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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
687960110308ba70c1dffb88 | 5499fef3506cd876 | cf1efbe71ef28fc9 | 50,368 | BPE | 50,009 | true | true | true | true | 116 | NFC | ByteLevel | ByteLevel | [
"NFC"
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"ByteLevel"
] | [
"ByteLevel"
] | 48 | ibm-granite/granite-embedding-small-english-r2 | ibm-granite | null | 1,054,334 | 5,286,836 | False | 2025-07-17T20:41:53Z | 2026-01-21T18:38:43Z | sentence-transformers | 65 | 0 | null | feature-extraction | null | [
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] | 2ab6fa8ea2d674564defd37171ae19079b864b33 | [
"sentence-transformers",
"pytorch",
"safetensors",
"modernbert",
"feature-extraction",
"granite",
"embeddings",
"transformers",
"mteb",
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"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["ModernBertModel"], "model_type": "modernbert", "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": ["en"], "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "feature-extraction", "tags": ["granite", "embeddings", "transformers", "mte... | # Granite-Embedding-Small-English-R2
<!-- Provide a quick summary of what the model is/does. -->
**Model Summary:** Granite-embedding-small-english-r2 is a 47M parameter dense biencoder embedding model from the Granite Embeddings collection that can be used to generate high quality text embeddings. This model produce... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"AutoModel",
"ModernBertModel",
"modernbert"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
641d9636847134d584b901f9 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | JiaqiLee/imdb-finetuned-bert-base-uncased | JiaqiLee | null | 100 | 215,129 | False | 2023-03-24T12:23:18Z | 2023-03-28T08:07:13Z | transformers | 6 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"tokenizer.json",
"vocab.txt"
] | 28cb550c2ef758c1e092e743326afc7f67ec44a4 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"dataset:imdb",
"license:openrail",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert"} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["imdb"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "openrail", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": null, "pipeline_tag": "text-classification", "tags": null} | ## Model description
This model is a fine-tuned version of the [bert-base-uncased](https://huggingface.co/transformers/model_doc/bert.html) model to classify the sentiment of movie reviews into one of two
categories: negative(label 0), positive(label 1).
## How to use
You can use the model with the following code.
`... | null | [
"openrail"
] | [
"imdb"
] | [
"en"
] | null | null | [
"accuracy"
] | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
637bd1c9ca8542a0ba8bf2aa | 040ad6a95131fbdc | ac6d4a011240d207 | 50,266 | BPE | 50,000 | false | true | true | true | 6 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 705 | Dizex/InstaFoodRoBERTa-NER | Dizex | null | 497 | 214,126 | False | 2022-11-21T19:30:17Z | 2024-04-03T07:03:08Z | transformers | 13 | 0 | null | token-classification | {"parameters": {"I64": 514, "F32": 124058115}, "total": 124058629} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 29d47a74b9b44750d43631caed2fb8b7e0b1d811 | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"token-classification",
"Instagram",
"NER",
"Named Entity Recognition",
"Food Entity Extraction",
"Social Media",
"Informal text",
"RoBERTa",
"en",
"dataset:Dizex/InstaFoodSet",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForTokenClassification"], "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": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["Dizex/InstaFoodSet"], "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": ["Instagram", "NER", "Named Entity Recognition", "Food Entity Extraction... | # InstaFoodRoBERTa-NER
## Model description
**InstaFoodRoBERTa-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** of Food entities on social media like informal text (e.g. Instagram, X, Reddit). It has been trained to recognize a single entity: food (FOOD).
Specifically, this mode... | null | [
"mit"
] | [
"Dizex/InstaFoodSet"
] | [
"en"
] | 124,058,629 | null | null | [
"roberta",
"AutoModelForTokenClassification",
"RobertaForTokenClassification"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
659cd1c7881feb6b9a0e9bee | bf1740d1d76cab01 | 1305f4ebba7df1b3 | 100,015 | BPE | 99,757 | true | true | true | true | 15 | Sequence | Sequence | ByteLevel | [] | [
"Split",
"Split",
"Split",
"Split",
"Split",
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 298 | deepseek-ai/deepseek-moe-16b-chat | deepseek-ai | null | 22,196 | 280,299 | False | 2024-01-09T04:55:35Z | 2024-02-05T08:02:28Z | transformers | 156 | 1 | null | text-generation | {"parameters": {"BF16": 16375728128}, "total": 16375728128} | [
".gitattributes",
"README.md",
"config.json",
"configuration_deepseek.py",
"generation_config.json",
"model-00001-of-00007.safetensors",
"model-00002-of-00007.safetensors",
"model-00003-of-00007.safetensors",
"model-00004-of-00007.safetensors",
"model-00005-of-00007.safetensors",
"model-00006-of... | eefd8ac7e8dc90e095129fe1a537d5e236b2e57c | [
"transformers",
"safetensors",
"deepseek",
"text-generation",
"conversational",
"custom_code",
"arxiv:2401.06066",
"license:other",
"region:us"
] | null | {"architectures": ["DeepseekForCausalLM"], "auto_map": {"AutoConfig": "configuration_deepseek.DeepseekConfig", "AutoModel": "modeling_deepseek.DeepseekModel", "AutoModelForCausalLM": "modeling_deepseek.DeepseekForCausalLM"}, "model_type": "deepseek", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content":... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_deepseek.DeepseekForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "other", "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-MoE/blob/main/LICENSE-MODEL", "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | <p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discor... | null | [
"other",
"deepseek",
"https://github.com/deepseek-ai/DeepSeek-MoE/blob/main/LICENSE-MODEL"
] | null | null | 16,375,728,128 | null | null | [
"deepseek",
"DeepseekForCausalLM",
"modeling_deepseek.DeepseekForCausalLM",
"AutoModelForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66a60cc2092c9d29f777b16d | 539b24ec3c9dbddd | 6ce596b7c0f62c6d | 151,647 | BPE | 151,387 | true | true | true | true | 4 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 234 | lmms-lab/llava-onevision-qwen2-0.5b-si | lmms-lab | null | 1,344 | 219,677 | False | 2024-07-28T09:17:54Z | 2024-09-02T07:23:47Z | transformers | 15 | 0 | [{"name": "llava-onevision-qwen-0.5b-si", "results": [{"task": {"type": "multimodal"}, "dataset": {"name": "AI2D", "type": "ai2d"}, "metrics": [{"type": "accuracy", "value": 54.2, "name": "accuracy", "verified": false}]}, {"task": {"type": "multimodal"}, "dataset": {"name": "ChartQA", "type": "chartqa"}, "metrics": [{"... | text-generation | {"parameters": {"BF16": 893618208}, "total": 893618208} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"preprocessor_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"trainer_state.json",
"training_args.bin",
"vocab.json"
] | c4b083af71ec78acfaad887db81db5167f272027 | [
"transformers",
"safetensors",
"llava",
"text-generation",
"multimodal",
"conversational",
"en",
"zh",
"dataset:lmms-lab/LLaVA-OneVision-Data",
"arxiv:2408.03326",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlavaQwenForCausalLM"], "model_type": "llava", "tokenizer_config": {"bos_token": null, "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoProcessor"
} | {"datasets": ["lmms-lab/LLaVA-OneVision-Data"], "language": ["en", "zh"], "library_name": "transformers", "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["multimodal"], "model-index": [{"name": "llava-onevision-qwen-0.5b-si", "results": [{"task": {"type": "multimodal"}, "dataset": {"name": "AI2D", "type": "a... | # LLaVA-OneVision
Play with the model on the [LLaVA OneVision Chat](https://llava-onevision.lmms-lab.com/).
## Table of Contents
1. [Model Summary](##model-summary)
2. [Use](##use)
3. [Limitations](##limitations)
4. [Training](##training)
5. [Licens... | null | [
"apache-2.0"
] | [
"lmms-lab/LLaVA-OneVision-Data"
] | [
"en",
"zh"
] | 893,618,208 | null | [
"accuracy"
] | [
"AutoModelForCausalLM",
"LlavaQwenForCausalLM",
"llava"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65623f88f725fc09723934fe | dab60c3f3f0e0ac9 | a7d0718ad11ad4ff | 32,003 | BPE | 61,249 | true | false | true | true | 6 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 706 | Locutusque/Orca-2-13b-SFT-v4 | Locutusque | {
"models": [
{
"_id": "6552c9841e9a46ca57c9e175",
"id": "microsoft/Orca-2-13b"
}
],
"relation": "finetune"
} | 23 | 213,026 | False | 2023-11-25T18:40:08Z | 2023-12-19T03:05:25Z | transformers | 5 | 0 | null | text-generation | {"parameters": {"BF16": 13015895040}, "total": 13015895040} | [
".gitattributes",
"LICENSE.txt",
"README.md",
"USE_POLICY.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00014.safetensors",
"model-00002-of-00014.safetensors",
"model-00003-of-00014.safetensors",
"model-00004-of-00014.safetensors",
"model-00005-of-00014.safetensor... | f3491a4c169a5b8307383499b72ab9e2174c37da | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"fr",
"es",
"hi",
"zh",
"code",
"dataset:HuggingFaceH4/no_robots",
"dataset:mlabonne/guanaco-llama2-1k",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"dataset:totally-not-an-llm/EverythingLM-data-V3",
"base_model:microsof... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "microsoft/Orca-2-13b", "datasets": ["HuggingFaceH4/no_robots", "mlabonne/guanaco-llama2-1k", "OpenAssistant/oasst_top1_2023-08-25", "totally-not-an-llm/EverythingLM-data-V3"], "eval_results": null, "language": ["en", "fr", "es", "hi", "zh", "code"], "library_name": null, "license": "other", "license_nam... | The "microsoft/Orca-2-13b" model fully fine-tuned on HuggingFaceH4/no_robots, totally-not-an-llm/EverythingLM-data-V3, mlabonne/guanaco-llama2-1k, and OpenAssistant/oasst_top1_2023-08-25. This model achieved a test loss of 0.18.
Make sure to comply with the microsoft research license. Please read it before using this ... | null | [
"other"
] | [
"HuggingFaceH4/no_robots",
"mlabonne/guanaco-llama2-1k",
"OpenAssistant/oasst_top1_2023-08-25",
"totally-not-an-llm/EverythingLM-data-V3"
] | [
"en",
"fr",
"es",
"hi",
"zh",
"code"
] | 13,015,895,040 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
672dec5fb852a01eae148d38 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
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"ByteLevel"
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"models": [
{
"_id": "66e6cfeb6998c3d86c14e163",
"id": "Qwen/Qwen2.5-0.5B"
}
],
"relation": "finetune"
} | 41,301 | 373,805 | False | 2024-11-08T10:47:59Z | 2024-11-18T12:52:36Z | transformers | 48 | 1 | null | text-generation | {"parameters": {"BF16": 494032768}, "total": 494032768} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 8123ea2e9354afb7ffcc6c8641d1b2f5ecf18301 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"qwen",
"qwen-coder",
"codeqwen",
"conversational",
"en",
"arxiv:2409.12186",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:finetune:Qwen/Qwen2.5-0.5B",
"license:apache-2.0",
"text-generation-inference... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Qwen/Qwen2.5-0.5B"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-... | # Qwen2.5-Coder-0.5B
## Introduction
Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings ... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B/blob/main/LICENSE"
] | null | [
"en"
] | 494,032,768 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f181abf | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | yjernite/bart_eli5 | yjernite | null | 2,107 | 215,908 | False | 2022-03-02T23:29:05Z | 2021-03-09T22:31:11Z | transformers | 11 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"tokenizer.json",
"vocab.json"
] | 38797dd2ef06f5542c6f7db853518703f6b3da21 | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:eli5",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BartForConditionalGeneration"], "model_type": "bart"} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["eli5"], "eval_results": null, "language": "en", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ## BART ELI5
Read the article at https://yjernite.github.io/lfqa.html and try the demo at https://huggingface.co/qa/ | null | [
"apache-2.0"
] | [
"eli5"
] | [
"en"
] | null | null | null | [
"bart",
"BartForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
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] | null | null | null |
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"dataset:h2oai/h2ogpt-fortune2000-personalized",
"dataset:h2oai/h2ogpt-oig-... | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
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"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": ["h2oai/h2ogpt-oig-oasst1-instruct-cleaned-v1", "h2oai/openassistant_oasst1_h2ogpt", "h2oai/h2ogpt-fortune2000-personalized", "h2oai/h2ogpt-oig-oasst1-instruct-cleaned-v3"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name"... | # h2oGPT Model Card
## Summary
H2O.ai's `h2ogpt-oig-oasst1-512-6_9b` is a 6.9 billion parameter instruction-following large language model licensed for commercial use.
- Base model: [EleutherAI/pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b)
- Fine-tuning dataset: [h2oai/h2ogpt-oig-oasst1-instruct-cleaned... | null | [
"apache-2.0"
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] | null | null | null | [
"gpt_neox",
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"Precompiled"
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"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 28 | eleldar/language-detection | eleldar | null | 514 | 213,874 | False | 2022-05-24T09:30:04Z | 2022-05-24T10:06:00Z | transformers | 14 | 0 | [{"name": "xlm-roberta-base-language-detection", "results": []}] | text-classification | null | [
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] | null | {"architectures": ["XLMRobertaForSequenceClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": f... | {
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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", "f1"], "model_name": "xlm-roberta-base-language-detection", "pipeline_tag": null, "tags": ["generated_from_trainer"]} | # Clone from [https://huggingface.co/papluca/xlm-roberta-base-language-detection](xlm-roberta-base-language-detection)
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [Language Identification](https://huggingface.co/datasets/papluca/language-identification#addit... | null | [
"mit"
] | null | null | null | null | [
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] | null | {"architectures": ["LlamaForSequenceClassification"], "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']... | {
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* **Authors**: Hanze Dong*, Wei Xiong*, Bo Pang*, Haoxiang Wang*, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang
* **Code**: https://github.com/RLHFlow/RLHF-Reward-Mode... | null | [
"cc-by-nc-4.0"
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"Replace"
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"... | 9c3bc62a67b6bc602beb76c35ae4409bef141a14 | [
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"conversational",
"ko",
"en",
"license:llama3",
"text-generation-inference",
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] | null | {"architectures": ["Gemma2ForCausalLM"], "model_type": "gemma2", "tokenizer_config": {"bos_token": "<bos>", "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{{ '<bos>' + system_message }}{% for message in messages %}{% set content = message['content']... | {
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ko", "en"], "library_name": "transformers", "license": "llama3", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": null} | # davidkim205/ko-gemma-2-9b-it
davidkim205/ko-gemma-2-9b-it is one of several models being researched to improve the performance of Korean language models.
(would be released soon)
## Model Details
* **Model Developers** : davidkim(changyeon kim)
* **Repository** : -
* **base mode** : google/gemma-2-9b-it
* **sf... | null | [
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} | 11,719 | 266,647 | False | 2024-01-31T11:17:21Z | 2024-04-01T16:01:00Z | transformers | 2 | 0 | [{"name": "BeagSake-7B", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 72.44, "name": "normalize... | text-generation | {"parameters": {"BF16": 7241732096}, "total": 7241732096} | [
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} | {"base_model": ["shadowml/BeagleSempra-7B", "shadowml/WestBeagle-7B"], "license": "cc-by-nc-4.0", "tags": ["merge", "mergekit", "lazymergekit"], "model-index": [{"name": "BeagSake-7B", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "... | # BeagSake-7B
BeagSake-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [shadowml/BeagleSempra-7B](https://huggingface.co/shadowml/BeagleSempra-7B)
* [shadowml/WestBeagle-7B](https://huggingface.co/shadowml/WestBeagle-7... | null | [
"cc-by-nc-4.0"
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<picture>
<source srcset="https://github.com/XiaomiMiMo/MiMo/raw/main/figures/Xiaomi_MiMo_darkmode.png?raw=true" media="(prefers-color-scheme: dark)">
<img src="https://github.com/XiaomiMiMo/MiMo/raw/main/figures/Xiaomi_MiMo.png?raw=true" width="60%" alt="Xiaomi-MiMo" />
</picture>
</di... | null | [
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] | 11 | google/switch-base-128 | google | null | 591 | 213,124 | False | 2022-11-04T07:59:22Z | 2023-01-24T17:20:02Z | transformers | 5 | 0 | null | null | null | [
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"dataset:c4",
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# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Bias, Risks, and Limitations](#bias-... | null | [
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] | 52 | katuni4ka/phi-3.5-moe-tiny-random | katuni4ka | null | 59 | 211,589 | False | 2025-03-26T10:34:36Z | 2025-03-26T10:35:58Z | transformers | 0 | 0 | null | text-generation | {"parameters": {"BF16": 1110112}, "total": 1110112} | [
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] | 22 | OpenMed/OpenMed-NER-PathologyDetect-SuperClinical-184M | OpenMed | null | 93,523 | 564,815 | False | 2025-07-18T08:55:44Z | 2025-08-05T09:45:04Z | transformers | 0 | 0 | null | token-classification | {"parameters": {"BF16": 183833859}, "total": 183833859} | [
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] | 237 | OpenMed/OpenMed-NER-DiseaseDetect-SuperClinical-141M | OpenMed | null | 91,278 | 557,052 | False | 2025-07-18T08:57:43Z | 2025-08-05T09:45:12Z | transformers | 0 | 0 | null | token-classification | {"parameters": {"BF16": 141306627}, "total": 141306627} | [
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"ByteLevel"
] | [
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"models": [
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} | 6,629 | 275,639 | False | 2024-12-06T20:00:37Z | 2025-01-31T04:05:57Z | transformers | 28 | 0 | null | text-generation | null | [
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] | null | {"architectures": ["GraniteMoeForCausalLM"], "model_type": "granitemoe", "tokenizer_config": {"bos_token": "<|end_of_text|>", "chat_template": "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set system_... | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
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} | {"base_model": ["ibm-granite/granite-3.1-3b-a800m-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": "text-generation", "tags": ["language", "granite-3... | # Granite-3.1-3B-A800M-Instruct
**Model Summary:**
Granite-3.1-3B-A800M-Instruct is a 3B parameter long-context instruct model finetuned from Granite-3.1-3B-A800M-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets tailored for solving long c... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"granitemoe",
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] | [
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] | [
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] | [
"text"
] | [
"text"
] |
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"ByteLevel"
] | [
"ByteLevel"
] | 707 | bofenghuang/whisper-large-v3-french | bofenghuang | null | 796 | 214,063 | False | 2023-11-27T14:59:26Z | 2024-02-05T15:47:57Z | transformers | 38 | 0 | [{"name": "whisper-large-v3-french", "results": [{"task": {"name": "Automatic Speech Recognition", "type": "automatic-speech-recognition"}, "dataset": {"name": "Common Voice 13.0", "type": "mozilla-foundation/common_voice_13_0", "config": "fr", "split": "test", "args": {"language": "fr"}}, "metrics": [{"name": "WER", "... | automatic-speech-recognition | {"parameters": {"F16": 1609879040}, "total": 1609879040} | [
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"dataset:google/fleurs",
"dataset:gigant/african_accented_french",
"a... | null | {"architectures": ["WhisperForConditionalGeneration"], "model_type": "whisper", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
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"pipeline_tag": "automatic-speech-recognition",
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} | {"datasets": ["mozilla-foundation/common_voice_13_0", "facebook/multilingual_librispeech", "facebook/voxpopuli", "google/fleurs", "gigant/african_accented_french"], "language": "fr", "library_name": "transformers", "license": "mit", "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition", "tags": ["automatic... | # Whisper-Large-V3-French
Whisper-Large-V3-French is fine-tuned on `openai/whisper-large-v3` to further enhance its performance on the French language. This model has been trained to predict casing, punctuation, and numbers. While this might slightly sacrifice performance, we believe it allows for broader usage.
This... | null | [
"mit"
] | [
"mozilla-foundation/common_voice_13_0",
"facebook/multilingual_librispeech",
"facebook/voxpopuli",
"google/fleurs",
"gigant/african_accented_french"
] | [
"fr"
] | 1,609,879,040 | null | [
"wer"
] | [
"WhisperForConditionalGeneration",
"whisper",
"AutoModelForSpeechSeq2Seq"
] | [
"automatic-speech-recognition"
] | [
"multimodal"
] | [
"audio"
] | [
"text"
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"NFC"
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"models": [
{
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"id": "Qwen/Qwen2.5-Coder-0.5B-Instruct"
}
],
"relation": "quantized"
} | 2,970 | 228,316 | False | 2024-11-12T00:57:50Z | 2024-11-12T02:35:57Z | transformers | 4 | 0 | null | text-generation | {"parameters": {"F32": 67536, "BF16": 136206208, "U8": 369065978}, "total": 505339722} | [
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] | af38bf399b7f3be66ba9e9e52a662af5f5a8cef2 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"code",
"qwen",
"qwen-coder",
"codeqwen",
"conversational",
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"base_model:Qwen/Qwen2.5-Coder-0.5B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-Coder-0.5B-Instruct",
"licen... | 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' %... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-Coder-0.5B-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 | [
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] | null | [
"en"
] | 505,339,722 | null | null | [
"AutoModelForCausalLM",
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"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 355 | BlueAvenir/sustainability_relevance_class_model | BlueAvenir | null | 2 | 2 | False | 2023-03-23T09:47:58Z | 2023-03-23T09:48:17Z | sentence-transformers | 0 | 0 | null | sentence-similarity | null | [
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] | 14ecd0033ddcccaaf55e21002eed7318001cf183 | [
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"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"text-embeddings-inference",
"endpoints_compatible",
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] | 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",
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"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "tr... | # {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when y... | null | null | null | null | null | null | null | [
"XLMRobertaModel",
"AutoModel",
"xlm-roberta"
] | [
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"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
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"biocuration",
"chem",
"en",
"arxiv:2508.01630",
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] | null | {"architectures": ["BertForTokenClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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"apache-2.0"
] | null | [
"en"
] | 108,893,955 | null | null | [
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"ByteLevel"
] | [
"ByteLevel"
] | 62 | rrivera1849/LUAR-CRUD | rrivera1849 | null | 4,454 | 218,512 | False | 2023-09-22T14:02:34Z | 2025-12-08T15:27:44Z | transformers | 4 | 0 | null | feature-extraction | {"parameters": {"F32": 82512128}, "total": 82512128} | [
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"README.md",
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"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 756d8a6131d833d33c4c3a513fa1515acda10f81 | [
"transformers",
"pytorch",
"safetensors",
"LUAR",
"feature-extraction",
"custom_code",
"en",
"license:apache-2.0",
"region:us"
] | null | {"architectures": ["LUAR"], "auto_map": {"AutoConfig": "config.LUARConfig", "AutoModel": "model.LUAR"}, "model_type": "LUAR", "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": "model.LUAR",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # rrivera1849/LUAR-CRUD
Author Style Representations using [LUAR](https://aclanthology.org/2021.emnlp-main.70.pdf).
The LUAR training and evaluation repository can be found [here](https://github.com/llnl/luar).
This particular model was trained on a subsample of the Pushshift Reddit Dataset (5 million users) for com... | null | [
"apache-2.0"
] | null | [
"en"
] | 82,512,128 | null | null | [
"LUAR",
"AutoModel",
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] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
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"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 86 | togethercomputer/RedPajama-INCITE-Instruct-3B-v1 | togethercomputer | null | 1,038 | 215,957 | False | 2023-05-05T05:12:00Z | 2023-05-09T14:59:36Z | transformers | 93 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
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] | 0c66778ee09a036886741707733620b91057909a | [
"transformers",
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"dataset:Muennighoff/natural-instructions",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"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": ["togethercomputer/RedPajama-Data-1T", "Muennighoff/P3", "Muennighoff/natural-instructions"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "... | # RedPajama-INCITE-Instruct-3B-v1
RedPajama-INCITE-Instruct-3B-v1 was developed by Together and leaders from the open-source AI community including Ontocord.ai, ETH DS3Lab, AAI CERC, Université de Montréal, MILA - Québec AI Institute, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research res... | null | [
"apache-2.0"
] | [
"togethercomputer/RedPajama-Data-1T",
"Muennighoff/P3",
"Muennighoff/natural-instructions"
] | [
"en"
] | null | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
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648b3259f594f7daf26eb76b | 5a6f3bee2b1d15f6 | b5bdc8c42aaa1103 | 99 | BPE | 0 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 708 | javirandor/passgpt-10characters | javirandor | null | 352 | 212,225 | False | 2023-06-15T15:46:33Z | 2023-07-20T10:45:01Z | transformers | 4 | 0 | null | text-generation | {"parameters": {"F32": 57566976, "BOOL": 8388608}, "total": 65955584} | [
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] | 4c70f3788d1c14654d76cf72df3c3cae71af2873 | [
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"gpt2",
"text-generation",
"passwords",
"cybersecurity",
"arxiv:2306.01545",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "cls_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": fa... | {
"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": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["passwords", "cybersecurity"], "extra_gated_fields": {"Institution": "text", ... | # PassGPT
PassGPT is a causal language model trained on password leaks. It was first introduced in [this paper](https://arxiv.org/abs/2306.01545). This version of the model was trained on passwords from the RockYou leak, after filtering those that were at most 10 characters long. If you need access to PassGPT trained ... | null | null | null | null | 65,955,584 | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
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672dec6a60ce5208843522f3 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
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"ByteLevel"
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{
"_id": "66e6d03f69d1083dd982a18c",
"id": "Qwen/Qwen2.5-3B"
}
],
"relation": "finetune"
} | 45,334 | 503,071 | False | 2024-11-08T10:48:10Z | 2024-11-18T12:53:33Z | transformers | 44 | 2 | null | text-generation | {"parameters": {"BF16": 3085938688}, "total": 3085938688} | [
".gitattributes",
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"license:other",
"text-generation-inference",
"end... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Qwen/Qwen2.5-3B"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": "qwen-research", "license_link": "https://huggingface.co/Qwen/Qwen2.5-Coder-3B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "tex... | # Qwen2.5-Coder-3B
## Introduction
Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings th... | null | [
"other",
"qwen-research",
"https://huggingface.co/Qwen/Qwen2.5-Coder-3B/blob/main/LICENSE"
] | null | [
"en"
] | 3,085,938,688 | null | null | [
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"Qwen2ForCausalLM",
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"text"
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"text"
] | [
"text"
] |
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"BertPreTokenizer"
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**IMPORTANT**: **nanoLLaVA-1.5** is out with a much better performance. Please find it [here](https://huggingface.co/qnguyen3/nanoLLaVA-1.5).
<p align="center">
<img src="https://i.postimg.cc/d15k3YNG/nanollava.webp" alt="Logo" width="350">
</p>
## Description
nanoLLaVA i... | null | [
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## Model Summary
**Granite Guardian 3.0 8B** is a fine-tuned Granite 3.0 8B Instruct model designed to detect risks in prompts and responses.
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"4-bit",
"awq",
"deploy:azure",
"region:us... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "awq"}, "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_u... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "de", "fr", "it", "pt", "hi", "es", "th"], "library_name": "transformers", "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["llama-3.1", "met... | > [!IMPORTANT]
> This repository is a community-driven quantized version of the original model [`meta-llama/Meta-Llama-3.1-405B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct) which is the FP16 half-precision official version released by Meta AI.
## Model Information
The Meta Llama 3.1 coll... | null | [
"llama3.1"
] | null | [
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th"
] | 410,081,247,232 | null | null | [
"AutoModelForCausalLM",
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"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6877f621425f7ca9d3e3ca7a | 6a67cbd79bab1e59 | cf1efbe71ef28fc9 | 50,368 | BPE | 50,009 | true | true | true | true | 116 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 395 | OpenMed/OpenMed-NER-PathologyDetect-ModernClinical-395M | OpenMed | null | 94,983 | 561,272 | False | 2025-07-16T18:57:37Z | 2025-08-05T09:34:29Z | transformers | 2 | 1 | null | token-classification | {"parameters": {"BF16": 395834371}, "total": 395834371} | [
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"tokenizer_config.json"
] | f591eba5f799c331e267b4c9cd1b129df740894f | [
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"named-entity-recognition",
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"disease-entity-recognition",
"medical-diagnosis",
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"pathology",
"disease",
"en",
"arxiv:2508.01630",
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687803d68840352c6b14a65e | b3310d158ab6e0dc | 66be3297e0980489 | 28,895 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
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"BertPreTokenizer"
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"text"
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621ffdc136468d709f177b41 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
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WellcomeBertMesh is build from the data science team at the WellcomeTrust to tag biomedical grants with Medical Subject Headings ([Mesh](https://www.nlm.nih.gov/mesh/meshhome.html)). Even though developed with the intention to be used towards research grants, it should be applicable to any type of b... | null | [
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68780229f3bc728230f5765d | 87ec02145bad27f5 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-T... | null | null | null | null | 4,385,920 | null | null | [
"BertModel",
"AutoModel",
"bert"
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"feature-extraction"
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6877f688fbfb5a9850c97db2 | 1f077a9a0005c931 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
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"transformers",
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"token-classification",
"named-entity-recognition",
"biomedical-nlp",
"disease-entity-recognition",
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"ncbi",
"pathology",
"disease",
"en",
"arxiv:2508.01630",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["XLMRobertaForTokenClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
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"pipeline_tag": "token-classification",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["token-classification", "named-entity-recognition", "biomedical-nlp", "transform... | null | null | [
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621ffdc136468d709f178855 | 906dff66b9bfacf6 | d0b832fec0f4f9f6 | 4,000 | Unigram | null | true | true | false | true | 1 | Sequence | Metaspace | Metaspace | [
"Precompiled",
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] | [
"Metaspace"
] | [
"Metaspace"
] | 709 | alaggung/bart-r3f | alaggung | null | 818 | 213,924 | False | 2022-03-02T23:29:05Z | 2022-01-11T16:18:32Z | transformers | 8 | 0 | null | summarization | null | [
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] | b0a1bafbf3396749e35b5b48bf9891d34f089f57 | [
"transformers",
"pytorch",
"tf",
"bart",
"text2text-generation",
"summarization",
"ko",
"endpoints_compatible",
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] | null | {"architectures": ["BartForConditionalGeneration"], "model_type": "bart", "tokenizer_config": {"bos_token": "[BOS]", "eos_token": "[EOS]", "cls_token": "[BOS]", "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
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"pipeline_tag": "text2text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ko"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["summarization"], "widget": [{"text": "[BOS]\ubc25 \u3131?[SEP]\uace0\uace0\uace0\uace0 ... | # BART R3F
[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.
[bart-pretrained](https://huggingface.co/alaggung/bart-pretrained) 모델에 [2021-dialogue-summary-competition](https://github.com/cosmoquester/2021-dialogue-summary-competition) 레포지토리의 R3F를 적용해 대화요약 Task를 학습한 모델입니다.
데이터는 [AIHub 한국어 대화요약]... | null | null | null | [
"ko"
] | null | null | null | [
"bart",
"BartForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"summarization"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6461dbdced3c7e6fa5e1dce5 | 1518b5b91df51a3a | 06c049f6287d7099 | 52,000 | BPE | 51,760 | true | true | true | true | 2 | NFKC | ByteLevel | ByteLevel | [
"NFKC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 604 | cyberagent/open-calm-3b | cyberagent | null | 216,615 | 824,259 | False | 2023-05-15T07:14:36Z | 2023-05-18T01:11:50Z | transformers | 20 | 0 | null | text-generation | null | [
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] | 64b7b9f1b88efd3404b01742096efeeba2460274 | [
"transformers",
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"gpt_neox",
"text-generation",
"japanese",
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"ja",
"dataset:wikipedia",
"dataset:cc100",
"dataset:mc4",
"license:cc-by-sa-4.0",
"text-generation-inference",
"region:us"
] | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|padding|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["wikipedia", "cc100", "mc4"], "eval_results": null, "language": ["ja"], "library_name": null, "license": "cc-by-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["japanese", "causal-lm"], "inference": false} | # OpenCALM-3B
## Model Description
OpenCALM is a suite of decoder-only language models pre-trained on Japanese datasets, developed by CyberAgent, Inc.
## Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("cyberagent/open-calm-3b",... | null | [
"cc-by-sa-4.0"
] | [
"wikipedia",
"cc100",
"mc4"
] | [
"ja"
] | null | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 230 | OpenMed/OpenMed-NER-ChemicalDetect-ElectraMed-560M | OpenMed | null | 91,610 | 550,135 | False | 2025-07-16T20:34:50Z | 2025-08-05T09:40:28Z | transformers | 0 | 0 | null | token-classification | {"parameters": {"BF16": 558843907}, "total": 558843907} | [
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... | null | {"architectures": ["XLMRobertaForTokenClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
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64527abaa0c0a664a241c069 | cb3f886e13ad7505 | 1f1d26e73b02d1f3 | 49,152 | BPE | 48,891 | false | true | false | true | 19 | null | Sequence | ByteLevel | null | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 70 | bigcode/starcoderbase | bigcode | null | 45 | 213,859 | auto | 2023-05-03T15:16:10Z | 2023-05-11T08:10:58Z | transformers | 416 | 0 | [{"name": "StarCoderBase", "results": [{"task": {"type": "text-generation"}, "dataset": {"type": "openai_humaneval", "name": "HumanEval"}, "metrics": [{"name": "pass@1", "type": "pass@1", "value": 0.304, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"type": "mbpp", "name": "MBPP"}, "metrics": ... | text-generation | null | [
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} | {"datasets": ["bigcode/the-stack-dedup"], "library_name": "transformers", "license": "bigcode-openrail-m", "metrics": ["code_eval"], "pipeline_tag": "text-generation", "tags": ["code"], "inference": true, "widget": [{"text": "def print_hello_world():", "example_title": "Hello world", "group": "Python"}], "extra_gated_p... | null | null | [
"bigcode-openrail-m"
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687814ac944952bae9207b6d | 6a67cbd79bab1e59 | cf1efbe71ef28fc9 | 50,368 | BPE | 50,009 | true | true | true | true | 116 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 395 | OpenMed/OpenMed-NER-ChemicalDetect-ModernClinical-149M | OpenMed | null | 93,971 | 557,984 | False | 2025-07-16T21:07:56Z | 2025-08-05T09:42:56Z | transformers | 0 | 0 | null | token-classification | {"parameters": {"BF16": 149607171}, "total": 149607171} | [
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] | ede2413cf32d81862a427f29f99d6a8f37d04637 | [
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"chem",
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"arxiv:2508.01630",
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"region:us"
] | null | {"architectures": ["ModernBertForTokenClassification"], "model_type": "modernbert", "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": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["token-classification", "named-entity-recognition", "biomedical-nlp", "transform... | null | null | [
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] | null | [
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66a9b02f8963e4a05d1f05ba | 54895f2d8faef315 | e6ffdc5dd5f1fd86 | 256,000 | BPE | 580,604 | true | true | true | true | 249 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
"Fuse"
] | 50 | unsloth/gemma-2-2b-bnb-4bit | unsloth | {
"models": [
{
"_id": "66962a41534f204a2b9982fd",
"id": "google/gemma-2-2b"
}
],
"relation": "quantized"
} | 3,936 | 241,409 | False | 2024-07-31T03:31:59Z | 2024-11-22T08:17:04Z | transformers | 12 | 0 | null | text-generation | {"parameters": {"F32": 173056, "BF16": 590065920, "U8": 2087597414}, "total": 2677836390} | [
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] | 341a487bce54fbeb136e13786461a1e66ec3ffbc | [
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"gemma",
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"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"deploy:azure",
"re... | null | {"architectures": ["Gemma2ForCausalLM"], "model_type": "gemma2", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": "<bos>", "eos_token": "<eos>", "pad_token": "<pad>", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "google/gemma-2-2b", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "gemma", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["unsloth", "transformers", "gemma2", "gemma", "google"]} | # Finetune Gemma, Llama 3, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a Google Colab Tesla T4 notebook for **Gemma 2 (9B)** here: https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%... | null | [
"gemma"
] | null | [
"en"
] | 2,677,836,390 | null | null | [
"AutoModelForCausalLM",
"gemma2",
"Gemma2ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
622cdf0c45261ac5cc0bdaf7 | 6cb1e7b9477371bb | 0c920ea67da16983 | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 457 | richielo/small-e-czech-finetuned-ner-wikiann | richielo | null | 10,250 | 250,914 | False | 2022-03-12T17:57:32Z | 2022-03-12T20:18:42Z | transformers | 3 | 0 | [{"name": "small-e-czech-finetuned-ner-wikiann", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "args": "cs"}, "metrics": [{"name": "Precision", "type": "precision", "value": 0.8713322894683097, "verified": false}, {"name": "Recal... | token-classification | null | [
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] | null | {"architectures": ["ElectraForTokenClassification"], "model_type": "electra", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForTokenClassification",
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"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"datasets": ["wikiann"], "license": "cc-by-4.0", "metrics": ["precision", "recall", "f1", "accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "small-e-czech-finetuned-ner-wikiann", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann"... | <!-- 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. -->
# small-e-czech-finetuned-ner-wikiann
This model is a fine-tuned version of [Seznam/small-e-czech](https://huggingface.co/Seznam/sm... | null | [
"cc-by-4.0"
] | [
"wikiann"
] | null | null | null | [
"precision",
"recall",
"f1",
"accuracy"
] | [
"ElectraForTokenClassification",
"AutoModelForTokenClassification",
"electra"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc036468d709f1753d3 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | Elron/bleurt-base-512 | Elron | null | 61,713 | 269,828 | False | 2022-03-02T23:29:04Z | 2021-10-04T13:23:33Z | transformers | 1 | 0 | null | text-classification | null | [
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"config.json",
"pytorch_model.bin",
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] | 4f4abeeba7c29ded45fc90b8a66eb49c8569f587 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | null | \n## BLEURT
Pytorch version of the original BLEURT models from ACL paper ["BLEURT: Learning Robust Metrics for Text Generation"](https://aclanthology.org/2020.acl-main.704/) by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
The code for model conversion was originated from [this notebook](http... | null | null | null | null | null | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
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6877fd38bc0a29a38cef4cec | e034328a2d067fa5 | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 237 | OpenMed/OpenMed-NER-PathologyDetect-SuperClinical-141M | OpenMed | null | 91,922 | 552,026 | False | 2025-07-16T19:27:52Z | 2025-08-05T09:35:32Z | transformers | 0 | 0 | null | token-classification | {"parameters": {"BF16": 141306627}, "total": 141306627} | [
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A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
> Call it little_berty
### BibTeX entry and citation info
Please cite in your publication as the following:
```bibtex
@misc{ALBERTPersian,
author = {Hooshvare Team},
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This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
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**Specialized model for Disease Entity Recognition - Disease entities from the BC5CDR dataset**
[](https://opensource.org... | null | [
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Долили сильно больше данных в sft, теперь стабильнее работает json и multiturn, слегка подточили параметры претрена модели
Added a lot more data to sft, now json and multiturn work more stable on long context and hard prompts
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