByteLevel"
] | [
"ByteLevel"
] | 303 | breezedeus/pix2text-mfr | breezedeus | null | 481,806 | 2,352,949 | False | 2024-02-11T16:01:39Z | 2024-05-05T14:23:13Z | transformers | 53 | 0 | null | image-to-text | null | [
".gitattributes",
"README.md",
"config.json",
"decoder_model.onnx",
"encoder_model.onnx",
"generation_config.json",
"preprocessor_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | bea257edb2653f2ae413b084f2ac0e8299d08df0 | [
"transformers",
"onnx",
"vision-encoder-decoder",
"image-text-to-text",
"latex-ocr",
"math-ocr",
"math-formula-recognition",
"mfr",
"pix2text",
"p2t",
"image-to-text",
"doi:10.57967/hf/1833",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["VisionEncoderDecoderModel"], "model_type": "vision-encoder-decoder", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["latex-ocr", "math-ocr", "math-formula-recognition", "mfr", "pix2text", "p2t", ... | # Model Card: Pix2Text-MFR
Mathematical Formula Recognition (MFR) model from [Pix2Text (P2T)](https://github.com/breezedeus/Pix2Text).
## Model Details / 模型细节
This MFR model utilizes the [TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr) architecture developed by Microsoft, starting with its initial va... | null | [
"mit"
] | null | null | null | null | null | [
"AutoModelForImageTextToText",
"vision-encoder-decoder",
"VisionEncoderDecoderModel"
] | [
"image-text-to-text",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
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"Metaspace"
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"Metaspace"
] | 304 | clips/mfaq | clips | null | 498 | 1,333,102 | False | 2022-03-02T23:29:05Z | 2021-10-15T06:21:13Z | sentence-transformers | 36 | 0 | null | sentence-similarity | null | [
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"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["clips/mfaq"], "eval_results": null, "language": ["cs", "da", "de", "en", "es", "fi", "fr", "he", "hr", "hu", "id", "it", "nl", "no", "pl", "pt", "ro", "ru", "sv", "tr", "vi"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "... | # MFAQ
We present a multilingual FAQ retrieval model trained on the [MFAQ dataset](https://huggingface.co/datasets/clips/mfaq), it ranks candidate answers according to a given question.
## Installation
```
pip install sentence-transformers transformers
```
## Usage
You can use MFAQ with sentence-transformers or dir... | null | [
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"xlm-roberta"
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] | [
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621ffdc136468d709f1799cf | 5afd7d59d2aa5e01 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
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] | [
"BertPreTokenizer"
] | [
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] | 125 | bhadresh-savani/bert-base-uncased-emotion | bhadresh-savani | null | 5,214 | 1,353,605 | False | 2022-03-02T23:29:05Z | 2023-03-22T08:43:48Z | transformers | 55 | 0 | [{"name": "bhadresh-savani/bert-base-uncased-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "default", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.9265, "name": "Accuracy", "verified": true, "ve... | text-classification | {"parameters": {"I64": 512, "F32": 109486854}, "total": 109487366} | [
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} | {"datasets": ["emotion"], "language": ["en"], "license": "apache-2.0", "metrics": ["Accuracy, F1 Score"], "tags": ["text-classification", "emotion", "pytorch"], "thumbnail": "https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4", "model-index": [{"name": "bhadresh-savan... | # bert-base-uncased-emotion
## Model description:
[Bert](https://arxiv.org/abs/1810.04805) is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective
[bert-base-uncased](https://huggingface.co/bert-base-uncased) finetuned on the emotion dataset using HuggingFace Traine... | null | [
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6259d478c59eb444b7f11eb6 | f56695671ec53fe4 | b46495902ed345f1 | 250,054 | Unigram | null | true | true | true | true | 57 | Sequence | Metaspace | Metaspace | [
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"Metaspace"
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] | null | {"architectures": ["MBartForConditionalGeneration"], "model_type": "mbart", "tokenizer_config": {"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": true, "__typ... | {
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": "uk", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["gec", "mbart-50"], "widget": [{"text": "\u044f \u0439 \u043d\u0435 \u0434\u0443\u043c\u04... | This model was finetuned on errorful sentences from the `train` subset of [UA-GEC](https://github.com/grammarly/ua-gec) corpus, introduced in [UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language](https://arxiv.org/abs/2103.16997) paper.
Only sentences containing errors were used; 8,874 s... | null | null | null | [
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] | null | null | null | [
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"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["trl"]} | # Tiny MistralForCausalLM
This is a minimal model built for unit tests in the [TRL](https://github.com/huggingface/trl) library. | null | null | null | null | 513,960 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
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] | [
"text"
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"text"
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65f1c5a622ea2a37a28c49a7 | 478c3acd2402b078 | 82619ce8e1dcaab5 | 100,000 | BPE | 99,776 | false | true | true | true | 15 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
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{
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"id": "VRLLab/TurkishBERTweet"
}
],
"relation": "finetune"
} | 529 | 1,309,420 | False | 2024-03-13T15:26:30Z | 2024-03-13T15:27:32Z | transformers | 2 | 0 | [{"name": "turkish_sentiment3", "results": []}] | text-classification | {"parameters": {"F32": 162844419}, "total": 162844419} | [
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] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
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} | {"base_model": "VRLLab/TurkishBERTweet", "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": "turkish_sentiment3", "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. -->
# turkish_sentiment3
This model is a fine-tuned version of [VRLLab/TurkishBERTweet](https://huggingface.co/VRLLab/TurkishBERTweet) ... | null | [
"mit"
] | null | null | 162,844,419 | null | [
"accuracy"
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"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
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] | [
"text"
] | [
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65874158126b8d7eae9e3e7b | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
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"Replace",
"ByteFallback",
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"models": [
{
"_id": "652cf112b118f26df7751f16",
"id": "stabilityai/japanese-stablelm-base-gamma-7b"
}
],
"relation": "finetune"
} | 18,154 | 1,366,608 | False | 2023-12-23T20:21:44Z | 2025-03-09T20:40:07Z | transformers | 18 | 0 | null | text-generation | {"parameters": {"BF16": 7241732096}, "total": 7241732096} | [
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"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["stabilityai/japanese-stablelm-base-gamma-7b"], "datasets": ["augmxnt/ultra-orca-boros-en-ja-v1"], "eval_results": null, "language": ["ja", "en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags":... | # shisa-gamma-7b-v1
For more information see our main [Shisa 7B](https://huggingface.co/augmxnt/shisa-7b-v1) model
We applied a version of our fine-tune data set onto [Japanese Stable LM Base Gamma 7B](https://huggingface.co/stabilityai/japanese-stablelm-base-gamma-7b) and it performed pretty well, just sharing since... | null | [
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"ja",
"en"
] | 7,241,732,096 | null | null | [
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6563cebe7061c2cbda3591c5 | 3772f0f7838bb6c2 | 291a9305acaae14c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
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] | 97 | ncbi/MedCPT-Cross-Encoder | ncbi | null | 43,403 | 1,487,882 | False | 2023-11-26T23:03:26Z | 2023-12-03T00:45:45Z | transformers | 24 | 0 | null | text-classification | null | [
".gitattributes",
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] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
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"pipeline_tag": "text-classification",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "other", "license_name": "public-domain", "license_link": "LICENSE", "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # Usage: Ranking articles for a given query
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ncbi/MedCPT-Cross-Encoder")
model = AutoModelForSequenceClassification.from_pretrained("ncbi/MedCPT-Cross-Encoder")
query = "diabete... | null | [
"other",
"public-domain",
"LICENSE"
] | null | null | null | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
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"Precompiled",
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"Metaspace"
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"rust_model.ot",
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] | 8fcd50a33ea773f3bacf3fecdf193c9ca2c060ac | [
"transformers",
"pytorch",
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"reformer",
"text-generation",
"endpoints_compatible",
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"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | null | ## Reformer Model trained on "Crime and Punishment"
Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English.
Crime and Punishment training data was taken from `gs://trax-ml/reformer/crime-and-punishment-2554.txt` and contains
roughly 0.5M tokens.
The ReformerLM model was trai... | null | null | null | null | null | null | null | [
"ReformerModelWithLMHead",
"reformer",
"AutoModelForCausalLM"
] | [
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"BertPreTokenizer"
] | [
"WordPiece"
] | 8 | bucketresearch/politicalBiasBERT | bucketresearch | {
"models": [
{
"_id": "621ffdc036468d709f174331",
"id": "google-bert/bert-base-cased"
}
],
"relation": "finetune"
} | 413 | 1,304,773 | False | 2023-01-31T06:01:54Z | 2025-05-08T06:48:40Z | transformers | 31 | 0 | null | text-classification | {"parameters": {"I64": 512, "F32": 108312579}, "total": 108313091} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"optimizer.pt",
"pytorch_model.bin",
"rng_state.pth",
"scaler.pt",
"scheduler.pt",
"tokenizer.json",
"trainer_state.json",
"training_args.bin"
] | 5e5aa509b3f0fe76a6cf102f56490bd82ca04e2f | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"en",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"doi:10.57967/hf/0870",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert"} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": ["google-bert/bert-base-cased"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # PoliticalBiasBERT
<!-- Provide a quick summary of what the model is/does. -->
BERT finetuned on many examples of politically biased texts
Paper and repository coming soon.
## Usage
```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
text = "your text here"
tokenizer = Au... | null | [
"mit"
] | null | [
"en"
] | 108,313,091 | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66e81d43f85e59f0618f3b24 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-Math-7B | Qwen | {
"models": [
{
"_id": "66e6d06401e2adb00b0be996",
"id": "Qwen/Qwen2.5-7B"
}
],
"relation": "finetune"
} | 117,201 | 2,683,059 | False | 2024-09-16T11:57:55Z | 2024-09-23T06:07:51Z | transformers | 107 | 1 | null | text-generation | {"parameters": {"BF16": 7615616512}, "total": 7615616512} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"tokenizer.json",
"... | b101308fe89651ea5ce025f25317fea6fc07e96e | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"arxiv:2409.12122",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"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": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'Please reason step by step, an... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-7B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-Math-7B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generat... | # Qwen2.5-Math-7B
> [!Warning]
> <div align="center">
> <b>
> 🚨 Qwen2.5-Math mainly supports solving English and Chinese math problems through CoT and TIR. We do not recommend using this series of models for other tasks.
> </b>
> </div>
## Introduction
In August 2024, we released the first series of mathematical LL... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-Math-7B/blob/main/LICENSE"
] | null | [
"en"
] | 7,615,616,512 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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