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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
621ffdc136468d709f17b2f6 | 7b83dff9fadba2d1 | 24d7bacb0a5d7c86 | 30,522 | WordPiece | null | true | true | true | true | 7 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 231 | funnel-transformer/small | funnel-transformer | null | 132,380 | 2,266,358 | False | 2022-03-02T23:29:05Z | 2024-02-29T09:00:06Z | transformers | 6 | 0 | null | feature-extraction | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 81e9124473a26bd4ff7244e1c1c544ef8e22ed08 | [
"transformers",
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["FunnelModel"], "model_type": "funnel", "tokenizer_config": {"unk_token": "<unk>", "sep_token": "<sep>", "pad_token": "<pad>", "cls_token": "<cls>", "mask_token": "<mask>", "bos_token": "<s>", "eos_token": "</s>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["bookcorpus", "wikipedia", "gigaword"], "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} | # Funnel Transformer small model (B4-4-4 with decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](https... | null | [
"apache-2.0"
] | [
"bookcorpus",
"wikipedia",
"gigaword"
] | [
"en"
] | null | null | null | [
"AutoModel",
"FunnelModel",
"funnel"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
621ffdc036468d709f174339 | 634a9186a6003172 | 5f9f736c7dc6fbfa | 28,996 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 8 | google-bert/bert-large-cased-whole-word-masking-finetuned-squad | google-bert | null | 40,735 | 2,157,682 | False | 2022-03-02T23:29:04Z | 2024-02-19T11:06:38Z | transformers | 1 | 0 | null | question-answering | {"parameters": {"F32": 333581314}, "total": 333581314} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"pytorch_model.bin",
"rust_model.ot",
"saved_model.tar.gz",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | de462ace242f74c8c469e35697b4d3a47c77d084 | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForQuestionAnswering"], "model_type": "bert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForQuestionAnswering",
"custom_class": null,
"pipeline_tag": "question-answering",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["bookcorpus", "wikipedia"], "eval_results": null, "language": "en", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # BERT large model (cased) whole word masking finetuned on SQuAD
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is cas... | null | [
"apache-2.0"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | 333,581,314 | null | null | [
"AutoModelForQuestionAnswering",
"bert",
"BertForQuestionAnswering"
] | [
"question-answering"
] | [
"text"
] | [
"text"
] | [
"text"
] |
625454cdcac3e87b7f0cacb9 | 83bf28f8b6767a2f | ae88e903e2b563a1 | 50,295 | BPE | 50,000 | false | true | true | true | 39 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 85 | Salesforce/codegen-350M-mono | Salesforce | null | 150,625 | 2,732,267 | False | 2022-04-11T16:18:21Z | 2025-01-31T20:16:47Z | transformers | 101 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | d9107f71cca463240db1143f4a75a927a27fcb27 | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"license:bsd-3-clause",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["CodeGenForCausalLM"], "model_type": "codegen", "tokenizer_config": {"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "bsd-3-clause", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # CodeGen (CodeGen-Mono 350M)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Sava... | null | [
"bsd-3-clause"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"codegen",
"CodeGenForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17ef5e | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | pparasurama/raceBERT-ethnicity | pparasurama | null | 123,464 | 2,640,328 | False | 2022-03-02T23:29:05Z | 2021-11-09T20:42:29Z | transformers | 4 | 0 | null | text-classification | null | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | babd5c78b799aaac6346b7d934b510674ae27d63 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"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"
} | null | null | null | null | null | null | null | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65c1b2e66ec23508cfe77bf2 | 6950482313cc6dd9 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 68 | hooman650/bge-m3-onnx-o4 | hooman650 | null | 125 | 2,223,667 | False | 2024-02-06T04:17:42Z | 2024-02-06T04:25:23Z | transformers | 11 | 0 | null | feature-extraction | null | [
".gitattributes",
"README.md",
"config.json",
"model_optimized.onnx",
"model_optimized.onnx.data",
"ort_config.json",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 848e8bc2408aad2c8849c7be9475f7aec3ee781a | [
"transformers",
"onnx",
"xlm-roberta",
"feature-extraction",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["XLMRobertaModel"], "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": "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": "feature-extraction", "tags": null} | # bge-m3-onnx-o4
This is `bge-m3-onnx-o4` weights of the original [`BAAI/bge-m3`](https://huggingface.co/BAAI/bge-m3). Why is this model cool?
- [x] Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse ret... | null | [
"mit"
] | null | null | null | null | null | [
"XLMRobertaModel",
"AutoModel",
"xlm-roberta"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
64f965cc1435cc7faa6ccb56 | 8e42935d2dbeb45e | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 204 | nfliu/deberta-v3-large_boolq | nfliu | {
"models": [
{
"_id": "621ffdc136468d709f17dee5",
"id": "microsoft/deberta-v3-large"
}
],
"relation": "finetune"
} | 1,321 | 1,987,435 | False | 2023-09-07T05:55:24Z | 2023-09-08T05:40:57Z | transformers | 3 | 0 | [{"name": "deberta-v3-large_boolq", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "boolq", "type": "boolq", "config": "default", "split": "validation", "args": "default"}, "metrics": [{"name": "Accuracy", "type": "accuracy", "value": 0.8834862385321101, "verifi... | text-classification | {"parameters": {"F32": 435063810}, "total": 435063810} | [
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"all_results.json",
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"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json",
"train_results.json",
"trainer_state.json",
"training_a... | 9df67c219a86b2611c177f288b8ccc82d7b97707 | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"dataset:boolq",
"base_model:microsoft/deberta-v3-large",
"base_model:finetune:microsoft/deberta-v3-large",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatibl... | null | {"architectures": ["DebertaV2ForSequenceClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "microsoft/deberta-v3-large", "datasets": ["boolq"], "license": "mit", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-v3-large_boolq", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "boolq", "type": ... | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large_boolq
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/debert... | null | [
"mit"
] | [
"boolq"
] | null | 435,063,810 | null | [
"accuracy"
] | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62d6b87ef00ba76e7a691e9e | 2092a9ca51a9724d | c678ba3e3a4740c3 | 57,532 | Unigram | null | true | true | true | true | 15 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 183 | naver-clova-ix/donut-base-finetuned-docvqa | naver-clova-ix | null | 18,199 | 2,179,208 | False | 2022-07-19T13:58:22Z | 2024-03-09T13:01:37Z | transformers | 273 | 1 | null | document-question-answering | null | [
".gitattributes",
".gitignore",
"README.md",
"added_tokens.json",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | b19d2e332684b0e2d35d9144ce34047767335cf8 | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"donut",
"image-to-text",
"vision",
"document-question-answering",
"arxiv:2111.15664",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["VisionEncoderDecoderModel"], "model_type": "vision-encoder-decoder", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": ... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "document-question-answering", "tags": ["donut", "image-to-text", "vision"], "widget": [{"text": "What i... | # Donut (base-sized model, fine-tuned on DocVQA)
Donut model fine-tuned on DocVQA. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
Disclaimer: The team releasing ... | null | [
"mit"
] | null | null | null | null | null | [
"AutoModelForImageTextToText",
"vision-encoder-decoder",
"VisionEncoderDecoderModel"
] | [
"image-text-to-text",
"document-question-answering",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
621ffdc136468d709f1794d9 | 1a6b1ef74338b373 | 607a0511dda1f7ab | 64,000 | WordPiece | null | true | true | true | true | 36 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 232 | aubmindlab/bert-base-arabert | aubmindlab | null | 806 | 2,010,500 | False | 2022-03-02T23:29:05Z | 2023-08-03T12:32:51Z | transformers | 33 | 0 | null | fill-mask | {"parameters": {"F32": 135851010}, "total": 135851010} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 470dc2a368cce42412068cf568d7728fd3869c53 | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"ar",
"arxiv:2003.00104",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "ar", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"text": " \u0639\u0627\u0635\u0645 +\u0629 \u0644\u0628\u0646\u0627\u0646... | # !!! A newer version of this model is available !!! [AraBERTv2](https://huggingface.co/aubmindlab/bert-base-arabertv2)
# AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png" width="100" align="left"/>
**AraBERT**... | null | null | null | [
"ar"
] | 135,851,010 | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65580f4b16de0c93ae634b8e | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | Titeiiko/OTIS-Official-Spam-Model | Titeiiko | null | 147 | 1,968,433 | False | 2023-11-18T01:11:39Z | 2023-11-23T22:52:24Z | transformers | 12 | 0 | null | text-classification | {"parameters": {"F32": 4386178}, "total": 4386178} | [
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"model.safetensors",
"pytorch_model.bin",
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"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 2586e3df31d9ba3643392b1d761e733eb1055e4d | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"text-classification",
"anti-spam",
"spam",
"en",
"license:bsd-3-clause",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
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"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "bsd-3-clause", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["anti-spam", "spam"]} | <!-- PROJECT LOGO -->
<br />
<div align="center">
<a href="https://github.com/JewishLewish/Otis">
<img src="https://cdn.discordapp.com/attachments/660227834500874276/1175310288212463706/47._Big_Tree_1.png?ex=656ac400&is=65584f00&hm=0518b63834cd0da8208e79c1b019fd41e170aaa860d4812695fb8a43d43abc55&" alt="Logo" widt... | null | [
"bsd-3-clause"
] | null | [
"en"
] | 4,386,178 | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f1802be | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | sentence-transformers/msmarco-MiniLM-L12-cos-v5 | sentence-transformers | null | 946,330 | 6,789,960 | False | 2022-03-02T23:29:05Z | 2024-11-05T16:57:30Z | sentence-transformers | 10 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 33360000}, "total": 33360512} | [
".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... | 09660d808c6038205655b469ac62c39fe7288ed4 | [
"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-L12-cos-v5
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500k (query, answer) pairs from the [MS MARCO Passages dataset](https://github.com/microsoft... | null | null | null | [
"en"
] | 33,360,512 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
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"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 233 | cointegrated/rubert-tiny-sentiment-balanced | cointegrated | null | 25,348 | 2,121,655 | False | 2022-03-02T23:29:05Z | 2023-03-20T09:53:10Z | transformers | 18 | 0 | null | text-classification | {"parameters": {"I64": 512, "F32": 11785107}, "total": 11785619} | [
".gitattributes",
"README.md",
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"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | e0679dd4460e48648f5d4d819ae0f505384f5a9d | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"russian",
"classification",
"sentiment",
"multiclass",
"ru",
"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": ["ru"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["russian", "classification", "sentiment", "multiclass"], "widget": [{"text": "\u041a\u04... | This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned for classification of sentiment for short Russian texts.
The problem is formulated as multiclass classification: `negative` vs `neutral` vs `positive`.
## Usage
The function below estimates the sentiment of the ... | null | null | null | [
"ru"
] | 11,785,619 | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17a83d | bbf489d4445ec8f5 | 291a9305acaae14c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 223 | dmis-lab/biosyn-sapbert-bc5cdr-chemical | dmis-lab | null | 236 | 1,942,501 | False | 2022-03-02T23:29:05Z | 2021-10-25T14:47:09Z | transformers | 2 | 0 | null | feature-extraction | null | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"sparse_encoder.pk",
"sparse_weight.pt",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | f9b9daf740698ac427bb6532fd456fc18bccdd80 | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"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"
} | null | null | null | null | null | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
67fe66d72d8ae79641b62bea | 46323c0f61df476d | 1fe366e13272caa0 | 262,145 | BPE | 514,906 | true | true | true | true | 6,415 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
"Fuse"
] | 49 | mlx-community/gemma-3-12b-it-qat-4bit | mlx-community | {
"models": [
{
"_id": "67ff92c1a93c489f94ab29c6",
"id": "OpenGVLab/InternVL3-1B-Instruct"
}
],
"relation": "finetune"
} | 125,715 | 2,493,857 | False | 2025-04-15T14:01:59Z | 2025-04-21T19:47:21Z | transformers | 18 | 0 | null | image-text-to-text | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"chat_template.json",
"config.json",
"generation_config.json",
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"preprocessor_config.json",
"processor_config.json",
"special_tokens_map.js... | 66fc51ef25778c03d33c4c8bc446973d062e73f4 | [
"transformers",
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"internvl",
"custom_code",
"mlx",
"conversational",
"multilingual",
"dataset:OpenGVLab/MMPR-v1.2",
"base_model:OpenGVLab/InternVL3-1B-Instruct",
"base_model:finetune:OpenGVLab/InternVL3-1B-Instruct",
"license:other",
"text-gene... | null | {"architectures": ["Gemma3ForConditionalGeneration"], "model_type": "gemma3", "processor_config": {"chat_template": "{{ bos_token }}\n{%- if messages[0]['role'] == 'system' -%}\n {%- if messages[0]['content'] is string -%}\n {%- set first_user_prefix = messages[0]['content'] + '\n\n' -%}\n {%- else -%}\n ... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": ["OpenGVLab/InternVL3-1B-Instruct"], "datasets": ["OpenGVLab/MMPR-v1.2"], "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": "other", "license_name": "qwen", "license_link": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE", "metrics": null, ... | null | null | [
"other",
"qwen",
"https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE"
] | [
"OpenGVLab/MMPR-v1.2"
] | [
"multilingual"
] | null | null | null | [
"AutoModelForImageTextToText",
"Gemma3ForConditionalGeneration",
"gemma3"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
667f8a95ed6d253729e8d369 | 539b24ec3c9dbddd | 6ce596b7c0f62c6d | 151,647 | BPE | 151,387 | true | true | true | true | 4 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 234 | lmms-lab/llava-onevision-qwen2-7b-ov | lmms-lab | null | 108,374 | 2,421,719 | False | 2024-06-29T04:16:21Z | 2024-09-02T07:25:03Z | transformers | 62 | 0 | [{"name": "llava-onevision-qwen-7b-ov", "results": [{"task": {"type": "multimodal"}, "dataset": {"name": "AI2D", "type": "ai2d"}, "metrics": [{"type": "accuracy", "value": 81.4, "name": "accuracy", "verified": false}]}, {"task": {"type": "multimodal"}, "dataset": {"name": "ChartQA", "type": "chartqa"}, "metrics": [{"ty... | text-generation | {"parameters": {"BF16": 8030348832}, "total": 8030348832} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
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"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"preprocess... | 0b07bf7565e244cf4f39982249eafe8cd799d6dd | [
"transformers",
"safetensors",
"llava",
"text-generation",
"multimodal",
"conversational",
"en",
"zh",
"dataset:lmms-lab/LLaVA-OneVision-Data",
"arxiv:2408.03326",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlavaQwenForCausalLM"], "model_type": "llava", "tokenizer_config": {"bos_token": null, "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoProcessor"
} | {"datasets": ["lmms-lab/LLaVA-OneVision-Data"], "language": ["en", "zh"], "library_name": "transformers", "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["multimodal"], "model-index": [{"name": "llava-onevision-qwen-7b-ov", "results": [{"task": {"type": "multimodal"}, "dataset": {"name": "AI2D", "type": "ai2... | # LLaVA-OneVision
Play with the model on the [LLaVA OneVision Chat](https://llava-onevision.lmms-lab.com/).
## Table of Contents
1. [Model Summary](##model-summary)
2. [Use](##use)
3. [Limitations](##limitations)
4. [Training](##training)
5. [Licens... | null | [
"apache-2.0"
] | [
"lmms-lab/LLaVA-OneVision-Data"
] | [
"en",
"zh"
] | 8,030,348,832 | null | [
"accuracy"
] | [
"AutoModelForCausalLM",
"LlavaQwenForCausalLM",
"llava"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6889ec2029577a514ed82d21 | ebba86274664c8c0 | 1734b703c6797817 | 151,688 | BPE | 151,387 | true | true | true | true | 45 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 235 | rednote-hilab/dots.ocr | rednote-hilab | null | 257,194 | 4,511,440 | False | 2025-07-30T09:55:44Z | 2025-10-31T08:49:31Z | dots_ocr | 1,281 | 4 | null | image-text-to-text | {"parameters": {"BF16": 3039179264}, "total": 3039179264} | [
".gitattributes",
"NOTICE",
"README.md",
"chat_template.json",
"config.json",
"configuration_dots.py",
"dots.ocr LICENSE AGREEMENT",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
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"modeling_dots_... | c0111ce6bc07803dbc267932ffef0ae3a51dc951 | [
"dots_ocr",
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"document-parse",
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"table",
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"transformers",
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"conversational",
"en",
"zh",
"multilingual",
"license:mit",
"eval-results",
"region:us"
] | null | {"architectures": ["DotsOCRForCausalLM"], "model_type": "dots_ocr", "auto_map": {"AutoConfig": "configuration_dots.DotsOCRConfig", "AutoModelForCausalLM": "modeling_dots_ocr.DotsOCRForCausalLM"}, "processor_config": {"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_dots_ocr.DotsOCRForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "zh", "multilingual"], "library_name": "dots_ocr", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["image-to-text", "ocr", "document-parse", "... | <div align="center">
<p align="center">
<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/logo.png" width="300"/>
<p>
<h1 align="center">
dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model
</h1>
[![Blog](https://img.shields.io/badge/Blog-View_on_GitHub... | null | [
"mit"
] | null | [
"en",
"zh",
"multilingual"
] | 3,039,179,264 | null | null | [
"AutoModelForCausalLM",
"DotsOCRForCausalLM",
"dots_ocr",
"modeling_dots_ocr.DotsOCRForCausalLM"
] | [
"image-text-to-text",
"image-to-text",
"text-generation"
] | [
"text",
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
664448c9ba3c6c8d118eb0e7 | fbd395e40dc9abcc | f540d0cefa06a5d9 | 100,002 | BPE | 99,757 | true | true | true | true | 2 | Sequence | Sequence | ByteLevel | [] | [
"Split",
"Split",
"Split",
"Split",
"Split",
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 236 | deepseek-ai/DeepSeek-V2-Lite | deepseek-ai | null | 250,700 | 2,768,265 | False | 2024-05-15T05:31:53Z | 2024-06-25T08:37:17Z | transformers | 168 | 0 | null | text-generation | {"parameters": {"BF16": 15706484224}, "total": 15706484224} | [
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"conversational",
"custom_code",
"arxiv:2405.04434",
"license:other",
"text-generation-inference",
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] | null | {"architectures": ["DeepseekV2ForCausalLM"], "auto_map": {"AutoConfig": "configuration_deepseek.DeepseekV2Config", "AutoModel": "modeling_deepseek.DeepseekV2Model", "AutoModelForCausalLM": "modeling_deepseek.DeepseekV2ForCausalLM"}, "model_type": "deepseek_v2", "tokenizer_config": {"bos_token": {"__type": "AddedToken",... | {
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<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V2" />
</div>
<hr>
<div align="center" style="line-... | null | [
"other",
"deepseek",
"https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL"
] | null | null | 15,706,484,224 | null | null | [
"AutoModelForCausalLM",
"DeepseekV2ForCausalLM",
"deepseek_v2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6244b6ca0acf2eaa113542ec | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | alisawuffles/roberta-large-wanli | alisawuffles | null | 886 | 1,920,980 | False | 2022-03-30T20:00:10Z | 2023-06-14T04:58:48Z | transformers | 10 | 0 | null | text-classification | {"parameters": {"I64": 514, "F32": 355362819}, "total": 355363333} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
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"pytorch_model.bin",
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"trainer_state.json",
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] | 42a3e357c1a17b97688c4b8c7b6959e446883f59 | [
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"roberta",
"text-classification",
"en",
"dataset:alisawuffles/WANLI",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta"} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["alisawuffles/WANLI"], "eval_results": null, "language": ["en"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["text-classification"], "widget": [{"text": "I almost forgot to eat lu... | This is an off-the-shelf roberta-large model finetuned on WANLI, the Worker-AI Collaborative NLI dataset ([Liu et al., 2022](https://aclanthology.org/2022.findings-emnlp.508/)). It outperforms the `roberta-large-mnli` model on eight out-of-domain test sets, including by 11% on HANS and 9% on Adversarial NLI.
### How t... | null | null | [
"alisawuffles/WANLI"
] | [
"en"
] | 355,363,333 | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65384bec5235cb10ae55f06e | 3772f0f7838bb6c2 | 291a9305acaae14c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 97 | ncbi/MedCPT-Query-Encoder | ncbi | null | 99,558 | 2,453,845 | False | 2023-10-24T22:57:48Z | 2023-12-03T00:45:30Z | transformers | 58 | 0 | null | feature-extraction | {"parameters": {"F32": 109482240}, "total": 109482240} | [
".gitattributes",
"LICENSE",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | d83a36cc6b8e3a5c5e9d9d6ba156808c1643dcbc | [
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"pytorch",
"safetensors",
"bert",
"feature-extraction",
"arxiv:2307.00589",
"license:other",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "other", "license_name": "public-domain", "license_link": "LICENSE", "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # MedCPT Introduction
**MedCPT generates embeddings of biomedical texts that can be used for semantic search (dense retrieval)**. The model contains two encoders:
- [MedCPT Query Encoder](https://huggingface.co/ncbi/MedCPT-Query-Encoder): compute the embeddings of short texts (e.g., questions, search queries, sentence... | null | [
"other",
"public-domain",
"LICENSE"
] | null | null | 109,482,240 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
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"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
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"models": [
{
"_id": "67ff9385c795b579b14acf5b",
"id": "OpenGVLab/InternVL3-8B-Instruct"
}
],
"relation": "finetune"
} | 97,057 | 2,537,493 | False | 2025-04-10T16:40:17Z | 2025-09-11T12:50:07Z | transformers | 103 | 0 | null | image-text-to-text | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"configuration_intern_vit.py",
"configuration_internvl_chat.py",
"conversation.py",
"examples/image1.jpg",
"examples/image2.jpg",
"examples/red-panda.mp4",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safeten... | 853e3a797a661694b1b8ece0cb72dc2b23e3dac9 | [
"transformers",
"safetensors",
"internvl_chat",
"feature-extraction",
"internvl",
"custom_code",
"image-text-to-text",
"conversational",
"multilingual",
"dataset:OpenGVLab/MMPR-v1.2",
"arxiv:2312.14238",
"arxiv:2404.16821",
"arxiv:2412.05271",
"arxiv:2411.10442",
"arxiv:2504.10479",
"a... | null | {"architectures": ["InternVLChatModel"], "auto_map": {"AutoConfig": "configuration_internvl_chat.InternVLChatConfig", "AutoModel": "modeling_internvl_chat.InternVLChatModel", "AutoModelForCausalLM": "modeling_internvl_chat.InternVLChatModel"}, "model_type": "internvl_chat", "tokenizer_config": {"bos_token": null, "chat... | {
"auto_model": "AutoModel",
"custom_class": "modeling_internvl_chat.InternVLChatModel",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"base_model": ["OpenGVLab/InternVL3-8B-Instruct"], "datasets": ["OpenGVLab/MMPR-v1.2"], "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": "apache-2.0", "license_name": "qwen", "license_link": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE", "metrics": n... | # InternVL3-8B
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271) [\[📜 InternVL2.5-MPO\]](https://huggingface.co/paper... | null | [
"apache-2.0",
"qwen",
"https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE"
] | [
"OpenGVLab/MMPR-v1.2"
] | [
"multilingual"
] | null | null | null | [
"AutoModel",
"internvl_chat",
"InternVLChatModel",
"modeling_internvl_chat.InternVLChatModel"
] | [
"image-text-to-text",
"feature-extraction"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text",
"embeddings"
] |
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"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 237 | OpenMed/OpenMed-NER-PharmaDetect-SuperClinical-434M | OpenMed | null | 245,599 | 3,669,259 | False | 2025-07-16T18:50:49Z | 2025-08-05T09:31:25Z | transformers | 23 | 0 | null | token-classification | {"parameters": {"BF16": 434015235}, "total": 434015235} | [
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] | d4259d05b0e924ad786f5c342ddedf46fe956331 | [
"transformers",
"safetensors",
"deberta-v2",
"token-classification",
"named-entity-recognition",
"biomedical-nlp",
"chemical-entity-recognition",
"drug-discovery",
"pharmacology",
"biocuration",
"chem",
"en",
"arxiv:2508.01630",
"license:apache-2.0",
"endpoints_compatible",
"region:us"... | null | {"architectures": ["DebertaV2ForTokenClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": 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": ["token-classification", "named-entity-recognition", "biomedical-nlp", "transform... | null | null | [
"apache-2.0"
] | null | [
"en"
] | 434,015,235 | null | null | [
"DebertaV2ForTokenClassification",
"AutoModelForTokenClassification",
"deberta-v2"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66e81d379ebf20e0639888fd | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-Math-1.5B | Qwen | {
"models": [
{
"_id": "66e6d032663c452ad2400439",
"id": "Qwen/Qwen2.5-1.5B"
}
],
"relation": "finetune"
} | 768,738 | 4,598,677 | False | 2024-09-16T11:57:43Z | 2024-09-23T06:06:55Z | transformers | 103 | 1 | null | text-generation | {"parameters": {"BF16": 1543714304}, "total": 1543714304} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 4a83ca6e4526a4f2da3aa259ec36c259f66b2ab2 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"arxiv:2409.12122",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'Please reason step by step, an... | {
"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-Math-1.5B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-gen... | # Qwen2.5-Math-1.5B
> [!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 ... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-Math-1.5B/blob/main/LICENSE"
] | null | [
"en"
] | 1,543,714,304 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f1779ad | 729fbde64e945265 | c906e3d2c0e399fb | 50,105 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 238 | TurkuNLP/bert-base-finnish-cased-v1 | TurkuNLP | null | 8,267 | 1,929,383 | False | 2022-03-02T23:29:05Z | 2024-02-20T11:56:47Z | transformers | 10 | 0 | null | fill-mask | {"parameters": {"F32": 125165755}, "total": 125165755} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"pytorch_model.bin",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 87b4eaac147499b3e54d2af200244b502501712c | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"fi",
"arxiv:1912.07076",
"arxiv:1908.04212",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "fi", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ## Quickstart
**Release 1.0** (November 25, 2019)
We generally recommend the use of the cased model.
Paper presenting Finnish BERT: [arXiv:1912.07076](https://arxiv.org/abs/1912.07076)
## What's this?
A version of Google's [BERT](https://github.com/google-research/bert) deep transfer learning model for Finnish. Th... | null | null | null | [
"fi"
] | 125,165,755 | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17ae06 | 34805e59899648ad | 080ea6fa4b958367 | 250,027 | Unigram | null | true | true | true | true | 30 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 239 | facebook/mbart-large-cc25 | facebook | null | 13,951 | 1,934,664 | False | 2022-03-02T23:29:05Z | 2023-03-28T09:36:03Z | transformers | 69 | 0 | null | translation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"generation_config_for_translation_en_to_ro.json",
"pytorch_model.bin",
"sentence.bpe.model",
"sentencepiece.bpe.model",
"tf_model.h5",
"tokenizer.json"
] | f417e5563320b2cc8aabe4329d986b238809067f | [
"transformers",
"pytorch",
"tf",
"mbart",
"text2text-generation",
"translation",
"en",
"ar",
"cs",
"de",
"et",
"fi",
"fr",
"gu",
"hi",
"it",
"ja",
"kk",
"ko",
"lt",
"lv",
"my",
"ne",
"nl",
"ro",
"ru",
"si",
"tr",
"vi",
"zh",
"multilingual",
"endpoints_co... | null | {"architectures": ["MBartForConditionalGeneration"], "model_type": "mbart"} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "ar", "cs", "de", "et", "fi", "fr", "gu", "hi", "it", "ja", "kk", "ko", "lt", "lv", "my", "ne", "nl", "ro", "ru", "si", "tr", "vi", "zh", "multilingual"], "library_name": null, "license": null, "license_name": null, "license_link": null, "m... | #### mbart-large-cc25
Pretrained (not finetuned) multilingual mbart model.
Original Languages
```
export langs=ar_AR,cs_CZ,de_DE,en_XX,es_XX,et_EE,fi_FI,fr_XX,gu_IN,hi_IN,it_IT,ja_XX,kk_KZ,ko_KR,lt_LT,lv_LV,my_MM,ne_NP,nl_XX,ro_RO,ru_RU,si_LK,tr_TR,vi_VN,zh_CN
```
Original Code: https://github.com/pytorch/fairseq/tre... | null | null | null | [
"en",
"ar",
"cs",
"de",
"et",
"fi",
"fr",
"gu",
"hi",
"it",
"ja",
"kk",
"ko",
"lt",
"lv",
"my",
"ne",
"nl",
"ro",
"ru",
"si",
"tr",
"vi",
"zh",
"multilingual"
] | null | null | null | [
"AutoModelForSeq2SeqLM",
"MBartForConditionalGeneration",
"mbart"
] | [
"text2text-generation",
"translation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 240 | google/flan-ul2 | google | null | 1,432 | 1,884,650 | False | 2023-03-03T10:37:27Z | 2023-11-07T15:11:54Z | transformers | 562 | 0 | null | null | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"pytorch_model-00001-of-00008.bin",
"pytorch_model-00002-of-00008.bin",
"pytorch_model-00003-of-00008.bin",
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"pytorch_model-00005-of-00008.bin",
"pytorch_model-00006-of-00008.bin",
"pytorch_mode... | 452d74ce28ac4a7f211d6ba3ef0717027f7a8074 | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"flan-ul2",
"en",
"fr",
"ro",
"de",
"multilingual",
"dataset:svakulenk0/qrecc",
"dataset:taskmaster2",
"dataset:djaym7/wiki_dialog",
"dataset:deepmind/code_contests",
"dataset:lambada",
"dataset:gsm8k",
"dataset:aqua_rat",
"d... | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["svakulenk0/qrecc", "taskmaster2", "djaym7/wiki_dialog", "deepmind/code_contests", "lambada", "gsm8k", "aqua_rat", "esnli", "quasc", "qed", "c4"], "eval_results": null, "language": ["en", "fr", "ro", "de", "multilingual"], "library_name": null, "license": "apache-2.0", "license_name": ... | # Model card for Flan-UL2
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Using the model](#using-the-model)
2. [Results](#results)
3. [Introduction to UL2](#introduction-to-ul2)
4. [Training](#training)
5. [Contrib... | null | [
"apache-2.0"
] | [
"svakulenk0/qrecc",
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"deepmind/code_contests",
"lambada",
"gsm8k",
"aqua_rat",
"esnli",
"quasc",
"qed",
"c4"
] | [
"en",
"fr",
"ro",
"de",
"multilingual"
] | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
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"Digits",
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] | [
"ByteLevel"
] | 241 | deepseek-ai/deepseek-coder-1.3b-instruct | deepseek-ai | null | 89,222 | 3,363,561 | False | 2023-10-29T12:43:40Z | 2024-03-07T13:23:21Z | transformers | 159 | 0 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"pytorch_model.bin",
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"tokenizer_config.json"
] | e063262dac8366fc1f28a4da0ff3c50ea66259ca | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<\uff5cbegin\u2581of\u2581sentence\uff5c>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|EOT|>", "lstr... | {
"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="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 | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 86 | EleutherAI/pythia-2.8b | EleutherAI | null | 68,600 | 2,018,364 | False | 2023-02-13T14:37:12Z | 2023-06-09T00:35:37Z | transformers | 33 | 0 | null | text-generation | {"parameters": {"F16": 2775209280, "U8": 134217728}, "total": 2909427008} | [
".gitattributes",
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"config.json",
"model.safetensors",
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"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 2a259cdd96a4beb1cdf467512e3904197345f6a9 | [
"transformers",
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"text-generation",
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"pythia",
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"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"reg... | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["EleutherAI/pile"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["pytorch", "causal-lm", "pythia"]} | The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and ... | null | [
"apache-2.0"
] | [
"EleutherAI/pile"
] | [
"en"
] | 2,909,427,008 | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
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6447e75e3e498d669191db01 | 71140a663949f258 | 8a01c02d4d0a401c | 119,547 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 9 | suno/bark | suno | null | 15,743 | 1,996,244 | False | 2023-04-25T14:44:46Z | 2023-10-04T14:17:55Z | transformers | 1,511 | 0 | null | text-to-speech | null | [
".gitattributes",
"README.md",
"coarse.pt",
"coarse_2.pt",
"config.json",
"fine.pt",
"fine_2.pt",
"generation_config.json",
"pytorch_model.bin",
"speaker_embeddings/announcer_coarse_prompt.npy",
"speaker_embeddings/announcer_fine_prompt.npy",
"speaker_embeddings/announcer_semantic_prompt.npy",... | 70a8a7d34168586dc5d028fa9666aceade177992 | [
"transformers",
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"bark",
"text-to-audio",
"audio",
"text-to-speech",
"en",
"de",
"es",
"fr",
"hi",
"it",
"ja",
"ko",
"pl",
"pt",
"ru",
"tr",
"zh",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BarkModel"], "model_type": "bark", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForTextToWaveform",
"custom_class": null,
"pipeline_tag": "text-to-audio",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "de", "es", "fr", "hi", "it", "ja", "ko", "pl", "pt", "ru", "tr", "zh"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-to-speech", "tags": ["ba... | # Bark
Bark is a transformer-based text-to-audio model created by [Suno](https://www.suno.ai).
Bark can generate highly realistic, multilingual speech as well as other audio - including music,
background noise and simple sound effects. The model can also produce nonverbal
communications like laughing, sighing and c... | null | [
"mit"
] | null | [
"en",
"de",
"es",
"fr",
"hi",
"it",
"ja",
"ko",
"pl",
"pt",
"ru",
"tr",
"zh"
] | null | null | null | [
"AutoModelForTextToWaveform",
"BarkModel",
"bark"
] | [
"text-to-speech",
"text-to-audio"
] | [
"text",
"audio"
] | [
"text"
] | [
"audio"
] |
621ffdc136468d709f17adea | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | facebook/dpr-question_encoder-multiset-base | facebook | null | 1,478 | 1,862,376 | False | 2022-03-02T23:29:05Z | 2022-12-21T15:20:05Z | transformers | 4 | 0 | null | feature-extraction | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 5325e4ee906435291d63046f535476cb3fc60d43 | [
"transformers",
"pytorch",
"tf",
"dpr",
"feature-extraction",
"en",
"dataset:nq_open",
"dataset:trivia_qa",
"dataset:web_questions",
"dataset:trec",
"arxiv:2004.04906",
"arxiv:1702.08734",
"arxiv:1910.09700",
"license:cc-by-nc-4.0",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DPRQuestionEncoder"], "model_type": "dpr", "tokenizer_config": {}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["nq_open", "trivia_qa", "web_questions", "trec"], "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-question_encoder-multiset-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)
- [Environ... | null | [
"cc-by-nc-4.0"
] | [
"nq_open",
"trivia_qa",
"web_questions",
"trec"
] | [
"en"
] | null | null | null | [
"AutoModel",
"dpr",
"DPRQuestionEncoder"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
6335cf694067f020755d93a9 | 5ef9eb7677e33416 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 19 | cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all | cardiffnlp | null | 22,540 | 1,946,587 | False | 2022-09-29T17:01:29Z | 2022-09-30T00:31:18Z | transformers | 11 | 0 | [{"name": "cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "cardiffnlp/tweet_topic_multi", "type": "cardiffnlp/tweet_topic_multi", "args": "cardiffnlp/tweet_topic_multi", "split": "test_2021"}, "metr... | text-classification | null | [
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"pytorch_model.bin",
"special_tokens_map.json",
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"tokenizer_config.json",
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] | b18c857625b4e6e5756957dc84b5b687d2a3c3c1 | [
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"pytorch",
"roberta",
"text-classification",
"dataset:cardiffnlp/tweet_topic_multi",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"datasets": ["cardiffnlp/tweet_topic_multi"], "metrics": ["f1", "accuracy"], "pipeline_tag": "text-classification", "widget": [{"text": "I'm sure the {@Tampa Bay Lightning@} would\u2019ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Island... | # cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the [tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). This model is fine-tu... | null | null | [
"cardiffnlp/tweet_topic_multi"
] | null | null | null | [
"f1",
"accuracy"
] | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
678a79e88b0ce22ae8998bf2 | 7ec8239dd3bcce7b | 46a701b36ce8cd6c | 49,280 | BPE | 48,900 | false | true | false | true | 145 | null | Sequence | ByteLevel | null | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 242 | HuggingFaceTB/SmolVLM-256M-Instruct | HuggingFaceTB | {
"models": [
{
"_id": "672388f6e1133cff79766318",
"id": "HuggingFaceTB/SmolLM2-135M-Instruct"
}
],
"relation": "quantized"
} | 308,787 | 3,154,299 | False | 2025-01-17T15:40:24Z | 2025-04-08T21:39:50Z | transformers | 345 | 1 | null | image-text-to-text | {"parameters": {"BF16": 256484928}, "total": 256484928} | [
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"onnx/decoder_model_merged_bnb4.onnx",
"onnx/decoder_model_merged_fp16.onnx",
"onnx/decoder_model_merged_int8.... | 7e3e67edbbed1bf9888184d9df282b700a323964 | [
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"base_model:HuggingFaceTB/SmolLM2-135M-Instruct",
"base_model:quantized:HuggingFaceTB/SmolLM2-135M-Instru... | 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",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": ["HuggingFaceTB/SmolLM2-135M-Instruct", "google/siglip-base-patch16-512"], "datasets": ["HuggingFaceM4/the_cauldron", "HuggingFaceM4/Docmatix"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, ... | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/SmolVLM_256_banner.png" width="800" height="auto" alt="Image description">
# SmolVLM-256M
SmolVLM-256M is the smallest multimodal model in the world. It accepts arbitrary sequences of image and text inputs to produce text outputs.... | null | [
"apache-2.0"
] | [
"HuggingFaceM4/the_cauldron",
"HuggingFaceM4/Docmatix"
] | [
"en"
] | 256,484,928 | null | null | [
"AutoModelForImageTextToText",
"Idefics3ForConditionalGeneration",
"idefics3"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
621ffdc136468d709f1799ce | 191e06b04083279c | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 26 | bhadresh-savani/bert-base-go-emotion | bhadresh-savani | null | 5,000 | 1,857,996 | False | 2022-03-02T23:29:05Z | 2021-11-29T10:43:10Z | transformers | 37 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
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] | 6ecebb2840243665ab089020504c52e086862848 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"go-emotion",
"en",
"dataset:go_emotions",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DistilBertForMultilabelSequenceClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "DistilBertForMultilabelSequenceClassification",
"custom_class": null,
"pipeline_tag": null,
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["go_emotions"], "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": ["text-classification", "go-emotion", "pytorch"], "thumbnail":... | # Bert-Base-Uncased-Go-Emotion
## Model description:
## Training Parameters:
```
Num examples = 169208
Num Epochs = 3
Instantaneous batch size per device = 16
Total train batch size (w. parallel, distributed & accumulation) = 16
Gradient Accumulation steps = 1
Total optimization steps = 31728
```
## TrainOutput:
```... | null | [
"apache-2.0"
] | [
"go_emotions"
] | [
"en"
] | null | null | [
"Accuracy"
] | [
"DistilBertForMultilabelSequenceClassification",
"bert"
] | [
null,
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17a83c | bbf489d4445ec8f5 | 291a9305acaae14c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 223 | dmis-lab/biosyn-sapbert-bc2gn | dmis-lab | null | 291 | 1,843,164 | False | 2022-03-02T23:29:05Z | 2022-02-25T13:32:53Z | transformers | 1 | 0 | null | feature-extraction | null | [
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] | 28ef41eace90e9aa6a9db372413c145883c72902 | [
"transformers",
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"bert",
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"auto_model": "AutoModel",
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"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | null | null | null | [
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"ByteLevel"
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] | 971e46dbc5c69c407838ca4247b210da3f8eda46 | [
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"text-generation",
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] | null | {"architectures": ["JAISLMHeadModel"], "auto_map": {"AutoConfig": "configuration_jais.JAISConfig", "AutoModel": "modeling_jais.JAISModel", "AutoModelForCausalLM": "modeling_jais.JAISLMHeadModel", "AutoModelForQuestionAnswering": "modeling_jais.JAISForQuestionAnswering", "AutoModelForSequenceClassification": "modeling_j... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_jais.JAISLMHeadModel",
"pipeline_tag": "text-generation",
"processor": null
} | null | null | null | null | null | null | null | null | null | [
"JAISLMHeadModel",
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"text-generation"
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"text"
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"text"
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621ffdc136468d709f179702 | 231a07db47cdf6c8 | 14a9d3e626148fd9 | 32,000 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
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"BertPreTokenizer"
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"WordPiece"
] | 244 | ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa | ayameRushia | null | 107,387 | 2,413,609 | False | 2022-03-02T23:29:05Z | 2025-08-23T02:42:01Z | transformers | 20 | 0 | [{"name": "bert-base-indonesian-1.5G-finetuned-sentiment-analysis-smsa", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "indonlu", "type": "indonlu", "args": "smsa"}, "metrics": [{"name": "Accuracy", "type": "accuracy", "value": 0.9373015873015873, "verified": f... | text-classification | null | [
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] | 89fa5baac367b63b27cf5240552add8649fe0a91 | [
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"id",
"dataset:indonlu",
"license:mit",
"model-index",
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] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
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"pipeline_tag": "text-classification",
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} | {"datasets": ["indonlu"], "language": "id", "license": "mit", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "widget": [{"text": "Saya mengapresiasi usaha anda"}], "model-index": [{"name": "bert-base-indonesian-1.5G-finetuned-sentiment-analysis-smsa", "results": [{"task": {"type": "text-classification", "... | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-indonesian-1.5G-finetuned-sentiment-analysis-smsa
This model is a fine-tuned version of [cahya/bert-base-indonesian-1.5... | null | [
"mit"
] | [
"indonlu"
] | [
"id"
] | null | null | [
"accuracy"
] | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
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"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 245 | unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit | unsloth | null | 19,176 | 1,878,392 | False | 2024-07-18T17:40:54Z | 2025-02-02T05:31:51Z | transformers | 31 | 0 | null | text-generation | {"parameters": {"F32": 741760, "BF16": 1342592000, "U8": 11246074494}, "total": 12589408254} | [
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"deploy:azure",
"region:us"
] | 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... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["mistral", "unsloth", "transformers"]} | # Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tesla T4 notebook for Mistral Nemo 12b here: https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Disco... | null | [
"apache-2.0"
] | null | [
"en"
] | 12,589,408,254 | null | null | [
"AutoModelForCausalLM",
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"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17c755 | 4192fa6cbbc4c213 | cf7a052aa6fec7ad | 30,000 | BPE | 15,977 | true | true | true | true | 229 | Sequence | Metaspace | Metaspace | [
"NFKC",
"BertNormalizer"
] | [
"Metaspace"
] | [
"Metaspace"
] | 246 | hyunwoongko/kobart | hyunwoongko | null | 64,593 | 2,122,648 | False | 2022-03-02T23:29:05Z | 2022-08-16T20:01:59Z | transformers | 9 | 0 | null | null | null | [
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"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 055dfc31314e710db1de2be582d42fc25c0d3995 | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"ko",
"license:mit",
"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"
} | {"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": ["bart"]} | ## KoBART-base-v2
With the addition of chatting data, the model is trained to handle the semantics of sequences longer than KoBART.
```python
from transformers import PreTrainedTokenizerFast, BartModel
tokenizer = PreTrainedTokenizerFast.from_pretrained('hyunwoongko/kobart')
model = BartModel.from_pretrained('hyunwo... | null | [
"mit"
] | null | [
"ko"
] | null | null | null | [
"bart",
"BartForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
62f876e03b991129e945a0de | 382b5a4eb98f0296 | d965e4a9140ecf72 | 21,128 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 33 | LTP/base | LTP | null | 375 | 1,813,545 | False | 2022-08-14T04:15:28Z | 2025-08-15T08:40:42Z | transformers | 4 | 0 | null | null | {"parameters": {"I64": 512, "F32": 138268816}, "total": 138269328} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 97d6e77d2d7e93c435ec0fb297800f54964323be | [
"transformers",
"pytorch",
"safetensors",
"arxiv:2009.11616",
"endpoints_compatible",
"region:us"
] | null | {"tokenizer_config": {}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | null | ------------------------------ | -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------... | null | null | null | null | 138,269,328 | null | null | [
"AutoModel"
] | [
null
] | null | null | null |
621ffdc036468d709f175df7 | f68a233b6e1cafdd | 7a200ecd99c055f3 | 42,000 | WordPiece | null | true | true | true | true | 110 | Sequence | BertPreTokenizer | WordPiece | [
"Replace",
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 247 | HooshvareLab/bert-fa-zwnj-base-ner | HooshvareLab | null | 114 | 1,801,857 | False | 2022-03-02T23:29:04Z | 2021-05-18T21:04:35Z | transformers | 5 | 0 | null | token-classification | null | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 17d4928f28c36fd74864c221a27134da8b6bf9bc | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"fa",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForTokenClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "fa", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # BertNER
This model fine-tuned for the Named Entity Recognition (NER) task on a mixed NER dataset collected from [ARMAN](https://github.com/HaniehP/PersianNER), [PEYMA](http://nsurl.org/2019-2/tasks/task-7-named-entity-recognition-ner-for-farsi/), and [WikiANN](https://elisa-ie.github.io/wikiann/) that covered ten ty... | null | null | null | [
"fa"
] | null | null | null | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
669736ebe45a00f3dfffbbc3 | c282b82b7bd21539 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 248 | Lajavaness/bilingual-embedding-small | Lajavaness | null | 6,498 | 1,844,671 | False | 2024-07-17T03:13:47Z | 2025-07-28T04:03:23Z | sentence-transformers | 8 | 0 | [{"name": "bilingual-embedding-small", "results": [{"task": {"type": "Clustering"}, "dataset": {"type": "lyon-nlp/alloprof", "name": "MTEB AlloProfClusteringP2P", "config": "default", "split": "test", "revision": "392ba3f5bcc8c51f578786c1fc3dae648662cb9b"}, "metrics": [{"type": "v_measure", "value": 63.19030822769444, ... | sentence-similarity | {"parameters": {"F32": 117653760}, "total": 117653760} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config.py",
"config_sentence_transformers.json",
"model.safetensors",
"modeling.py",
"modules.json",
"sentence_bert_config.json",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_confi... | 80f7adb74544464c380b0c5dfda441f075400043 | [
"sentence-transformers",
"safetensors",
"bilingual",
"feature-extraction",
"sentence-similarity",
"transformers",
"sentence-embedding",
"mteb",
"custom_code",
"fr",
"en",
"arxiv:2010.08240",
"arxiv:1911.02116",
"arxiv:1908.10084",
"license:apache-2.0",
"model-index",
"endpoints_compa... | null | {"architectures": ["BilingualModel"], "auto_map": {"AutoConfig": "config.BilingualConfig", "AutoModel": "modeling.BilingualModel", "AutoModelForMaskedLM": "dangvantuan/bilingual_impl--modeling.BilingualForMaskedLM", "AutoModelForMultipleChoice": "dangvantuan/bilingual_impl--modeling.BilingualForMultipleChoice", "AutoMo... | {
"auto_model": "AutoModel",
"custom_class": "modeling.BilingualModel",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"language": ["fr", "en"], "library_name": "sentence-transformers", "license": "apache-2.0", "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "sentence-embedding", "mteb"], "model-index": [{"name": "bilingual-embedding-small", "results... | null | null | [
"apache-2.0"
] | null | [
"fr",
"en"
] | 117,653,760 | null | null | [
"BilingualModel",
"AutoModel",
"modeling.BilingualModel",
"bilingual"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
66bcfb053c885d04493fa0d2 | cb3f886e13ad7505 | 1f1d26e73b02d1f3 | 49,152 | BPE | 48,891 | false | true | false | true | 19 | null | Sequence | ByteLevel | null | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 70 | ibm-research/PowerMoE-3b | ibm-research | null | 613,701 | 3,536,013 | False | 2024-08-14T18:44:21Z | 2024-09-24T22:04:31Z | transformers | 18 | 1 | [{"name": "ibm/PowerMoE-3b", "results": [{"task": {"type": "text-generation"}, "dataset": {"type": "lm-eval-harness", "name": "ARC"}, "metrics": [{"name": "accuracy-norm", "type": "accuracy-norm", "value": 58.1, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"type": "lm-eval-harness", "name": "... | text-generation | {"parameters": {"F32": 3374286336}, "total": 3374286336} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00003.safetensors",
"model-00002-of-00003.safetensors",
"model-00003-of-00003.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 13fcb5a98001438bed01cf1ac4b423751dc4c2ea | [
"transformers",
"safetensors",
"granitemoe",
"text-generation",
"arxiv:2408.13359",
"license:apache-2.0",
"model-index",
"region:us"
] | null | {"architectures": ["GraniteMoeForCausalLM"], "model_type": "granitemoe", "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"
} | {"library_name": "transformers", "license": "apache-2.0", "pipeline_tag": "text-generation", "inference": false, "model-index": [{"name": "ibm/PowerMoE-3b", "results": [{"task": {"type": "text-generation"}, "dataset": {"name": "ARC", "type": "lm-eval-harness"}, "metrics": [{"type": "accuracy-norm", "value": 58.1, "name... | ## Model Summary
PowerMoE-3B is a 3B sparse Mixture-of-Experts (sMoE) language model trained with the Power learning rate scheduler. It sparsely activates 800M parameters for each token. It is trained on a mix of open-source and proprietary datasets. PowerMoE-3B has shown promising results compared to other dense model... | null | [
"apache-2.0"
] | null | null | 3,374,286,336 | null | null | [
"granitemoe",
"AutoModelForCausalLM",
"GraniteMoeForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
62de52134dcb9177d4bdd8a5 | 32694b540d0fd0f3 | 8eba6449bcfd5626 | 32,000 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 249 | KDHyun08/TAACO_STS | KDHyun08 | null | 40 | 2,166,531 | False | 2022-07-25T08:19:31Z | 2022-08-01T05:00:14Z | sentence-transformers | 2 | 0 | null | sentence-similarity | null | [
".gitattributes",
".gitignore",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 53f72d8d0c35934fe6dee16fd443411fcdcac53d | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"TAACO",
"ko",
"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": "ko", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "sentence-similarity", "transformers", "TAACO"]} | # TAACO_Similarity
본 모델은 [Sentence-transformers](https://www.SBERT.net)를 기반으로 하며 KLUE의 STS(Sentence Textual Similarity) 데이터셋을 통해 훈련을 진행한 모델입니다.
필자가 제작하고 있는 한국어 문장간 결속성 측정 도구인 K-TAACO(가제)의 지표 중 하나인 문장 간 의미적 결속성을 측정하기 위해 본 모델을 제작하였습니다.
또한 모두의 말뭉치의 문장간 유사도 데이터 등 다양한 데이터를 구해 추가 훈련을 진행할 예정입니다.
## Train Data
KLUE-s... | null | null | null | [
"ko"
] | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f17ed04 | 6a792251ace4fa20 | ceb5045914a9e9c7 | 40,000 | BPE | 39,739 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 250 | pdelobelle/robbert-v2-dutch-base | pdelobelle | null | 28,997 | 2,034,377 | False | 2022-03-02T23:29:05Z | 2023-12-04T15:14:12Z | transformers | 34 | 0 | null | fill-mask | {"parameters": {"I64": 514, "F32": 116803648}, "total": 116804162} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 271b8bf12b7e429434ce953efb432e8373e84453 | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"roberta",
"fill-mask",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"BERT",
"nl",
"dataset:oscar",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2001.06286",
"arxiv:2004.02814",
"a... | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta", "tokenizer_config": {"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normaliz... | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["oscar", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "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", "Ro... | <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"
] | [
"oscar",
"dbrd",
"lassy-ud",
"europarl-mono",
"conll2002"
] | [
"nl"
] | 116,804,162 | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62a2447c8d94f0a9b12c4aa1 | 4496c5c12cbe29e0 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 163 | biu-nlp/lingmess-coref | biu-nlp | null | 78,539 | 2,387,337 | False | 2022-06-09T19:05:32Z | 2022-10-26T08:55:32Z | transformers | 12 | 0 | [{"name": "biu-nlp/lingmess-coref", "results": [{"task": {"type": "coreference-resolution", "name": "coreference-resolution"}, "dataset": {"name": "ontonotes", "type": "coreference"}, "metrics": [{"name": "Avg. F1", "type": "CoNLL", "value": 81.4, "verified": false}]}]}] | null | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | fa5d8a827a09388d03adbe9e800c7d8c509c3935 | [
"transformers",
"pytorch",
"longformer",
"coreference-resolution",
"en",
"dataset:ontonotes",
"arxiv:2205.12644",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LingMessCoref"], "model_type": "longformer", "tokenizer_config": {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
"auto_model": "LingMessCoref",
"custom_class": null,
"pipeline_tag": null,
"processor": "AutoTokenizer"
} | {"datasets": ["ontonotes"], "language": ["en"], "license": "mit", "metrics": ["CoNLL"], "tags": ["coreference-resolution"], "task_categories": ["coreference-resolution"], "model-index": [{"name": "biu-nlp/lingmess-coref", "results": [{"task": {"type": "coreference-resolution", "name": "coreference-resolution"}, "datase... | ## LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution
[LingMess](https://arxiv.org/abs/2205.12644) is a linguistically motivated categorization of mention-pairs into 6 types of coreference decisions and learn a dedicated trainable scoring function for each category. This significantly im... | null | [
"mit"
] | [
"ontonotes"
] | [
"en"
] | null | null | [
"CoNLL"
] | [
"longformer",
"LingMessCoref"
] | [
null
] | null | null | null |
6225fa93c6e650de3a65dc7f | 5e8264d4d7547d12 | d18454166678a15d | 50,257 | BPE | 50,000 | false | true | true | true | 1 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 251 | nferruz/ProtGPT2 | nferruz | null | 22,221 | 1,867,107 | False | 2022-03-07T12:29:07Z | 2024-11-02T10:01:26Z | transformers | 113 | 0 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"merges.txt",
"ppl-plddt.png",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"vocab.json"
] | f71aa6cf063ad784ebd53881d11332fd098eaa58 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"doi:10.57967/hf/5299",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2"} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": null, "widget": [{"text": "<|endoftext|>"}], "inference": {"parameters... | # **ProtGPT2**
ProtGPT2 ([peer-reviewed paper](https://www.nature.com/articles/s41467-022-32007-7)) is a language model that speaks the protein language and can be used for de novo protein design and engineering. ProtGPT2 generated sequences conserve natural proteins' critical features (amino acid propensities, second... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f180300 | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | sentence-transformers/stsb-roberta-large | sentence-transformers | null | 23,759 | 1,930,761 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:22:33Z | sentence-transformers | 4 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 355359744}, "total": 355360258} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"added_tokens.json",
"config.json",
"config_sentence_transformers.json",
"flax_model.msgpack",
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"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"... | 796387975f5090fecdb2a99a4864fc9e9041ca0c | [
"sentence-transformers",
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"tf",
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"openvino",
"roberta",
"feature-extraction",
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"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaModel"], "model_type": "roberta", "tokenizer_config": {"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": t... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/stsb-roberta-large
This is a [sentence-transforme... | null | [
"apache-2.0"
] | null | null | 355,360,258 | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
65484cfeaf7c27330d5b1cb9 | 460ba6f8e3139abe | 1aa87660a7683e97 | 52,493 | BPE | 50,000 | false | true | true | true | 2,233 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 252 | KomeijiForce/bart-large-emojilm | KomeijiForce | null | 312,512 | 3,654,346 | False | 2023-11-06T02:18:38Z | 2023-11-10T22:21:05Z | transformers | 0 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | c9c71887deb6fd84e32ee406998ef813dc6eafb7 | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:KomeijiForce/Text2Emoji",
"arxiv:2311.01751",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BartForConditionalGeneration"], "model_type": "bart", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["KomeijiForce/Text2Emoji"], "eval_results": null, "language": ["en"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": ["bertscore"], "model_name": null, "pipeline_tag": "text2text-generation", "tags": null} | # EmojiLM
This is a [BART](https://huggingface.co/facebook/bart-large) model pre-trained on the [Text2Emoji](https://huggingface.co/datasets/KomeijiForce/Text2Emoji) dataset to translate setences into series of emojis.
For instance, "I love pizza" will be translated into "🍕😍".
An example implementation for transla... | null | null | [
"KomeijiForce/Text2Emoji"
] | [
"en"
] | null | null | [
"bertscore"
] | [
"bart",
"BartForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6709e0d9bb2498474f4e8ee2 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | nvidia/Llama-3.1-Nemotron-70B-Instruct-HF | nvidia | {
"models": [
{
"_id": "66969ad27a033bf62173f3e2",
"id": "meta-llama/Llama-3.1-70B-Instruct"
}
],
"relation": "finetune"
} | 11,425 | 1,804,973 | False | 2024-10-12T02:37:13Z | 2025-04-13T04:12:19Z | transformers | 2,063 | 0 | null | text-generation | {"parameters": {"BF16": 70553706496}, "total": 70553706496} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00030.safetensors",
"model-00002-of-00030.safetensors",
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"model-00004-of-00030.safetensors",
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"model-0... | 031d4042f36adc1a52cca51b331d25cbe3cf1022 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"nvidia",
"llama3.1",
"conversational",
"en",
"dataset:nvidia/HelpSteer2",
"arxiv:2410.01257",
"arxiv:2405.01481",
"arxiv:2406.08673",
"base_model:meta-llama/Llama-3.1-70B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-70B-In... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_messag... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Llama-3.1-70B-Instruct", "datasets": ["nvidia/HelpSteer2"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["nvidia", "... | # Model Overview
## Description:
Llama-3.1-Nemotron-70B-Instruct is a large language model customized by NVIDIA to improve the helpfulness of LLM generated responses to user queries.
This model reaches [Arena Hard](https://github.com/lmarena/arena-hard-auto) of 85.0, [AlpacaEval 2 LC](https://tatsu-lab.github.io/al... | null | [
"llama3.1"
] | [
"nvidia/HelpSteer2"
] | [
"en"
] | 70,553,706,496 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
662a2d8b084d6b7ab42d8a15 | 3f287b04362fff24 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 158 | MLP-KTLim/llama-3-Korean-Bllossom-8B | MLP-KTLim | {
"models": [
{
"_id": "661f97d48e7f3438386f755d",
"id": "meta-llama/Meta-Llama-3-8B"
}
],
"relation": "finetune"
} | 3,864 | 2,126,318 | False | 2024-04-25T10:16:43Z | 2024-12-18T12:18:13Z | transformers | 388 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
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"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"... | ed9647c18477ee09a03690c613c859eddca24362 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"ko",
"arxiv:2403.10882",
"arxiv:2403.11399",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:finetune:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"text-generation-inference",
"endpoints_compatible",... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|s... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["meta-llama/Meta-Llama-3-8B"], "datasets": null, "eval_results": null, "language": ["en", "ko"], "library_name": "transformers", "license": "llama3", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | <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!
* ~~[2024.08.09] Llama3.1 버전을 기반으로한 Bllossom-8B로 모델을 업데이트 했습니다. 기존 llama3기반 Bllossom 보다 평균 5%정도 성능 ... | null | [
"llama3"
] | null | [
"en",
"ko"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
689372f8ec7fe05a86c150c7 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | lmstudio-community/Qwen3-4B-Thinking-2507-MLX-4bit | lmstudio-community | {
"models": [
{
"_id": "6891e4c34b616cd1bb82f50e",
"id": "Qwen/Qwen3-4B-Thinking-2507"
}
],
"relation": "quantized"
} | 77,821 | 2,151,432 | False | 2025-08-06T15:21:28Z | 2025-08-06T15:22:07Z | transformers | 11 | 0 | null | text-generation | {"parameters": {"BF16": 125892096, "U32": 502784000}, "total": 628676096} | [
".gitattributes",
"README.md",
"added_tokens.json",
"chat_template.jinja",
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"merges.txt",
"model.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 1fc4709626fbf06a76b7d0231293a3d23a539f31 | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"mlx",
"conversational",
"base_model:Qwen/Qwen3-4B-Thinking-2507",
"base_model:quantized:Qwen/Qwen3-4B-Thinking-2507",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"region:us"
] | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "quantization_config": {"bits": 4}, "tokenizer_config": {"bos_token": null, "eos_token": "<|im_end|>", "pad_token": "<|endoftext|>", "unk_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'sy... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen3-4B-Thinking-2507", "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_ta... | ## 💫 Community Model> Qwen3-4B-Thinking-2507 by Qwen
_👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)_.
**Model creator**: [Qwen](https://huggingface.co/Qwen)<br>
**Origin... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507/blob/main/LICENSE"
] | null | null | 628,676,096 | null | null | [
"AutoModelForCausalLM",
"Qwen3ForCausalLM",
"qwen3"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66069a17909dafbfb909873a | db1f70281be29946 | 2fb7ed124e7316ec | 1,024 | BPE | 766 | true | true | true | true | 2 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 253 | echarlaix/tiny-random-PhiForCausalLM | echarlaix | null | 19,689 | 1,854,478 | False | 2024-03-29T10:38:15Z | 2024-05-14T13:50:41Z | transformers | 0 | 0 | null | text-generation | {"parameters": {"F32": 80074}, "total": 80074} | [
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] | acb0ac41d787dd9c9bbed0a70a16ed05c9e2e8db | [
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"text-generation",
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"text-generation-inference",
"endpoints_compatible",
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] | null | {"architectures": ["PhiForCausalLM"], "model_type": "phi", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
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"apache-2.0"
] | null | null | 80,074 | null | null | [
"AutoModelForCausalLM",
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"PhiForCausalLM"
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"text"
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"text"
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678de32a08277d61535e073c | 023aba82521a81c5 | cf1efbe71ef28fc9 | 50,368 | BPE | 50,009 | true | true | true | true | 116 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 254 | Alibaba-NLP/gte-reranker-modernbert-base | Alibaba-NLP | {
"models": [
{
"_id": "6759799ed862e62a825b5413",
"id": "answerdotai/ModernBERT-base"
}
],
"relation": "finetune"
} | 924,983 | 4,919,160 | False | 2025-01-20T05:46:18Z | 2025-07-04T10:03:42Z | transformers | 88 | 1 | null | text-ranking | {"parameters": {"F32": 149605633}, "total": 149605633} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
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"onnx/model_q4.onnx",
"onnx/model_q4f16.onnx",
"onnx/model_quantized.onnx",
"onnx/model_uint8.onnx",
"special_tokens_map.json",
"tok... | f7481e6055501a30fb19d090657df9ec1f79ab2c | [
"transformers",
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"safetensors",
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"text-classification",
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"text-embeddings-inference",
"text-ranking",
"en",
"arxiv:2308.03281",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"licen... | null | {"architectures": ["ModernBertForSequenceClassification"], "model_type": "modernbert", "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": ["answerdotai/ModernBERT-base"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-ranking", "tags": ["sentence-transformers", "transfo... | null | null | [
"apache-2.0"
] | null | [
"en"
] | 149,605,633 | null | null | [
"ModernBertForSequenceClassification",
"modernbert",
"AutoModelForSequenceClassification"
] | [
"text-ranking",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65f697701d7d373a9af1d4aa | 71140a663949f258 | 8a01c02d4d0a401c | 119,547 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 9 | microsoft/llmlingua-2-bert-base-multilingual-cased-meetingbank | microsoft | null | 131,855 | 2,458,869 | False | 2024-03-17T07:10:40Z | 2025-01-08T02:12:02Z | transformers | 50 | 0 | null | token-classification | {"parameters": {"F32": 177341186}, "total": 177341186} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 5f0c82792b7ea14c6484e015b6a072009496b7f2 | [
"transformers",
"safetensors",
"bert",
"token-classification",
"arxiv:2403.12968",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForTokenClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # 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 [BERT multilingual base model (cased)](https://huggingface.co/googl... | null | [
"apache-2.0"
] | null | null | 177,341,186 | null | null | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
629722f365c0fc78a71df16b | e68b0b111c49dac2 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 28 | cross-encoder/mmarco-mMiniLMv2-L12-H384-v1 | cross-encoder | {
"models": [
{
"_id": "621ffdc136468d709f17e8fe",
"id": "nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large"
}
],
"relation": "quantized"
} | 491,012 | 3,094,750 | False | 2022-06-01T08:27:31Z | 2025-04-15T08:03:02Z | sentence-transformers | 69 | 0 | null | text-ranking | {"parameters": {"I64": 514, "F32": 117641089}, "total": 117641603} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/model_qint8_avx512.onnx",
"onnx/model_qint8_avx512_vnni.onnx",
"onnx/model_quint8... | 1427fd652930e4ba29e8149678df786c240d8825 | [
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"onnx",
"safetensors",
"openvino",
"xlm-roberta",
"text-classification",
"transformers",
"text-ranking",
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"fr",
"de",
"hi",
"in",
"it",
"ja",
"pt",
"ru",
"es",
"vi",
"multilingual",
"dataset:unicamp-dl/mmarco"... | null | {"architectures": ["XLMRobertaForSequenceClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": f... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": ["nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large"], "datasets": ["unicamp-dl/mmarco"], "eval_results": null, "language": ["en", "ar", "zh", "nl", "fr", "de", "hi", "in", "it", "ja", "pt", "ru", "es", "vi", "multilingual"], "library_name": "sentence-transformers", "license": "apache-2.0", "license_... | null | null | [
"apache-2.0"
] | [
"unicamp-dl/mmarco"
] | [
"en",
"ar",
"zh",
"nl",
"fr",
"de",
"hi",
"in",
"it",
"ja",
"pt",
"ru",
"es",
"vi",
"multilingual"
] | 117,641,603 | null | null | [
"AutoModelForSequenceClassification",
"XLMRobertaForSequenceClassification",
"xlm-roberta"
] | [
"text-ranking",
"text-classification"
] | [
"text"
] | [
"text"
] | [
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## **Overview**
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docs: https://huggingface.co/transformers/model_doc/bart.html
finetuning: examples/seq2seq/ (as of Aug 20, 2020)
Metrics: ROUGE > 22 on xsum.
variants: search for distilbart
paper: https://arxiv.org/abs/1910.13461 | null | [
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_👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)_.
**Model creator**: [Qwen](https://huggingface.co/Qwen)<br>
**Origin... | null | [
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"https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507/blob/main/LICENSE"
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} | {"base_model": ["Qwen/Qwen2.5-VL-7B-Instruct"], "datasets": ["nvidia/Cosmos-Reason1-SFT-Dataset", "nvidia/Cosmos-Reason1-RL-Dataset", "nvidia/Cosmos-Reason1-Benchmark"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": "nvidia-open-model-license", "license_li... | # **Cosmos-Reason1: Physical AI Common Sense and Embodied Reasoning Models**
[**Cosmos**](https://huggingface.co/collections/nvidia/cosmos-reason1-67c9e926206426008f1da1b7) | [**Code**](https://github.com/nvidia-cosmos/cosmos-reason1) | [**Paper**](https://arxiv.org/abs/2503.15558) | [**Paper Website**](https://resear... | null | [
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] | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "quantization_config": {"bits": 6}, "tokenizer_config": {"bos_token": null, "eos_token": "<|im_end|>", "pad_token": "<|endoftext|>", "unk_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'sy... | {
"auto_model": "AutoModelForCausalLM",
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_👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)_.
**Model creator**: [Qwen](https://huggingface.co/Qwen)<br>
**Origin... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507/blob/main/LICENSE"
] | null | null | 880,068,096 | null | null | [
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"region:us"
] | null | {"architectures": ["Blip2ForConditionalGeneration"], "model_type": "blip-2", "tokenizer_config": {"bos_token": "</s>", "eos_token": "</s>", "pad_token": "<pad>", "unk_token": "</s>"}} | {
"auto_model": "AutoModelForVisualQuestionAnswering",
"custom_class": null,
"pipeline_tag": "visual-question-answering",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["vision", "image-to-text", "image-captioning", "visual-question-answering... | # BLIP-2, OPT-6.7b, fine-tuned on COCO
BLIP-2 model, leveraging [OPT-6.7b](https://huggingface.co/facebook/opt-6.7b) (a large language model with 6.7 billion parameters).
It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxi... | null | [
"mit"
] | null | [
"en"
] | 7,753,460,736 | null | null | [
"Blip2ForConditionalGeneration",
"AutoModelForVisualQuestionAnswering",
"blip-2"
] | [
"image-text-to-text",
"visual-question-answering",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text",
"logits"
] |
689278858f8b43b04dac57ad | 441f36ff7235b58e | 985b920c8f6b14f1 | 200,019 | BPE | 446,189 | false | true | true | true | 21 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 54 | lmstudio-community/gpt-oss-20b-MLX-8bit | lmstudio-community | {
"models": [
{
"_id": "68913539bd3d0a833438591d",
"id": "openai/gpt-oss-20b"
}
],
"relation": "quantized"
} | 3,880 | 1,728,899 | False | 2025-08-05T21:32:53Z | 2025-08-05T21:43:15Z | transformers | 50 | 0 | null | text-generation | {"parameters": {"BF16": 660339264, "U32": 5226946560}, "total": 20914755648} | [
".gitattributes",
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"transformers",
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"base_model:openai/gpt-oss-20b",
"base_model:quantized:openai/gpt-oss-20b",
"license:apache-2.0",
"endpoints_compatible",
"8-bit",
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] | null | {"architectures": ["GptOssForCausalLM"], "model_type": "gpt_oss", "quantization_config": {"bits": 8}, "tokenizer_config": {"bos_token": "<|startoftext|>", "eos_token": "<|return|>", "pad_token": "<|endoftext|>"}, "chat_template_jinja": "{#-\n In addition to the normal inputs of `messages` and `tools`, this template al... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "openai/gpt-oss-20b", "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["vllm", "mlx"]} | ## 💫 Community Model> gpt-oss-20b by openai
_👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)_.
**Model creator**: [openai](https://huggingface.co/openai)<br>
**Original mo... | null | [
"apache-2.0"
] | null | null | 20,914,755,648 | null | null | [
"AutoModelForCausalLM",
"GptOssForCausalLM",
"gpt_oss"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64da7d7670446182be5a26ec | 324d1780c7dcba21 | 9befc50479ed313c | 31,090 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 126 | allenai/specter2_aug2023refresh_base | allenai | null | 8,198 | 1,747,550 | False | 2023-08-14T19:16:06Z | 2024-05-14T23:39:35Z | transformers | 3 | 0 | null | feature-extraction | null | [
".gitattributes",
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"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 084e9624d354a1cbc464ef6cc1e3646d236b95d9 | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"dataset:allenai/scirepeval",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
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"pipeline_tag": "feature-extraction",
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} | {"base_model": null, "datasets": ["allenai/scirepeval"], "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ## SPECTER2
<!-- Provide a quick summary of what the model is/does. -->
SPECTER2 is a family of models that succeeds [SPECTER](https://huggingface.co/allenai/specter) and is capable of generating task specific embeddings for scientific tasks when paired with [adapters](https://huggingface.co/models?search=allenai/spe... | null | [
"apache-2.0"
] | [
"allenai/scirepeval"
] | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
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65795ce8a415f6989a53e299 | 2d0341bd3a73bae6 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 32 | zhiqiulin/clip-flant5-xxl | zhiqiulin | {
"models": [
{
"_id": "6352c0d3507b679c3c5f41f4",
"id": "google/flan-t5-xxl"
}
],
"relation": "finetune"
} | 9,442 | 1,759,626 | False | 2023-12-13T07:27:36Z | 2024-10-06T20:29:03Z | transformers | 5 | 0 | null | text-generation | null | [
".gitattributes",
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"generation_config.json",
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"pytorch_model.bin.index.json",
"special_tokens_map.json",
"spiece.model",
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"tokenizer_config.jso... | 89bad6fffe1126b24d4360c1e1f69145eb6103aa | [
"transformers",
"pytorch",
"t5",
"text-generation",
"en",
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"base_model:finetune:google/flan-t5-xxl",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["CLIPT5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["google/flan-t5-xxl"], "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} | # CLIP-FlanT5-XXL (VQAScore)
<!-- Provide a quick summary of what the model is/does. -->
This model is a fine-tuned version of google/flan-t5-xxl designed for image-text retrieval tasks, as presented in the VQAScore paper.
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"t5",
"CLIPT5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63ea852ce35a4dfbb8e094db | 58967ba68263f0aa | 2f8d5857ae5fa330 | 50,277 | BPE | 50,009 | true | true | true | true | 25 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 86 | EleutherAI/pythia-410m | EleutherAI | null | 133,146 | 2,038,428 | False | 2023-02-13T18:45:00Z | 2023-07-09T16:01:42Z | transformers | 36 | 0 | null | text-generation | {"parameters": {"F16": 405334208, "U8": 100663296}, "total": 505997504} | [
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"README.md",
"config.json",
"model.safetensors",
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] | 9879c9b5f8bea9051dcb0e68dff21493d67e9d4f | [
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"arxiv:2201.07311",
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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",
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"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"
] | 505,997,504 | null | null | [
"gpt_neox",
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"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
657a7375270ef0b785e7db8d | 7248a1200524b64a | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 100 | qandos0/SentimentArEng | qandos0 | {
"models": [
{
"_id": "621ffdc136468d709f179e40",
"id": "cardiffnlp/twitter-xlm-roberta-base-sentiment"
}
],
"relation": "finetune"
} | 141,059 | 4,521,126 | False | 2023-12-14T03:16:05Z | 2023-12-14T10:19:22Z | transformers | 0 | 0 | [{"name": "result", "results": []}] | text-classification | {"parameters": {"F32": 278045955}, "total": 278045955} | [
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"training_args.bin"
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"transformers",
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"text-embeddings-inference",
"endpoints_compatible",
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] | null | {"architectures": ["XLMRobertaForSequenceClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
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"pipeline_tag": "text-classification",
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} | {"base_model": "cardiffnlp/twitter-xlm-roberta-base-sentiment", "datasets": null, "eval_results": [], "language": ["ar", "en"], "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": "result", "pipeline_tag": "text-classification", "tags": nul... | null | null | null | null | [
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"text"
] | [
"text"
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"logits"
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67b47164ebd454aee6ae7a8e | 58ca26a5d92a2509 | e6ffdc5dd5f1fd86 | 256,000 | BPE | 580,604 | true | false | true | true | 217 | Replace | null | Sequence | [
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse"
] | 69 | google/siglip2-base-patch16-naflex | google | null | 486,414 | 5,113,847 | False | 2025-02-18T11:39:16Z | 2025-02-21T04:04:09Z | transformers | 25 | 2 | null | zero-shot-image-classification | {"parameters": {"F32": 375234050}, "total": 375234050} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"preprocessor_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | b53b807d3a2d5e2b3911292f2d69e5341cdc064c | [
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"arxiv:2303.15343",
"arxiv:2209.06794",
"license:apache-2.0",
"endpoints_compatible",
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] | null | {"architectures": ["Siglip2Model"], "model_type": "siglip2", "tokenizer_config": {"bos_token": "<bos>", "eos_token": "<eos>", "pad_token": "<pad>", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
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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 | 375,234,050 | null | null | [
"Siglip2Model",
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] | [
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621ffdc136468d709f17cbd1 | bc12a8d6a9749cb9 | 8eba6449bcfd5626 | 32,000 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 258 | jhgan/ko-sbert-sts | jhgan | null | 68,526 | 2,020,911 | False | 2022-03-02T23:29:05Z | 2022-08-16T12:45:38Z | sentence-transformers | 13 | 0 | null | sentence-similarity | null | [
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"similarity_evaluation_sts-test_results.csv",
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"sentence-transformers",
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"sentence-similarity",
"transformers",
"arxiv:2004.03289",
"text-embeddings-inference",
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] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "tr... | # ko-sbert-sts
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when y... | null | null | null | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
64e7867286fc685a30ba3882 | 271640fddfc1bd19 | 704a2955bcffd2d4 | 32,016 | BPE | 61,260 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 212 | codellama/CodeLlama-13b-Instruct-hf | codellama | null | 9,242 | 1,745,104 | False | 2023-08-24T16:33:54Z | 2024-04-12T14:19:04Z | transformers | 159 | 0 | null | text-generation | {"parameters": {"BF16": 13016028160}, "total": 13016028160} | [
".gitattributes",
"LICENSE",
"README.md",
"USE_POLICY.md",
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"generation_config.json",
"model-00001-of-00003.safetensors",
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"model-00003-of-00003.safetensors",
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"pytorch_model-00001-of-00003.bin",
"pytorch_model-000... | 745795438019e47e4dad1347a0093e11deee4c68 | [
"transformers",
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"safetensors",
"llama",
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"llama-2",
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"code",
"arxiv:2308.12950",
"license:llama2",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% f... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["code"], "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["llama-2"]} | # **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 13 instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding.... | null | [
"llama2"
] | null | [
"code"
] | 13,016,028,160 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
68c04b9ba4d1823e9b0ee2bd | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Qwen/Qwen3-Next-80B-A3B-Thinking | Qwen | null | 50,718 | 2,282,134 | False | 2025-09-09T15:45:31Z | 2025-09-15T09:12:48Z | transformers | 486 | 4 | null | text-generation | null | [
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"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen3NextForCausalLM"], "model_type": "qwen3_next", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou m... | {
"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-Next-80B-A3B-Thinking/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation... | # Qwen3-Next-80B-A3B-Thinking
<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>
Over the past few months, we have observed increasingly ... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking/blob/main/LICENSE"
] | null | null | null | null | null | [
"qwen3_next",
"Qwen3NextForCausalLM",
"AutoModelForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
640b3b92dd397b27036db4f8 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | JungleLee/bert-toxic-comment-classification | JungleLee | null | 73,015 | 1,975,409 | False | 2023-03-10T14:15:46Z | 2023-03-28T08:07:53Z | transformers | 22 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 9c59104291a49a877078424cbfd9f3af23c26ef9 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"doi:10.57967/hf/3170",
"license:afl-3.0",
"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": ["en"], "library_name": "transformers", "license": "afl-3.0", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": null, "pipeline_tag": "text-classification", "tags": null} | ## Model description
This model is a fine-tuned version of the [bert-base-uncased](https://huggingface.co/transformers/model_doc/bert.html) model to classify toxic comments.
## How to use
You can use the model with the following code.
```python
from transformers import BertForSequenceClassification, BertTokenizer, T... | null | [
"afl-3.0"
] | null | [
"en"
] | null | null | [
"accuracy"
] | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66f4591241aafc2b3032aeb8 | 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-Instruct | unsloth | {
"models": [
{
"_id": "66eaedefece5ee215637cc82",
"id": "meta-llama/Llama-3.2-1B-Instruct"
}
],
"relation": "finetune"
} | 198,217 | 2,450,444 | False | 2024-09-25T18:40:18Z | 2025-05-09T20:24:41Z | transformers | 91 | 1 | null | text-generation | {"parameters": {"BF16": 1235814400}, "total": 1235814400} | [
".gitattributes",
"README.md",
"chat_template.jinja",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 5a8abab4a5d6f164389b1079fb721cfab8d7126c | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3",
"meta",
"facebook",
"unsloth",
"conversational",
"en",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"license:llama3.2",
"text-generation-inference",
"endpoints_... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|eot_id|>", "pad_token": "<|finetune_right_pad_id|>", "unk_token": null, "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- en... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Llama-3.2-1B-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"
] | 1,235,814,400 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64e7869fb159a6f87be2188e | e23b6bdbe047bb7e | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | codellama/CodeLlama-34b-hf | codellama | null | 2,836 | 1,688,113 | False | 2023-08-24T16:34:39Z | 2024-04-12T14:16:52Z | transformers | 178 | 0 | null | text-generation | {"parameters": {"BF16": 33743970304}, "total": 33743970304} | [
".gitattributes",
"LICENSE",
"README.md",
"USE_POLICY.md",
"config.json",
"generation_config.json",
"model-00001-of-00007.safetensors",
"model-00002-of-00007.safetensors",
"model-00003-of-00007.safetensors",
"model-00004-of-00007.safetensors",
"model-00005-of-00007.safetensors",
"model-00006-o... | 6008b9656730b71c7d19a15370c7ff6d2902f4ef | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"llama-2",
"code",
"arxiv:2308.12950",
"license:llama2",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": true, "rstrip":... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["code"], "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["llama-2"]} | # **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the base 34B version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to... | null | [
"llama2"
] | null | [
"code"
] | 33,743,970,304 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63436f897bdcbf6a1cb9b2a3 | 04f5fbc309982132 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 259 | MingZhong/unieval-sum | MingZhong | null | 540 | 1,713,733 | False | 2022-10-10T01:04:09Z | 2022-10-14T01:06:32Z | transformers | 5 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"optimizer.pt",
"pytorch_model.bin",
"rng_state_0.pth",
"rng_state_1.pth",
"scheduler.pt",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json",
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] | ed8da5a8aa01b19d49a5f8738d382ced54b73ad0 | [
"transformers",
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"t5",
"text2text-generation",
"arxiv:2210.07197",
"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"
} | null | Pre-trained evaluator in EMNLP 2022 paper
*[Towards a Unified Multi-Dimensional Evaluator for Text Generation](https://arxiv.org/abs/2210.07197)*
## Introduction
**Multi-dimensional evaluation** is the dominant paradigm for human evaluation in Natural Language Generation (NLG), i.e., evaluating the generated text fr... | null | null | null | null | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
621ffdc136468d709f17f011 | a2ff85d8cedaf0ee | 291a9305acaae14c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 260 | pritamdeka/S-PubMedBert-MS-MARCO | pritamdeka | null | 158,083 | 2,131,262 | False | 2022-03-02T23:29:05Z | 2024-09-05T09:39:02Z | sentence-transformers | 41 | 0 | null | sentence-similarity | null | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"topics.json",
"vocab.txt"
] | 96786c7024f95c5aac7f2b9a18086c7b97b23036 | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:cc-by-nc-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "cc-by-nc-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-simila... | # pritamdeka/S-PubMedBert-MS-MARCO
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.
This is the [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://hu... | null | [
"cc-by-nc-2.0"
] | null | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6796251e22990ae89b1f60f1 | 56f06bcfe8412019 | e833dc43b2428575 | 100,603 | BPE | 99,757 | true | true | true | true | 603 | Sequence | Sequence | ByteLevel | [] | [
"Split",
"Split",
"Split",
"Split",
"Split",
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 261 | deepseek-ai/Janus-Pro-7B | deepseek-ai | null | 41,063 | 1,872,496 | False | 2025-01-26T12:05:50Z | 2025-02-01T08:00:16Z | transformers | 3,555 | 2 | null | any-to-any | null | [
".gitattributes",
"README.md",
"config.json",
"janus_pro_teaser1.png",
"janus_pro_teaser2.png",
"preprocessor_config.json",
"processor_config.json",
"pytorch_model-00001-of-00002.bin",
"pytorch_model-00002-of-00002.bin",
"pytorch_model.bin.index.json",
"special_tokens_map.json",
"tokenizer.jso... | 5c3eb3fb2a3b61094328465ba61fcd4272090d67 | [
"transformers",
"pytorch",
"multi_modality",
"muiltimodal",
"text-to-image",
"unified-model",
"any-to-any",
"arxiv:2501.17811",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"model_type": "multi_modality", "tokenizer_config": {"bos_token": "<\uff5cbegin\u2581of\u2581sentence\uff5c>", "eos_token": "<\uff5cend\u2581of\u2581sentence\uff5c>", "pad_token": null, "unk_token": null, "use_default_system_prompt": true}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "mit", "license_name": "deepseek", "license_link": "LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "any-to-any", "tags": ["muiltimodal", "text-to-image", "unified-model"]} | ## 1. Introduction
Janus-Pro is a novel autoregressive framework that unifies multimodal understanding and generation.
It addresses the limitations of previous approaches by decoupling visual encoding into separate pathways, while still utilizing a single, unified transformer architecture for processing. The decoupli... | null | [
"mit",
"deepseek",
"LICENSE"
] | null | null | null | null | null | [
"multi_modality",
"AutoModel"
] | [
null,
"text-to-image",
"any-to-any"
] | [
"vision",
"multimodal"
] | [
"text",
"any"
] | [
"any",
"image"
] |
6537e750333f48930f559161 | 99808a0afd9c6504 | aa8af6ff55c14796 | 51,865 | BPE | 49,999 | false | true | true | true | 1,608 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 262 | distil-whisper/distil-large-v2 | distil-whisper | null | 12,101 | 1,701,250 | False | 2023-10-24T15:48:32Z | 2025-03-06T17:05:25Z | transformers | 515 | 0 | null | automatic-speech-recognition | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"generation_config.json",
"ggml-large-32-2.en.bin",
"ggml-large-32-2.fp32.en.bin",
"merges.txt",
"model.fp32.safetensors",
"model.safetensors",
"normalizer.json",
"onnx/decoder_model.onnx",
"onnx/decod... | ccb3693e5247bdd734c99eeab96688f8cda3ba72 | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"onnx",
"safetensors",
"whisper",
"automatic-speech-recognition",
"audio",
"transformers.js",
"en",
"arxiv:2311.00430",
"arxiv:2210.13352",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["WhisperForConditionalGeneration"], "model_type": "whisper", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForSpeechSeq2Seq",
"custom_class": null,
"pipeline_tag": "automatic-speech-recognition",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "automatic-speech-recognition", "tags": ["audio", "automatic-speech-recognition", "transform... | # Distil-Whisper: distil-large-v2
Distil-Whisper was proposed in the paper [Robust Knowledge Distillation via Large-Scale Pseudo Labelling](https://arxiv.org/abs/2311.00430).
It is a distilled version of the Whisper model that is **6 times faster**, 49% smaller, and performs
**within 1% WER** on out-of-distribution ... | null | [
"mit"
] | null | [
"en"
] | null | null | null | [
"WhisperForConditionalGeneration",
"whisper",
"AutoModelForSpeechSeq2Seq"
] | [
"automatic-speech-recognition"
] | [
"multimodal"
] | [
"audio"
] | [
"text"
] |
66f4a45e8ec8643247714bf3 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | mlx-community/Llama-3.2-1B-Instruct-8bit | mlx-community | null | 857 | 1,662,519 | False | 2024-09-26T00:01:34Z | 2024-09-26T00:09:01Z | transformers | 1 | 0 | null | text-generation | {"parameters": {"F16": 38684672, "U32": 308936704}, "total": 347621376} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | d48cdf0a4ea22d893b7c63a99d6a693e24822795 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"mlx",
"conversational",
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"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"license:llama3.2",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:u... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_messag... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "de", "fr", "it", "pt", "hi", "es", "th"], "library_name": "transformers", "license": "llama3.2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["facebook", "meta... | # mlx-community/Llama-3.2-1B-Instruct-8bit
The Model [mlx-community/Llama-3.2-1B-Instruct-8bit](https://huggingface.co/mlx-community/Llama-3.2-1B-Instruct-8bit) was converted to MLX format from [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) using mlx-lm version **0.17.1**.
... | null | [
"llama3.2"
] | null | [
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th"
] | 347,621,376 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63710e1d967e405db11ea5cf | 85fa9c1177badd68 | 30d48bc6c539b72a | 250,000 | WordPiece | null | false | true | true | false | 29 | null | Whitespace | null | null | [
"Whitespace"
] | null | 263 | ai4bharat/IndicBERTv2-MLM-only | ai4bharat | null | 25,805 | 1,781,763 | False | 2022-11-13T15:32:45Z | 2024-09-05T06:43:18Z | transformers | 9 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 8598f13fe52443bc3fc054fcd665944560145b5c | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"indicbert2",
"ai4bharat",
"multilingual",
"as",
"bn",
"brx",
"doi",
"en",
"gom",
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"ta",
"te",
"u... | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["as", "bn", "brx", "doi", "en", "gom", "gu", "hi", "kn", "ks", "kas", "mai", "ml", "mr", "mni", "mnb", "ne", "or", "pa", "sa", "sat", "sd", "snd", "ta", "te", "ur"], "library_name": null, "license": "mit", "license_name": null, "license_link": nu... | # IndicBERT
A multilingual language model trained on IndicCorp v2 and evaluated on IndicXTREME benchmark. The model has 278M parameters and is available in 23 Indic languages and English. The models are trained with various objectives and datasets. The list of models are as follows:
- IndicBERT-MLM [[Model](https://hu... | null | [
"mit"
] | null | [
"as",
"bn",
"brx",
"doi",
"en",
"gom",
"gu",
"hi",
"kn",
"ks",
"kas",
"mai",
"ml",
"mr",
"mni",
"mnb",
"ne",
"or",
"pa",
"sa",
"sat",
"sd",
"snd",
"ta",
"te",
"ur"
] | null | null | [
"accuracy"
] | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
683f07454f6e7115e88614de | 883c2d6535a54097 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 215 | Qwen/Qwen3-Embedding-4B | Qwen | {
"models": [
{
"_id": "680f0c5b4e1c1da6491f3b9d",
"id": "Qwen/Qwen3-4B-Base"
}
],
"relation": "finetune"
} | 1,534,702 | 5,150,411 | False | 2025-06-03T14:31:33Z | 2025-06-20T09:30:56Z | sentence-transformers | 240 | 3 | null | feature-extraction | null | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
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"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"modules.json",
"tokenizer.json",
"token... | 5cf2132abc99cad020ac570b19d031efec650f2b | [
"sentence-transformers",
"safetensors",
"qwen3",
"text-generation",
"transformers",
"sentence-similarity",
"feature-extraction",
"text-embeddings-inference",
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"base_model:Qwen/Qwen3-4B-Base",
"base_model:finetune:Qwen/Qwen3-4B-Base",
"license:apache-2.0",
"endpoints_compat... | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Qwen/Qwen3-4B-Base"], "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["transformers", "sentence-transformers", "sentence-similarity", ... | # Qwen3-Embedding-4B
<p align="center">
<img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/logo_qwen3.png" width="400"/>
<p>
## Highlights
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"Qwen3ForCausalLM",
"qwen3"
] | [
"sentence-similarity",
"feature-extraction",
"text-generation"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"text",
"embeddings"
] |
67b5949fc1f004c14454b878 | 1c26bff8e19ff3a9 | b530ae66fac73b8d | 126,349 | BPE | 125,824 | true | true | true | true | 269 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 264 | GSAI-ML/LLaDA-8B-Instruct | GSAI-ML | null | 516,639 | 3,354,818 | False | 2025-02-19T08:21:51Z | 2025-10-21T11:17:43Z | transformers | 351 | 1 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"configuration_llada.py",
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"model-00005-of-00006.safetensors",
"model-00006-of-00... | 08b83a6feb34df1a6011b80c3c00c7563e963b07 | [
"transformers",
"safetensors",
"llada",
"text-generation",
"conversational",
"custom_code",
"license:mit",
"region:us"
] | null | {"architectures": ["LLaDAModelLM"], "auto_map": {"AutoConfig": "configuration_llada.LLaDAConfig", "AutoModelForCausalLM": "modeling_llada.LLaDAModelLM", "AutoModel": "modeling_llada.LLaDAModelLM"}, "model_type": "llada", "tokenizer_config": {"bos_token": "<|startoftext|>", "chat_template": "{% set loop_messages = messa... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_llada.LLaDAModelLM",
"pipeline_tag": "text-generation",
"processor": null
} | {"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": "text-generation", "tags": null} | # LLaDA-8B-Instruct
We introduce LLaDA, a diffusion model with an unprecedented 8B scale, trained entirely from scratch, rivaling LLaMA3 8B in performance.
[Project Page](https://ml-gsai.github.io/LLaDA-demo/)
[Code](https://github.com/ML-GSAI/LLaDA) | null | [
"mit"
] | null | null | null | null | null | [
"LLaDAModelLM",
"llada",
"modeling_llada.LLaDAModelLM",
"AutoModelForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64b6c679d16e945399007406 | 6949732424a8d297 | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 42 | TheBloke/Llama-2-7B-GPTQ | TheBloke | {
"models": [
{
"_id": "64b0234d53bd91402e6ad49c",
"id": "meta-llama/Llama-2-7b-hf"
}
],
"relation": "quantized"
} | 30,391 | 1,726,163 | False | 2023-07-18T17:06:01Z | 2023-09-27T12:44:46Z | transformers | 81 | 0 | null | text-generation | {"parameters": {"F32": 2048, "I32": 6476005376, "F16": 263770112}, "total": 6739777536} | [
".gitattributes",
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"text-generation-inference",
"4-bit",
"gptq",
"depl... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "cont... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Llama-2-7b-hf", "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": "Llama 2 7B", "pipeline_tag": "text-generation", "tags": ["facebook", "meta", "pytorch", "llama", "... | <!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<d... | null | [
"llama2"
] | null | [
"en"
] | 6,739,777,536 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
653321216141b3927abffaec | bcdced20559a6ff4 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 265 | Falconsai/text_summarization | Falconsai | null | 24,584 | 1,783,198 | False | 2023-10-21T00:53:53Z | 2024-02-17T07:55:14Z | transformers | 292 | 0 | null | summarization | {"parameters": {"F32": 60506624}, "total": 60506624} | [
".gitattributes",
"README.md",
"config.json",
"coreml/text2text-generation/decoder_float32_model.mlpackage/Data/com.apple.CoreML/model.mlmodel",
"coreml/text2text-generation/decoder_float32_model.mlpackage/Data/com.apple.CoreML/weights/weight.bin",
"coreml/text2text-generation/decoder_float32_model.mlpack... | 6e505f907968c4a9360773ff57885cdc6dca4bfd | [
"transformers",
"pytorch",
"coreml",
"onnx",
"safetensors",
"t5",
"text2text-generation",
"summarization",
"en",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"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": 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": "summarization", "tags": null, "widget": [{"text": "Hugging Face: Revolutionizing Natural Langu... | # Model Card: Fine-Tuned T5 Small for Text Summarization
## Model Description
The **Fine-Tuned T5 Small** is a variant of the T5 transformer model, designed for the task of text summarization. It is adapted and fine-tuned to generate concise and coherent summaries of input text.
The model, named "t5-small," is pre-t... | null | [
"apache-2.0"
] | null | [
"en"
] | 60,506,624 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"summarization"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17e711 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | nielsr/layoutlmv2-finetuned-funsd | nielsr | {
"models": [
{
"_id": "621ffdc136468d709f17def5",
"id": "microsoft/layoutlmv2-base-uncased"
}
],
"relation": "finetune"
} | 226 | 1,648,662 | False | 2022-03-02T23:29:05Z | 2023-09-11T12:30:41Z | transformers | 13 | 0 | null | token-classification | {"parameters": {"I64": 512, "F32": 200493133}, "total": 200493645} | [
".gitattributes",
".gitignore",
"README.md",
"config.json",
"model.safetensors",
"preprocessor_config.json",
"pytorch_model.bin",
"runs/Jul09_13-10-53_0589b24a56c9/1625836259.3002846/events.out.tfevents.1625836259.0589b24a56c9.1885.3",
"runs/Jul09_13-10-53_0589b24a56c9/events.out.tfevents.1625836259... | 405e52225928302d4c3c239ad71db42e15da2670 | [
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"tensorboard",
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"layoutlmv2",
"token-classification",
"generated_from_trainer",
"dataset:funsd",
"base_model:microsoft/layoutlmv2-base-uncased",
"base_model:finetune:microsoft/layoutlmv2-base-uncased",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LayoutLMv2ForTokenClassification"], "model_type": "layoutlmv2", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoProcessor"
} | {"base_model": "microsoft/layoutlmv2-base-uncased", "datasets": ["funsd"], "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["generated_from_trainer"], "model_index": [{"name": "l... | <!-- 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. -->
# layoutlmv2-finetuned-funsd
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/micro... | null | null | [
"funsd"
] | null | 200,493,645 | null | null | [
"layoutlmv2",
"LayoutLMv2ForTokenClassification",
"AutoModelForTokenClassification"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6560df0b620c177ae059ca9e | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | vibhorag101/roberta-base-suicide-prediction-phr | vibhorag101 | {
"models": [
{
"_id": "621ffdc036468d709f174350",
"id": "FacebookAI/roberta-base"
}
],
"relation": "finetune"
} | 72,406 | 1,940,243 | False | 2023-11-24T17:36:11Z | 2025-10-22T14:40:30Z | transformers | 12 | 0 | [{"name": "roberta-base-suicide-prediction-phr", "results": [{"task": {"type": "text-classification", "name": "Suicidal Tendency Prediction in text"}, "dataset": {"type": "vibhorag101/roberta-base-suicide-prediction-phr", "name": "Suicide Prediction Dataset", "split": "test"}, "metrics": [{"type": "accuracy", "value": ... | text-classification | null | [
".gitattributes",
".gitignore",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
] | 6e9b96d32711c81d8fc7bd0b221355f2d497ba44 | [
"transformers",
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"dataset:vibhorag101/suicide_prediction_dataset_phr",
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"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"model-index",
"text-embeddings-inference",
"... | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "roberta-base", "datasets": ["vibhorag101/suicide_prediction_dataset_phr"], "language": ["en"], "library_name": "transformers", "license": "mit", "metrics": ["accuracy", "recall", "precision", "f1"], "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-suicide-prediction-phr", "resu... | # roberta-base-suicide-prediction-phr
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on this [dataset](https://huggingface.co/datasets/vibhorag101/suicide_prediction_dataset_phr) sourced from Reddit.
It achieves the following results on the evaluation/validation set:
- Los... | null | [
"mit"
] | [
"vibhorag101/suicide_prediction_dataset_phr"
] | [
"en"
] | null | null | [
"accuracy",
"recall",
"precision",
"f1"
] | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62b0888a058e22045db8f819 | 8a8c94e1a59f5186 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 11 | optimum/t5-small | optimum | null | 15,846 | 1,666,484 | False | 2022-06-20T14:47:38Z | 2023-01-19T17:56:30Z | transformers | 10 | 0 | null | translation | null | [
".gitattributes",
"README.md",
"config.json",
"decoder_model.onnx",
"decoder_with_past_model.onnx",
"encoder_model.onnx",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | c5c8ea1bc3f5940643d88d792094e851fa80e06f | [
"transformers",
"onnx",
"t5",
"text2text-generation",
"summarization",
"translation",
"en",
"fr",
"ro",
"de",
"multilingual",
"dataset:c4",
"arxiv:1910.10683",
"license:apache-2.0",
"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": ["c4"], "eval_results": null, "language": ["en", "fr", "ro", "de", "multilingual"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["summarization", "translation"]} | ## [t5-small](https://huggingface.co/t5-small) exported to the ONNX format
## Model description
[T5](https://huggingface.co/docs/transformers/model_doc/t5#t5) is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text f... | null | [
"apache-2.0"
] | [
"c4"
] | [
"en",
"fr",
"ro",
"de",
"multilingual"
] | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"translation",
"summarization"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64b6d9d4bb1d8f2832c8be93 | 6949732424a8d297 | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 42 | TheBloke/Llama-2-13B-chat-GPTQ | TheBloke | {
"models": [
{
"_id": "64b01418c2f8f5e34a7f1302",
"id": "meta-llama/Llama-2-13b-chat-hf"
}
],
"relation": "quantized"
} | 561 | 1,652,666 | False | 2023-07-18T18:28:36Z | 2023-09-27T12:44:48Z | transformers | 363 | 0 | null | text-generation | {"parameters": {"F32": 2560, "I32": 12687769600, "F16": 330224640}, "total": 13017996800} | [
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"USE_POLICY.md",
"config.json",
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"model.safetensors",
"quantize_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | ea078917a7e91c896787c73dba935f032ae658e9 | [
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"llama",
"text-generation",
"facebook",
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"arxiv:2307.09288",
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"license:llama2",
"text-generation-inference",
"4-bit",
"gp... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "cont... | {
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} | {"base_model": "meta-llama/Llama-2-13b-chat-hf", "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": "Llama 2 13B Chat", "pipeline_tag": "text-generation", "tags": ["facebook", "meta", "pytorch"... | <!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<d... | null | [
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] | null | [
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] | 13,017,996,800 | null | null | [
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"models": [
{
"_id": "621ffdc036468d709f174350",
"id": "FacebookAI/roberta-base"
}
],
"relation": "finetune"
} | 8,500 | 1,750,191 | False | 2022-05-11T12:42:35Z | 2025-10-23T08:53:05Z | sentence-transformers | 23 | 1 | null | sentence-similarity | null | [
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"tokenizer_config.js... | d7d0f5ca829316a8f5695e49dfce80b86db5e76c | [
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"endpoints... | null | {"architectures": ["RobertaModel"], "model_type": "roberta", "tokenizer_config": {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": ["FacebookAI/roberta-base"], "datasets": ["AnnaWegmann/StyleEmbeddingData"], "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transforme... | # Style Embedding
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.
for more info see [Style-Embeddings](https://github.com/nlpsoc/Style-Embeddings)
see published paper a... | null | null | [
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] | null | null | null | null | [
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] | 266 | GanymedeNil/text2vec-large-chinese | GanymedeNil | null | 2,774 | 1,643,169 | False | 2023-03-07T03:32:14Z | 2024-06-25T09:51:38Z | transformers | 761 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 325522432}, "total": 325522944} | [
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] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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News
2024-06-25 [text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese-onnx) onnxruntime version.
Talk to me: https://twitter.... | null | [
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] | null | [
"zh"
] | 325,522,944 | null | null | [
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"Split",
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] | 241 | deepseek-ai/deepseek-coder-6.7b-base | deepseek-ai | null | 63,490 | 1,746,735 | False | 2023-10-23T16:15:39Z | 2024-03-19T03:54:51Z | transformers | 122 | 0 | null | text-generation | {"parameters": {"BF16": 6740512768}, "total": 6740512768} | [
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"to... | ce2207a8bfef3ee92bd7dd4cc31c52cfa0046912 | [
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"llama",
"text-generation",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<\uff5cbegin\u2581of\u2581sentence\uff5c>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<\uff5cend\u258... | {
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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",
"LICENSE"
] | null | null | 6,740,512,768 | null | null | [
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] | [
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] |
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"WordPiece"
] | 0 | michaelfeil/bge-small-en-v1.5 | michaelfeil | null | 48,896 | 1,835,688 | False | 2024-03-12T05:20:48Z | 2024-03-18T16:03:12Z | sentence-transformers | 3 | 0 | null | feature-extraction | {"parameters": {"I64": 512, "F32": 33360000}, "total": 33360512} | [
".gitattributes",
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"t... | ab7b31bd10f9bfbb915a28662ec4726b06c6552a | [
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"en",
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"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
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] | 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",
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"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"]} | <h1 align="center">Infinity Embedding Model</h1>
This is the stable default model for infinity.
```bash
pip install infinity_emb[all]
```
More details about the infinity inference project please refer to the Github: [Infinity](https://github.com/michaelfeil/infinity).
## Usage for Embedding Model via infinity in ... | null | [
"mit"
] | null | [
"en"
] | 33,360,512 | null | null | [
"BertModel",
"AutoModel",
"bert"
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"feature-extraction"
] | [
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"multimodal"
] | [
"text"
] | [
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] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 267 | mpi-inno-comp/paecter | mpi-inno-comp | null | 2,215 | 1,797,712 | False | 2024-02-29T09:34:49Z | 2024-10-25T12:30:17Z | sentence-transformers | 15 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 344702976}, "total": 344703488} | [
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"arxiv:2402.19411",
"doi:10.57967/hf/2003",
"license:apache-2.0",
"text-embeddings-inferen... | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["mpi-inno-comp/paecter_dataset"], "eval_results": null, "language": "en", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["patent-similarity", "sentence-trans... | # PaECTER - a Patent Similarity Model
PaECTER (Patent Embeddings using Citationinformed TransformERs) is a patent similarity model.
Built upon Google's BERT for Patents as its base model, it generates 1024-dimensional dense vector embeddings from patent text.
These vectors encapsulate the semantic essence of the giv... | null | [
"apache-2.0"
] | [
"mpi-inno-comp/paecter_dataset"
] | [
"en"
] | 344,703,488 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
675f7dfb72d14a2cffa1002c | 21feada71fab2765 | 12ebeebff692ebb1 | 131,072 | BPE | 269,443 | false | true | true | true | 1,000 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 245 | Nitral-AI/Captain-Eris_Violet-V0.420-12B | Nitral-AI | {
"models": [
{
"_id": "66e30fa2a007fc9b30e0501a",
"id": "Epiculous/Violet_Twilight-v0.2"
},
{
"_id": "67242ea18fa663371051b323",
"id": "Nitral-AI/Captain_BMO-12B"
}
],
"relation": "merge"
} | 52 | 1,616,554 | False | 2024-12-16T01:10:19Z | 2025-05-11T03:31:34Z | transformers | 68 | 1 | null | text-generation | {"parameters": {"BF16": 12247782400}, "total": 12247782400} | [
".gitattributes",
"README.md",
"ST/ST-formatting-captain-x-2025-05-11.json",
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"mergekit_config.yml",
"model-00001-of-00005.safetensors",
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"model-00003-of-00005.safetensors",
"model-00004-of-00005.safetensors",
"model-0000... | 9da28dddf4530bb0a195c73a9fe8f844245d68f5 | [
"transformers",
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"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"en",
"base_model:Epiculous/Violet_Twilight-v0.2",
"base_model:merge:Epiculous/Violet_Twilight-v0.2",
"base_model:Nitral-AI/Captain_BMO-12B",
"base_model:merge:Nitral-AI/Captain_BMO-12B",
"lic... | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = mes... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Epiculous/Violet_Twilight-v0.2", "Nitral-AI/Captain_BMO-12B"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["mergekit", "mer... | 
# Instruct/Context import + Textgen preset combined available: [Here](https://hu... | null | [
"other"
] | null | [
"en"
] | 12,247,782,400 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66bd92d455a1210c53365577 | 86f7385b380f33a8 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 268 | dangvantuan/vietnamese-document-embedding | dangvantuan | null | 489,845 | 2,921,685 | False | 2024-08-15T05:32:04Z | 2024-11-22T13:56:46Z | sentence-transformers | 26 | 0 | null | sentence-similarity | {"parameters": {"F32": 305368320}, "total": 305368320} | [
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"modules.json",
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"special_tokens_map.json",
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] | 6fa4e2f8ed2d33120b0f4442cc81f8f973c3f56b | [
"sentence-transformers",
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"Vietnamese",
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"sentence-similarity",
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"phobert",
"vietnamese",
"sentence-embedding",
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"vi",
"arxiv:1908.10084",
"arxiv:2407.19669",
"arxiv:2308.03281",
"arxiv:2402.14776",
"license:apache-2.0",
... | null | {"architectures": ["VietnameseModel"], "auto_map": {"AutoConfig": "dangvantuan/Vietnamese_impl--configuration.VietnameseConfig", "AutoModel": "dangvantuan/Vietnamese_impl--modeling.VietnameseModel", "AutoModelForMaskedLM": "dangvantuan/Vietnamese_impl--modeling.VietnameseForMaskedLM", "AutoModelForMultipleChoice": "dan... | {
"auto_model": "AutoModel",
"custom_class": "dangvantuan/Vietnamese_impl--modeling.VietnameseModel",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["vi"], "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["pearsonr", "spearmanr"], "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers"... | ## Model Description:
[**vietnamese-document-embedding**](https://huggingface.co/dangvantuan/vietnamese-document-embedding) is the Document Embedding Model for Vietnamese language with context length up to 8096 tokens. This model is a specialized long text-embedding trained specifically for the Vietnamese language, wh... | null | [
"apache-2.0"
] | null | [
"vi"
] | 305,368,320 | null | [
"pearsonr",
"spearmanr"
] | [
"AutoModel",
"dangvantuan/Vietnamese_impl--modeling.VietnameseModel",
"Vietnamese",
"VietnameseModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
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"ByteLevel"
] | [
"ByteLevel"
] | 6 | microsoft/trocr-large-stage1 | microsoft | null | 1,493 | 1,675,794 | False | 2022-03-02T23:29:05Z | 2024-05-27T20:12:53Z | transformers | 26 | 0 | null | image-to-text | {"parameters": {"F32": 608119809}, "total": 608119809} | [
".gitattributes",
".gitignore",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
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"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 4f6e6d7a2cba548292ddb6f5649b2d64eb7124d8 | [
"transformers",
"pytorch",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"trocr",
"image-to-text",
"arxiv:2109.10282",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["VisionEncoderDecoderModel"], "model_type": "vision-encoder-decoder", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "cls_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "nor... | {
"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": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["trocr", "image-to-text"]} | # TrOCR (large-sized model, pre-trained only)
TrOCR pre-trained only model. It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/mas... | null | null | null | null | 608,119,809 | null | null | [
"AutoModelForImageTextToText",
"vision-encoder-decoder",
"VisionEncoderDecoderModel"
] | [
"image-text-to-text",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
67de202b6599e7158f6912f4 | d902613821ac8766 | 3a65d0d018eee5a3 | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 269 | Qwen/Qwen2.5-Omni-7B | Qwen | null | 475,907 | 2,938,626 | False | 2025-03-22T02:27:55Z | 2025-04-30T13:48:45Z | transformers | 1,877 | 2 | 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-00005.safetensors",
"model-00002-of-00005.safetensors",
"model-00003-of-00005.safetensors",
"model-00004-of-00005.safetensors",
"model-0... | ae9e1690543ffd5c0221dc27f79834d0294cba00 | [
"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": "apache-2.0", "license_link": "https://huggingface.co/Qwen/Qwen2.5-Omni-7B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "any-to-any", "tags": [... | null | null | [
"other",
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-Omni-7B/blob/main/LICENSE"
] | null | [
"en"
] | null | null | null | [
"Qwen2_5OmniModel",
"AutoModel",
"qwen2_5_omni"
] | [
null,
"any-to-any"
] | [
"multimodal"
] | [
"any"
] | [
"any"
] |
6578547a0766f274f3d2974f | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | upstage/SOLAR-10.7B-Instruct-v1.0 | upstage | {
"models": [
{
"_id": "657874e50c22bb8984c717f7",
"id": "upstage/SOLAR-10.7B-v1.0"
}
],
"relation": "finetune"
} | 30,818 | 1,730,656 | False | 2023-12-12T12:39:22Z | 2024-09-10T21:44:55Z | transformers | 649 | 0 | null | text-generation | {"parameters": {"F16": 10731524096}, "total": 10731524096} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00005.safetensors",
"model-00002-of-00005.safetensors",
"model-00003-of-00005.safetensors",
"model-00004-of-00005.safetensors",
"model-00005-of-00005.safetensors",
"model.safetensors.index.json",
"solar-api-b... | 090d405e6fe7b4c7b0b999a67f5c6dee0514a7ad | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:c-s-ale/alpaca-gpt4-data",
"dataset:Open-Orca/OpenOrca",
"dataset:Intel/orca_dpo_pairs",
"dataset:allenai/ultrafeedback_binarized_cleaned",
"arxiv:2312.15166",
"arxiv:2309.12284",
"arxiv:2403.19270",
... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{% if message['content']%}{{'### System:\n' + message['content']+'\n\n'}}{% endif %}{% elif message['role'] == 'user' %}{{'### User:\n' + message['conten... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["upstage/SOLAR-10.7B-v1.0"], "datasets": ["c-s-ale/alpaca-gpt4-data", "Open-Orca/OpenOrca", "Intel/orca_dpo_pairs", "allenai/ultrafeedback_binarized_cleaned"], "eval_results": null, "language": ["en"], "library_name": null, "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics"... | <p align="left">
<a href="https://console.upstage.ai/">
<img src="https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0/resolve/main/solar-api-banner.png" width="100%"/>
</a>
<p>
# **Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!**
**(This model is [upstage/SOLAR-10.7B-v1.0](https:/... | null | [
"cc-by-nc-4.0"
] | [
"c-s-ale/alpaca-gpt4-data",
"Open-Orca/OpenOrca",
"Intel/orca_dpo_pairs",
"allenai/ultrafeedback_binarized_cleaned"
] | [
"en"
] | 10,731,524,096 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6460fc90b2ae2983b1064972 | d6cc075dc4f0e1b5 | 7f171e98bdd0da5f | 83,828 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 270 | seara/rubert-tiny2-russian-sentiment | seara | null | 40,269 | 1,859,999 | False | 2023-05-14T15:21:52Z | 2024-10-29T18:17:02Z | transformers | 32 | 0 | null | text-classification | {"parameters": {"I64": 2048, "F32": 29194707}, "total": 29196755} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | ad9be38671d4444945734cce7cdaadacc5002cc4 | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"text-classification",
"sentiment-analysis",
"multi-class-classification",
"sentiment analysis",
"rubert",
"sentiment",
"tiny",
"russian",
"multiclass",
"classification",
"ru",
"dataset:sismetanin/rureviews",
"dataset... | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["sismetanin/rureviews", "RuSentiment", "LinisCrowd2015", "LinisCrowd2016", "KaggleRussianNews"], "eval_results": null, "language": ["ru"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["f1", "roc_auc", "precision", "recall"], "model_nam... | This is [RuBERT-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) model fine-tuned for __sentiment classification__ of short __Russian__ texts.
The task is a __multi-class classification__ with the following labels:
```yaml
0: neutral
1: positive
2: negative
```
Label to Russian label:
```yaml
neutral: нейтра... | null | [
"mit"
] | [
"sismetanin/rureviews",
"RuSentiment",
"LinisCrowd2015",
"LinisCrowd2016",
"KaggleRussianNews"
] | [
"ru"
] | 29,196,755 | null | [
"f1",
"roc_auc",
"precision",
"recall"
] | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62d9d087cfed764363b43770 | 2012b52fa41ec860 | db117e9883c4d0ab | 50,257 | BPE | 50,000 | false | true | true | true | 1 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 113 | succinctly/text2image-prompt-generator | succinctly | null | 7,298 | 1,616,932 | False | 2022-07-21T22:17:43Z | 2022-08-20T06:01:10Z | transformers | 312 | 1 | null | text-generation | null | [
".gitattributes",
".gitignore",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
] | b7e96e38b77149daaded8f5101cdc81482330b4b | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"text2image",
"prompting",
"en",
"dataset:succinctly/midjourney-prompts",
"license:cc-by-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["succinctly/midjourney-prompts"], "eval_results": null, "language": ["en"], "library_name": null, "license": "cc-by-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["text2image", "prompting"], "thumbnail": "https://dr... | This is a GPT-2 model fine-tuned on the [succinctly/midjourney-prompts](https://huggingface.co/datasets/succinctly/midjourney-prompts) dataset, which contains 250k text prompts that users issued to the [Midjourney](https://www.midjourney.com/) text-to-image service over a month period. For more details on how this data... | null | [
"cc-by-2.0"
] | [
"succinctly/midjourney-prompts"
] | [
"en"
] | null | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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