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DeciCoder 1B is a 1 billion parameter decoder-only code completion model
trained on the Python, Java, and Javascript subsets of [Starcoder Training Dataset](https://huggingface.co/datasets/bigcode/starcoderdata).
The model uses Grouped Query Attention and has a context window of 2048
toke... | null | [
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<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 | [
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T5-Efficient-TINY is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
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"text-generation-inference",
"endpoints_... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<unk>", "unk_token": "<unk>", "use_default_system_prompt": true}} | {
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## Model Description
LLaMA-2-7B-32K is an open-source, long context language model developed by Together, fine-tuned from Meta's original Llama-2 7B model.
This model represents our efforts to contribute to the rapid progress of the open-source ecosystem for large language models.
The model has bee... | null | [
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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... | {
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Directly quantized 4bit model with `bitsandbytes`.
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.githubuserconten... | null | [
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"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"tokenizer_config": {"bos_token": "<|endoftext|>", "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assista... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": "xiuyul/mamba-2.8b-ultrachat", "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "eval_results": [], "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": "mamba-2.8b-zephyr", "pipeline_tag": null, "tags": null} | # mamba-2.8b-zephyr
This model is a fine-tuned version of [xiuyul/mamba-2.8b-ultrachat](https://huggingface.co/xiuyul/mamba-2.8b-ultrachat) on the [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) dataset trained using [Direct Preference Optimization (DPO)](... | null | [
"apache-2.0"
] | [
"HuggingFaceH4/ultrafeedback_binarized"
] | null | 2,768,345,600 | null | null | [
"AutoModel"
] | [
null
] | null | null | null |
687812c5362d1a11aee2ac75 | 0f8423b4fa6a301e | 5f9f736c7dc6fbfa | 28,996 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 418 | OpenMed/OpenMed-NER-PharmaDetect-BioPatient-108M | OpenMed | null | 131,376 | 1,330,041 | False | 2025-07-16T20:59:49Z | 2025-08-05T09:42:22Z | transformers | 1 | 0 | null | token-classification | {"parameters": {"BF16": 107721987}, "total": 107721987} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"openmed_vs_sota_grouped_bars.png",
"special_tokens_map.json",
"test_results.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | c6a50beb895afa62fce8ced457a704f89b915a21 | [
"transformers",
"safetensors",
"bert",
"token-classification",
"named-entity-recognition",
"biomedical-nlp",
"chemical-entity-recognition",
"drug-discovery",
"pharmacology",
"biocuration",
"chem",
"en",
"arxiv:2508.01630",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["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": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["token-classification", "named-entity-recognition", "biomedical-nlp", "transform... | null | null | [
"apache-2.0"
] | null | [
"en"
] | 107,721,987 | null | null | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
677ef480edc5741bc2f6e66a | 7ebc9855769c36cb | 09fc399b14d43f49 | 100,352 | BPE | 100,000 | false | true | false | true | 97 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 444 | unsloth/phi-4 | unsloth | {
"models": [
{
"_id": "67597bd1b9cdca50cb621f94",
"id": "microsoft/phi-4"
}
],
"relation": "finetune"
} | 26,522 | 627,946 | False | 2025-01-08T21:56:16Z | 2025-01-13T18:54:20Z | transformers | 90 | 0 | null | text-generation | {"parameters": {"BF16": 14659507200}, "total": 14659507200} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00006.safetensors",
"model-00002-of-00006.safetensors",
"model-00003-of-00006.safetensors",
"model-00004-of-00006.safetensors",
"model-00005-of-00006.safetensors",
"model-00006-of-00006.safetens... | c6220bde10fff762dbd72c3331894aa4cade249d | [
"transformers",
"safetensors",
"llama",
"text-generation",
"phi",
"phi4",
"unsloth",
"nlp",
"math",
"code",
"chat",
"conversational",
"en",
"arxiv:2412.08905",
"base_model:microsoft/phi-4",
"base_model:finetune:microsoft/phi-4",
"license:mit",
"text-generation-inference",
"endpoi... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|endoftext|>", "chat_template": "{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start... | {
"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.***
# unsloth/Phi-4
We have converted Phi-4 to Llama's architecture for improved ease of use, better fine-tuning, and greater accuracy. Also cont... | null | [
"mit",
"https://huggingface.co/microsoft/phi-4/resolve/main/LICENSE"
] | null | [
"en"
] | 14,659,507,200 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
68187ec7128a3add1fe4fd08 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Qwen/Qwen3-30B-A3B-GPTQ-Int4 | Qwen | {
"models": [
{
"_id": "680da7c9aa6c98cb395660ac",
"id": "Qwen/Qwen3-30B-A3B"
}
],
"relation": "quantized"
} | 60,648 | 1,956,997 | False | 2025-05-05T09:03:03Z | 2025-05-21T06:08:09Z | transformers | 49 | 0 | null | text-generation | {"parameters": {"I32": 29896998912, "F16": 635123712}, "total": 30532122624} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"quantize_config.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 9b534e4318b7ebc3c961a839f13eb18b1833f441 | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"arxiv:2309.00071",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-30B-A3B",
"base_model:quantized:Qwen/Qwen3-30B-A3B",
"license:apache-2.0",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen3-30B-A3B", "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/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generati... | # Qwen3-30B-A3B-GPTQ-Int4
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Qwen3 Highlights
Qwen3 is the latest generation of large... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE"
] | null | null | 30,532,122,624 | null | null | [
"qwen3_moe",
"AutoModelForCausalLM",
"Qwen3MoeForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
685f879e6a9cf2c4405f4d9e | bc23e733a7166c39 | 61daf977e586b75c | 101,304 | BPE | 182,926 | true | false | true | true | 1,029 | Sequence | null | Sequence | [
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse"
] | 487 | baidu/ERNIE-4.5-0.3B-PT | baidu | null | 12,078 | 537,291 | False | 2025-06-28T06:11:42Z | 2025-08-29T06:48:30Z | transformers | 103 | 0 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"added_tokens.json",
"chat_template.jinja",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | b565cf6caebdb7a1eadf00100857b1ed5e044f12 | [
"transformers",
"safetensors",
"ernie4_5",
"text-generation",
"ERNIE4.5",
"conversational",
"en",
"zh",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Ernie4_5ForCausalLM"], "model_type": "ernie4_5", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<|begin_of_sentence|>", "eos_token": "</s>", "mask_token": "<mask:1>", "pad_token": "<unk>", "sep_token": "<|end_of_sentence|>", "unk_token": "<unk>", "use_default_system_prompt": false}, "chat_te... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "zh"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["ERNIE4.5"]} | <div align="center" style="line-height: 1;">
<a href="https://ernie.baidu.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖_Chat-ERNIE_Bot-blue" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/baidu" target="_blank" s... | null | [
"apache-2.0"
] | null | [
"en",
"zh"
] | null | null | null | [
"AutoModelForCausalLM",
"ernie4_5",
"Ernie4_5ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63c21bc71b5c1a514dd54600 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | YuxinJiang/sup-promcse-roberta-large | YuxinJiang | null | 7 | 488,606 | False | 2023-01-14T03:04:39Z | 2023-04-05T14:03:50Z | transformers | 3 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"tokenizer.json",
"vocab.json"
] | 33ce350610acae31a27790152d24f83ee98f6bdd | [
"transformers",
"pytorch",
"roberta",
"arxiv:2203.06875",
"arxiv:1908.10084",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForCL"], "model_type": "roberta"} | {
"auto_model": "RobertaForCL",
"custom_class": null,
"pipeline_tag": null,
"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} | # PromCSE: Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning
[](https://colab.research.google.com/drive/1lanXViJzbmGM1bwm8AflNUKmrvDidg_3?usp=sharing)
arXiv link: https://arxiv.org/abs/2203.0... | null | [
"mit"
] | null | null | null | null | null | [
"roberta",
"RobertaForCL"
] | [
null
] | null | null | null |
621ffdc136468d709f1794d4 | a4ea8f3be4c3e2c7 | 873dd6f00e997067 | 64,000 | BPE | 63,740 | false | true | true | true | 4 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 488 | aubmindlab/aragpt2-base | aubmindlab | null | 7,774 | 684,043 | False | 2022-03-02T23:29:05Z | 2023-10-30T13:53:25Z | transformers | 31 | 0 | null | text-generation | {"parameters": {"F32": 134994432, "U8": 12582912}, "total": 147577344} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"runs/eval/events.out.tfevents.1608681654.tpu-mother",
"runs/eval_results.txt",
"runs/events.out.tfevents.1608138983.tpu-mother",
"runs/events.out.tfevents.160... | 257d7289dac3605a6cd903ff71d67a52fc5d09aa | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"ar",
"arxiv:2012.15520",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2"} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "ar", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"text": "\u064a\u062d\u0643\u0649 \u0623\u0646 \u0645\u0632\u0627\u0631\u... | # Arabic GPT2
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/AraGPT2.png" width="100" align="left"/>
You can find more information in our paper [AraGPT2](https://arxiv.org/abs/2012.15520)
The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning G... | null | null | null | [
"ar"
] | 147,577,344 | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65743679a4ee9a4fe7750e5e | 6950482313cc6dd9 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 68 | OrdalieTech/Solon-embeddings-large-0.1 | OrdalieTech | null | 25,468 | 559,021 | False | 2023-12-09T09:42:17Z | 2024-03-26T15:53:50Z | transformers | 52 | 0 | [{"name": "Solon-embeddings-large-0.1", "results": [{"task": {"type": "sentence-similarity", "name": "Passage Retrieval"}, "dataset": {"type": "unicamp-dl/mmarco", "name": "mMARCO-fr", "config": "french", "split": "validation"}, "metrics": [{"type": "recall_at_500", "name": "Recall@500", "value": 92.7, "verified": fals... | feature-extraction | {"parameters": {"F32": 559890432}, "total": 559890432} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"sentence_bert_config.json",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | 9f6465f6ea2f6d10c6294bc15d84edf87d47cdef | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"mteb",
"fr",
"license:mit",
"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": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"language": ["fr"], "license": "mit", "tags": ["mteb"], "model-index": [{"name": "Solon-embeddings-large-0.1", "results": [{"task": {"type": "sentence-similarity", "name": "Passage Retrieval"}, "dataset": {"name": "mMARCO-fr", "type": "unicamp-dl/mmarco", "config": "french", "split": "validation"}, "metrics": [{"type"... | # Solon Embeddings — large 0.1
SOTA Open source french embedding model.
**Instructions :**
Add "query : " before the *query* to retrieve to increase performance of retrieval.
No instructions needed for *passages*.
| Model | Mean Score |
| --- | --- |
| **OrdalieTech/Solon-embeddings-large-0.1** | 0.7490 |
| coh... | null | [
"mit"
] | null | [
"fr"
] | 559,890,432 | null | null | [
"XLMRobertaModel",
"AutoModel",
"xlm-roberta"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
66a0e8f313f9b1a830b6b08c | c6de6331832e687a | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 142 | hugging-quants/Meta-Llama-3.1-70B-Instruct-GPTQ-INT4 | hugging-quants | null | 895 | 596,263 | False | 2024-07-24T11:43:47Z | 2024-08-07T07:28:59Z | transformers | 23 | 0 | null | text-generation | {"parameters": {"I32": 68451041280, "F16": 2109382656}, "total": 70560423936} | [
".gitattributes",
"LICENSE",
"README.md",
"USE_POLICY.md",
"config.json",
"generation_config.json",
"model-00001-of-00009.safetensors",
"model-00002-of-00009.safetensors",
"model-00003-of-00009.safetensors",
"model-00004-of-00009.safetensors",
"model-00005-of-00009.safetensors",
"model-00006-o... | 1b0ae7f9d6da8b79f36fdc24912f950ecb2b6e91 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3.1",
"meta",
"autogptq",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"license:llama3.1",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"deploy:azure",
"region:... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "de", "fr", "it", "pt", "hi", "es", "th"], "library_name": "transformers", "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["llama-3.1", "met... | > [!IMPORTANT]
> This repository is a community-driven quantized version of the original model [`meta-llama/Meta-Llama-3.1-70B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct) which is the FP16 half-precision official version released by Meta AI.
## Model Information
The Meta Llama 3.1 collec... | null | [
"llama3.1"
] | null | [
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th"
] | 70,560,423,936 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"Prepend",
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"Replace",
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"model-00005-of-00005.safetensors",
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"solar_logo.... | 5a06298f374dae1fb918d73fba72c7722ce71d36 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2312.15166",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": null, "unk_token": "<unk>", "use_default_system_prompt": true}} | {
"auto_model": "AutoModelForCausalLM",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | <p align="left">
<a href="https://console.upstage.ai/">
<img src="https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0/resolve/main/solar-api-banner.png" width="100%"/>
</a>
<p>
# **Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!**
# **Introduction**
We introduce SOLAR-10.7B, an a... | null | [
"apache-2.0"
] | null | null | 10,731,524,096 | null | null | [
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] | 2a050a4c59d687a85324d32e147517992117ed30 | [
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"text-generation",
"conversational",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<\uff5cbegin\u2581of\u2581sentence\uff5c>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|EOT|>", "lstr... | {
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<img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https... | null | [
"other",
"deepseek",
"LICENSE"
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"BertPreTokenizer"
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"en"
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"README.md",
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"tokenizer.json",
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] | a6083667f229a8b1503c816c863fd21be053871d | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"license:apache-2.0",
"text-generation-inference",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "unk_token": "<unk>", "use_default_system_prompt": true}} | {
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MistralLite is a fine-tuned [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) language model, with enhanced capabilities of processing long context (up to 32K tokens). By utilizing an adapted Rotary Embedding and sliding window during fine-tuning, MistralLite is able to **perform ... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
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"text"
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"fill-mask",
"en",
"arxiv:1406.2661",
"license:apache-2.0",
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] | null | {"architectures": ["ElectraForMaskedLM"], "model_type": "electra", "tokenizer_config": {}} | {
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**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" inp... | null | [
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] | null | [
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] | null | null | null | [
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"cell-line-name",
"en",
"arxiv:2508.01630",
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"Metaspace"
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"id": "google/flan-t5-base"
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],
"relation": "finetune"
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"base_model:finetune:google/flan-t5-base",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
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"pipeline_tag": "text2text-generation",
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} | {"base_model": ["google/flan-t5-base"], "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | This is a flan-t5-based model pre-trained on VG scene graph parsing dataset first and then fine-tuned on FACTUAL scene graph parsing dataset.
See model details from 'https://github.com/zhuang-li/FACTUAL/tree/main '.
If you use the model, please cite:
```
@inproceedings{li-etal-2023-factual,
title = "{FACTUAL}: A ... | null | null | null | null | 247,577,856 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
621ffdc136468d709f17a0d1 | ee291bff3d5dc545 | 483fa5044ce5bdc8 | 50,500 | BPE | 50,000 | false | true | true | true | 240 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 292 | climatebert/distilroberta-base-climate-sentiment | climatebert | null | 727 | 483,078 | False | 2022-03-02T23:29:05Z | 2023-06-02T13:53:52Z | transformers | 5 | 0 | null | text-classification | {"parameters": {"I64": 514, "F32": 82301187}, "total": 82301701} | [
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"tokenizer.json",
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] | e9f9a94ee4263f5ad5cfc97b8539a497fc88aa7d | [
"transformers",
"pytorch",
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"roberta",
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"en",
"dataset:climatebert/climate_sentiment",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": fa... | {
"auto_model": "AutoModelForSequenceClassification",
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"pipeline_tag": "text-classification",
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} | {"base_model": null, "datasets": ["climatebert/climate_sentiment"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": null, "pipeline_tag": null, "tags": null} | # Model Card for distilroberta-base-climate-sentiment
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the climate-related sentiment classes opportunity, neutral, or risk.
Using the [climatebert/distilroberta-base-climat... | null | [
"apache-2.0"
] | [
"climatebert/climate_sentiment"
] | [
"en"
] | 82,301,701 | null | [
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] | [
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"text"
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672b1f86fbdaf22d9dd81520 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
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"Split",
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"ByteLevel"
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"models": [
{
"_id": "672dec5fb852a01eae148d38",
"id": "Qwen/Qwen2.5-Coder-0.5B"
}
],
"relation": "finetune"
} | 469,882 | 13,518,856 | False | 2024-11-06T07:49:26Z | 2024-11-18T12:52:41Z | transformers | 65 | 0 | null | text-generation | {"parameters": {"BF16": 494032768}, "total": 494032768} | [
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] | ea3f2471cf1b1f0db85067f1ef93848e38e88c25 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"codeqwen",
"chat",
"qwen",
"qwen-coder",
"conversational",
"en",
"arxiv:2409.12186",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-Coder-0.5B",
"base_model:finetune:Qwen/Qwen2.5-Coder-0.5B",
"license:apache-2.0",
"tex... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Aliba... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Qwen/Qwen2.5-Coder-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-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeli... | # Qwen2.5-Coder-0.5B-Instruct
## 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-Code... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | 494,032,768 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"Punctuation",
"ByteLevel",
"Digits",
"Split"
] | [
"ByteLevel"
] | 45 | trl-internal-testing/tiny-FalconMambaForCausalLM | trl-internal-testing | null | 86,446 | 660,600 | False | 2024-11-25T15:05:23Z | 2025-12-19T19:09:37Z | transformers | 0 | 0 | null | text-generation | {"parameters": {"BF16": 525400}, "total": 525400} | [
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This is a minimal model built for unit tests in the [TRL](https://github.com/huggingface/trl) library. | null | null | null | null | 525,400 | null | null | [
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*BioClinical ModernBERT is available in two sizes: [base](https://huggingface.co/thomas-sounack/BioClinical-ModernBERT-base) (150M parameters) and [large](https://huggingface.co/thomas-sounack/BioClinical-ModernBERT-large) (396M parameters). The model training checkpoints can be found [here](h... | null | [
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## 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. Qwen... | null | [
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<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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<p style="margin-bottom: 0; margin-top: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/qwen3-680edabfb790c8c34a242f95">our collection</a> for all versions of Qwen3 including GGUF, 4-bit & 16-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em>Learn to run Qwen3 correct... | null | [
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<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 | [
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BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) pre-training procedure.... | null | [
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"added_tokens.json",
"all_results.json",
"config.json",
"eval_results.json",
"model.safetensors",
"onnx/added_tokens.json",
"onnx/config.json",
"onnx/model.onnx",
"onnx/special_tokens_map.json",
"onnx/spm.model",
"onnx/tokenizer.json",
"onnx/tokenizer_config.... | 9814d1113e03f72e8d383423188a51b43d5534c9 | [
"transformers",
"onnx",
"safetensors",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"en",
"dataset:ai4privacy/pii-masking-200k",
"dataset:Isotonic/pii-masking-200k",
"base_model:microsoft/deberta-v3-base",
"base_model:quantized:microsoft/deberta-v3-base",
"license:cc-by-nc-4... | null | {"architectures": ["DebertaV2ForTokenClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": "microsoft/deberta-v3-base", "datasets": ["ai4privacy/pii-masking-200k", "Isotonic/pii-masking-200k"], "eval_results": [], "language": ["en"], "library_name": null, "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": ["seqeval"], "model_name": "deberta-v3-base_finetuned_ai4p... | # deberta-v3-base_finetuned_ai4privacy_v2
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the [ai4privacy/pii-masking-200k](https://huggingface.co/ai4privacy/pii-masking-200k) dataset.
## Useage
GitHub Implementation: [Ai4Privacy](https://github.c... | null | [
"cc-by-nc-4.0"
] | [
"ai4privacy/pii-masking-200k",
"Isotonic/pii-masking-200k"
] | [
"en"
] | 183,916,911 | null | [
"seqeval"
] | [
"DebertaV2ForTokenClassification",
"AutoModelForTokenClassification",
"deberta-v2"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
67aa64f16024056209c124c0 | 5b8a6156b5b6e013 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 37 | WebOrganizer/TopicClassifier-NoURL | WebOrganizer | {
"models": [
{
"_id": "66232e36df8e46344246d0f6",
"id": "Alibaba-NLP/gte-base-en-v1.5"
}
],
"relation": "finetune"
} | 23,692 | 567,798 | False | 2025-02-10T20:43:29Z | 2025-02-19T17:22:19Z | transformers | 14 | 0 | null | text-classification | {"parameters": {"F32": 137385240}, "total": 137385240} | [
".gitattributes",
"README.md",
"config.json",
"configuration.py",
"model.safetensors",
"modeling.py",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 8f60a8f97177f5664a81ad1c2789412fbb38351d | [
"transformers",
"safetensors",
"new",
"text-classification",
"custom_code",
"dataset:WebOrganizer/TopicAnnotations-Llama-3.1-8B",
"dataset:WebOrganizer/TopicAnnotations-Llama-3.1-405B-FP8",
"arxiv:2502.10341",
"base_model:Alibaba-NLP/gte-base-en-v1.5",
"base_model:finetune:Alibaba-NLP/gte-base-en-... | null | {"architectures": ["NewForSequenceClassification"], "auto_map": {"AutoConfig": "configuration.NewConfig", "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel", "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM", "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice", ... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": "modeling.NewForSequenceClassification",
"pipeline_tag": "text-classification",
"processor": null
} | {"base_model": ["Alibaba-NLP/gte-base-en-v1.5"], "datasets": ["WebOrganizer/TopicAnnotations-Llama-3.1-8B", "WebOrganizer/TopicAnnotations-Llama-3.1-405B-FP8"], "eval_results": null, "language": null, "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_na... | # WebOrganizer/TopicClassifier-NoURL
[[Paper](https://arxiv.org/abs/2502.10341)] [[Website](https://weborganizer.allenai.org)] [[GitHub](https://github.com/CodeCreator/WebOrganizer)]
The TopicClassifier-NoURL organizes web content into 17 categories based on the text contents of web pages (without using URL informati... | null | null | [
"WebOrganizer/TopicAnnotations-Llama-3.1-8B",
"WebOrganizer/TopicAnnotations-Llama-3.1-405B-FP8"
] | null | 137,385,240 | null | null | [
"new",
"NewForSequenceClassification",
"AutoModelForSequenceClassification",
"modeling.NewForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc036468d709f17432d | a3d54d6c1d5c62b4 | f87d7bfce4fd9617 | 30,000 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Replace",
"Replace",
"NFKD",
"StripAccents",
"Lowercase",
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 12 | albert/albert-xlarge-v2 | albert | null | 1,130 | 474,035 | False | 2022-03-02T23:29:04Z | 2024-04-10T09:57:46Z | transformers | 13 | 0 | null | fill-mask | {"parameters": {"F32": 59047392}, "total": 59047392} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"spiece.model",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"with-prefix-tf_model.h5"
] | 4fd2c2aa9aeb305f87704a7e595be7bfffa3db88 | [
"transformers",
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["AlbertForMaskedLM"], "model_type": "albert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["bookcorpus", "wikipedia"], "eval_results": null, "language": "en", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # ALBERT XLarge v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not mak... | null | [
"apache-2.0"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | 59,047,392 | null | null | [
"albert",
"AutoModelForMaskedLM",
"AlbertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17b79e | 2d0341bd3a73bae6 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 32 | google/t5-large-lm-adapt | google | null | 163 | 469,784 | False | 2022-03-02T23:29:05Z | 2023-01-24T16:52:08Z | transformers | 8 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json"
] | 50221db6cb0b917d3d0f7441b2af8c6928d563ee | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"t5-lm-adapt",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["c4"], "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": ["t5-lm-adapt"]} | [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 - LM-Adapted
## Version 1.1 - LM-Adapted
[T5 Version 1.1 - LM Adapted](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) includes the follo... | null | [
"apache-2.0"
] | [
"c4"
] | [
"en"
] | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
621ffdc136468d709f17a13f | 52bad4bf7b21579a | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 39 | cointegrated/roberta-large-cola-krishna2020 | cointegrated | null | 755 | 472,454 | False | 2022-03-02T23:29:05Z | 2023-06-13T09:38:15Z | transformers | 7 | 0 | null | text-classification | {"parameters": {"I64": 514, "F32": 355361794}, "total": 355362308} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | efffc0dbebedfdb84747a32b30ec0c3df15812b1 | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"arxiv:2010.05700",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | null | This is a RoBERTa-large classifier trained on the CoLA corpus [Warstadt et al., 2019](https://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00290),
which contains sentences paired with grammatical acceptability judgments. The model can be used to evaluate fluency of machine-generated English sentences, e.g. for eval... | null | null | null | null | 355,362,308 | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17e10c | cde80f9ffa04f8c6 | a4df73d11717bfec | 32,500 | WordPiece | null | true | true | true | true | 7 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 491 | monologg/kobigbird-bert-base | monologg | null | 3,403 | 481,268 | False | 2022-03-02T23:29:05Z | 2023-06-12T12:30:09Z | transformers | 23 | 0 | null | fill-mask | {"parameters": {"I64": 4096, "F32": 114380022}, "total": 114384118} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 8a37e13f8166f9c9ffbd32c4040ef0d919406b66 | [
"transformers",
"pytorch",
"safetensors",
"big_bird",
"fill-mask",
"korean",
"ko",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BigBirdForMaskedLM"], "model_type": "big_bird", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "bos_token": "<s>", "eos_token": "</s>"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "ko", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["korean"], "mask_token": "[MASK]", "widget": [{"text": "\ub300\ud55c\ubbfc\uad6d\uc758 \uc... | # KoBigBird
<img src="https://user-images.githubusercontent.com/28896432/140442206-e34b02d5-e279-47e5-9c2a-db1278b1c14d.png" width="200"/>
Pretrained BigBird Model for Korean (**kobigbird-bert-base**)
## About
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to m... | null | null | null | [
"ko"
] | 114,384,118 | null | null | [
"big_bird",
"AutoModelForMaskedLM",
"BigBirdForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
649a8cd32e34c69015602a0f | c2e19ed484926649 | 84fb34b2e1636fc5 | 30,527 | WordPiece | null | true | true | true | true | 6 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 492 | haddadalwi/multi-qa-mpnet-base-dot-v1-finetuned-squad2-all | haddadalwi | null | 1 | 9 | False | 2023-06-27T07:16:35Z | 2023-06-27T07:29:46Z | transformers | 0 | 0 | [{"name": "multi-qa-mpnet-base-dot-v1-finetuned-squad2-all", "results": []}] | question-answering | null | [
".gitattributes",
".gitignore",
"README.md",
"config.json",
"pytorch_model.bin",
"runs/Jun27_07-16-30_10c431b6def2/events.out.tfevents.1687850200.10c431b6def2.1196.0",
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"tokenizer.json",
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"transformers",
"pytorch",
"tensorboard",
"mpnet",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["MPNetForQuestionAnswering"], "model_type": "mpnet", "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": "AutoModelForQuestionAnswering",
"custom_class": null,
"pipeline_tag": "question-answering",
"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": "multi-qa-mpnet-base-dot-v1-finetuned-squad2-all", "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. -->
# multi-qa-mpnet-base-dot-v1-finetuned-squad2-all
This model is a fine-tuned version of [sentence-transformers/multi-qa-mpnet-base-... | null | null | null | null | null | null | null | [
"AutoModelForQuestionAnswering",
"MPNetForQuestionAnswering",
"mpnet"
] | [
"question-answering"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66f0cf1cfcc1c455f85fb460 | 643d190440f20cd9 | 84110b1ed2b9294f | 151,666 | BPE | 151,387 | true | true | true | true | 23 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 493 | unsloth/Qwen2.5-32B-bnb-4bit | unsloth | {
"models": [
{
"_id": "66e6d09952865ca1e41c6566",
"id": "Qwen/Qwen2.5-32B"
}
],
"relation": "quantized"
} | 2,902 | 473,322 | False | 2024-09-23T02:14:52Z | 2025-04-28T04:15:47Z | transformers | 3 | 0 | null | text-generation | {"parameters": {"F32": 2026496, "BF16": 1558254592, "U8": 32180952044}, "total": 33741233132} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"special_to... | eb5bd6f3b0aaaa1475bb75d43e1cce7c8df90bc4 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-32B",
"base_model:quantized:Qwen/Qwen2.5-32B",
"license:apache-2.0",... | 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": "<|PAD_TOKEN|>", "unk_token": null}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-32B", "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, "p... | # 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.resear... | null | [
"apache-2.0"
] | null | [
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara"
] | 33,741,233,132 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66f6347bc2673e97c895e4dd | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | NousResearch/Llama-3.2-1B | NousResearch | null | 19,795 | 562,253 | False | 2024-09-27T04:28:43Z | 2024-09-27T04:33:27Z | transformers | 19 | 0 | null | text-generation | {"parameters": {"BF16": 1235814400}, "total": 1235814400} | [
".gitattributes",
"LICENSE.txt",
"README.md",
"USE_POLICY.md",
"config.json",
"generation_config.json",
"model.safetensors",
"original/consolidated.00.pth",
"original/params.json",
"original/tokenizer.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | a9745ffc3556f145a830ac0c203509ba860582a3 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"license:llama3.2",
"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": null, "datasets": null, "eval_results": null, "language": ["en", "de", "fr", "it", "pt", "hi", "es", "th"], "library_name": "transformers", "license": "llama3.2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["facebook", "meta... | ## Model Information
The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agen... | null | [
"llama3.2"
] | null | [
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th"
] | 1,235,814,400 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
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"text"
] | [
"text"
] | [
"text"
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621ffdc136468d709f180ccf | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | tbs17/MathBERT | tbs17 | null | 2,152 | 478,919 | False | 2022-03-02T23:29:05Z | 2021-08-05T00:44:29Z | transformers | 27 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"tokenizer.json",
"tokenizer_config.json",
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] | e26235ccf2b14614ef278b19caa44fdb5dcf050f | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | null | #### MathBERT model (original vocab)
*Disclaimer: the format of the documentation follows the official BERT model readme.md*
Pretrained model on pre-k to graduate math language (English) using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and English.... | null | null | null | null | null | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
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"text"
] | [
"text"
] | [
"logits"
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"Lowercase",
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"Replace",
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 494 | google/siglip-base-patch16-256-multilingual | google | null | 27,335 | 540,244 | False | 2024-01-08T13:24:51Z | 2024-09-26T08:21:48Z | transformers | 52 | 0 | null | zero-shot-image-classification | {"parameters": {"F32": 370626050}, "total": 370626050} | [
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] | 8952a4eafcde3cb7ab46b1dd629b33f8784ca9c6 | [
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"vision",
"arxiv:2303.15343",
"arxiv:2209.06794",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["SiglipModel"], "model_type": "siglip", "tokenizer_config": {"eos_token": "</s>", "pad_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForZeroShotImageClassification",
"custom_class": null,
"pipeline_tag": "zero-shot-image-classification",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["vision"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_image... | # SigLIP (base-sized model, multilingual)
SigLIP model pre-trained on WebLi at resolution 256x256. It was introduced in the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Zhai et al. and first released in [this repository](https://github.com/google-research/big_vision).
Dis... | null | [
"apache-2.0"
] | null | null | 370,626,050 | null | null | [
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"siglip",
"SiglipModel"
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680f77951f10e4a4ecbd4ca5 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
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"ByteLevel"
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{
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"id": "Qwen/Qwen3-32B"
}
],
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} | 76,250 | 3,269,026 | False | 2025-04-28T12:41:57Z | 2025-07-26T03:46:13Z | transformers | 82 | 1 | null | text-generation | null | [
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] | 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 {%-... | {
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} | {"base_model": ["Qwen/Qwen3-32B"], "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-32B-FP8/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation... | null | null | [
"apache-2.0",
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] | null | null | null | null | null | [
"AutoModelForCausalLM",
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66167dfaa0d3d76bfa2ed169 | ec51150e70ba291f | 711660f9db8f81ba | 79,099 | BPE | 152,143 | true | false | true | true | 17 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 495 | Vikhrmodels/Vikhr-7B-instruct_0.4 | Vikhrmodels | null | 478 | 462,982 | False | 2024-04-10T11:54:34Z | 2024-05-24T10:10:50Z | transformers | 35 | 0 | null | text-generation | {"parameters": {"BF16": 7627567104}, "total": 7627567104} | [
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... | dd960329b333cbe7f67396c5fe715f477b997639 | [
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"ru",
"en",
"arxiv:2405.13929",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["MistralForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "chat_template": "<s>{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|i... | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ru", "en"], "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # Релиз вихря 0.3-0.4
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
- [Google Colab](https://colab.research.google.com/drive/15O9LwZhVU... | null | null | null | [
"ru",
"en"
] | 7,627,567,104 | null | null | [
"AutoModelForCausalLM",
"llama",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
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66231225c8920ec35160357e | 803e3c8528c55bf1 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 352 | aaditya/Llama3-OpenBioLLM-8B | aaditya | {
"models": [
{
"_id": "661f97d48e7f3438386f755d",
"id": "meta-llama/Meta-Llama-3-8B"
}
],
"relation": "finetune"
} | 39,038 | 516,397 | False | 2024-04-20T00:53:57Z | 2025-01-18T07:40:39Z | transformers | 236 | 1 | [{"name": "OpenBioLLM-8B", "results": []}] | text-generation | null | [
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"... | 70d6bb521cab6ca755b675ade38831eedf89d31c | [
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"llama-3",
"Mixtral",
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"DPO",
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"base_model:meta-llama... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|end_of_text|>", "pad_token": "<|end_of_text|>"}} | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
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} | {"base_model": "meta-llama/Meta-Llama-3-8B", "datasets": null, "eval_results": [], "language": ["en"], "library_name": null, "license": "llama3", "license_name": null, "license_link": null, "metrics": null, "model_name": "OpenBioLLM-8B", "pipeline_tag": null, "tags": ["llama-3", "llama", "Mixtral", "instruct", "finetun... | <div align="center">
<img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div>
<div align="center">
<h1>Advanci... | null | [
"llama3"
] | null | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
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"text"
] | [
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] |
66faecf1a15bc0b3f0c55924 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | nvidia/NVLM-D-72B | nvidia | null | 106,378 | 948,845 | False | 2024-09-30T18:24:49Z | 2025-01-14T18:34:52Z | transformers | 775 | 0 | null | image-text-to-text | null | [
".gitattributes",
"Dockerfile",
"Notice.txt",
"README.md",
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"configuration_intern_vit.py",
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"eval/eval_dataset.py",
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"eval/mmmu_utils.py",
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... | 11e9e0f8d265d65ddaa8f9445b6f55fdec9d686f | [
"transformers",
"safetensors",
"NVLM_D",
"nvidia",
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] | null | {"architectures": ["NVLM_D"], "auto_map": {"AutoConfig": "configuration_nvlm_d.NVLM_D_Config", "AutoModel": "modeling_nvlm_d.NVLM_D_Model", "AutoModelForCausalLM": "modeling_nvlm_d.NVLM_D_Model"}, "model_type": "NVLM_D", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>sys... | {
"auto_model": "NVLM_D",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["nvidia", "NVLM", "pytorch", "multimodal", "conversa... | <p align="center">
<img src="nvlm-logo-light.png" alt="Image Description" width="300" >
</p>
# Model Overview
## Description
This family of models performs vision-language and text-only tasks including optical character recognition, multimodal reasoning, localization, common sense reasoning, world knowledge utiliz... | null | [
"cc-by-nc-4.0"
] | null | [
"en"
] | null | null | null | [
"NVLM_D"
] | [
"image-text-to-text",
null
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
62de62570c4f109908cf19bd | 8e42935d2dbeb45e | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 204 | deepset/deberta-v3-large-squad2 | deepset | null | 28,136 | 521,009 | False | 2022-07-25T09:28:55Z | 2024-09-24T16:02:07Z | transformers | 52 | 0 | [{"name": "deepset/deberta-v3-large-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 88.0876, "name": "Exact Match", "verified": true,... | question-answering | {"parameters": {"I64": 512, "F32": 434014210}, "total": 434014722} | [
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] | ff971b7ff7946434a93edc8271fa4de35eab29f7 | [
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"deberta-v2",
"question-answering",
"deberta",
"deberta-v3",
"deberta-v3-large",
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"dataset:squad_v2",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DebertaV2ForQuestionAnswering"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForQuestionAnswering",
"custom_class": null,
"pipeline_tag": "question-answering",
"processor": "AutoTokenizer"
} | {"datasets": ["squad_v2"], "language": "en", "license": "cc-by-4.0", "tags": ["deberta", "deberta-v3", "deberta-v3-large"], "model-index": [{"name": "deepset/deberta-v3-large-squad2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "... | # deberta-v3-large for Extractive QA
This is the [deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Ques... | null | [
"cc-by-4.0"
] | [
"squad_v2"
] | [
"en"
] | 434,014,722 | null | null | [
"AutoModelForQuestionAnswering",
"deberta-v2",
"DebertaV2ForQuestionAnswering"
] | [
"question-answering"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17ee54 | 71140a663949f258 | 8a01c02d4d0a401c | 119,547 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 9 | philschmid/distilbert-base-multilingual-cased-sentiment-2 | philschmid | null | 60 | 460,963 | False | 2022-03-02T23:29:05Z | 2022-01-24T15:08:50Z | transformers | 4 | 0 | [{"name": "distilbert-base-multilingual-cased-sentiment-2", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "amazon_reviews_multi", "type": "amazon_reviews_multi", "args": "all_languages"}, "metrics": [{"name": "Accuracy", "type": "accuracy", "value": 0.747566666... | text-classification | null | [
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"tokenizer.json"... | 83ff874f93aacbba79642abfe2a274a3c874232b | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"datasets": ["amazon_reviews_multi"], "license": "apache-2.0", "metrics": ["accuracy", "f1"], "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-multilingual-cased-sentiment-2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_r... | <!-- 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. -->
# distilbert-base-multilingual-cased-sentiment-2
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https:/... | null | [
"apache-2.0"
] | [
"amazon_reviews_multi"
] | null | null | null | [
"accuracy",
"f1"
] | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17e362 | 337fa60bc59000b3 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 496 | mrsinghania/asr-question-detection | mrsinghania | null | 275 | 460,724 | False | 2022-03-02T23:29:05Z | 2021-09-21T06:44:23Z | transformers | 7 | 0 | null | text-classification | null | [
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"config.json",
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"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
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] | 90b29f15265e6819044d484039b1ae9ca683342d | [
"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 | <i>Question vs Statement classifier</i> trained on more than 7k samples which were coming from spoken data in an interview setting
<b>Code for using in Transformers:</b>
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mrsinghania/asr-question-de... | null | null | null | null | null | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
63942be3a360270231557ac4 | 30094a1eabdbef67 | db117e9883c4d0ab | 50,257 | BPE | 50,000 | false | true | true | true | 1 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 4 | FredZhang7/distilgpt2-stable-diffusion-v2 | FredZhang7 | null | 1,388 | 465,952 | False | 2022-12-10T06:49:07Z | 2023-03-16T20:06:26Z | transformers | 100 | 0 | null | text-generation | {"parameters": {"F32": 81912576, "U8": 6291456}, "total": 88204032} | [
".gitattributes",
"README.md",
"config.json",
"constrastive_search.png",
"contrastive_comma_style.png",
"model.safetensors",
"pytorch_model.bin",
"tokenizer.json",
"training_args.bin"
] | f839bc9217d4bc3694e4c5285934b5e671012f85 | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"stable-diffusion",
"prompt-generator",
"arxiv:2210.14140",
"dataset:FredZhang7/stable-diffusion-prompts-2.47M",
"dataset:poloclub/diffusiondb",
"dataset:Gustavosta/Stable-Diffusion-Prompts",
"dataset:bartman081523/stable-dif... | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2"} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["FredZhang7/stable-diffusion-prompts-2.47M", "poloclub/diffusiondb", "Gustavosta/Stable-Diffusion-Prompts", "bartman081523/stable-diffusion-discord-prompts"], "eval_results": null, "language": null, "library_name": null, "license": "creativeml-openrail-m", "license_name": null, "licens... | # Fast GPT2 PromptGen
<style>
.container {
padding-left: 20px;
border-left: 5px solid gray;
}
</style>
<div class="container">
<p><strong><a href="https://huggingface.co/FredZhang7/anime-anything-promptgen-v2">Fast Anime PromptGen</a></strong> generates descriptive safebooru and danbooru tags for anime text-to-... | null | [
"creativeml-openrail-m"
] | [
"FredZhang7/stable-diffusion-prompts-2.47M",
"poloclub/diffusiondb",
"Gustavosta/Stable-Diffusion-Prompts",
"bartman081523/stable-diffusion-discord-prompts"
] | null | 88,204,032 | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66995c227f28c43e09622b77 | 595f48517e0303f3 | 12ebeebff692ebb1 | 131,072 | BPE | 269,443 | false | true | true | true | 1,000 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 497 | RedHatAI/Mistral-Nemo-Instruct-2407-FP8 | RedHatAI | null | 561 | 474,409 | False | 2024-07-18T18:17:06Z | 2024-07-19T13:06:48Z | transformers | 18 | 0 | null | text-generation | {"parameters": {"BF16": 1342592000, "F8_E4M3": 10905190400}, "total": 12247782400} | [
".gitattributes",
"README.md",
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"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"... | 2545394475682e98b69596fde20b7fa7b43789e1 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"fp8",
"vllm",
"conversational",
"en",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "quantization_config": {"quant_method": "fp8"}, "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{%... | {
"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": "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 | [
"en"
] | 12,247,782,400 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66eb44b9561b0d0436c6571b | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | unsloth/Qwen2.5-7B-Instruct | unsloth | {
"models": [
{
"_id": "66e81cbcd683a3f4e5291bbf",
"id": "Qwen/Qwen2.5-7B-Instruct"
}
],
"relation": "finetune"
} | 93,109 | 779,178 | False | 2024-09-18T21:23:05Z | 2025-04-28T04:16:22Z | transformers | 23 | 1 | null | text-generation | {"parameters": {"BF16": 7615616512}, "total": 7615616512} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"special_to... | a75c9dc945567a9b6f568b8503a0307731607bee | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"conversational",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"arxiv:2309.00071",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:fi... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Aliba... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-7B-Instruct", "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": ... | # 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.resear... | null | [
"apache-2.0"
] | null | [
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara"
] | 7,615,616,512 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
68780842a5dbb701d7f2b6db | e034328a2d067fa5 | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 237 | OpenMed/OpenMed-NER-GenomeDetect-SuperClinical-434M | OpenMed | null | 107,595 | 912,042 | False | 2025-07-16T20:14:58Z | 2025-08-05T09:31:21Z | transformers | 5 | 0 | null | token-classification | {"parameters": {"BF16": 434015235}, "total": 434015235} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"openmed_vs_sota_grouped_bars.png",
"special_tokens_map.json",
"spm.model",
"test_results.json",
"tokenizer.json",
"tokenizer_config.json"
] | 28a9248f0cf3f071775baf0ead7937811038872b | [
"transformers",
"safetensors",
"deberta-v2",
"token-classification",
"named-entity-recognition",
"biomedical-nlp",
"gene-recognition",
"protein-recognition",
"genomics",
"molecular-biology",
"gene/protein",
"en",
"arxiv:2508.01630",
"license:apache-2.0",
"endpoints_compatible",
"region... | null | {"architectures": ["DebertaV2ForTokenClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["token-classification", "named-entity-recognition", "biomedical-nlp", "transform... | null | null | [
"apache-2.0"
] | null | [
"en"
] | 434,015,235 | null | null | [
"DebertaV2ForTokenClassification",
"AutoModelForTokenClassification",
"deberta-v2"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17b1c6 | e62415c9ea18c5f6 | 192ec7dae77281ac | 32,102 | Unigram | null | true | true | true | true | 105 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 498 | flax-community/t5-recipe-generation | flax-community | null | 931 | 457,817 | False | 2022-03-02T23:29:05Z | 2023-08-03T00:04:15Z | transformers | 75 | 0 | null | text-generation | {"parameters": {"F32": 222903552}, "total": 222903552} | [
".gitattributes",
"README.md",
"chef-transformer.png",
"config.json",
"data/test.csv",
"data/test_generated.csv",
"demo/.gitignore",
"demo/Build Ingredients Vocab.ipynb",
"demo/README.md",
"demo/beam_search.py",
"demo/config.json",
"demo/images/chef-transformer.png",
"demo/images/logo.png",
... | 12ba57f5c4924642afdf556a761124638e06af51 | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"seq2seq",
"text-generation",
"recipe-generation",
"en",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text2text-generation", "tags": ["seq2seq", "t5", "text-generation", "recipe-generation"], "widget": [{"t... | 
# Chef Transformer (T5)
> This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/recipe-generation-model/7475), organized by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google.
Want to give it a try? Then what's the wait, head over to Hugg... | null | null | null | [
"en"
] | 222,903,552 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
650c03f4c275bbb4427507ff | 7935b8f626500ef9 | 830fa437e6f588f3 | 50,000 | BPE | 49,721 | true | true | true | true | 23 | NFKC | Sequence | ByteLevel | [
"NFKC"
] | [
"Split",
"Digits",
"Split",
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 499 | facebook/nougat-small | facebook | null | 2,334 | 475,316 | False | 2023-09-21T08:51:00Z | 2023-11-20T09:27:37Z | transformers | 30 | 0 | null | image-to-text | {"parameters": {"I64": 48020, "F32": 247383672}, "total": 247431692} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | dced72ae7bcdfcb82a9ef55543a90a7a5e4d32e6 | [
"transformers",
"pytorch",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"vision",
"nougat",
"image-to-text",
"arxiv:2308.13418",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["VisionEncoderDecoderModel"], "model_type": "vision-encoder-decoder", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "cc-by-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-to-text", "tags": ["vision", "nougat"]} | # Nougat model, small-sized version
Nougat model trained on PDF-to-markdown. It was introduced in the paper [Nougat: Neural Optical Understanding for Academic Documents](https://arxiv.org/abs/2308.13418) by Blecher et al. and first released in [this repository](https://github.com/facebookresearch/nougat/tree/main).
... | null | [
"cc-by-4.0"
] | null | null | 247,431,692 | null | null | [
"AutoModelForImageTextToText",
"vision-encoder-decoder",
"VisionEncoderDecoderModel"
] | [
"image-text-to-text",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
621ffdc136468d709f177990 | e3dd5ca733d28913 | c88295bc9f3d465b | 30,001 | Unigram | null | true | true | true | true | 9 | Sequence | Metaspace | Metaspace | [
"Replace",
"Replace",
"NFKD",
"StripAccents",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 500 | TsinghuaAI/CPM-Generate | TsinghuaAI | null | 2,574 | 457,816 | False | 2022-03-02T23:29:05Z | 2021-07-29T19:03:51Z | transformers | 42 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"spiece.model",
"tf_model.h5",
"tokenizer.json"
] | 0e4bcd995f9a9e70ba7c31f67df60e73a922676f | [
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"cpm",
"zh",
"arxiv:2012.00413",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2"} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["zh"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["cpm"]} | # CPM-Generate
## Model description
CPM (Chinese Pre-trained Language Model) is a Transformer-based autoregressive language model, with 2.6 billion parameters and 100GB Chinese training data. To the best of our knowledge, CPM is the largest Chinese pre-trained language model, which could facilitate downstream Chinese... | null | [
"mit"
] | null | [
"zh"
] | null | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
650c0d70c4724c21198859ed | e68b0b111c49dac2 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 28 | corrius/cross-encoder-mmarco-mMiniLMv2-L12-H384-v1 | corrius | null | 823 | 456,256 | False | 2023-09-21T09:31:28Z | 2023-09-28T13:31:41Z | transformers | 2 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 8a6d8730ad581e680e43e3b25bff04b93a7890d3 | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["XLMRobertaForSequenceClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": f... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | null | # Model Name: mmarco-mMiniLMv2-L12-H384-v1
This model is a re-upload of the `mmarco-mMiniLMv2-L12-H384-v1` from the `cross-encoder` user on HuggingFace. The intent is to provide continued access to the community after its original removal from HuggingFace Model Hub.
## Original Model Information
- **Author**: cross-... | null | null | null | null | null | null | null | [
"AutoModelForSequenceClassification",
"XLMRobertaForSequenceClassification",
"xlm-roberta"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65aa9a2c2582acc636e92622 | b679a407515528d6 | 66824fb64a2f47bb | 100,289 | BPE | 100,000 | false | true | false | true | 33 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 501 | stabilityai/stablelm-2-zephyr-1_6b | stabilityai | null | 5,146 | 480,790 | False | 2024-01-19T15:50:04Z | 2024-06-03T15:16:39Z | transformers | 186 | 0 | null | text-generation | {"parameters": {"F16": 1644515328}, "total": 1644515328} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"configuration_stablelm.py",
"generation_config.json",
"merges.txt",
"model.safetensors",
"modeling_stablelm.py",
"special_tokens_map.json",
"stablelm-2-zephyr-1_6b-OpenVINO-4bit.bin",
"stablelm-2-zephyr-1_6b-OpenVINO-4bit.xml",
"stabl... | 2f275b1127d59fc31e4f7c7426d528768ada9ea4 | [
"transformers",
"safetensors",
"gguf",
"stablelm",
"text-generation",
"causal-lm",
"conversational",
"en",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:allenai/ultrafeedback_binarized_cleaned",
"dataset:meta-math/MetaMathQA",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"dataset:op... | {"total": 1644515328, "architecture": "stablelm", "context_length": 4096, "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif messa... | {"architectures": ["StableLmForCausalLM"], "model_type": "stablelm", "tokenizer_config": {"bos_token": "<|endoftext|>", "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + me... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["HuggingFaceH4/ultrachat_200k", "allenai/ultrafeedback_binarized_cleaned", "meta-math/MetaMathQA", "WizardLM/WizardLM_evol_instruct_V2_196k", "openchat/openchat_sharegpt4_dataset", "LDJnr/Capybara", "Intel/orca_dpo_pairs", "hkust-nlp/deita-10k-v0"], "eval_results": null, "language": ["... | # `StableLM 2 Zephyr 1.6B`
## Model Description
`Stable LM 2 Zephyr 1.6B` is a 1.6 billion parameter instruction tuned language model inspired by [HugginFaceH4's Zephyr 7B](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) training pipeline. The model is trained on a mix of publicly available datasets and syntheti... | null | [
"other"
] | [
"HuggingFaceH4/ultrachat_200k",
"allenai/ultrafeedback_binarized_cleaned",
"meta-math/MetaMathQA",
"WizardLM/WizardLM_evol_instruct_V2_196k",
"openchat/openchat_sharegpt4_dataset",
"LDJnr/Capybara",
"Intel/orca_dpo_pairs",
"hkust-nlp/deita-10k-v0"
] | [
"en"
] | 1,644,515,328 | 1,644,515,328 | null | [
"AutoModelForCausalLM",
"stablelm",
"StableLmForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67a9d4cade8e0e83d380a6d2 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | AIDC-AI/Ovis2-8B | AIDC-AI | null | 2,107 | 486,094 | False | 2025-02-10T10:28:26Z | 2025-08-15T15:20:02Z | transformers | 75 | 0 | null | image-text-to-text | {"parameters": {"BF16": 8935282678}, "total": 8935282678} | [
".gitattributes",
"NOTICE",
"README.md",
"added_tokens.json",
"config.json",
"configuration_aimv2.py",
"configuration_ovis.py",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-... | c0730f752cf605d44788a08151cfede0caab714d | [
"transformers",
"safetensors",
"ovis",
"text-generation",
"MLLM",
"image-text-to-text",
"conversational",
"custom_code",
"en",
"zh",
"dataset:AIDC-AI/Ovis-dataset",
"arxiv:2405.20797",
"license:apache-2.0",
"region:us"
] | null | {"architectures": ["Ovis"], "auto_map": {"AutoConfig": "configuration_ovis.OvisConfig", "AutoModelForCausalLM": "modeling_ovis.Ovis"}, "model_type": "ovis", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_ovis.Ovis",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": ["AIDC-AI/Ovis-dataset"], "eval_results": null, "language": ["en", "zh"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["MLLM"]} | # Ovis2-8B
<div align="center">
<img src=https://cdn-uploads.huggingface.co/production/uploads/637aebed7ce76c3b834cea37/3IK823BZ8w-mz_QfeYkDn.png width="30%"/>
</div>
<span style="color: #ED7D31; font-size: 22px;">It is recommended to use the latest version: [Ovis2.5](https://huggingface.co/collections/AIDC-AI/ovis2... | null | [
"apache-2.0"
] | [
"AIDC-AI/Ovis-dataset"
] | [
"en",
"zh"
] | 8,935,282,678 | null | null | [
"ovis",
"AutoModelForCausalLM",
"modeling_ovis.Ovis",
"Ovis"
] | [
"image-text-to-text",
"text-generation"
] | [
"text",
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
66c526e07690afd2f4b3e057 | f5e7f943c396c76b | ddde944c7b357600 | 32,011 | BPE | 61,249 | true | false | true | true | 14 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 52 | unsloth/Phi-3.5-mini-instruct-bnb-4bit | unsloth | {
"models": [
{
"_id": "66bfbb1a43a701a837a745ac",
"id": "microsoft/Phi-3.5-mini-instruct"
}
],
"relation": "quantized"
} | 13,855 | 519,963 | False | 2024-08-20T23:29:36Z | 2024-11-22T08:17:06Z | transformers | 13 | 0 | null | text-generation | {"parameters": {"F32": 282112, "BF16": 197200896, "U8": 3737201864}, "total": 3934684872} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 9274d79d2f899c2b719ce874f7ee3bfc4a8a2b24 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"phi3",
"phi",
"microsoft",
"conversational",
"multilingual",
"arxiv:2404.14219",
"arxiv:2407.13833",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:quantized:microsoft/Phi-3.5-mini-instruct",
"license:mit",... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}{% if message['role'] == 'system' and message['content'] %}{{'<|sys... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "microsoft/Phi-3.5-mini-instruct", "datasets": null, "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": "https://huggingface.co/microsoft/Phi-3.5-mini-instruct/resolve/main/LICENSE", "metrics": null, "model_name": nu... | # Finetune Phi-3.5, Llama 3.1, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tesla T4 notebook for Phi-3.5 (mini) here: https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/D... | null | [
"mit",
"https://huggingface.co/microsoft/Phi-3.5-mini-instruct/resolve/main/LICENSE"
] | null | [
"multilingual"
] | 3,934,684,872 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6294c1351e87ffbe5c04c007 | 7b07d3f5339a7fda | 84fb34b2e1636fc5 | 30,527 | WordPiece | null | true | true | true | true | 6 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 449 | AAUBS/PatentSBERTa_V2 | AAUBS | null | 2,324 | 461,145 | False | 2022-05-30T13:05:57Z | 2024-08-26T09:54:54Z | sentence-transformers | 16 | 0 | null | sentence-similarity | null | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"eval/similarity_evaluation_sts-dev_results.csv",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
... | cf95524055d476037e453021653562556374160e | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MPNetModel"], "model_type": "mpnet", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "[UNK]", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "tr... | # PatentSBERTa_V2
PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT
Aalborg University Business School, AI: Growth-Lab
https://www.sciencedirect.com/science/article/abs/pii/S0040162524003329
https://github.com/AI-Growth-Lab/PatentSBERTa
This is a [sentence-tran... | null | null | null | null | null | null | null | [
"MPNetModel",
"AutoModel",
"mpnet"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
67e3f120abb5b6a5a3e6c1f3 | 616c061030c03d7b | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 195 | Qwen/Qwen2.5-VL-32B-Instruct-AWQ | Qwen | {
"models": [
{
"_id": "67dd8463d7019776f5fc5b23",
"id": "Qwen/Qwen2.5-VL-32B-Instruct"
}
],
"relation": "quantized"
} | 181,884 | 1,046,124 | False | 2025-03-26T12:20:48Z | 2025-04-06T16:25:43Z | transformers | 60 | 0 | null | image-text-to-text | {"parameters": {"I32": 31205621760, "BF16": 2246637824, "F16": 458752}, "total": 33452718336} | [
".gitattributes",
"README.md",
"added_tokens.json",
"chat_template.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00006.safetensors",
"model-00002-of-00006.safetensors",
"model-00003-of-00006.safetensors",
"model-00004-of-00006.safetensors",
"model-00005-of-00006... | 66c370b74a18e7b1e871c97918f032ed3578dfef | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"multimodal",
"conversational",
"en",
"arxiv:2309.00071",
"arxiv:2502.13923",
"base_model:Qwen/Qwen2.5-VL-32B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-VL-32B-Instruct",
"license:apache-2.0",
"text-generation-inference",
... | null | {"architectures": ["Qwen2_5_VLForConditionalGeneration"], "model_type": "qwen2_5_vl", "quantization_config": {"bits": 4, "quant_method": "awq"}, "processor_config": {"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and me... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": ["Qwen/Qwen2.5-VL-32B-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": "image-text-to-text", "tags": ["multimodal"]} | # Qwen2.5-VL-32B-Instruct-AWQ
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Latest Updates:
In addition to the original formul... | null | [
"apache-2.0"
] | null | [
"en"
] | 33,452,718,336 | null | null | [
"qwen2_5_vl",
"AutoModelForImageTextToText",
"Qwen2_5_VLForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
659ea27eb6ad10c28f57e2c6 | d4d5f7cb385da884 | 2d4d455c0dc5b195 | 31,102 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 473 | nickprock/multi-sentence-BERTino | nickprock | null | 438 | 455,549 | False | 2024-01-10T13:58:22Z | 2025-03-14T08:58:02Z | sentence-transformers | 7 | 0 | null | sentence-similarity | {"parameters": {"F32": 67576320}, "total": 67576320} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"onnx/model_qint8_avx512_vnni.onnx",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"... | 900bdf903f6712d51cd098279375a90f06d69022 | [
"sentence-transformers",
"onnx",
"safetensors",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"it",
"dataset:stsb_multi_mt",
"dataset:unicamp-dl/mmarco",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["DistilBertModel"], "model_type": "distilbert", "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": ["stsb_multi_mt", "unicamp-dl/mmarco"], "eval_results": null, "language": ["it"], "library_name": "sentence-transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transfo... | # {multi-sentence-BERTino}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This model is trained from [indigo-ai/BERTino](https://huggingface.co/indigo-ai/BERTino) usin... | null | [
"mit"
] | [
"stsb_multi_mt",
"unicamp-dl/mmarco"
] | [
"it"
] | 67,576,320 | null | null | [
"AutoModel",
"distilbert",
"DistilBertModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
662f7848367f6e4f9fd56f30 | 3f287b04362fff24 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 158 | gradientai/Llama-3-8B-Instruct-Gradient-1048k | gradientai | null | 25,583 | 546,089 | False | 2024-04-29T10:36:56Z | 2024-10-29T16:13:18Z | transformers | 680 | 0 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
".gitattributes",
"License",
"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",
"special_tokens_map.json",
"tokeniz... | cd3069b65a8eb13da639d332a5f61b0fbb29fa73 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"meta",
"llama-3",
"conversational",
"en",
"arxiv:2309.00071",
"arxiv:2402.08268",
"arxiv:2305.14233",
"doi:10.57967/hf/3372",
"license:llama3",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|e... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "llama3", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["meta", "llama-3"]} | <a href="https://www.gradient.ai" target="_blank"><img src="https://cdn-uploads.huggingface.co/production/uploads/655bb613e8a8971e89944f3e/TSa3V8YpoVagnTYgxiLaO.png" width="200"/></a>
# Llama-3 8B Gradient Instruct 1048k
Join our custom agent and long context (262k-1M+) waitlist: https://forms.gle/L6TDY7dozx8TuoUv7
... | null | [
"llama3"
] | null | [
"en"
] | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc036468d709f176266 | 83e4d61ac694d3d7 | 4a714e39d07d2a5f | 50,325 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 502 | KBLab/sentence-bert-swedish-cased | KBLab | null | 37,026 | 552,848 | False | 2022-03-02T23:29:04Z | 2025-11-07T22:48:16Z | sentence-transformers | 32 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 124690944}, "total": 124691456} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"Sentence-Bert-Swedish-Cased-124M-BF16.gguf",
"config.json",
"config_sentence_transformers.json",
"eval/mse_evaluation_TED2020-en-sv-dev.tsv.gz_results.csv",
"eval/mse_evaluation_Tatoeba-eng-swe-dev.tsv.gz_results.csv",
"eval/translation_evalua... | 6b5e83cd29c03729cfdc33d13b1423399b0efb5c | [
"sentence-transformers",
"pytorch",
"safetensors",
"gguf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"sv",
"arxiv:2004.09813",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | {"total": 124100352, "architecture": "bert", "context_length": 512, "causal": false, "bos_token": "[CLS]", "eos_token": "[SEP]"} | {"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": ["sv"], "library_name": null, "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-simila... | # KBLab/sentence-bert-swedish-cased
This is a [sentence-transformers](https://www.SBERT.net) model: It maps Swedish sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model is a bilingual Swedish-English model trained according to instructi... | null | [
"apache-2.0"
] | null | [
"sv"
] | 124,691,456 | 124,100,352 | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
659bec69ac728bc303263386 | 7a9379003b337737 | 9ff9438e6447a43c | 32,000 | Unigram | null | true | true | true | true | 3 | Sequence | Metaspace | Metaspace | [
"Lowercase",
"Replace",
"Replace",
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 27 | google/siglip-base-patch16-384 | google | null | 22,105 | 617,988 | False | 2024-01-08T12:36:57Z | 2024-09-26T08:21:51Z | transformers | 11 | 0 | null | zero-shot-image-classification | {"parameters": {"F32": 203447810}, "total": 203447810} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"preprocessor_config.json",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | 41aec1c83b32e0a6fca20ad88ba058aa5b5ea394 | [
"transformers",
"safetensors",
"siglip",
"zero-shot-image-classification",
"vision",
"arxiv:2303.15343",
"arxiv:2209.06794",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["SiglipModel"], "model_type": "siglip", "tokenizer_config": {"eos_token": "</s>", "pad_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForZeroShotImageClassification",
"custom_class": null,
"pipeline_tag": "zero-shot-image-classification",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["vision"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_image... | # SigLIP (base-sized model)
SigLIP model pre-trained on WebLi at resolution 384x384. It was introduced in the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Zhai et al. and first released in [this repository](https://github.com/google-research/big_vision).
Disclaimer: The t... | null | [
"apache-2.0"
] | null | null | 203,447,810 | null | null | [
"AutoModelForZeroShotImageClassification",
"siglip",
"SiglipModel"
] | [
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
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"Precompiled",
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] | [
"Metaspace"
] | [
"Metaspace"
] | 503 | pedramyazdipoor/persian_xlm_roberta_large | pedramyazdipoor | null | 19 | 449,569 | False | 2022-09-18T15:46:12Z | 2025-04-12T18:31:31Z | transformers | 3 | 0 | null | question-answering | null | [
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] | null | {"architectures": ["XLMRobertaForQuestionAnswering"], "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>... | {
"auto_model": "AutoModelForQuestionAnswering",
"custom_class": null,
"pipeline_tag": "question-answering",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | null | null | null | [
"AutoModelForQuestionAnswering",
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"xlm-roberta"
] | [
"question-answering"
] | [
"text"
] | [
"text"
] | [
"text"
] |
633085ec395a0edcad1fac21 | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | neulab/codebert-c | neulab | null | 18,398 | 971,693 | False | 2022-09-25T16:46:36Z | 2023-02-27T20:56:38Z | transformers | 6 | 0 | null | fill-mask | null | [
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] | 1e7a39ebc098045c1e959f259aea833de2fdecf5 | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2302.05527",
"endpoints_compatible",
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] | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "cls_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstri... | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | null | This is a `microsoft/codebert-base-mlm` model, trained for 1,000,000 steps (with `batch_size=32`) on **C** code from the `codeparrot/github-code-clean` dataset, on the masked-language-modeling task.
It is intended to be used in CodeBERTScore: [https://github.com/neulab/code-bert-score](https://github.com/neulab/code-... | null | null | null | null | null | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
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65d4e83bc2e85b8b98af701f | ac35e3244a1fc1f8 | 5ff1c70e91becad4 | 49,152 | BPE | 48,872 | false | true | false | true | 38 | null | Sequence | ByteLevel | null | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 121 | bigcode/starcoder2-15b | bigcode | null | 10,053 | 517,419 | False | 2024-02-20T17:58:19Z | 2024-06-05T19:52:45Z | transformers | 664 | 3 | [{"name": "starcoder2-15b", "results": [{"task": {"type": "text-generation"}, "dataset": {"name": "CruxEval-I", "type": "cruxeval-i"}, "metrics": [{"type": "pass@1", "value": 48.1, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "DS-1000", "type": "ds-1000"}, "metrics": [{"type": "pass@1... | text-generation | null | [
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"model-00006-of-00014.safetens... | 46d44742909c03ac8cee08eb03fdebce02e193ec | [
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"text-generation",
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"dataset:bigcode/the-stack-v2-train",
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"arxiv:2207.14255",
"arxiv:2402.19173",
"license:bigcode-openrail-m",
"model-index",
"text-generation-inference",
"end... | null | {"architectures": ["Starcoder2ForCausalLM"], "model_type": "starcoder2", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"datasets": ["bigcode/the-stack-v2-train"], "library_name": "transformers", "license": "bigcode-openrail-m", "pipeline_tag": "text-generation", "tags": ["code"], "inference": {"parameters": {"temperature": 0.2, "top_p": 0.95}}, "widget": [{"text": "def print_hello_world():", "example_title": "Hello world", "group": "P... | # StarCoder2
<center>
<img src="https://huggingface.co/datasets/bigcode/admin_private/resolve/main/starcoder2_banner.png" alt="SC2" width="900" height="600">
</center>
## Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training](#training)
5. [License](#lic... | null | [
"bigcode-openrail-m"
] | [
"bigcode/the-stack-v2-train"
] | null | null | null | null | [
"AutoModelForCausalLM",
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"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64d3ca87afffa438fb3da35b | e23b6bdbe047bb7e | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | h2oai/h2ogpt-4096-llama2-13b-chat | h2oai | null | 197 | 449,438 | False | 2023-08-09T17:19:03Z | 2023-08-24T18:35:40Z | transformers | 12 | 0 | null | text-generation | {"parameters": {"F32": 2560, "F16": 13015864320}, "total": 13015866880} | [
".gitattributes",
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"model-00002-of-00003.safetensors",
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"pytorch_model-00001-... | 4a1968218a334323d847ed2fcf6cb661b74b5e68 | [
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"llama-2",
"h2ogpt",
"en",
"license:llama2",
"text-generation-inference",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": false, "rstrip... | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["facebook", "meta", "pytorch", "llama", "llama-2", "h2ogpt"], "inferenc... | h2oGPT clone of [Meta's Llama 2 13B Chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf).
Try it live on our [h2oGPT demo](https://gpt.h2o.ai) with side-by-side LLM comparisons and private document chat!
See how it compares to other models on our [LLM Leaderboard](https://evalgpt.ai/)!
See more at [H2O.ai](h... | null | [
"llama2"
] | null | [
"en"
] | 13,015,866,880 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
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"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f18019e | ccf483eb28c1c710 | d05332da13658bea | 120,138 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 71 | ai-forever/sbert_large_mt_nlu_ru | ai-forever | null | 1,405 | 456,098 | False | 2022-03-02T23:29:05Z | 2025-04-17T11:32:02Z | transformers | 26 | 0 | null | feature-extraction | {"parameters": {"F32": 426908672}, "total": 426908672} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 950ff429588164c6e2e6cfedb93efafa1169d20e | [
"transformers",
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"bert",
"feature-extraction",
"PyTorch",
"Transformers",
"ru",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"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": ["ru"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["PyTorch", "Transformers"]} | # BERT large model multitask (cased) for Sentence Embeddings in Russian language.
The model is described [in this article](https://habr.com/ru/company/sberdevices/blog/560748/)
Russian SuperGLUE [metrics](https://russiansuperglue.com/login/submit_info/944)
For better quality, use mean token embeddings.
## Usage (Hug... | null | [
"mit"
] | null | [
"ru"
] | 426,908,672 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"feature-extraction"
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"multimodal"
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"text"
] | [
"embeddings"
] |
640a000851028b5b07b1ab92 | 7ca70421579da9a5 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 504 | gijs/aces-roberta-13 | gijs | null | 5 | 446,177 | False | 2023-03-09T15:49:28Z | 2023-03-09T15:52:02Z | transformers | 0 | 0 | [{"name": "aces-roberta-13", "results": []}] | token-classification | null | [
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] | b62e0364cdb3552a245259a28523d734b6d308c7 | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"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": null, "eval_results": [], "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["precision", "recall", "f1", "accuracy"], "model_name": "aces-roberta-13", "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. -->
# aces-roberta-13
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset... | null | [
"mit"
] | null | null | null | null | [
"precision",
"recall",
"f1",
"accuracy"
] | [
"roberta",
"AutoModelForTokenClassification",
"RobertaForTokenClassification"
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"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
687fa5b678e85b9b93b7bac2 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Qwen/Qwen3-Coder-480B-A35B-Instruct | Qwen | null | 92,041 | 856,480 | False | 2025-07-22T14:52:38Z | 2025-08-21T10:18:07Z | transformers | 1,316 | 3 | null | text-generation | {"parameters": {"BF16": 480154875392}, "total": 480154875392} | [
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"model-00005-of-00241.safetens... | 9d90cf8fca1bf7b7acca42d3fc9ae694a2194069 | [
"transformers",
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"text-generation",
"conversational",
"arxiv:2505.09388",
"license:apache-2.0",
"eval-results",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "tokenizer_config": {"bos_token": null, "chat_template": "{% macro render_extra_keys(json_dict, handled_keys) %}\n {%- if json_dict is mapping %}\n {%- for json_key in json_dict if json_key not in handled_keys %}\n {%- if json_di... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generat... | # Qwen3-Coder-480B-A35B-Instruct
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Highlights
Today, we're announcing **Qwen3-Coder*... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct/blob/main/LICENSE"
] | null | null | 480,154,875,392 | null | null | [
"qwen3_moe",
"AutoModelForCausalLM",
"Qwen3MoeForCausalLM"
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"text-generation"
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"text"
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"text"
] | [
"text"
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667b636c8c44b132c0e75ca7 | 6e6a83dc4b556c95 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 111 | Sao10K/L3-8B-Lunaris-v1 | Sao10K | null | 1,799 | 449,463 | False | 2024-06-26T00:40:12Z | 2024-06-29T18:21:32Z | transformers | 141 | 0 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
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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",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "llama3", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | A generalist / roleplaying model merge based on Llama 3. Models are selected from my personal experience while using them.
I personally think this is an improvement over Stheno v3.2, considering the other models helped balance out its creativity and at the same time improving its logic.
Settings:
```
Instruct // Cont... | null | [
"llama3"
] | null | [
"en"
] | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
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"text-generation"
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] | [
"text"
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"text"
] |
66e81d2494457ccb513ef9c9 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-Coder-7B | Qwen | {
"models": [
{
"_id": "66e6d06401e2adb00b0be996",
"id": "Qwen/Qwen2.5-7B"
}
],
"relation": "finetune"
} | 115,379 | 1,218,262 | False | 2024-09-16T11:57:24Z | 2024-11-18T12:54:02Z | transformers | 139 | 0 | null | text-generation | {"parameters": {"BF16": 7615616512}, "total": 7615616512} | [
".gitattributes",
"LICENSE",
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"... | 0396a76181e127dfc13e5c5ec48a8cee09938b02 | [
"transformers",
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"code",
"qwen",
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"codeqwen",
"conversational",
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"base_model:finetune:Qwen/Qwen2.5-7B",
"license:apache-2.0",
"text-ge... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Aliba... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Qwen/Qwen2.5-7B"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-Coder-7B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-gene... | # Qwen2.5-Coder-7B
## 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 | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-Coder-7B/blob/main/LICENSE"
] | null | [
"en"
] | 7,615,616,512 | null | null | [
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"Qwen2ForCausalLM",
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] | [
"text"
] | [
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[](https://paperswithcode.com/sota/machine-translation-on-iwslt2015-english-1?p=mtet-multi-domain-translation-for-english)
[![PWC](h... | null | [
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FinTwitBERT-sentiment is a finetuned model for classifying the sentiment of financial tweets. It uses [FinTwitBERT](https://huggingface.co/StephanAkkerman/FinTwitBERT) as a base model, which has been pre-trained on 10 million financial tweets.
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} | 13,272 | 487,724 | False | 2024-02-11T15:55:35Z | 2026-03-05T17:43:44Z | transformers | 117 | 0 | [{"name": "GritLM-7B", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 81.1791044... | text-generation | {"parameters": {"BF16": 7241732096}, "total": 7241732096} | [
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<!-- markdownlint-disable html -->
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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" />
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<hr>
<div align="center" style="line-... | null | [
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interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
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621ffdc036468d709f175399 | 2a9c14dcf982bf18 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
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] | [
"WhitespaceSplit",
"Metaspace"
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] | 509 | Einmalumdiewelt/T5-Base_GNAD | Einmalumdiewelt | null | 87 | 446,802 | False | 2022-03-02T23:29:04Z | 2022-08-26T15:55:55Z | transformers | 22 | 0 | [{"name": "T5-Base_GNAD", "results": []}] | summarization | null | [
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] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
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} | {"base_model": null, "datasets": null, "eval_results": [], "language": ["de"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": ["rouge"], "model_name": "T5-Base_GNAD", "pipeline_tag": null, "tags": ["generated_from_trainer", "summarization"]} | <!-- 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. -->
# T5-Base_GNAD
This model is a fine-tuned version of [Einmalumdiewelt/T5-Base_GNAD](https://huggingface.co/Einmalumdiewelt/T5-Base_... | null | null | null | [
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} | 129 | 441,712 | False | 2023-05-13T08:18:23Z | 2023-09-27T12:44:18Z | transformers | 319 | 0 | null | text-generation | {"parameters": {"F32": 330227200, "I32": 12687769600}, "total": 13017996800} | [
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"4-bit"... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "cont... | {
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} | {"base_model": "ehartford/Wizard-Vicuna-13B-Uncensored", "datasets": ["ehartford/wizard_vicuna_70k_unfiltered"], "eval_results": null, "language": ["en"], "library_name": null, "license": "other", "license_name": null, "license_link": null, "metrics": null, "model_name": "Wizard Vicuna 13B Uncensored", "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 | [
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"NFC"
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"Split",
"ByteLevel"
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"ByteLevel"
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"model-00005-of-00008.safetenso... | 1a758c50ecb6350748b9ce0a99d2352fd9fc11c9 | [
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"text-generation",
"pretrained",
"moe",
"conversational",
"en",
"license:other",
"endpoints_compatible",
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] | null | {"architectures": ["Qwen2MoeForCausalLM"], "model_type": "qwen2_moe", "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['ro... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "other", "license_name": "tongyi-qianwen", "license_link": "https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ... | # Qwen1.5-MoE-A2.7B
## Introduction
Qwen1.5-MoE is a transformer-based MoE decoder-only language model pretrained on a large amount of data.
For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen-moe/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).
## Model Details
Qwen1.5-MoE... | null | [
"other",
"tongyi-qianwen",
"https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B/blob/main/LICENSE"
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} | {"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-Math/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-Math/blob/main/LICENSE-MODEL"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 511 | Salesforce/instructblip-flan-t5-xl | Salesforce | null | 10,522 | 509,485 | False | 2023-05-28T13:29:18Z | 2025-02-03T06:41:46Z | transformers | 30 | 0 | null | image-text-to-text | null | [
".gitattributes",
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"instructblip",
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"vision",
"image-captioning",
"en",
"arxiv:2305.06500",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["InstructBlipForConditionalGeneration"], "model_type": "instructblip", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["vision", "image-captioning"]} | # InstructBLIP model
InstructBLIP model using [Flan-T5-xl](https://huggingface.co/google/flan-t5-xl) as language model. InstructBLIP was introduced in the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Dai et al.
Disclaimer: The team r... | null | [
"mit"
] | null | [
"en"
] | null | null | null | [
"instructblip",
"InstructBlipForConditionalGeneration",
"AutoModelForImageTextToText"
] | [
"image-text-to-text"
] | [
"multimodal"
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"text",
"image"
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"text"
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6877fc158895c64ed859d5fd | e034328a2d067fa5 | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 237 | OpenMed/OpenMed-NER-DNADetect-SuperClinical-434M | OpenMed | null | 97,491 | 863,796 | False | 2025-07-16T19:23:01Z | 2025-08-05T09:35:08Z | transformers | 0 | 0 | null | token-classification | {"parameters": {"BF16": 434023435}, "total": 434023435} | [
".gitattributes",
"README.md",
"added_tokens.json",
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"openmed_vs_sota_grouped_bars.png",
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"spm.model",
"test_results.json",
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] | 1ccc35aaedee9347cefae72584aed8349daf316b | [
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"biomedical-nlp",
"protein-recognition",
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"genomics",
"dna",
"rna",
"cell_line",
"cell_type",
"protein",
"en",
"arxiv:2508.01630",
"license:apach... | null | {"architectures": ["DebertaV2ForTokenClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForTokenClassification",
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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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"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
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"models": [
{
"_id": "680da769038897aa905586a7",
"id": "Qwen/Qwen3-4B"
}
],
"relation": "quantized"
} | 414,692 | 1,445,072 | False | 2025-05-05T14:41:59Z | 2025-05-21T06:13:28Z | transformers | 26 | 1 | null | text-generation | {"parameters": {"I32": 3633315840, "BF16": 389152256}, "total": 4022468096} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 74d4bd2bd4bff9cafc9345221320bffb08b406a3 | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2309.00071",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-4B",
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"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "quantization_config": {"bits": 4, "quant_method": "awq"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\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/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags... | # Qwen3-4B-AWQ
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Qwen3 Highlights
Qwen3 is the latest generation of large language m... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE"
] | null | null | 4,022,468,096 | null | null | [
"AutoModelForCausalLM",
"Qwen3ForCausalLM",
"qwen3"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
659f8757d2e705b3fb660a96 | 89a5128d694e9242 | 081b543d8f95004e | 32,002 | BPE | 58,980 | true | false | true | true | 5 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 419 | NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO | NousResearch | {
"models": [
{
"_id": "6569aa681d7c2ca7b7bfcc80",
"id": "mistralai/Mixtral-8x7B-v0.1"
}
],
"relation": "finetune"
} | 9,441 | 494,310 | False | 2024-01-11T06:14:47Z | 2024-04-30T18:09:51Z | transformers | 452 | 0 | [{"name": "Nous-Hermes-2-Mixtral-8x7B-DPO", "results": []}] | text-generation | {"parameters": {"BF16": 46702809088}, "total": 46702809088} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
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"model-00005-of-00019.safetensors",
"model-00006-of-00019.s... | 286ae6737d048ad1d965c2e830864df02db50f2f | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"Mixtral",
"instruct",
"finetune",
"chatml",
"DPO",
"RLHF",
"gpt4",
"synthetic data",
"distillation",
"conversational",
"en",
"dataset:teknium/OpenHermes-2.5",
"base_model:mistralai/Mixtral-8x7B-v0.1",
"base_model:finetun... | null | {"architectures": ["MixtralForCausalLM"], "model_type": "mixtral", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{{bos_token}}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistan... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "mistralai/Mixtral-8x7B-v0.1", "datasets": ["teknium/OpenHermes-2.5"], "eval_results": [], "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": "Nous-Hermes-2-Mixtral-8x7B-DPO", "pipeline_tag": null, "tags": ["Mixtra... | # Nous Hermes 2 - Mixtral 8x7B - DPO
## Model description
Nous Hermes 2 Mixtral 8x7B DPO is the new flagship Nous Research model trained over the [Mixtral 8x7B MoE LLM](https://huggingface.co/mist... | null | [
"apache-2.0"
] | [
"teknium/OpenHermes-2.5"
] | [
"en"
] | 46,702,809,088 | null | null | [
"mixtral",
"MixtralForCausalLM",
"AutoModelForCausalLM"
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"text-generation"
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"text"
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"text"
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"text"
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"Precompiled",
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"dataset:Marqo/google-shopping-general-eval",
"license:apache-2.0",
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] | null | {"auto_map": {"AutoConfig": "marqo_fashionSigLIP.MarqoFashionSigLIPConfig", "AutoModel": "marqo_fashionSigLIP.MarqoFashionSigLIP", "AutoProcessor": "marqo_fashionSigLIP.MarqoFashionSigLIPProcessor"}, "model_type": "siglip", "tokenizer_config": {"eos_token": "</s>", "pad_token": "</s>", "unk_token": "<unk>"}} | {
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} | {"base_model": null, "datasets": ["Marqo/amazon-products-eval", "Marqo/google-shopping-general-eval"], "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": ["clip", "ecommerce... | <div style="display: flex; align-items: center; gap: 10px;">
<a href="https://www.marqo.ai/blog/introducing-marqos-ecommerce-embedding-models">
<img src="https://img.shields.io/badge/Model_Release-Blog-blue?logo=font-awesome&logoColor=white&style=flat&logo=pencil-alt" alt="Blog">
</a>
<a href="https://github.... | null | [
"apache-2.0"
] | [
"Marqo/amazon-products-eval",
"Marqo/google-shopping-general-eval"
] | null | 652,150,784 | null | null | [
"AutoModel",
"siglip"
] | [
null
] | null | null | null |
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"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 389 | solidrust/Mistral-7B-Instruct-v0.3-AWQ | solidrust | {
"models": [
{
"_id": "664dc170474f2283fa5c8659",
"id": "mistralai/Mistral-7B-Instruct-v0.3"
}
],
"relation": "quantized"
} | 4,135 | 814,111 | False | 2024-05-23T22:22:41Z | 2024-09-03T08:50:39Z | transformers | 8 | 0 | null | text-generation | {"parameters": {"I32": 6979321856, "F16": 268701696}, "total": 7248023552} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"quant_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 95b1295ddd1a8673117cdc7bd2a4da2a457bb3f7 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"conversational",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:quantized:mistralai/Mistral-7B-Instruct-v0.3",
"license:apache-2.0",
"text-generation-in... | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "quantization_config": {"bits": 4, "quant_method": "awq"}, "tokenizer_config": {"bos_token": "<s>", "chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversat... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "mistralai/Mistral-7B-Instruct-v0.3", "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": ["4-bit", "AWQ", "text-gener... | # mistralai/Mistral-7B-Instruct-v0.3 AWQ
- Model creator: [mistralai](https://huggingface.co/mistralai)
- Original model: [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)
## Model Summary
The Mistral-7B-Instruct-v0.3 Large Language Model (LLM) is an instruct fine-tuned version of... | null | [
"apache-2.0"
] | null | null | 7,248,023,552 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17d40a | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | kssteven/ibert-roberta-base | kssteven | null | 7,603 | 452,837 | False | 2022-03-02T23:29:05Z | 2021-11-22T10:09:32Z | transformers | 1 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 4f98e9110b04a8958444d3af8ed39287834fbb90 | [
"transformers",
"pytorch",
"ibert",
"fill-mask",
"arxiv:1907.11692",
"arxiv:2101.01321",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["IBertForMaskedLM"], "model_type": "ibert", "tokenizer_config": {"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized":... | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | null | # I-BERT base model
This model, `ibert-roberta-base`, is an integer-only quantized version of [RoBERTa](https://arxiv.org/abs/1907.11692), and was introduced in [this paper](https://arxiv.org/abs/2101.01321).
I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only... | null | null | null | null | null | null | null | [
"ibert",
"AutoModelForMaskedLM",
"IBertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6644926443cfcec03046d621 | f6bd53b02735a215 | 3f3879d485dc5e16 | 63,992 | BPE | 110,029 | true | false | false | true | 231 | Sequence | null | Sequence | [
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 512 | 01-ai/Yi-1.5-9B-Chat-16K | 01-ai | null | 9,178 | 483,000 | False | 2024-05-15T10:45:56Z | 2024-06-26T10:43:20Z | transformers | 36 | 0 | null | text-generation | {"parameters": {"BF16": 8829407232}, "total": 8829407232} | [
".gitattributes",
"NOTICE",
"README.md",
"config.json",
"generation_config.json",
"md5",
"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_map.json",
... | 2f8753bed95bd4fede3fd64f8305428620b4b212 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2403.04652",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|startoftext|>", "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in mes... | {
"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": null} | <div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter... | null | [
"apache-2.0"
] | null | null | 8,829,407,232 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f18127b | 787f7cf5bfa1a4ee | edab60f922c71503 | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 433 | unicamp-dl/translation-en-pt-t5 | unicamp-dl | null | 293 | 456,951 | False | 2022-03-02T23:29:05Z | 2021-10-11T03:47:21Z | transformers | 22 | 0 | null | translation | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | 8418d7e9b1837687137af06624cb3596b45c9343 | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"translation",
"en",
"pt",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "pt"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": ["bleu"], "model_name": null, "pipeline_tag": null, "tags": ["translation"]} | # Introduction
This repository brings an implementation of T5 for translation in EN-PT tasks using a modest hardware setup. We propose some changes in tokenizator and post-processing that improves the result and used a Portuguese pretrained model for the translation. You can collect more informations in [our repositor... | null | null | null | [
"en",
"pt"
] | null | null | [
"bleu"
] | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"translation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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