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621ffdc036468d709f175dff | b87b9dd0c17a851c | 2be8c7f5be1973c9 | 42,000 | BPE | 41,733 | false | true | true | true | 11 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 848 | HooshvareLab/roberta-fa-zwnj-base | HooshvareLab | null | 44 | 132,265 | False | 2022-03-02T23:29:04Z | 2023-02-15T19:52:24Z | transformers | 7 | 0 | null | fill-mask | null | [
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This model can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.
## Questions?
Post a Github issue on the [ParsRoBERTa Issues](https://github.com/hooshvare/parsbert/issues) repo. | null | [
"apache-2.0"
] | null | [
"fa"
] | null | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
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"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 9 | voidful/dpr-ctx_encoder-bert-base-multilingual | voidful | null | 6 | 130,904 | False | 2022-03-02T23:29:05Z | 2021-02-21T09:00:44Z | transformers | 6 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | c7a3dc617754e93efe785aa88dc1f52b4f7cb688 | [
"transformers",
"pytorch",
"dpr",
"multilingual",
"dataset:NQ",
"dataset:Trivia",
"dataset:SQuAD",
"dataset:MLQA",
"dataset:DRCD",
"arxiv:2004.04906",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["DPRContextEncoder"], "model_type": "dpr", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "DPRContextEncoder",
"custom_class": null,
"pipeline_tag": null,
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["NQ", "Trivia", "SQuAD", "MLQA", "DRCD"], "eval_results": null, "language": "multilingual", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # dpr-ctx_encoder-bert-base-multilingual
## Description
Multilingual DPR Model base on bert-base-multilingual-cased.
[DPR model](https://arxiv.org/abs/2004.04906)
[DPR repo](https://github.com/facebookresearch/DPR)
## Data
1. [NQ](https://github.com/facebookresearch/DPR/blob/master/data/download_data.py)
2. [Trivia... | null | null | [
"NQ",
"Trivia",
"SQuAD",
"MLQA",
"DRCD"
] | [
"multilingual"
] | null | null | null | [
"DPRContextEncoder",
"dpr"
] | [
null
] | null | null | null |
642ed3f1d78b742f61c65b95 | 1c29c6e3ba996b0d | 5f9f736c7dc6fbfa | 28,996 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 849 | jjzha/jobbert_knowledge_extraction | jjzha | null | 3,464 | 138,260 | False | 2023-04-06T14:15:13Z | 2023-10-26T10:25:41Z | transformers | 11 | 0 | null | token-classification | {"parameters": {"I64": 512, "F32": 107721987}, "total": 107722499} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 9dea9759b7e290413cb4d92e8cae338affab9be4 | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"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"
} | null | This is a demo using the models from:
```
@inproceedings{zhang-etal-2022-skillspan,
title = "{S}kill{S}pan: Hard and Soft Skill Extraction from {E}nglish Job Postings",
author = "Zhang, Mike and
Jensen, Kristian and
Sonniks, Sif and
Plank, Barbara",
booktitle = "Proceedings of the 2022... | null | null | null | null | 107,722,499 | null | null | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6594d7fe43971eed45cc4d22 | caee50e13b69dec7 | 477ef93aefa1d4b1 | 48,000 | BPE | 58,980 | true | false | true | true | 16,003 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 850 | LDCC/LDCC-SOLAR-10.7B | LDCC | null | 771 | 134,549 | False | 2024-01-03T03:43:58Z | 2024-02-08T01:59:04Z | transformers | 16 | 0 | null | text-generation | {"parameters": {"BF16": 10862596096}, "total": 10862596096} | [
".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",
"special_tok... | f0162665973def1ad517005d74c9a3df3e4a9b56 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ko",
"arxiv:2312.15166",
"license:cc-by-nc-4.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "eos_token": "<|im_end|>", "pad_token": "</s>", "unk_token": "<unk>", "use_default_system_prompt": true}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ko"], "library_name": null, "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # Model Card for LDCC-SOLAR-10.7B
## Developed by : Wonchul Kim ([Lotte Data Communication](https://www.ldcc.co.kr) AI Technical Team)
## Hardware and Software
* **Hardware**: We utilized an A100x4 * 1 for training our model
* **Training Factors**: We fine-tuned this model using a combination of the [DeepSpeed libra... | null | [
"cc-by-nc-4.0"
] | null | [
"ko"
] | 10,862,596,096 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63a0ef223c8841cfe2d1b94b | 634a9186a6003172 | 5f9f736c7dc6fbfa | 28,996 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 8 | andrewt-cam/bert-finetuned-animacy | andrewt-cam | null | 3 | 130,604 | False | 2022-12-19T23:09:22Z | 2022-12-25T09:29:17Z | transformers | 0 | 0 | null | token-classification | null | [
".gitattributes",
".gitignore",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | 68db52992f2b6b7169b731cca48b81a111f06936 | [
"transformers",
"pytorch",
"bert",
"token-classification",
"endpoints_compatible",
"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"
} | null | null | null | null | null | null | null | null | null | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
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"token-classification"
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"text"
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"text"
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666c78cc3ae28e1bcfe0c577 | 34093184d10f76a9 | 35b001f3a491f9f9 | 151,646 | BPE | 151,387 | true | true | true | true | 3 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 53 | RedHatAI/Qwen2-7B-Instruct-FP8 | RedHatAI | null | 6,064 | 172,173 | False | 2024-06-14T17:07:24Z | 2024-07-18T19:14:31Z | transformers | 2 | 0 | null | text-generation | {"parameters": {"BF16": 1090328064, "F8_E4M3": 6525288448}, "total": 7615616512} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json... | 3d039b6fd899c75713dbf695c003260eca9e2246 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"fp8",
"vllm",
"conversational",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"quant_method": "fp8"}, "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' }... | {
"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": null, "tags": ["fp8", "vllm"]} | null | null | [
"apache-2.0"
] | null | null | 7,615,616,512 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
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"text"
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621ffdc136468d709f17b15e | 07cf022cbacdd3c2 | 116d4b3e41dc2064 | 50,266 | BPE | 50,004 | false | true | true | true | 6 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 851 | flax-community/dansk-gpt-wiki | flax-community | null | 23 | 130,560 | False | 2022-03-02T23:29:05Z | 2023-09-11T19:38:56Z | transformers | 4 | 0 | null | text-generation | {"parameters": {"F32": 124439808, "U8": 12582912}, "total": 137022720} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"evaluate.py",
"events.out.tfevents.1626210352.t1v-n-905ccd3e-w-0.81578.3.v2",
"flax_model.msgpack",
"make_config.py",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"run_clm_flax.py",
"save_model.py",
"special_tok... | 3d92586d61d7a50e64dfab77af7245026f96e785 | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"da",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "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": "da", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"text": "Jeg elsker livet"}]} | # GPT2-svenska-wikipedia
A Danish GPT2 style model trained using Flax CLM pipeline on the Danish
part of the wiki40b dataset.
https://huggingface.co/datasets/wiki40b
## Model series
This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.
## Gpt models
## Swedish... | null | null | null | [
"da"
] | 137,022,720 | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
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"text"
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"text"
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"text"
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"ByteLevel"
] | [
"ByteLevel"
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"models": [
{
"_id": "63bf1d8ea2f6fcbfa7fa28eb",
"id": "lchaloupsky/czech-gpt2-oscar"
}
],
"relation": "finetune"
} | 29 | 130,295 | False | 2023-04-05T09:39:25Z | 2025-01-03T20:44:10Z | transformers | 3 | 0 | null | text-generation | {"parameters": {"F32": 124439808}, "total": 124439808} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
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"arxiv:2407.12790",
"base_model:lchaloupsky/czech-gpt2-oscar",
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"license:cc-by-sa-4.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region... | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["lchaloupsky/czech-gpt2-oscar"], "datasets": null, "eval_results": null, "language": ["cs"], "library_name": null, "license": "cc-by-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": null, "widget": [{"text": "# ABBA # 19... | ### Czech Poetry GPT
GPT2 finetuned on Czech poetry from github project by
Institute of Czech Literature, Czech Academy of Sciences.
https://github.com/versotym/corpusCzechVerse
Done using GPT-Czech-Poet
https://github.com/jinymusim/GPT-Czech-Poet
https://arxiv.org/abs/2407.12790
## Usage
Use as any other GPT2 ... | null | [
"cc-by-sa-4.0"
] | null | [
"cs"
] | 124,439,808 | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc036468d709f175dfd | fa2b3ade5a3a25fc | 2be8c7f5be1973c9 | 42,000 | BPE | 41,733 | false | true | true | true | 11 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 853 | HooshvareLab/gpt2-fa | HooshvareLab | null | 1,846 | 138,246 | False | 2022-03-02T23:29:04Z | 2021-05-21T10:51:23Z | transformers | 19 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
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"tf_model.h5",
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"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "fa", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"text": "\u062f\u0631 \u06cc\u06a9 \u0627\u062a\u0641\u0627\u0642... | # ParsGPT2
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@misc{ParsGPT2,
author = {Hooshvare Team},
title = {ParsGPT2 the Persian version of GPT2},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/hoo... | null | [
"apache-2.0"
] | null | [
"fa"
] | null | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6663672b46a5a30a270c280f | 4bfbae05ec28a18e | f148453ab4599061 | 100,000 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 854 | AMR-KELEG/NADI2024-baseline | AMR-KELEG | null | 2,738 | 179,854 | False | 2024-06-07T20:01:47Z | 2024-09-22T11:42:46Z | transformers | 0 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 65b3d251e04ef6ee34a528f1de572c6f57f73ab5 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"ar",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ar"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-classification", "tags": null, "widget": [{"text": "\u062e\u0644\u064a\u0644\u064a \u0641\u064a \... | # Model Card for NADI-2024-baseline
<!-- Provide a quick summary of what the model is/does. -->
A BERT-based model fine-tuned to perform single-label Arabic Dialect Identification (ADI). Instead of predicting the most probable dialect, the logits are used to generate multilabel predictions.
### Model Description
<!-... | null | null | null | [
"ar"
] | null | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6811256b174c79b9d5176ed4 | 281a63d7efbcc099 | 0d1b0351a03fb052 | 200,029 | BPE | 199,742 | false | true | false | true | 12 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 855 | microsoft/Phi-4-mini-reasoning | microsoft | null | 19,684 | 208,146 | False | 2025-04-29T19:15:55Z | 2025-12-10T20:24:22Z | transformers | 220 | 1 | null | text-generation | null | [
".gitattributes",
"CODE_OF_CONDUCT.md",
"LICENSE",
"NOTICE.md",
"Phi-4-Mini-Reasoning.pdf",
"README.md",
"SECURITY.md",
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"added_tokens.json",
"config.json",
"data_summary_card.md",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00... | 0e3b1e2d02ee478a3743abe3f629e9c0cb722e0a | [
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"safetensors",
"phi3",
"text-generation",
"nlp",
"math",
"code",
"conversational",
"en",
"arxiv:2504.21233",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Phi3ForCausalLM"], "model_type": "phi3", "tokenizer_config": {"bos_token": "<|endoftext|>", "chat_template": "{{ '<|system|>Your name is Phi, an AI math expert developed by Microsoft.' }}{% for message in messages %}{% if message['role'] == 'system' %} {{ message['content'] }}{% if 'tools' in messag... | {
"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": "mit", "license_name": null, "license_link": "https://huggingface.co/microsoft/Phi-4-mini-instruct-reasoning/resolve/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-gener... | null | null | [
"mit",
"https://huggingface.co/microsoft/Phi-4-mini-instruct-reasoning/resolve/main/LICENSE"
] | null | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"phi3",
"Phi3ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
687d268589a9cd0263a8a859 | 1774aa5af215e36a | 6b3612a55f8124d9 | 151,657 | BPE | 151,387 | true | true | true | true | 14 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 576 | trl-internal-testing/tiny-Qwen2VLForConditionalGeneration | trl-internal-testing | null | 92,156 | 407,835 | False | 2025-07-20T17:25:25Z | 2026-01-16T11:47:44Z | transformers | 0 | 0 | null | image-text-to-text | {"parameters": {"BF16": 3538080}, "total": 3538080} | [
".gitattributes",
"README.md",
"added_tokens.json",
"chat_template.jinja",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"preprocessor_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"video_preprocessor_config.json",
"vocab.... | 90d4f13a36eed3daf4061f13b98f099211d419da | [
"transformers",
"safetensors",
"qwen2_vl",
"image-text-to-text",
"trl",
"conversational",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen2VLForConditionalGeneration"], "model_type": "qwen2_vl", "tokenizer_config": {"bos_token": null, "eos_token": "<|im_end|>", "pad_token": "<|endoftext|>", "unk_token": null}, "chat_template_jinja": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["trl"]} | # Tiny Qwen2VLForConditionalGeneration
This is a minimal model built for unit tests in the [TRL](https://github.com/huggingface/trl) library. | null | null | null | null | 3,538,080 | null | null | [
"AutoModelForImageTextToText",
"qwen2_vl",
"Qwen2VLForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
64c50d3357e5b2cd8ada4840 | 8a8c94e1a59f5186 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 11 | ai-forever/T5-large-spell | ai-forever | null | 658 | 139,188 | False | 2023-07-29T12:59:31Z | 2024-08-02T12:51:38Z | transformers | 13 | 0 | null | null | {"parameters": {"F32": 737668096}, "total": 737668096} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | c32125039b7df52d4d7cda059a0f2f74c29e8e02 | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"spellchecking",
"NLP",
"T5",
"natural language generation",
"en",
"arxiv:2308.09435",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": 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": ["spellchecking", "NLP", "T5", "pytorch", "natural language generation"]} | # T5-large-spell model
### Summary
The model corrects spelling errors and typos by bringing all words in the text to the standard English language.
The proofreader was trained based on the [T5-large](https://huggingface.co/t5-large) model.
An extensive dataset with “artificial” errors was taken as a training corpus: ... | null | [
"mit"
] | null | [
"en"
] | 737,668,096 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
621ffdc136468d709f17a0d7 | 7f15c183214cfbb9 | 2d4d455c0dc5b195 | 31,102 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 856 | clip-italian/clip-italian | clip-italian | null | 543 | 132,073 | False | 2022-03-02T23:29:05Z | 2023-03-16T16:06:34Z | transformers | 16 | 0 | null | feature-extraction | null | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 8b1e1e8162f7df366afb8e895fd19af214c78126 | [
"transformers",
"pytorch",
"jax",
"vision-text-dual-encoder",
"feature-extraction",
"italian",
"bert",
"vit",
"vision",
"it",
"dataset:wit",
"dataset:ctl/conceptualCaptions",
"dataset:mscoco-it",
"arxiv:2108.08688",
"arxiv:2103.01913",
"arxiv:2103.00020",
"license:gpl-3.0",
"endpoi... | null | {"architectures": ["VisionTextDualEncoderModel"], "model_type": "vision-text-dual-encoder", "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": "AutoProcessor"
} | {"base_model": null, "datasets": ["wit", "ctl/conceptualCaptions", "mscoco-it"], "eval_results": null, "language": "it", "library_name": null, "license": "gpl-3.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["italian", "bert", "vit", "vision"]} | # Italian CLIP
Paper: [Contrastive Language-Image Pre-training for the Italian Language](https://arxiv.org/abs/2108.08688)
With a few tricks, we have been able to fine-tune a competitive Italian CLIP model with **only 1.4 million** training samples. Our Italian CLIP model is built upon the [Italian BERT](https://hugg... | null | [
"gpl-3.0"
] | [
"wit",
"ctl/conceptualCaptions",
"mscoco-it"
] | [
"it"
] | null | null | null | [
"VisionTextDualEncoderModel",
"AutoModel",
"vision-text-dual-encoder"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
64cb7b8fa81988d073466297 | e23b6bdbe047bb7e | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | TheBloke/vicuna-13B-v1.5-GPTQ | TheBloke | {
"models": [
{
"_id": "64c4993e3e30498cca8dd34e",
"id": "lmsys/vicuna-13b-v1.5"
}
],
"relation": "quantized"
} | 9 | 129,582 | False | 2023-08-03T10:03:59Z | 2023-09-27T12:45:20Z | transformers | 12 | 0 | null | text-generation | {"parameters": {"F32": 2560, "I32": 12687769600, "F16": 330224640}, "total": 13017996800} | [
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"tokenizer.json",
"tokenizer.model",
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] | fd55a8ca80cf9c70510360b961a5e5c0ade72287 | [
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"license:llama2",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "con... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "lmsys/vicuna-13b-v1.5", "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": "Vicuna 13B v1.5", "pipeline_tag": null, "tags": null, "inference": false, "model_creator": "lmsys", "mo... | <!-- 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 | null | 13,017,996,800 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
62a738b8ce772ca0badcb6b3 | 9a415b19aec14031 | ad4b234fa1a3ca7b | 31,102 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 857 | Anjoe/gbert-large | Anjoe | null | 2 | 129,375 | False | 2022-06-13T13:16:40Z | 2023-08-01T10:24:21Z | transformers | 0 | 0 | [{"name": "gbert-large", "results": []}] | fill-mask | {"parameters": {"I64": 512, "F32": 335768958}, "total": 335769470} | [
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".gitignore",
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"special_t... | 48b9dc751bc9422ca65c23ca919b847158c1232b | [
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"tf",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"endpoints_compatible",
"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": [], "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": "gbert-large", "pipeline_tag": null, "tags": ["generated_from_trainer"]} | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gbert-large
This model is a fine-tuned version of deepset/gbert-large
It was fine-tuned on poetry from Projekt Gutenberg in ord... | null | null | null | null | 335,769,470 | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
622f52b83f3276a897728c30 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | cambridgeltl/sst_mobilebert-uncased | cambridgeltl | null | 7 | 129,429 | False | 2022-03-14T14:35:36Z | 2022-11-04T19:20:23Z | transformers | 1 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | af21b1e50f26e62e17f378d54c750c43c912dd02 | [
"transformers",
"pytorch",
"mobilebert",
"text-classification",
"arxiv:2004.02984",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MobileBertForSequenceClassification"], "model_type": "mobilebert", "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 | This model provides a MobileBERT [(Sun et al., 2020)](https://arxiv.org/abs/2004.02984) fine-tuned on the SST data with three sentiments (0 -- negative, 1 -- neutral, and 2 -- positive).
## Example Usage
Below, we provide illustrations on how to use this model to make sentiment predictions.
```python
import torch... | null | null | null | null | null | null | null | [
"mobilebert",
"MobileBertForSequenceClassification",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
664e34ecbc40cce7a86db736 | 278b7bd720762cfd | fadac82906efdc90 | 32,768 | BPE | 58,980 | false | true | true | true | 771 | null | Metaspace | Sequence | null | [
"Metaspace"
] | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 216 | unsloth/mistral-7b-instruct-v0.3 | unsloth | null | 4,967 | 156,776 | False | 2024-05-22T18:09:48Z | 2024-09-11T02:09:51Z | transformers | 10 | 0 | null | text-generation | {"parameters": {"BF16": 7248023552}, "total": 7248023552} | [
".gitattributes",
"README.md",
"config.json",
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"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer.model.v... | 60b99dcbc7f853b7cd18472a607e8d045b96f03e | [
"transformers",
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"mistral",
"text-generation",
"unsloth",
"mistral-7b",
"mistral-instruct",
"instruct",
"conversational",
"en",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | 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": 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": ["unsloth", "transformers", "mistral", "mistral-7b", "mistral-instruct"... | # Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
We have a Google Colab Tesla T4 notebook for Mistral v3 7b here: https://colab.research.google.com/drive/1_yNCks4BTD5zOnjozppphh5GzMFaMKq_?usp=sharing
For conversational ShareGPT style and using Mistral v3 Instruct: https://colab.research.... | null | [
"apache-2.0"
] | null | [
"en"
] | 7,248,023,552 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6422c4b4bbbbabc97c8942da | 716852fad96cc799 | 64541a3d0db00c39 | 1,024 | BPE | 767 | false | true | true | true | 1 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 599 | fxmarty/onnx-tiny-random-gpt2-without-merge | fxmarty | null | 233 | 130,691 | False | 2023-03-28T10:43:00Z | 2023-03-28T10:45:16Z | transformers | 1 | 0 | null | feature-extraction | null | [
".gitattributes",
"README.md",
"config.json",
"decoder_model.onnx",
"decoder_with_past_model.onnx",
"generation_config.json",
"merges.txt",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | a348808940b73dc20771830a352de90304007387 | [
"transformers",
"onnx",
"gpt2",
"feature-extraction",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPT2Model"], "model_type": "gpt2", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized": true... | {
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<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated... | null | null | null | null | null | null | null | [
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"license:a... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %... | {
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} | {"base_model": "Qwen/Qwen2.5-Coder-3B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["unsloth", "transformers", "code", "qwen",... | # Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a Qwen 2.5 (all model sizes) [free Google Colab Tesla T4 notebook](https://colab.research.google.com/drive/1Kose-ucXO1IBaZq5BvbwWieuubP7hxvQ?usp=sharing).
Also a [Qwen 2.5 conversational style notebook](https://colab.research.... | null | [
"apache-2.0"
] | null | [
"en"
] | 3,172,967,424 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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] | 859 | hatemnoaman/bert-base-arabic-finetuned-emotion | hatemnoaman | null | 29 | 129,122 | False | 2022-10-15T15:52:10Z | 2023-12-03T19:48:10Z | transformers | 3 | 0 | [{"name": "bert-base-arabic-finetuned-emotion", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "emotone_ar", "type": "emotone_ar", "config": "default", "split": "train[:90%]", "args": "default"}, "metrics": [{"name": "Accuracy", "type": "accuracy", "value": 0.74... | text-classification | null | [
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should probably proofread and complete it, then remove this comment. -->
# bert-base-arabic-finetuned-emotion
This model is a fine-tuned version of [asafaya/bert-base-arabic](https://huggingface.co/asafay... | null | null | [
"emotone_ar"
] | null | null | null | [
"accuracy",
"f1"
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66f101b25084238a4a8c6c1a | 643d190440f20cd9 | 84110b1ed2b9294f | 151,666 | BPE | 151,387 | true | true | true | true | 23 | NFC | Sequence | ByteLevel | [
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{
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"relation": "quantized"
} | 6,429 | 170,757 | False | 2024-09-23T05:50:42Z | 2024-11-12T02:54:07Z | transformers | 16 | 0 | null | text-generation | {"parameters": {"F32": 451584, "BF16": 1090328064, "U8": 6729271284}, "total": 7820050932} | [
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We have a Qwen 2.5 (all model sizes) [free Google Colab Tesla T4 notebook](https://colab.research.google.com/drive/1Kose-ucXO1IBaZq5BvbwWieuubP7hxvQ?usp=sharing).
Also a [Qwen 2.5 conversational style notebook](https://colab.research.... | null | [
"apache-2.0"
] | null | [
"en"
] | 7,820,050,932 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"transformers",
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"en",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nSei un assistente utile.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n... | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["it", "en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # Qwen2 1.5B: Almost the Same Performance as ITALIA (iGenius) but 6 Times Smaller 🚀
### Model Overview
**Model Name:** Qwen2 1.5B Fine-tuned for Italian Language
**Version:** 1.5b
**Model Type:** Language Model
**Parameter Count:** 1.5 billion
**Language:** Italian
**Comparable Model:** [ITALIA by iGenius]... | null | [
"apache-2.0"
] | null | [
"it",
"en"
] | 1,543,714,304 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
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] | [
"text"
] | [
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"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 214 | ku-nlp/deberta-v2-large-japanese | ku-nlp | null | 1,403 | 133,167 | False | 2023-01-07T07:45:25Z | 2023-05-12T14:10:35Z | transformers | 9 | 0 | null | fill-mask | {"parameters": {"I64": 512, "F32": 372638208}, "total": 372638720} | [
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## Model description
This is a Japanese DeBERTa V2 large model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the
Japanese portion of OSCAR.
## How to use
You can use this model for masked language modeling as follows:
```python
from transformers i... | null | [
"cc-by-sa-4.0"
] | [
"wikipedia",
"cc100",
"oscar"
] | [
"ja"
] | 372,638,720 | null | [
"accuracy"
] | [
"DebertaV2ForMaskedLM",
"AutoModelForMaskedLM",
"deberta-v2"
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"fill-mask"
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"text"
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"text"
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648015e2db6dca2bd255ecd3 | 8bc6b61a7b6c29c2 | 4c6e091e8dffb5aa | 30,003 | BPE | 29,740 | false | true | true | true | 7 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 299 | nlpai-lab/kullm-polyglot-5.8b-v2 | nlpai-lab | null | 215 | 129,372 | False | 2023-06-07T05:30:10Z | 2023-06-07T06:45:30Z | transformers | 24 | 0 | null | text-generation | null | [
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] | 5981236c4fd4e624eca2326312d40419e6441256 | [
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] | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>"}} | {
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"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": ["nlpai-lab/kullm-v2"], "eval_results": null, "language": ["ko"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # KULLM-Polyglot-5.8B-v2
This model is a parameter-efficient fine-tuned version of [EleutherAI/polyglot-ko-5.8b](https://huggingface.co/EleutherAI/polyglot-ko-5.8b) on a KULLM v2
Detail Codes are available at [KULLM Github Repository](https://github.com/nlpai-lab/KULLM)
## Training procedure
### Training hyperpar... | null | [
"apache-2.0"
] | [
"nlpai-lab/kullm-v2"
] | [
"ko"
] | null | null | null | [
"gpt_neox",
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"text"
] | [
"text"
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"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "agpl-3.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["text generation", "conversational"], "thumbnail": null, "inference": false} | # Pygmalion 1.3B
## Model description
Pymalion 1.3B is a proof-of-concept dialogue model based on EleutherAI's [pythia-1.3b-deduped](https://huggingface.co/EleutherAI/pythia-1.3b-deduped).
**Warning:** This model is **NOT** suitable for use by minors. It **will** output X-rated content under certain circumstances.
... | null | [
"agpl-3.0"
] | null | [
"en"
] | 1,515,311,488 | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64bc0cf3b8cd00871918f501 | ee854d15c33f212f | b8b83ffed1887163 | 250,057 | Unigram | null | true | true | true | true | 60 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 860 | MRNH/mbart-english-grammar-corrector | MRNH | null | 19 | 128,268 | False | 2023-07-22T17:08:03Z | 2023-08-19T09:41:51Z | transformers | 1 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
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"pytorch_model.bin",
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"tokenizer.json",
"tokenizer_config.json"
] | 1f33647b63876ad65de3e04d5221ad8e809f8c59 | [
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"mbart",
"text2text-generation",
"grammatical error correction",
"GEC",
"english",
"en",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["MBartForConditionalGeneration"], "model_type": "mbart", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": "<pad>", "sep... | {
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"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": ["f1"], "model_name": null, "pipeline_tag": "text2text-generation", "tags": ["grammatical error correction", "GEC", "english"]} | This is a fine-tuned version of Multilingual Bart trained (610M) on English in particular on the public dataset FCE for Grammatical Error Correction.
To initialize the model:
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
model = MBartForConditionalGeneration.from_pretrained... | null | null | null | [
"en"
] | null | null | [
"f1"
] | [
"AutoModelForSeq2SeqLM",
"MBartForConditionalGeneration",
"mbart"
] | [
"text2text-generation",
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 415 | facebook/xglm-1.7B | facebook | null | 1,204 | 145,237 | False | 2022-03-02T23:29:05Z | 2023-01-24T16:35:37Z | transformers | 20 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
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"pytorch_model.bin",
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"tf_model.h5",
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] | d23a5e8e2164af31a84a26756b9b17f925143050 | [
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"pytorch",
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"xglm",
"text-generation",
"multilingual",
"en",
"ru",
"zh",
"de",
"es",
"fr",
"ja",
"it",
"pt",
"el",
"ko",
"fi",
"id",
"tr",
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"vi",
"th",
"bg",
"ca",
"hi",
"et",
"bn",
"ta",
"ur",
"sw",
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"eu",
"my",
"ht"... | null | {"architectures": ["XGLMForCausalLM"], "model_type": "xglm", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual", "en", "ru", "zh", "de", "es", "fr", "ja", "it", "pt", "el", "ko", "fi", "id", "tr", "ar", "vi", "th", "bg", "ca", "hi", "et", "bn", "ta", "ur", "sw", "te", "eu", "my", "ht", "qu"], "library_name": null, "license": "mit", "license_... | # XGLM-1.7B
XGLM-1.7B is a multilingual autoregressive language model (with 1.7 billion parameters) trained on a balanced corpus of a diverse set of languages totaling 500 billion sub-tokens. It was introduced in the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Vi... | null | [
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] | null | [
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"de",
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] | null | null | null | [
"AutoModelForCausalLM",
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] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64548722c4cbe32fbe30ee0f | 5b8a6156b5b6e013 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 37 | medical-ner-proj/bert-medical-ner-proj | medical-ner-proj | null | 25 | 128,092 | False | 2023-05-05T04:33:38Z | 2023-05-07T02:46:47Z | transformers | 35 | 0 | null | token-classification | null | [
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"tokenizer.json... | 40c26571554b333e97cab3cfa6266810438f70c9 | [
"transformers",
"pytorch",
"bert",
"token-classification",
"license:openrail",
"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": "openrail", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | Medical documents NER model by fine tuning BERT
widget:
- example_title: "example 1" text: "John Doe has a history of hypertension, which is well-controlled with medication. He has no history of allergies or surgeries. He is not currently taking any medication except for his blood pressure medication."
- example_title... | null | [
"openrail"
] | null | null | null | null | null | [
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] | [
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] | [
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] | [
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] | [
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"ByteLevel"
] | [
"ByteLevel"
] | 13 | RogerKam/roberta_RCADE_fine_tuned_sentiment_covid_news | RogerKam | null | 7 | 127,878 | False | 2022-06-22T16:58:47Z | 2022-06-22T19:15:59Z | transformers | 0 | 0 | [{"name": "roberta_RCADE_fine_tuned_sentiment_covid_news", "results": []}] | text-classification | null | [
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"generated_from_trainer",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
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} | {"base_model": null, "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": "roberta_RCADE_fine_tuned_sentiment_covid_news", "pipeline_tag": null, "tags": ["generated_from_trainer"]} | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_RCADE_fine_tuned_sentiment_covid_news
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta... | null | [
"mit"
] | null | null | null | null | [
"accuracy"
] | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
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] | [
"text"
] | [
"logits"
] |
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"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 861 | unikei/bert-base-smiles | unikei | null | 2,293 | 191,766 | False | 2023-09-14T10:23:48Z | 2025-07-25T11:43:49Z | transformers | 9 | 0 | null | fill-mask | {"parameters": {"F32": 109705010}, "total": 109705010} | [
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"README.md",
"config.json",
"model.safetensors",
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"tokenizer.json",
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"training_args.bin",
"vocab.txt"
] | af666f7fc52add55d533e0eef217f9550c1ba6de | [
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"medical",
"fill-mask",
"dataset:ChEMBL",
"license:bigscience-openrail-m",
"endpoints_compatible",
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] | null | {"architectures": ["BertForPreTraining"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForPreTraining",
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} | {"base_model": null, "datasets": ["ChEMBL"], "eval_results": null, "language": null, "library_name": null, "license": "bigscience-openrail-m", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "fill-mask", "tags": ["biology", "medical"], "widget": [{"text": "O=C([C@@H](c1c... | null | null | [
"bigscience-openrail-m"
] | [
"ChEMBL"
] | null | 109,705,010 | null | null | [
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"AutoModelForPreTraining",
"BertForPreTraining"
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"fill-mask",
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] |
65fc67075fa7358962dc9792 | 2a63bc9e8054b53c | 7d28abbec99b8db3 | 256,000 | BPE | 580,604 | true | true | true | true | 249 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
"Fuse"
] | 546 | google/codegemma-7b-it | google | null | 3,992 | 143,199 | manual | 2024-03-21T16:57:43Z | 2024-08-07T18:27:26Z | transformers | 250 | 1 | null | text-generation | null | [
".gitattributes",
"README.md",
"codegemma_nl_benchmarks.png",
"config.json",
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"model-00001-of-00004.safetensors",
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"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"special_tokens_m... | 078cdc51070553d1636d645c9a238f3b0914459a | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["GemmaForCausalLM"], "model_type": "gemma", "tokenizer_config": {"bos_token": "<bos>", "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 %... | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "gemma", "license_name": null, "license_link": "https://ai.google.dev/gemma/terms", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": null, "extra_gated_heading": "Acce... | null | null | [
"gemma",
"https://ai.google.dev/gemma/terms"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"gemma",
"GemmaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f177c73 | a7ec3f9825480642 | 9a433cde9e2781dd | 32,119 | Unigram | null | true | true | true | true | 122 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 862 | Xenova/sponsorblock-small | Xenova | null | 307 | 127,749 | False | 2022-03-02T23:29:05Z | 2022-02-08T16:56:09Z | transformers | 3 | 0 | null | null | null | [
".gitattributes",
"added_tokens.json",
"config.json",
"pytorch_model.bin",
"rng_state.pth",
"scheduler.pt",
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"tokenizer.json",
"tokenizer_config.json",
"trainer_state.json",
"training_args.bin"
] | 5261e7056338c5a91dd6e153314536f44a182b03 | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"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 | null | null | null | null | null | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
65899ad3595e258218b7f21f | 0975dae6194733f3 | f9c0dc94ccf9ccd2 | 32,003 | BPE | 61,249 | true | false | true | true | 6 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 863 | unum-cloud/uform-gen | unum-cloud | {
"models": [
{
"_id": "63f8c65d677cd902e9690aad",
"id": "unum-cloud/uform-vl-english"
}
],
"relation": "finetune"
} | 36 | 127,456 | False | 2023-12-25T15:08:03Z | 2023-12-31T01:36:45Z | transformers | 44 | 0 | null | image-to-text | {"parameters": {"F32": 1489559936}, "total": 1489559936} | [
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"dataset:ChristophSchuhmann/MS_COCO_2017_URL_TEXT",
"base_model:unum-cloud/uform-vl... | null | {"architectures": ["VLMForCausalLM"], "model_type": "vlm", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + ' <|im_end|>'... | {
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} | {"base_model": "unum-cloud/uform-vl-english", "datasets": ["sbu_captions", "visual_genome", "HuggingFaceM4/VQAv2", "ChristophSchuhmann/MS_COCO_2017_URL_TEXT"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_nam... | <Gallery />
<h1 align="center">UForm</h1>
<h3 align="center">
Pocket-Sized Multimodal AI<br/>
For Content Understanding and Generation<br/>
</h3>
## Description
UForm-Gen is a small generative vision-language model primarily designed for Image Captioning and Visual Question Answering. The model consists of two part... | null | [
"apache-2.0"
] | [
"sbu_captions",
"visual_genome",
"HuggingFaceM4/VQAv2",
"ChristophSchuhmann/MS_COCO_2017_URL_TEXT"
] | [
"en"
] | 1,489,559,936 | null | null | [
"AutoModelForCausalLM",
"VLMForCausalLM",
"vlm"
] | [
"visual-question-answering",
"text-generation",
"image-to-text"
] | [
"text",
"multimodal"
] | [
"text",
"image"
] | [
"logits",
"text"
] |
65a642051cc39bf956b8799b | fc29ccd738d51f6b | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 1 | Qdrant/gte-large-onnx | Qdrant | null | 708 | 130,059 | False | 2024-01-16T08:44:53Z | 2024-07-15T12:55:14Z | transformers | 2 | 0 | null | sentence-similarity | null | [
".gitattributes",
"README.md",
"config.json",
"model.onnx",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 770e825c74a004f165b78793f7c8fc4a95280878 | [
"transformers",
"onnx",
"bert",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": null} | ONNX port of [thenlper/gte-large](https://huggingface.co/thenlper/gte-large) for text classification and similarity searches.
### Usage
Here's an example of performing inference using the model with [FastEmbed](https://github.com/qdrant/fastembed).
```py
from fastembed import TextEmbedding
documents = [
"You sh... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
64e2eb73315c08ea11cde8ae | 1231e8e9514eb495 | 14f594dbc72e886f | 50,260 | BPE | 50,000 | false | true | true | true | 4 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 864 | Locutusque/gpt2-xl-conversational | Locutusque | null | 842 | 131,849 | False | 2023-08-21T04:43:31Z | 2023-11-21T17:17:46Z | transformers | 18 | 0 | null | text-generation | {"parameters": {"F16": 1557616000}, "total": 1557616000} | [
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"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["Locutusque/InstructMix"], "eval_results": null, "language": ["en"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["bleu", "perplexity", "loss", "accuracy"], "model_name": null, "pipeline_tag": "text-generation", "tags": null, "widget":... | # Model Card
## Model Details
- Model Name: gpt2-xl-conversational
- Model Type: Language Modeling
- Task: Generating Conversational Responses
- Hardware: 1x Nvidia Titan V
- Description: This model is trained on a dataset of conversations between a user and an AI assistant, with the goal of generating a coherent and ... | null | [
"mit"
] | [
"Locutusque/InstructMix"
] | [
"en"
] | 1,557,616,000 | null | [
"bleu",
"perplexity",
"loss",
"accuracy"
] | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66daf6eaec009ab887cccd93 | 539b24ec3c9dbddd | 6ce596b7c0f62c6d | 151,647 | BPE | 151,387 | true | true | true | true | 4 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 234 | lmms-lab/llava-onevision-qwen2-72b-ov-chat | lmms-lab | null | 30 | 127,223 | False | 2024-09-06T12:34:50Z | 2024-10-09T04:59:22Z | transformers | 9 | 0 | null | image-text-to-text | {"parameters": {"BF16": 73180522016}, "total": 73180522016} | [
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"model-... | f78a770cb228ab72e644736e3b6af6fec58117c1 | [
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"arxiv:2408.03326",
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"license:apache-2.0",
"endpoints_compatible",
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] | 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... | {
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"custom_class": null,
"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": ["lmms-lab/LLaVA-OneVision-Data"], "eval_results": null, "language": ["en", "zh"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["multimodal... | # 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. [License... | null | [
"apache-2.0"
] | [
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"zh"
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"text",
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
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"ByteLevel"
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} | 33 | 127,614 | False | 2024-10-26T19:00:14Z | 2024-11-02T15:28:48Z | transformers | 1 | 0 | [{"name": "l3.1-8b-inst-fft-induction-barc-heavy-200k-old-200k-lr1e-5-ep2", "results": []}] | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
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should probably proofread and complete it, then remove this comment. -->
# l3.1-8b-inst-fft-induction-barc-heavy-200k-old-200k-lr1e-5-ep2
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8... | null | [
"llama3.1"
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"barc0/induction_100k_gpt4o-mini_generated_problems_seed100.jsonl_messages_format_0.3"
] | null | 8,030,261,248 | null | null | [
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} | 2,650 | 138,768 | False | 2024-08-23T07:41:13Z | 2024-09-28T06:03:06Z | transformers | 43 | 0 | null | image-to-text | {"parameters": {"F16": 822949977}, "total": 822949977} | [
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] | null | {"architectures": ["Florence2ForConditionalGeneration"], "auto_map": {"AutoConfig": "configuration_florence2.Florence2Config", "AutoModelForCausalLM": "modeling_florence2.Florence2ForConditionalGeneration"}, "model_type": "florence2", "tokenizer_config": {}} | {
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This repository contains a fine-tuned version of the `microsoft/Florence-2-large` model. The model has been tuned on a 40,000 image subset of the `Ejafa/ye-pop` dataset, with captions generated using `THUDM/cogvlm2-llama3-chat-19B`.
## Training... | null | [
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"image-to-text"
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"text",
"image"
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"text"
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66bb66c6f1dfeeafae30af57 | c6de6331832e687a | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
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"ByteLevel"
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],
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} | 2,469 | 134,078 | False | 2024-08-13T13:59:34Z | 2025-06-11T10:05:04Z | transformers | 17 | 0 | [{"name": "Llama-3.1-8b-ITA", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "IFEval (0-Shot)", "type": "HuggingFaceH4/ifeval", "args": {"num_few_shot": 0}}, "metrics": [{"type": "inst_level_strict_acc and prompt_level_strict_acc", "value": 79.17, "name": "strict accura... | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
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} | {"base_model": "meta-llama/Meta-Llama-3.1-8B-Instruct", "language": ["en", "it"], "library_name": "transformers", "model-index": [{"name": "Llama-3.1-8b-ITA", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "IFEval (0-Shot)", "type": "HuggingFaceH4/ifeval", "args": {"num... | ---
**💡 Found this resource helpful?** Creating and maintaining open source AI models and datasets requires significant computational resources. If this work has been valuable to you, consider [supporting my research](https://buymeacoffee.com/michele.montebovi) to help me continue building tools that benefit the enti... | null | null | null | [
"en",
"it"
] | 8,030,261,248 | null | null | [
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"Prepend",
"Replace"
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"Replace",
"ByteFallback",
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] | 220 | openbmb/MiniCPM-2B-sft-fp32 | openbmb | null | 240 | 128,467 | False | 2024-01-30T02:13:10Z | 2024-09-07T05:39:13Z | transformers | 296 | 0 | null | text-generation | null | [
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<h1>
MiniCPM
</h1>
</div>
<p align="center">
<a href="https://shengdinghu.notion.site/MiniCPM-c805a17c5c8046398914e47f0542095a?pvs=4" target="_blank">MiniCPM 技术报告</a><a href="https://shengdinghu.notion.site/MiniCPM-Unveiling-the-Potential-of-End-side-Large-Language-Models-d4d3a8c426424654a4e80e4... | null | [
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632e9453736ea97ebc6ece7c | 26e59f8e38f730c6 | befceb9191a2d18d | 50,358 | BPE | 50,000 | true | true | true | true | 102 | NFKC | ByteLevel | ByteLevel | [
"NFKC"
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"ByteLevel"
] | [
"ByteLevel"
] | 867 | lcw99/t5-base-korean-text-summary | lcw99 | null | 584 | 130,042 | False | 2022-09-24T05:23:31Z | 2023-04-13T02:30:33Z | transformers | 13 | 0 | [{"name": "t5-base-korean-text-summary", "results": []}] | null | {"parameters": {"F32": 275579136}, "total": 275579136} | [
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"auto_model": "AutoModelForSeq2SeqLM",
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This model is a fine-tuning of [paust/pko-t5-base](https://huggingface.co/paust/pko-t5-base) model using AIHUB "summary and report generation data". This model provides a short summary of long sentences in Korean.
이 모델은 paust/pko-t5-base model을 AIHUB "요약문 및 레포트 생성 데이터"를 이용하여 fine tunning... | null | null | null | [
"ko"
] | 275,579,136 | null | null | [
"t5",
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"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
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"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 868 | opendatalab/MinerU2.5-2509-1.2B | opendatalab | null | 42,075 | 3,504,911 | False | 2025-09-17T02:42:29Z | 2025-09-29T06:58:38Z | transformers | 343 | 3 | null | image-text-to-text | {"parameters": {"BF16": 1156026624}, "total": 1156026624} | [
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"eval-results",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen2VLForConditionalGeneration"], "model_type": "qwen2_vl", "processor_config": {"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpfu... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["zh", "en"], "library_name": "transformers", "license": "agpl-3.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": null} | # MinerU2.5
We are releasing MinerU2.5, a 1.2B-parameter visual-language model specialized in OCR and document parsing, enabling more accurate and robust parsing of complex and diverse real-world documents.
The model weights are stable and available for use, primarily intended for internal development and demonstrati... | null | [
"agpl-3.0"
] | null | [
"zh",
"en"
] | 1,156,026,624 | null | null | [
"AutoModelForImageTextToText",
"qwen2_vl",
"Qwen2VLForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
642ecc1ff75545112eb77314 | 1c29c6e3ba996b0d | 5f9f736c7dc6fbfa | 28,996 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 849 | jjzha/jobbert_skill_extraction | jjzha | null | 4,239 | 136,000 | False | 2023-04-06T13:41:51Z | 2023-10-26T10:25:11Z | transformers | 21 | 0 | null | token-classification | {"parameters": {"I64": 512, "F32": 107721987}, "total": 107722499} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | a6211b675e287f3a07ffd4834b8182a2b519589b | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"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"
} | null | This is a demo using the models from:
```
@inproceedings{zhang-etal-2022-skillspan,
title = "{S}kill{S}pan: Hard and Soft Skill Extraction from {E}nglish Job Postings",
author = "Zhang, Mike and
Jensen, Kristian and
Sonniks, Sif and
Plank, Barbara",
booktitle = "Proceedings of the 2022... | null | null | null | null | 107,722,499 | null | null | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
640245cf17f1f2c384676945 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | sjiang1/codecse | sjiang1 | null | 5 | 126,549 | False | 2023-03-03T19:09:03Z | 2023-03-09T12:49:03Z | transformers | 3 | 0 | null | feature-extraction | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
] | aab1a7d4c18e30a7e5e59fd98b9bedbe25e9b79d | [
"transformers",
"pytorch",
"roberta_for_cl",
"code",
"sentence embedding",
"feature-extraction",
"multilingual",
"dataset:CodeSearchNet",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["GraphCodeBERTForCL"], "model_type": "roberta_for_cl", "tokenizer_config": {"bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "no... | {
"auto_model": "GraphCodeBERTForCL",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": ["CodeSearchNet"], "eval_results": null, "language": ["multilingual"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "feature-extraction", "tags": ["code", "sentence embedding"]} | # Model Card for CodeCSE
A simple pre-trained model for code and comment sentence embeddings using contrastive learning. This model was pretrained using [CodeSearchNet](https://huggingface.co/datasets/code_search_net).
Please [**clone the CodeCSE repository**](https://github.com/emu-se/CodeCSE) to get `GraphCodeBERTFo... | null | [
"mit"
] | [
"CodeSearchNet"
] | [
"multilingual"
] | null | null | null | [
"GraphCodeBERTForCL",
"roberta_for_cl"
] | [
null,
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
621ffdc136468d709f17f014 | e387f2f9dec84ea9 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 516 | prithivida/active_to_passive_styletransfer | prithivida | null | 31 | 125,876 | False | 2022-03-02T23:29:05Z | 2021-06-23T13:43:58Z | transformers | 4 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | d3deb88bab2ae342e5233d160eb0d454d7eb2f57 | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"text-generation-inference",
"endpoints_compatible",
"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 | ## This model belongs to the Styleformer project
[Please refer to github page](https://github.com/PrithivirajDamodaran/Styleformer) | null | null | null | null | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
62deef6701dd68486922e532 | 91642f76e1681385 | ab089c4bfa2e1ada | 50,001 | Unigram | null | true | true | true | true | 5 | NFKC | Sequence | Metaspace | [
"NFKC"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 364 | sdadas/st-polish-paraphrase-from-mpnet | sdadas | null | 2,671 | 132,725 | False | 2022-07-25T19:30:47Z | 2026-01-27T17:14:39Z | sentence-transformers | 5 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 124442880}, "total": 124443394} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"unigram.json"
] | a95278b3ff89bcd4887875d01191079cba9d2c36 | [
"sentence-transformers",
"pytorch",
"safetensors",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"pl",
"license:lgpl",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaModel"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["pl"], "library_name": null, "license": "lgpl", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity",... | # sdadas/st-polish-paraphrase-from-mpnet
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... | null | [
"lgpl"
] | null | [
"pl"
] | 124,443,394 | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6796963e30ee45cd52b47339 | 7ef55b16de52028f | 8e02c06835f99f51 | 68,549 | BPE | 49,721 | true | true | true | true | 18,572 | NFKC | Sequence | ByteLevel | [
"NFKC"
] | [
"Split",
"Digits",
"Split",
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 869 | datalab-to/texify | datalab-to | null | 816 | 127,152 | False | 2025-01-26T20:08:30Z | 2025-01-29T14:38:00Z | transformers | 2 | 0 | null | null | {"parameters": {"I64": 73728, "F16": 168970392}, "total": 169044120} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"preprocessor_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | 8f1d761762b3e977e9e62cebfca487d489556abc | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["TexifyModel"], "model_type": "vision-encoder-decoder", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "cc-by-nc-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | LaTeX OCR model for [surya](https://github.com/VikParuchuri/surya). | null | [
"cc-by-nc-sa-4.0"
] | null | null | 169,044,120 | null | null | [
"AutoModel",
"vision-encoder-decoder",
"TexifyModel"
] | [
null
] | null | null | null |
621ffdc136468d709f17adaf | 4211ba98bb2477fe | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 870 | fabriceyhc/bert-base-uncased-ag_news | fabriceyhc | null | 1,231 | 127,571 | False | 2022-03-02T23:29:05Z | 2021-09-21T00:54:07Z | transformers | 0 | 0 | [{"name": "bert-base-uncased-ag_news", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "ag_news", "type": "ag_news", "args": "default"}, "metrics": [{"name": "Accuracy", "type": "accuracy", "value": 0.9375, "verified": false}]}]}] | text-classification | null | [
".gitattributes",
".gitignore",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | c14e3b32fe1f757639b9751bdff3ea3c8c3b4a6b | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"sibyl",
"dataset:ag_news",
"license:apache-2.0",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"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"
} | {"datasets": ["ag_news"], "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["generated_from_trainer", "sibyl"], "model-index": [{"name": "bert-base-uncased-ag_news", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "ag_news", "type": "ag_news", "args": "d... | <!-- 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-uncased-ag_news
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on ... | null | [
"apache-2.0"
] | [
"ag_news"
] | null | null | null | [
"accuracy"
] | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f1802b6 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | sentence-transformers/facebook-dpr-question_encoder-multiset-base | sentence-transformers | null | 182 | 127,497 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:20:23Z | sentence-transformers | 2 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 109482240}, "total": 109482752} | [
".gitattributes",
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"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
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"onnx/model_O1.onnx",
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"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/m... | 6d1a675cd90cc7719e3e62b7b042c64b22a07537 | [
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"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/facebook-dpr-question_encoder-multiset-base
This is a port of the [DPR Model](https://github.com/facebookresearch/DPR) to [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 sem... | null | [
"apache-2.0"
] | null | null | 109,482,752 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
688a9e5651065cefb11fdd9f | bdd8a4397719b5b0 | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
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"models": [
{
"_id": "6888aae7c54baad7997d7b4b",
"id": "Qwen/Qwen3-30B-A3B-Thinking-2507"
}
],
"relation": "quantized"
} | 2,511 | 158,358 | False | 2025-07-30T22:36:06Z | 2026-03-23T07:20:00Z | transformers | 16 | 0 | null | text-generation | {"parameters": {"I64": 37248, "I32": 29896998912, "BF16": 1569404928}, "total": 5306567040} | [
".gitattributes",
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"license:apache-2.0",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "quantization_config": {"quant_method": "compressed-tensors"}, "tokenizer_config": {"bos_token": null, "eos_token": "<|im_end|>", "pad_token": "<|endoftext|>", "unk_token": null}, "chat_template_jinja": "{%- if tools %}\n {{- '<|im_start|>system\\... | {
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"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Qwen/Qwen3-30B-A3B-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/Qwen3-30B-A3B-Thinking-2507/blob/main/LICENSE", "metrics": null, "model_name": null, ... | null | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507/blob/main/LICENSE"
] | null | null | 5,306,567,040 | null | null | [
"qwen3_moe",
"AutoModelForCausalLM",
"Qwen3MoeForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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] | 86 | fxmarty/tiny-mpt-random-remote-code | fxmarty | null | 274 | 125,385 | False | 2023-07-06T04:45:38Z | 2023-07-06T05:11:36Z | transformers | 0 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
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"attention.py",
"blocks.py",
"config.json",
"configuration_mpt.py",
"custom_embedding.py",
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"hf_prefixlm_converter.py",
"meta_init_context.py",
"modeling_mpt.py",
"norm.py",
"param_init_... | 55f6a763b3465051e04c46c6f1f91e3ef71f7540 | [
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"pytorch",
"mpt",
"text-generation",
"custom_code",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["MPTForCausalLM"], "auto_map": {"AutoConfig": "mosaicml/mpt-7b--configuration_mpt.MPTConfig", "AutoModelForCausalLM": "mosaicml/mpt-7b--modeling_mpt.MPTForCausalLM"}, "model_type": "mpt", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
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"custom_class": "mosaicml/mpt-7b--modeling_mpt.MPTForCausalLM",
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "task": ["text-generation"]} | null | null | [
"mit"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"mpt",
"mosaicml/mpt-7b--modeling_mpt.MPTForCausalLM",
"MPTForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
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"text"
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"models": [
{
"_id": "639336a66c6bb189f25823dd",
"id": "rafalposwiata/deproberta-large-v1"
}
],
"relation": "finetune"
} | 372 | 127,379 | False | 2022-12-16T15:10:42Z | 2024-01-04T07:27:01Z | transformers | 9 | 0 | null | text-classification | {"parameters": {"I64": 514, "F32": 355362819}, "total": 355363333} | [
".gitattributes",
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"merges.txt",
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"safetensors",
"roberta",
"text-classification",
"depression",
"en",
"base_model:rafalposwiata/deproberta-large-v1",
"base_model:finetune:rafalposwiata/deproberta-large-v1",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": fa... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "rafalposwiata/deproberta-large-v1", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["depression", "roberta"]} | Fine-tuned [DepRoBERTa](https://huggingface.co/rafalposwiata/deproberta-large-v1) model for detecting the level of depression as **not depression**, **moderate** or **severe**, based on social media posts in English.
Model was part of the winning solution for [the Shared Task on Detecting Signs of Depression
from Soci... | null | null | null | [
"en"
] | 355,363,333 | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
663ef86857090e5742df8d85 | 6e6a83dc4b556c95 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 111 | Kukedlc/NeuralLLaMa-3-8b-DT-v0.1 | Kukedlc | {
"models": [
{
"_id": "66268445b395d3ae7875f7a2",
"id": "mlabonne/ChimeraLlama-3-8B-v2"
},
{
"_id": "66387752fa232f0f5620addb",
"id": "nbeerbower/llama-3-stella-8B"
},
{
"_id": "663cd52f34f4cefb3ee42c46",
"id": "uygarkurt/llama-3-merged-linear"
}
],
"re... | 7,873 | 162,791 | False | 2024-05-11T04:47:36Z | 2024-09-22T19:07:24Z | transformers | 1 | 0 | [{"name": "NeuralLLaMa-3-8b-DT-v0.1", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "IFEval (0-Shot)", "type": "HuggingFaceH4/ifeval", "args": {"num_few_shot": 0}}, "metrics": [{"type": "inst_level_strict_acc and prompt_level_strict_acc", "value": 43.71, "name": "stric... | text-generation | {"parameters": {"F16": 8030261248}, "total": 8030261248} | [
".config/.last_opt_in_prompt.yaml",
".config/.last_survey_prompt.yaml",
".config/.last_update_check.json",
".config/active_config",
".config/config_sentinel",
".config/configurations/config_default",
".config/default_configs.db",
".config/gce",
".config/logs/2024.05.23/13.23.45.334202.log",
".conf... | e69b4f7c30346e5666814a632f6c7af95dd740ae | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"mlabonne/ChimeraLlama-3-8B-v2",
"nbeerbower/llama-3-stella-8B",
"uygarkurt/llama-3-merged-linear",
"base_model:mlabonne/ChimeraLlama-3-8B-v2",
"base_model:merge:mlabonne/ChimeraLlama-3-8B-v2",
"b... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|end_of_text|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["mlabonne/ChimeraLlama-3-8B-v2", "nbeerbower/llama-3-stella-8B", "uygarkurt/llama-3-merged-linear"], "license": "other", "tags": ["merge", "mergekit", "lazymergekit", "mlabonne/ChimeraLlama-3-8B-v2", "nbeerbower/llama-3-stella-8B", "uygarkurt/llama-3-merged-linear"], "model-index": [{"name": "NeuralLLaM... | # NeuralLLaMa-3-8b-DT-v0.1
NeuralLLaMa-3-8b-DT-v0.1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
*... | null | [
"other"
] | null | null | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
659b19fcc6457161ca1051f3 | 9586ce7745e6020b | aa8af6ff55c14796 | 51,865 | BPE | 49,999 | false | true | true | true | 1,608 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 872 | NbAiLabBeta/nb-whisper-small | NbAiLabBeta | {
"models": [
{
"_id": "63314bef9e3604f3f17e5b9e",
"id": "openai/whisper-small"
}
],
"relation": "quantized"
} | 263 | 126,606 | False | 2024-01-07T21:39:08Z | 2024-01-27T14:25:59Z | transformers | 1 | 0 | null | automatic-speech-recognition | {"parameters": {"F32": 241734912}, "total": 241734912} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"config_hf.json",
"ct2/config.json",
"ct2/model.bin",
"ct2/vocabulary.json",
"export_models.sh",
"flax_model.msgpack",
"generation_config.json",
"ggml-model-q5_0.bin",
"ggml-model.bin",
"merges.txt",
"model.bin",
"model... | 3788b9d42ad6d804407e6d289c89ba13e15f1c9d | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"onnx",
"safetensors",
"whisper",
"automatic-speech-recognition",
"audio",
"asr",
"hf-asr-leaderboard",
"no",
"nb",
"nn",
"en",
"dataset:NbAiLab/ncc_speech",
"dataset:NbAiLab/NST",
"dataset:NbAiLab/NPSC",
"arxiv:2212.04356",
"bas... | 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": "openai/whisper-small", "datasets": ["NbAiLab/ncc_speech", "NbAiLab/NST", "NbAiLab/NPSC"], "eval_results": null, "language": ["no", "nb", "nn", "en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["wer", "cer"], "model_name": null, "pipel... | # NB-Whisper Small (Release Candidate)
**IMPORTANT:** These models are currently Release Candidates. We are in the final stages of testing. If everything proceeds smoothly, we plan to officially release the models later this month.
Introducing the **_Norwegian NB-Whisper Small model_**, proudly developed by the Natio... | null | [
"apache-2.0"
] | [
"NbAiLab/ncc_speech",
"NbAiLab/NST",
"NbAiLab/NPSC"
] | [
"no",
"nb",
"nn",
"en"
] | 241,734,912 | null | [
"wer",
"cer"
] | [
"WhisperForConditionalGeneration",
"whisper",
"AutoModelForSpeechSeq2Seq"
] | [
"automatic-speech-recognition"
] | [
"multimodal"
] | [
"audio"
] | [
"text"
] |
662bd9b8b026a05e7307c10d | 6e6a83dc4b556c95 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 111 | HPAI-BSC/Llama3-Aloe-8B-Alpha | HPAI-BSC | null | 936 | 127,687 | False | 2024-04-26T16:43:36Z | 2026-01-17T17:35:44Z | transformers | 65 | 0 | null | question-answering | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
".gitattributes",
".gitignore",
"LICENSE",
"LLAMA3-COMMUNITY-LICENSE-AGREEMENT",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetens... | e6e785c30b96b7512dc483de23e8f85865683130 | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"biology",
"medical",
"question-answering",
"en",
"dataset:argilla/dpo-mix-7k",
"dataset:nvidia/HelpSteer",
"dataset:jondurbin/airoboros-3.2",
"dataset:hkust-nlp/deita-10k-v0",
"dataset:LDJnr/Capybara",
"dataset:HPAI-B... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|e... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["argilla/dpo-mix-7k", "nvidia/HelpSteer", "jondurbin/airoboros-3.2", "hkust-nlp/deita-10k-v0", "LDJnr/Capybara", "HPAI-BSC/CareQA", "GBaker/MedQA-USMLE-4-options", "lukaemon/mmlu", "bigbio/pubmed_qa", "openlifescienceai/medmcqa", "bigbio/med_qa", "HPAI-BSC/better-safe-than-sorry", "HPA... | <div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/6620f941eba5274b5c12f83d/DlPW2CN-qErpC3QJqdNho.png" width="400" alt="HPAI"/>
</div>
<hr style="margin: 15px">
<div align="center" style="line-height: 1;">
<a href="https://hpai.bsc.es/" target="_blank" style="margin: 1px;">
<im... | null | [
"cc-by-nc-4.0"
] | [
"argilla/dpo-mix-7k",
"nvidia/HelpSteer",
"jondurbin/airoboros-3.2",
"hkust-nlp/deita-10k-v0",
"LDJnr/Capybara",
"HPAI-BSC/CareQA",
"GBaker/MedQA-USMLE-4-options",
"lukaemon/mmlu",
"bigbio/pubmed_qa",
"openlifescienceai/medmcqa",
"bigbio/med_qa",
"HPAI-BSC/better-safe-than-sorry",
"HPAI-BSC/... | [
"en"
] | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"question-answering",
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
664a6ae921628f8d8bcc6f58 | 336c4bf4be7d271a | 87995784a2525d28 | 32,005 | BPE | 61,249 | true | false | true | true | 8 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 761 | ibm-granite/granite-7b-instruct | ibm-granite | {
"models": [
{
"_id": "66229dfe8b4f9558fc466d31",
"id": "ibm-granite/granite-7b-base"
}
],
"relation": "finetune"
} | 1,068 | 128,182 | False | 2024-05-19T21:11:05Z | 2024-12-19T21:05:30Z | transformers | 8 | 0 | null | text-generation | {"parameters": {"BF16": 6738481152}, "total": 6738481152} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"model-00001-of-00003.safetensors",
"model-00002-of-00003.safetensors",
"model-00003-of-00003.safetensors",
"model-card/Model Card for Merlinite 7b 28cc0b72cf574a4a828140d3539ede4a_Screenshot_2024-02-22_at_1... | 0ffe79e2482862881528834d0a106a46143c0e9c | [
"transformers",
"safetensors",
"llama",
"text-generation",
"granite",
"ibm",
"lab",
"labrador",
"labradorite",
"conversational",
"en",
"arxiv:2403.01081",
"base_model:ibm-granite/granite-7b-base",
"base_model:finetune:ibm-granite/granite-7b-base",
"license:apache-2.0",
"text-generation... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{'<|system|>'+ '\n' + message['content'] + '\n'}}{% elif message['role'] == 'user' %}{{'<|user|>' + '\n' + message['content'] + '\n'... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "ibm/granite-7b-base", "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["granite", "ibm", "lab", "labrador", "labradorite"... | # Model Card for Granite-7b-lab [Paper](https://arxiv.org/abs/2403.01081)
### Overview
### Performance
| Model | Alignment | Base | Teacher | MTBen... | null | [
"apache-2.0"
] | null | [
"en"
] | 6,738,481,152 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6638473473c2c55f2debb4ac | 6e6a83dc4b556c95 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 111 | meetkai/functionary-small-v2.5 | meetkai | null | 59 | 124,875 | False | 2024-05-06T02:57:56Z | 2024-10-16T09:26:05Z | transformers | 9 | 0 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"modeling_functionary.py",
"runs/May06_03-00-08_... | c5c46b5e438e6a924e304919c231dc61d7651e6c | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"conversational",
"custom_code",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM", "FunctionaryForCausalLM"], "auto_map": {"AutoModelForCausalLM": "modeling_functionary.FunctionaryForCausalLM"}, "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{# version=v2.llama3 #}{%- macro append_new_param_info(param_declaration,... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # Model Card for functionary-small-v2.5
[https://github.com/MeetKai/functionary](https://github.com/MeetKai/functionary)
<img src="https://huggingface.co/meetkai/functionary-medium-v2.2/resolve/main/functionary_logo.jpg" alt="Functionary Logo" width="300"/>
Functionary is a language model that can interpret and exec... | null | [
"mit"
] | null | null | 8,030,261,248 | null | null | [
"FunctionaryForCausalLM",
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
642d3ef1ba46f82e91aa608b | 867a3890c9943bc8 | d831300fa2535eef | 32,005 | Unigram | null | true | true | true | true | 7 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 276 | CATIE-AQ/QAmembert-large | CATIE-AQ | null | 1,091 | 126,871 | False | 2023-04-05T09:27:13Z | 2024-11-26T10:46:45Z | transformers | 13 | 0 | null | question-answering | {"parameters": {"I64": 514, "F32": 335613954}, "total": 335614468} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 6cd0775fb62d7967e30c1e8a464c18b37089000c | [
"transformers",
"pytorch",
"safetensors",
"camembert",
"question-answering",
"fr",
"dataset:etalab-ia/piaf",
"dataset:fquad",
"dataset:lincoln/newsquadfr",
"dataset:pragnakalp/squad_v2_french_translated",
"arxiv:1910.09700",
"license:mit",
"co2_eq_emissions",
"endpoints_compatible",
"dep... | null | {"architectures": ["CamembertForQuestionAnswering"], "model_type": "camembert", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": "<pad>", ... | {
"auto_model": "AutoModelForQuestionAnswering",
"custom_class": null,
"pipeline_tag": "question-answering",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["etalab-ia/piaf", "fquad", "lincoln/newsquadfr", "pragnakalp/squad_v2_french_translated"], "eval_results": null, "language": "fr", "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": ["f1", "exact_match"], "model_name": null, "pipeli... | # Model Card for QAmembert-large
## Model Description
We present **QAmemBERT**, which is a [CamemBERT large](https://huggingface.co/camembert/camembert-large) fine-tuned for the Question-Answering task for the French language on four French Q&A datasets composed of contexts and questions with their answers inside the... | null | [
"mit"
] | [
"etalab-ia/piaf",
"fquad",
"lincoln/newsquadfr",
"pragnakalp/squad_v2_french_translated"
] | [
"fr"
] | 335,614,468 | null | [
"f1",
"exact_match"
] | [
"AutoModelForQuestionAnswering",
"camembert",
"CamembertForQuestionAnswering"
] | [
"question-answering"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63e2701e84566d7b44befa44 | 2d0341bd3a73bae6 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 32 | Salesforce/blip2-flan-t5-xl-coco | Salesforce | null | 559 | 128,189 | False | 2023-02-07T15:37:02Z | 2025-02-03T06:39:32Z | transformers | 16 | 0 | null | image-to-text | {"parameters": {"F32": 3943037952}, "total": 3943037952} | [
".gitattributes",
"README.md",
"config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"preprocessor_config.json",
"pytorch_model-00001-of-00002.bin",
"pytorch_model-00002-of-00002.bin",
"pytorch_model.bin.index.json",
"special_token... | c9ae253c75005208861ffa6743b245a30a69a98a | [
"transformers",
"pytorch",
"safetensors",
"blip-2",
"visual-question-answering",
"vision",
"image-to-text",
"image-captioning",
"en",
"arxiv:2301.12597",
"arxiv:2210.11416",
"license:mit",
"region:us"
] | null | {"architectures": ["Blip2ForConditionalGeneration"], "model_type": "blip-2", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForVisualQuestionAnswering",
"custom_class": null,
"pipeline_tag": "visual-question-answering",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-to-text", "tags": ["vision", "image-to-text", "image-captioning", "visual-question-answering"], "... | # BLIP-2, Flan T5-xl, fine-tuned on COCO
BLIP-2 model, leveraging [Flan T5-xl](https://huggingface.co/google/flan-t5-xl) (a large language model).
It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by... | null | [
"mit"
] | null | [
"en"
] | 3,943,037,952 | null | null | [
"Blip2ForConditionalGeneration",
"AutoModelForVisualQuestionAnswering",
"blip-2"
] | [
"visual-question-answering",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits",
"text"
] |
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"Split",
"ByteLevel"
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"ByteLevel"
] | 618 | thkim0305/llama3.2_1B_vl | thkim0305 | null | 146 | 127,024 | False | 2025-01-03T09:16:50Z | 2025-01-07T04:09:04Z | transformers | 0 | 0 | null | image-text-to-text | {"parameters": {"BF16": 1545621504}, "total": 1545621504} | [
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] | null | {"architectures": ["LlavaForConditionalGeneration"], "model_type": "llava", "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_i... | {
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<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated... | null | null | null | null | 1,545,621,504 | null | null | [
"AutoModelForImageTextToText",
"LlavaForConditionalGeneration",
"llava"
] | [
"image-text-to-text"
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"image"
] | [
"text"
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"models": [
{
"_id": "67597bd1b9cdca50cb621f94",
"id": "microsoft/phi-4"
}
],
"relation": "quantized"
} | 3,697 | 149,599 | False | 2025-01-08T22:39:05Z | 2025-01-13T18:54:01Z | transformers | 16 | 0 | null | text-generation | {"parameters": {"F32": 908160, "BF16": 1028019200, "U8": 14057568638}, "total": 15086495998} | [
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"endpo... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": "<|endoftext|>", "chat_template": "{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>syst... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "microsoft/phi-4", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": "https://huggingface.co/microsoft/phi-4/resolve/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "ta... | ## ***See [our collection](https://huggingface.co/collections/unsloth/phi-4-all-versions-677eecf93784e61afe762afa) for versions of Phi-4 including GGUF, 4-bit & more formats.***
# Finetune Phi-4, Llama 3.3 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tesla T4 notebook for Phi-4 here: https... | null | [
"mit",
"https://huggingface.co/microsoft/phi-4/resolve/main/LICENSE"
] | null | [
"en"
] | 15,086,495,998 | null | null | [
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"llama",
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"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
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66216b009f3f246fd2733860 | 6e6a83dc4b556c95 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
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] | [
"ByteLevel"
] | 111 | unsloth/llama-3-70b-bnb-4bit | unsloth | null | 262 | 127,365 | False | 2024-04-18T18:48:32Z | 2024-11-22T07:13:21Z | transformers | 48 | 1 | null | text-generation | null | [
".gitattributes",
"LICENSE",
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|end_of_text|>", "pad_token": "<|reserved_special_token_250|>"}} | {
"auto_model": "AutoModelForCausalLM",
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} | {"base_model": "meta-llama/Llama-3-70B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["unsloth", "transformers", "llama", "llam... | # Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
We have a Google Colab Tesla T4 notebook for Llama-3 8b here: https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
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"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
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] |
67a41fb635a7aa0f914011fc | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | unsloth/Qwen2.5-1.5B-unsloth-bnb-4bit | unsloth | {
"models": [
{
"_id": "66e6d032663c452ad2400439",
"id": "Qwen/Qwen2.5-1.5B"
}
],
"relation": "quantized"
} | 33,796 | 219,733 | False | 2025-02-06T02:34:30Z | 2025-04-28T04:13:44Z | transformers | 3 | 0 | null | text-generation | {"parameters": {"F32": 117088, "BF16": 409679360, "U8": 1169533908}, "total": 1579330356} | [
".gitattributes",
"README.md",
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] | c1ea14ccfb9a82f6913933183fbee80331cb18d2 | [
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"license:... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": null, "eos_token": "<|endoftext|>", "pad_token": "<|vision_pad|>", "unk_token": null}} | {
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"pipeline_tag": "text-generation",
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} | {"base_model": "Qwen/Qwen2.5-1.5B", "datasets": null, "eval_results": null, "language": ["zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "... | <div>
<p style="margin-bottom: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/qwen-25-66fe4c08fb9ada518e8a0d3f">our collection</a> for versions of Qwen2.5 including 4-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em>Unsloth's <a href="https://unsloth.ai/blog/dynamic-... | null | [
"apache-2.0"
] | null | [
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara"
] | 1,579,330,356 | null | null | [
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"Qwen2ForCausalLM",
"qwen2"
] | [
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] | [
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] | [
"text"
] | [
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] |
66a27f8d2b049bde87aa588b | e2c43495aed6e9fb | e6ffdc5dd5f1fd86 | 256,000 | BPE | 580,604 | true | false | true | true | 217 | Replace | null | Sequence | [
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse"
] | 106 | BAAI/bge-reranker-v2.5-gemma2-lightweight | BAAI | null | 5,734 | 135,252 | False | 2024-07-25T16:38:37Z | 2024-09-06T09:24:43Z | sentence-transformers | 53 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
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"generation_config.json",
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"model.safetensors.index.json",
"special_... | fabc9f6f51698e30890300fa3917f0560fdbcd75 | [
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"text-classification",
"custom_code",
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"arxiv:2312.15503",
"arxiv:2402.03216",
"license:gemma",
"region:us"
] | null | {"architectures": ["CostWiseGemmaForCausalLM"], "auto_map": {"AutoConfig": "BAAI/bge-reranker-v2.5-gemma2-lightweight--gemma_config.CostWiseGemmaConfig", "AutoModel": "BAAI/bge-reranker-v2.5-gemma2-lightweight--gemma_model.CostWiseGemmaModel", "AutoModelForCausalLM": "BAAI/bge-reranker-v2.5-gemma2-lightweight--gemma_mo... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "BAAI/bge-reranker-v2.5-gemma2-lightweight--gemma_model.CostWiseGemmaForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual"], "library_name": null, "license": "gemma", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-classification", "tags": ["transformers", "sentence-transformers"]} | # Reranker
**More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/tree/master).**
- [Model List](#model-list)
- [Usage](#usage)
- [Fine-tuning](#fine-tune)
- [Evaluation](#evaluation)
- [Citation](#citation)
Different from embedding model, reranker uses question and docu... | null | [
"gemma"
] | null | [
"multilingual"
] | null | null | null | [
"AutoModelForCausalLM",
"CostWiseGemmaForCausalLM",
"BAAI/bge-reranker-v2.5-gemma2-lightweight--gemma_model.CostWiseGemmaForCausalLM",
"cost_wise_gemma"
] | [
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"text-classification"
] | [
"text"
] | [
"text"
] | [
"text",
"logits"
] |
668e38e5a249df5a132a7ba7 | 539b24ec3c9dbddd | 6ce596b7c0f62c6d | 151,647 | BPE | 151,387 | true | true | true | true | 4 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 234 | llava-hf/llava-interleave-qwen-7b-hf | llava-hf | null | 917 | 127,358 | False | 2024-07-10T07:31:49Z | 2025-01-27T11:28:29Z | transformers | 29 | 0 | null | image-text-to-text | {"parameters": {"BF16": 8141100576}, "total": 8141100576} | [
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"model.s... | 0f07e6c4a6fb1c5f0bbe2354d94ef6a3c8e05d23 | [
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"vision",
"conversational",
"en",
"arxiv:2407.07895",
"license:other",
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"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "other", "license_name": "tongyi-qianwen-research", "license_link": "LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["vision", "image-text-to-text"]} | # LLaVA Interleave Model Card
## Model Details
**Model type:**
LLaVA Interleave is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture.
Base LLM: [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/... | null | [
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"en"
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631d60e1a31a0a462ca5c24b | 69d9860d38b5c798 | cf7a052aa6fec7ad | 30,000 | BPE | 15,977 | true | true | false | true | 229 | Sequence | Metaspace | Metaspace | [
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] | 282 | circulus/kobart-trans-ko-en-v2 | circulus | null | 21 | 123,883 | False | 2022-09-11T04:15:29Z | 2022-09-13T11:38:33Z | transformers | 1 | 0 | null | null | null | [
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"bart",
"text2text-generation",
"endpoints_compatible",
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"auto_model": "AutoModelForSeq2SeqLM",
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} | null | null | null | null | null | null | null | null | null | [
"bart",
"BartForConditionalGeneration",
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6647cb40ab655d312d772484 | 803e3c8528c55bf1 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
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"ByteLevel"
] | 352 | princeton-nlp/Llama-3-Base-8B-SFT | princeton-nlp | null | 2,058 | 132,506 | False | 2024-05-17T21:25:20Z | 2024-06-17T11:43:26Z | transformers | 4 | 0 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
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"... | c8ea4f8ac59a942750b4b8e58ddc3fb0acd05757 | [
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|e... | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | null | This is a model released from the preprint: *[SimPO: Simple Preference Optimization with a Reference-Free Reward](https://arxiv.org/abs/2405.14734)* Please refer to our [repository](https://github.com/princeton-nlp/SimPO) for more details. | null | null | null | null | 8,030,261,248 | null | null | [
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638f0ca555db6cb66caa1193 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
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"BertPreTokenizer"
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"WordPiece"
] | 0 | microsoft/git-base-textcaps | microsoft | null | 266 | 124,133 | False | 2022-12-06T09:34:29Z | 2023-02-08T10:49:59Z | transformers | 8 | 0 | null | image-to-text | null | [
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] | 3708bb659e51815348366a12d87840227cb5aaf4 | [
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] | null | {"architectures": ["GitForCausalLM"], "model_type": "git", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": "microsoft/git-base-textcaps", "pipeline_tag": "image-to-text", "tags": ["vision", "image-captioning"]} | # GIT (GenerativeImage2Text), base-sized, fine-tuned on TextCaps
GIT (short for GenerativeImage2Text) model, base-sized version, fine-tuned on TextCaps. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first releas... | null | [
"mit"
] | null | [
"en"
] | null | null | null | [
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"text"
] |
653d39885e4f2c3e886c6a45 | 62ca7d057713d34d | f75a77ceae7d7f8c | 35,073 | Unigram | null | true | true | true | true | 3,077 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 873 | QizhiPei/biot5-base | QizhiPei | null | 95 | 156,509 | False | 2023-10-28T16:40:40Z | 2025-02-20T07:15:05Z | transformers | 9 | 0 | null | null | {"parameters": {"F32": 252101376}, "total": 252101376} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | 5126d0516fc96ae344c9ded949e863ae5fd97864 | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"biology",
"chemistry",
"en",
"dataset:QizhiPei/BioT5_finetune_dataset",
"arxiv:2310.07276",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["QizhiPei/BioT5_finetune_dataset"], "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": ["biology", "chemistry"]} | ## Example Usage
```python
from transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("QizhiPei/biot5-base", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrained('QizhiPei/biot5-base')
```
## References
For more information, please refer to our pap... | null | [
"mit"
] | [
"QizhiPei/BioT5_finetune_dataset"
] | [
"en"
] | 252,101,376 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
680f756751220d888dbd9406 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Qwen/Qwen3-4B-FP8 | Qwen | {
"models": [
{
"_id": "680da769038897aa905586a7",
"id": "Qwen/Qwen3-4B"
}
],
"relation": "quantized"
} | 48,765 | 484,208 | False | 2025-04-28T12:32:39Z | 2025-07-26T03:46:01Z | transformers | 38 | 0 | null | text-generation | {"parameters": {"BF16": 778330176, "F8_E4M3": 3633315840}, "total": 4411646016} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 96b30dc13593a244a5e59e84687309f53c375cfa | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2309.00071",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-4B",
"base_model:quantized:Qwen/Qwen3-4B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"fp8",
"region:us"
] | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "quantization_config": {"quant_method": "fp8"}, "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 {%-... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Qwen/Qwen3-4B"], "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-FP8/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation",... | null | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-4B-FP8/blob/main/LICENSE"
] | null | null | 4,411,646,016 | null | null | [
"AutoModelForCausalLM",
"Qwen3ForCausalLM",
"qwen3"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
656beb31efd0eea7c5951e51 | 2f2e1b110c4a4759 | 625319e7b610da21 | 128,001 | Unigram | null | true | true | true | true | 5 | NFKC | Metaspace | Metaspace | [
"NFKC"
] | [
"Metaspace"
] | [
"Metaspace"
] | 874 | radlab/polish-cross-encoder | radlab | null | 3,240 | 142,841 | False | 2023-12-03T02:42:57Z | 2025-04-05T19:11:07Z | sentence-transformers | 4 | 0 | null | text-ranking | {"parameters": {"F32": 434962433}, "total": 434962433} | [
".gitattributes",
"CECorrelationEvaluator_sts-dev_results.csv",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"unigram.json"
] | 7c2c5b9d023e199fe1d5d698c2c457445e4c080d | [
"sentence-transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"feature-extraction",
"sentence-similarity",
"transformers",
"text-ranking",
"pl",
"dataset:radlab/polish-sts-dataset",
"license:cc-by-sa-4.0",
"text-embeddings-inference",
"endpoints_compatible",
"regio... | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["radlab/polish-sts-dataset"], "eval_results": null, "language": ["pl"], "library_name": "sentence-transformers", "license": "cc-by-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-ranking", "tags": ["sentence-transformers",... | ## Sample model usage
Below is an example of using the model:
```python
from sentence_transformers.cross_encoder import CrossEncoder
model_path = "radlab/polish-cross-encoder"
model = CrossEncoder(model_path)
questions = [
"Jaką mamy dziś pogodę? bo Andrzej nic nie mówił.",
"Gdzie jedzie Andrzej? Bo wczor... | null | [
"cc-by-sa-4.0"
] | [
"radlab/polish-sts-dataset"
] | [
"pl"
] | 434,962,433 | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"sentence-similarity",
"feature-extraction",
"text-ranking",
"text-classification"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6557b61de36a60780a2395bb | 827f6451ef99df30 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 355 | sdadas/mmlw-e5-large | sdadas | null | 160 | 129,293 | False | 2023-11-17T18:51:09Z | 2026-02-12T11:03:47Z | sentence-transformers | 0 | 0 | [{"name": "mmlw-e5-large", "results": [{"task": {"type": "Clustering"}, "dataset": {"type": "PL-MTEB/8tags-clustering", "name": "MTEB 8TagsClustering", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "v_measure", "value": 30.623921415441725, "verified": false}]}, {"task": {"type": "Class... | sentence-similarity | {"parameters": {"F32": 559890432}, "total": 559890432} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | a7d9af002b465bef87fca48845446311bb60a806 | [
"sentence-transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"pl",
"license:apache-2.0",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["XLMRobertaModel"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": "<pad>", "sep_token":... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"language": "pl", "license": "apache-2.0", "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "mteb"], "widget": [{"source_sentence": "query: Jak do\u017cy\u0107 100 lat?", "sentences": ["passage: Trzeba zdrowo si\u0119 od\u017cywia\u01... | <h1 align="center">MMLW-e5-large</h1>
MMLW (muszę mieć lepszą wiadomość) are neural text encoders for Polish.
This is a distilled model that can be used to generate embeddings applicable to many tasks such as semantic similarity, clustering, information retrieval. The model can also serve as a base for further fine-tu... | null | [
"apache-2.0"
] | null | [
"pl"
] | 559,890,432 | null | null | [
"XLMRobertaModel",
"AutoModel",
"xlm-roberta"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
680f79bb99eb7ed877f285c9 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | unsloth/Qwen3-4B-Base-unsloth-bnb-4bit | unsloth | {
"models": [
{
"_id": "680f0c5b4e1c1da6491f3b9d",
"id": "Qwen/Qwen3-4B-Base"
}
],
"relation": "quantized"
} | 13,590 | 194,815 | False | 2025-04-28T12:51:07Z | 2025-05-13T02:31:18Z | transformers | 2 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | b2df1c74f36a88893924e1a21eeb17b8e788e75a | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"unsloth",
"base_model:Qwen/Qwen3-4B-Base",
"base_model:quantized:Qwen/Qwen3-4B-Base",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": null, "eos_token": "<|endoftext|>", "pad_token": "<|vision_pad|>", "unk_token": null}} | {
"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": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["unsloth"]} | # Qwen3-4B-Base
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models.
Building upon extensive advancements in training data, model architecture, and optimization techniques, Qwen3 delivers the following... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"Qwen3ForCausalLM",
"qwen3"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6626884a212635de6b662796 | 23c93a93ffd15959 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 875 | rungalileo/distilbert-sexism | rungalileo | null | 12 | 124,864 | False | 2024-04-22T15:54:50Z | 2024-12-02T19:34:52Z | transformers | 1 | 0 | null | text-classification | null | [
".gitattributes",
"config.json",
"model.onnx",
"model_fp16.onnx",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | a8c94546d504da2de6a39898855f02e26fc83adf | [
"transformers",
"pytorch",
"onnx",
"distilbert",
"text-classification",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | null | null | null | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
64f570ff15d186f6983e0379 | 1eee0b97f95ca02b | a8f500fcd9eb4f8e | 50,304 | BPE | 50,000 | false | true | true | true | 48 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 876 | 42dot/42dot_LLM-PLM-1.3B | 42dot | null | 905 | 127,962 | False | 2023-09-04T05:54:07Z | 2024-02-13T05:57:31Z | transformers | 34 | 0 | null | text-generation | {"parameters": {"F32": 1439271680}, "total": 1439271680} | [
".gitattributes",
"README.md",
"added_tokens.json",
"asset/42dot_LLM_EN_score_white_background.png",
"asset/42dot_LLM_PLM_KO_score_background.png",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"t... | d63893df7ce0a6bc5354f807693b8eef0228fc4a | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"causal-lm",
"42dot_llm",
"en",
"ko",
"license:cc-by-nc-4.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<||bos||>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized": ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "ko"], "library_name": null, "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["pytorch", "llama", "causal-lm", "42dot_llm"]} | # 42dot_LLM-PLM-1.3B
**42dot LLM-PLM** is a pre-trained language model (PLM) developed by [**42dot**](https://42dot.ai/) and is a part of **42dot LLM** (large language model). 42dot LLM-PLM is pre-trained using Korean and English text corpus and can be used as a foundation language model for several Korean and English... | null | [
"cc-by-nc-4.0"
] | null | [
"en",
"ko"
] | 1,439,271,680 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
624709240569474a96249075 | 099fc7b2924a795c | 5f9f736c7dc6fbfa | 28,996 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 453 | batterydata/bde-pos-bert-cased-base | batterydata | null | 63 | 1,190,992 | False | 2022-04-01T14:16:04Z | 2022-05-31T15:02:27Z | transformers | 0 | 0 | null | token-classification | null | [
".gitattributes",
"README.md",
"all_results.json",
"config.json",
"eval_results.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"train_results.json",
"trainer_state.json",
"training_args.bin",
"vocab.txt"
] | 000ab3da061ae07190e5606cd787b8a5e981b6ae | [
"transformers",
"pytorch",
"bert",
"token-classification",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForTokenClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | null | null | [
"mit"
] | null | null | null | null | null | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
68f4e3f4f82416217114139c | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Qwen/Qwen3-VL-2B-Instruct | Qwen | null | 2,528,954 | 18,035,950 | False | 2025-10-19T13:13:24Z | 2025-10-23T11:30:44Z | transformers | 355 | 4 | null | image-text-to-text | {"parameters": {"BF16": 2127532032}, "total": 2127532032} | [
".gitattributes",
"README.md",
"chat_template.json",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"preprocessor_config.json",
"tokenizer.json",
"tokenizer_config.json",
"video_preprocessor_config.json",
"vocab.json"
] | 89644892e4d85e24eaac8bacfd4f463576704203 | [
"transformers",
"safetensors",
"qwen3_vl",
"image-text-to-text",
"conversational",
"arxiv:2505.09388",
"arxiv:2502.13923",
"arxiv:2409.12191",
"arxiv:2308.12966",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen3VLForConditionalGeneration"], "model_type": "qwen3_vl", "processor_config": {"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {%- if messages[0].content is string %}\n {{- messages[0].content }}\n {%- el... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": null} | null | null | [
"apache-2.0"
] | null | null | 2,127,532,032 | null | null | [
"AutoModelForImageTextToText",
"qwen3_vl",
"Qwen3VLForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
6627492e0601587f0be631ea | 39b1e434a495263c | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 349 | winninghealth/WiNGPT2-Llama-3-8B-Base | winninghealth | {
"models": [
{
"_id": "661f97d48e7f3438386f755d",
"id": "meta-llama/Meta-Llama-3-8B"
}
],
"relation": "finetune"
} | 7,870 | 162,654 | False | 2024-04-23T05:37:50Z | 2026-01-26T08:14:37Z | transformers | 8 | 0 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
".gitattributes",
"LICENSE",
"README.md",
"USE_POLICY.md",
"config.json",
"generation_config.json",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"special_tokens_ma... | 8261856faa9e9cc7fc224c01289393d29ce98f64 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"medical",
"en",
"zh",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:finetune:meta-llama/Meta-Llama-3-8B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|end_of_text|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["meta-llama/Meta-Llama-3-8B"], "datasets": null, "eval_results": null, "language": ["en", "zh"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["medical"]} | ## WiNGPT2
[WiNGPT](https://github.com/winninghealth/WiNGPT2) 是一个基于GPT的医疗垂直领域大模型，旨在将专业的医学知识、医疗信息、数据融会贯通，为医疗行业提供智能化的医疗问答、诊断支持和医学知识等信息服务，提高诊疗效率和医疗服务质量。
## 更新日志
[2024/04/23] 更新 WiNGPT2-Llama-3-8B-Base 模型（中文增强/多语言）与测评结果
[2024/04/01] 更新 WiNEval 测评结果
[2024/03/05] 开源7B/14B-Chat-4bit模型权重: [🤗](https://huggingface.co/winni... | null | [
"apache-2.0"
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"en",
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<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated... | null | null | null | null | 109,486,854 | null | null | [
"BertForSequenceClassification",
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"transformers",
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"safetensors",
"bert",
"text-classification",
"de",
"dataset:deepset/germandpr",
"license:mit",
"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",
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} | {"base_model": null, "datasets": ["deepset/germandpr"], "eval_results": null, "language": "de", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ## Overview
**Language model:** gbert-base-germandpr-reranking
**Language:** German
**Training data:** GermanDPR train set (~ 56MB)
**Eval data:** GermanDPR test set (~ 6MB)
**Infrastructure**: 1x V100 GPU
**Published**: June 3rd, 2021
## Details
- We trained a text pair classification model in FARM, which ... | null | [
"mit"
] | [
"deepset/germandpr"
] | [
"de"
] | 109,929,730 | null | null | [
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"Replace",
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] | 46 | ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf | ISTA-DASLab | null | 98 | 122,641 | False | 2024-01-18T10:42:32Z | 2024-03-11T20:53:05Z | transformers | 5 | 0 | null | text-generation | {"parameters": {"F16": 381210624, "I16": 809500672}, "total": 1190711296} | [
".gitattributes",
"README.md",
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"model.safetensors",
"special_tokens_map.json",
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"tokenizer.model",
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"text-generation",
"arxiv:2401.06118",
"text-generation-inference",
"endpoints_compatible",
"aqlm",
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"quant_method": "aqlm"}, "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | null | ------|-------------|----------------|----------------|--------------------------------------------------------------------------|
| Llama-2-7b (THIS) | 1x16 | 5.92 | 2.4 | [Link](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf) |
| Llama-2-7b | 2x8 | 6.69 | 2... | null | null | null | null | 1,190,711,296 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
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"text-generation"
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"NFC"
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"Split",
"ByteLevel"
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"ByteLevel"
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"models": [
{
"_id": "667fbfa0b0ba06a1848a6108",
"id": "Alibaba-NLP/gte-Qwen2-1.5B-instruct"
}
],
"relation": "finetune"
} | 8,925 | 149,455 | False | 2025-02-08T12:01:08Z | 2026-01-05T06:43:27Z | sentence-transformers | 45 | 0 | null | sentence-similarity | {"parameters": {"BF16": 1543268864}, "total": 1543268864} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
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"special_tokens_map.json",
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"tokenizer... | 476f2840b314cce3ef5a1de240012069c55687fa | [
"sentence-transformers",
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"qwen2",
"feature-extraction",
"transformers",
"sentence-similarity",
"custom_code",
"en",
"zh",
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"base_model:finetune:Alibaba-NLP/gte-Qwen2-1.5B-instruct",
"license:apache-2.0",
"text-embeddings-inferen... | null | {"architectures": ["Qwen2Model"], "auto_map": {"AutoModel": "modeling_qwen.Qwen2Model", "AutoModelForCausalLM": "modeling_qwen.Qwen2ForCausalLM", "AutoModelForSequenceClassification": "modeling_qwen.Qwen2ForSequenceClassification"}, "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{% for... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": ["Alibaba-NLP/gte-Qwen2-1.5B-instruct"], "datasets": null, "eval_results": null, "language": ["en", "zh"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["sentence-transformers", "transformers"... | # INF-Retriever-v1-1.5B
## Model Overview
- **INF-Retriever-v1-1.5B** is a lightweight version of the [**INF-Retriever-v1**](https://huggingface.co/infly/inf-retriever-v1), an LLM-based dense retrieval model developed by [INF TECH](https://www.infly.cn/en).
It is built upon the [gte-Qwen2-1.5B-instruct](https://huggin... | null | [
"apache-2.0"
] | null | [
"en",
"zh"
] | 1,543,268,864 | null | null | [
"AutoModel",
"Qwen2Model",
"qwen2"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
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"ByteLevel"
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"tokenizer_config.json",
"training_args.bin",
"vocab.json"
] | 7b95192c0f959dff59a3d401e38e7520efd59590 | [
"transformers",
"pytorch",
"safetensors",
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"text-classification",
"banking",
"intent",
"multiclass",
"en",
"dataset:banking77",
"text-embeddings-inference",
"endpoints_compatible",
"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"
} | {"base_model": null, "datasets": ["banking77"], "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": ["banking", "intent", "multiclass"], "widget": [{"text": "How long until my transf... | # distilroberta-base fine-tuned on banking77 dataset for intent classification
Test set accuray: 0.896
## How to use
```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
ckpt = 'mrm8488/distilroberta-finetuned-banking77'
tokenizer = AutoTokenizer.from_pretrained(ckpt)
model = Au... | null | null | [
"banking77"
] | [
"en"
] | 82,178,127 | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
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"NFC"
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".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"h2oai_pipeline.py",
"h2ogpt-oasst1-512-12b.h2oaiopenassistant_oasst1_h2ogpt.2_epochs.fcaae7ef70600de8c97c9b38cb3f0075467cdad1.2.zip",
"pythia-12b-deduped.h2oaiopenassistant_oasst1_h2ogpt_graded.3_epochs.2ccf687ea3f3f3775a501838e81c... | c6bb0fe363e0105839d34ca757793b61c9606f95 | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"gpt",
"llm",
"large language model",
"open-source",
"en",
"dataset:h2oai/openassistant_oasst1_h2ogpt_graded",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | 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": ["h2oai/openassistant_oasst1_h2ogpt_graded"], "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": ["gpt", "llm", "large language ... | # h2oGPT Model Card
## Summary
H2O.ai's `h2ogpt-oasst1-512-12b` is a 12 billion parameter instruction-following large language model licensed for commercial use.
- Base model: [EleutherAI/pythia-12b](https://huggingface.co/EleutherAI/pythia-12b)
- Fine-tuning dataset: [h2oai/openassistant_oasst1_h2ogpt_graded](https:... | null | [
"apache-2.0"
] | [
"h2oai/openassistant_oasst1_h2ogpt_graded"
] | [
"en"
] | null | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"Digits",
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".gitattributes",
"1_Pooling/config.json",
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"config.json",
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"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenization_codesage.py",
"tokenizer.json",... | 6e5d6dc15db3e310c37c6dbac072409f95ffa5c5 | [
"transformers",
"pytorch",
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] | null | {"architectures": ["CodeSage"], "auto_map": {"AutoConfig": "config_codesage.CodeSageConfig", "AutoTokenizer": "tokenization_codesage.CodeSageTokenizer", "AutoModel": "modeling_codesage.CodeSageModel", "AutoModelForMaskedLM": "modeling_codesage.CodeSageForMaskedLM", "AutoModelForSequenceClassification": "modeling_codesa... | {
"auto_model": "CodeSage",
"custom_class": null,
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} | {"base_model": null, "datasets": ["bigcode/the-stack-dedup", "bigcode/the-stack-v2"], "eval_results": null, "language": ["code"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ## CodeSage-Large-v2
### Updates
* 1. <span style="color:blue">We are excited to announce the release of the CodeSage V2 model family with largely improved performance!</span> <strong>Please check out our [blogpost](https://code-representation-learning.github.io/codesage-v2.html) for more details</strong>.
* 2. In add... | null | [
"apache-2.0"
] | [
"bigcode/the-stack-dedup",
"bigcode/the-stack-v2"
] | [
"code"
] | null | null | null | [
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null
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] | 85 | amgadhasan/phi-2 | amgadhasan | null | 21 | 121,431 | False | 2023-12-13T17:00:55Z | 2023-12-13T18:16:42Z | transformers | 17 | 0 | null | text-generation | {"parameters": {"F32": 2779683840}, "total": 2779683840} | [
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"safetensors",
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"custom_class": "modeling_mixformer_sequential.MixFormerSequentialForCausalLM",
"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "other", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # Disclaimer
I do **NOT** own this model. It belongs to its developer (Microsoft). See the license file for more details.
# Overview
This repo contains the parameters of phi-2, which is a large language model developed by Microsoft.
# How to run
This model requires 12.5 GB of vRAM in float32.
Should take roughly 6.... | null | [
"other"
] | null | null | 2,779,683,840 | null | null | [
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<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>
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We have a Qwen 2.5 (all model sizes) [free Google Colab Tesla T4 notebook](https://colab.research.google.com/drive/1Kose-ucXO1IBaZq5BvbwWieuubP7hxvQ?usp=sharing).
Also a [Qwen 2.5 conversational style notebook](https://colab.resear... | null | [
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"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"arxiv:2012.15828",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["XLMRobertaModel"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": ... | {
"auto_model": "AutoModel",
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"pipeline_tag": "feature-extraction",
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} | null | # Model
mMiniLM-L12xH384 XLM-R model proposed in [MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers](https://arxiv.org/abs/2012.15828) that we fine-tune using the direct assessment annotations collected in the Workshop on Statistical Machine Translation (WMT) 2015 to 202... | null | null | null | null | null | null | null | [
"XLMRobertaModel",
"AutoModel",
"xlm-roberta"
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"other"
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] | 9a9bd26b6bf85a359fbb91aca2d2b70564774558 | [
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"electra",
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"exbert",
"de",
"dataset:deepset/germanquad",
"license:mit",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["ElectraForQuestionAnswering"], "model_type": "electra", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
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## Overview
**Language model:** gelectra-base-germanquad-distilled
**Language:** German
**Training data:** GermanQuAD train set (~ 12MB)
**Eval data:** GermanQuAD test set (~ 5MB)
**Code:** See [an example extractive QA pipeline built with Haystack](https://hayst... | null | [
"mit"
] | [
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] | [
"de"
] | 109,339,138 | null | null | [
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"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "ehartford/dolphin-2.0-mistral-7b", "datasets": ["ehartford/dolphin", "jondurbin/airoboros-2.2.1"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": "Dolphin 2.0 Mistral 7B", "pipeline_tag":... | <!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<d... | null | [
"apache-2.0"
] | [
"ehartford/dolphin",
"jondurbin/airoboros-2.2.1"
] | [
"en"
] | 7,243,108,352 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
68da221e75098b1868dc16ef | 9baf32eaba6e2bf5 | 581b243d041ef3f5 | 128,815 | BPE | 127,741 | true | true | true | true | 818 | Sequence | Sequence | ByteLevel | [] | [
"Split",
"Split",
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 341 | deepseek-ai/DeepSeek-V3.2-Exp | deepseek-ai | {
"models": [
{
"_id": "68d9f24b32997c444be3a213",
"id": "deepseek-ai/DeepSeek-V3.2-Exp-Base"
}
],
"relation": "finetune"
} | 92,223 | 463,994 | False | 2025-09-29T06:07:26Z | 2025-11-18T02:39:54Z | transformers | 972 | 0 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"assets/chat_template.jinja",
"assets/cost.png",
"config.json",
"generation_config.json",
"inference/README.md",
"inference/config_671B_v3.2.json",
"inference/convert.py",
"inference/generate.py",
"inference/kernel.py",
"inference/model.py",
"infer... | 194c67e12b1b0d6df0ef373ddcf215bc84027409 | [
"transformers",
"safetensors",
"deepseek_v32",
"text-generation",
"conversational",
"base_model:deepseek-ai/DeepSeek-V3.2-Exp-Base",
"base_model:finetune:deepseek-ai/DeepSeek-V3.2-Exp-Base",
"license:mit",
"endpoints_compatible",
"fp8",
"region:us"
] | null | {"architectures": ["DeepseekV32ForCausalLM"], "model_type": "deepseek_v32", "quantization_config": {"quant_method": "fp8"}, "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<\uff5cbegin\u2581of\u2581sentence\uff5c>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": ["deepseek-ai/DeepSeek-V3.2-Exp-Base"], "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "base_model_relation": "finetune"} | null | null | [
"mit"
] | null | null | null | null | null | [
"deepseek_v32",
"AutoModelForCausalLM",
"DeepseekV32ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
684410e4a9c0247c09c2fbde | 46323c0f61df476d | 1fe366e13272caa0 | 262,145 | BPE | 514,906 | true | true | true | true | 6,415 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
"Fuse"
] | 49 | sarvamai/sarvam-translate | sarvamai | {
"models": [
{
"_id": "67b79c8700245b72c5706777",
"id": "google/gemma-3-4b-it"
}
],
"relation": "finetune"
} | 22,648 | 271,059 | False | 2025-06-07T10:13:56Z | 2025-09-01T10:10:04Z | transformers | 127 | 0 | null | translation | {"parameters": {"BF16": 4300079472}, "total": 4300079472} | [
".gitattributes",
"LICENSE.txt",
"README.md",
"added_tokens.json",
"chat_template.jinja",
"config.json",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"preprocessor_config.json",
"processor_config.json",
"spe... | 37da6e80728634b661c3cc3ee2a3d9fa33ae9150 | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"translation",
"as",
"bn",
"brx",
"doi",
"gom",
"gu",
"en",
"hi",
"kn",
"ks",
"mai",
"ml",
"mni",
"mr",
"ne",
"or",
"pa",
"sa",
"sat",
"sd",
"ta",
"te",
"ur",
"base_model:google/gemma-3-4b-it",
"... | null | {"architectures": ["Gemma3ForConditionalGeneration"], "model_type": "gemma3", "tokenizer_config": {"bos_token": "<bos>", "eos_token": "<eos>", "pad_token": "<pad>", "unk_token": "<unk>", "use_default_system_prompt": false}, "chat_template_jinja": "{{ bos_token }}\n{%- if messages[0]['role'] == 'system' -%}\n {%- if ... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": ["google/gemma-3-4b-it"], "datasets": null, "eval_results": null, "language": ["as", "bn", "brx", "doi", "gom", "gu", "en", "hi", "kn", "ks", "mai", "ml", "mni", "mr", "ne", "or", "pa", "sa", "sat", "sd", "ta", "te", "ur"], "library_name": "transformers", "license": "gpl-3.0", "license_name": null, "lice... | # Sarvam-Translate
<p align="center">
<a href="https://dashboard.sarvam.ai/translate"
target="_blank" rel="noopener noreferrer">
<img
src="https://img.shields.io/badge/🚀 Try on Sarvam Playground-1488CC?style=for-the-badge&logo=rocket"
alt="Try on Sarvam Playground"
/>
</a>
</p>
Sarvam... | null | [
"gpl-3.0"
] | null | [
"as",
"bn",
"brx",
"doi",
"gom",
"gu",
"en",
"hi",
"kn",
"ks",
"mai",
"ml",
"mni",
"mr",
"ne",
"or",
"pa",
"sa",
"sat",
"sd",
"ta",
"te",
"ur"
] | 4,300,079,472 | null | null | [
"AutoModelForImageTextToText",
"Gemma3ForConditionalGeneration",
"gemma3"
] | [
"image-text-to-text",
"translation"
] | [
"text",
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
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