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} | {"base_model": "deepset/gbert-large", "datasets": ["deutsche-telekom/ger-backtrans-paraphrase"], "eval_results": null, "language": ["de"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-tr... | # German BERT large paraphrase euclidean
This is a [sentence-transformers](https://www.SBERT.net) model.
It maps sentences & paragraphs (text) into a 1024 dimensional dense vector space.
The model is intended to be used together with [SetFit](https://github.com/huggingface/setfit)
to improve German few-shot text classi... | null | [
"mit"
] | [
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"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
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] | null | {"architectures": ["DistilBertModel"], "model_type": "distilbert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/nq-distilbert-base-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you ... | null | [
"apache-2.0"
] | null | null | 66,362,880 | null | null | [
"AutoModel",
"distilbert",
"DistilBertModel"
] | [
"sentence-similarity",
"feature-extraction"
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"multimodal"
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"text"
] | [
"logits",
"embeddings"
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675d1af66205e9e0d9f1a3d8 | c0e1b90c863bae56 | fef4c97d7f951c6a | 131,072 | BPE | 128,810 | false | true | false | true | 2,024 | null | Sequence | ByteLevel | null | [
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"ByteLevel",
"Digits"
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] | 558 | tiiuae/Falcon3-1B-Instruct | tiiuae | {
"models": [
{
"_id": "675c21ee74d64986e3649d0c",
"id": "tiiuae/Falcon3-1B-Base"
}
],
"relation": "finetune"
} | 11,504 | 332,272 | False | 2024-12-14T05:43:18Z | 2025-01-10T06:58:42Z | transformers | 46 | 1 | null | text-generation | {"parameters": {"BF16": 1669408768}, "total": 1669408768} | [
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"config.json",
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] | 28ba2251970a01dd1edc7ba7dad2eb71216ccfdf | [
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"safetensors",
"llama",
"text-generation",
"falcon3",
"conversational",
"en",
"fr",
"es",
"pt",
"base_model:tiiuae/Falcon3-1B-Base",
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"license:other",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"re... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"chat_template": "{%- if tools %}\n{{- '<|system|>\\n' }}\n{%- if messages[0]['role'] == 'system' %}\n{{- messages[0]['content'] }}\n{%- set remaining_messages = messages[1:] %}\n{%- else %}\n{%- set remaining_messages = messages %}\n{%... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "tiiuae/Falcon3-1B-Base", "datasets": null, "eval_results": null, "language": ["en", "fr", "es", "pt"], "library_name": "transformers", "license": "other", "license_name": "falcon-llm-license", "license_link": "https://falconllm.tii.ae/falcon-terms-and-conditions.html", "metrics": null, "model_name": nul... | <div align="center">
<img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt="drawing" width="500"/>
</div>
# Falcon3-1B-Instruct
**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters.
This r... | null | [
"other",
"falcon-llm-license",
"https://falconllm.tii.ae/falcon-terms-and-conditions.html"
] | null | [
"en",
"fr",
"es",
"pt"
] | 1,669,408,768 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67847e071ceb7569fc09eb5f | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | jinaai/ReaderLM-v2 | jinaai | null | 16,808 | 333,820 | False | 2025-01-13T02:44:23Z | 2025-03-04T06:52:36Z | transformers | 772 | 2 | null | text-generation | {"parameters": {"BF16": 1543714304}, "total": 1543714304} | [
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"onnx/model_fp16.onnx",
"onnx/model_fp16.onnx_data",
"onnx/model_int8.onnx",
"onnx/model_q4.onn... | 1d07078459ee1e880a22d67387b5e683d50a6e4b | [
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"conversational",
"multilingual",
"arxiv:2503.01151",
"license:cc-by-nc-4.0",
"text-generation-inference",
"deploy:azure",
"region:eu"
] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are an AI assistant developed by Jina AI.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": null, "inference": false} | <br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
[Blog](https://jina.ai/news/reade... | null | [
"cc-by-nc-4.0"
] | null | [
"multilingual"
] | 1,543,714,304 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
641466634f654136a9054d35 | 6ad670d2742d0236 | 5f06a539f7067498 | 50,400 | BPE | 50,000 | false | true | true | true | 144 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 112 | TehVenom/PPO_Pygway-V8p4_Dev-6b | TehVenom | null | 920 | 289,883 | False | 2023-03-17T13:08:51Z | 2023-05-19T18:01:37Z | transformers | 7 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
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"pytorch_model-00005-of-00006.bin",
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"pytorch_mode... | f30709dba36c665869f9ac8cd0cef5a8a2e7c8df | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"en",
"license:apache-2.0",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPTJForCausalLM"], "model_type": "gptj", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized"... | {
"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": null, "commercial": "no", "inference": false} | # GPT-J 6B - PPO_Pygway Mix
## Model description
This is a merged model, using a weighted parameter blend strategy at a (20:20:60) ratio between the models:
- [20%] - KoboldAI/GPT-J-6B-Janeway: https://huggingface.co/KoboldAI/GPT-J-6B-Janeway
- [20%] - reciprocate/ppo_hh_gpt-j: https://huggingface.co/reciprocate/ppo_h... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"gptj",
"AutoModelForCausalLM",
"GPTJForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f18063d | de0f98cd504e94de | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 317 | slauw87/bart_summarisation | slauw87 | null | 134 | 286,462 | False | 2022-03-02T23:29:05Z | 2021-09-20T05:27:36Z | transformers | 59 | 0 | [{"name": "bart-large-cnn-samsum", "results": [{"task": {"name": "Abstractive Text Summarization", "type": "abstractive-text-summarization"}, "dataset": {"name": "SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization", "type": "samsum"}, "metrics": [{"name": "Validation ROGUE-1", "type": "rogu... | summarization | null | [
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"v... | e6097186162a3d9d75ba0a1297640f985baadf52 | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"sagemaker",
"summarization",
"en",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BartForConditionalGeneration"], "model_type": "bart", "tokenizer_config": {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"datasets": ["samsum"], "language": "en", "license": "apache-2.0", "tags": ["sagemaker", "bart", "summarization"], "widget": [{"text": "Sugi: I am tired of everything in my life. \nTommy: What? How happy you life is! I do envy you.\nSugi: You don't know that I have been over-protected by my mother these years. I am re... | ## `bart-large-cnn-samsum`
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
For more information look at:
- [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html)
- [Example Notebooks](https://github.com/huggingface/notebooks/... | null | [
"apache-2.0"
] | [
"samsum"
] | [
"en"
] | null | null | null | [
"bart",
"BartForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"summarization"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65e70ca839138fa017deedcf | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | lgodwangl/new_01m | lgodwangl | null | 1 | 18 | False | 2024-03-05T12:14:32Z | 2024-04-03T18:28:34Z | transformers | 1 | 0 | null | text-generation | {"parameters": {"BF16": 7241732096}, "total": 7241732096} | [
".gitattributes",
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"text-generation",
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"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": []} | # Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated... | null | null | null | null | 7,241,732,096 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
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"text"
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6624a370dd416a585a87e691 | bebd39f410449ff1 | 59a167c194ea6508 | 32,000 | BPE | 61,249 | true | false | true | true | 5 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
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] | 611 | Snowflake/snowflake-arctic-instruct | Snowflake | null | 13,117 | 327,883 | False | 2024-04-21T05:26:08Z | 2024-05-21T01:08:33Z | transformers | 360 | 0 | null | text-generation | null | [
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"arctic",
"text-generation",
"snowflake",
"moe",
"conversational",
"custom_code",
"license:apache-2.0",
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] | null | {"architectures": ["ArcticForCausalLM"], "auto_map": {"AutoConfig": "configuration_arctic.ArcticConfig", "AutoModel": "modeling_arctic.ArcticModel", "AutoModelForCausalLM": "modeling_arctic.ArcticForCausalLM", "AutoModelForSequenceClassification": "modeling_arctic.ArcticForSequenceClassification"}, "model_type": "arcti... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_arctic.ArcticForCausalLM",
"pipeline_tag": "text-generation",
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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": ["snowflake", "arctic", "moe"]} | ## Model Details
Arctic is a dense-MoE Hybrid transformer architecture pre-trained from scratch by the Snowflake AI
Research Team. We are releasing model checkpoints for both the base and instruct-tuned versions of
Arctic under an Apache-2.0 license. This means you can use them freely in your own research,
prototyp... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"arctic",
"AutoModelForCausalLM",
"ArcticForCausalLM",
"modeling_arctic.ArcticForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6633398c888a4d3b08503f24 | 344b7c49c2732e43 | 8386a692be535934 | 51,866 | BPE | 50,000 | false | true | true | true | 1,609 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 21 | bond005/whisper-large-v3-ru-podlodka | bond005 | null | 1,518 | 287,215 | False | 2024-05-02T06:58:20Z | 2026-02-06T09:41:32Z | transformers | 13 | 1 | [{"name": "Whisper Large V3 Russian Podlodka by Ivan Bondarenko", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Podlodka.io", "type": "bond005/podlodka_speech", "args": "ru"}, "metrics": [{"type": "wer", "value": 20.91, "name": "WER (with punctuation a... | automatic-speech-recognition | {"parameters": {"F32": 1543490560}, "total": 1543490560} | [
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"text"
] | |
688b25d301116efa6ae21836 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8 | Qwen | null | 349,040 | 1,761,345 | False | 2025-07-31T08:14:11Z | 2025-12-03T08:20:23Z | transformers | 171 | 2 | null | text-generation | {"parameters": {"BF16": 636948480, "F8_E4M3": 29896998912}, "total": 30533947392} | [
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"model-00004-of-00004.safetensors",
"model.safetensors.index.json"... | dcaee4d4dfc5ee71ad501f01f530e5652438fde0 | [
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"conversational",
"arxiv:2505.09388",
"license:apache-2.0",
"endpoints_compatible",
"fp8",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "quantization_config": {"quant_method": "fp8"}, "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 no... | {
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-gener... | # Qwen3-Coder-30B-A3B-Instruct-FP8
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Highlights
**Qwen3-Coder** is available in mult... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8/blob/main/LICENSE"
] | null | null | 30,533,947,392 | null | null | [
"qwen3_moe",
"AutoModelForCausalLM",
"Qwen3MoeForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6654e003965ea394ee6aa251 | 6e6a83dc4b556c95 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 111 | mlabonne/NeuralDaredevil-8B-abliterated | mlabonne | {
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{
"_id": "665348155ff9a88671a3d02c",
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}
],
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} | 15,865 | 349,424 | False | 2024-05-27T19:33:23Z | 2026-01-23T10:43:36Z | transformers | 264 | 2 | [{"name": "Daredevil-8B-abliterated-dpomix", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 69.28... | text-generation | {"parameters": {"F16": 8030261248}, "total": 8030261248} | [
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... | 6567010926ff93a5e9fb809534d61ab667a86674 | [
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"text-generation-inference",
"end... | 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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} | {"base_model": ["mlabonne/Daredevil-8B-abliterated"], "datasets": ["mlabonne/orpo-dpo-mix-40k"], "license": "llama3", "tags": ["dpo"], "model-index": [{"name": "Daredevil-8B-abliterated-dpomix", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25... | null | null | [
"llama3"
] | [
"mlabonne/orpo-dpo-mix-40k"
] | null | 8,030,261,248 | null | null | [
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"text"
] | [
"text"
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63b84f46878265fccc66281e | 2d0341bd3a73bae6 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 32 | jordiclive/flan-t5-3b-summarizer | jordiclive | null | 63 | 282,500 | False | 2023-01-06T16:41:42Z | 2024-02-10T21:01:46Z | transformers | 37 | 0 | null | summarization | null | [
".gitattributes",
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"spiece.model",
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] | e1f3331b8e0de87602d944da709aa80ece72743f | [
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"tf",
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"license:apache-2.0",
"license:bsd-3-clause",
"text-generation-inference",... | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
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} | {"base_model": null, "datasets": ["jordiclive/scored_summarization_datasets"], "eval_results": null, "language": ["en"], "library_name": null, "license": ["apache-2.0", "bsd-3-clause"], "license_name": null, "license_link": null, "metrics": ["rouge"], "model_name": null, "pipeline_tag": null, "tags": ["summarization", ... | # Multi-purpose Summarizer (Fine-tuned 3B google/flan-t5-xl on several Summarization datasets)
<a href="https://colab.research.google.com/drive/1EYfnIoG-r5lL2-3oiO_YdYEVKB0pAa9h">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
A fine-tuned version of [google/flan-t5-... | null | [
"apache-2.0",
"bsd-3-clause"
] | [
"jordiclive/scored_summarization_datasets"
] | [
"en"
] | null | null | [
"rouge"
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"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"summarization"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67a4e86ba8543cc064d3bfa7 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | RedHatAI/Qwen2.5-VL-72B-Instruct-FP8-dynamic | RedHatAI | {
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"_id": "6797079422990ae89b5aff86",
"id": "Qwen/Qwen2.5-VL-72B-Instruct"
}
],
"relation": "quantized"
} | 1,857 | 346,658 | False | 2025-02-06T16:50:51Z | 2025-04-25T15:00:04Z | transformers | 15 | 0 | null | image-text-to-text | {"parameters": {"BF16": 3204989184, "F8_E4M3": 70212648960}, "total": 73417638144} | [
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"... | null | {"architectures": ["Qwen2_5_VLForConditionalGeneration"], "model_type": "qwen2_5_vl", "quantization_config": {"quant_method": "compressed-tensors"}, "processor_config": {"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first an... | {
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} | {"base_model": "Qwen/Qwen2.5-VL-72B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md", "metrics": null, "model_na... | null | null | [
"apache-2.0",
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65b8ed7b23d948d884d8ac41 | 8ff9081ace3242e1 | 77b2d67c18d33823 | 128,000 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 612 | TURKCELL/bert-offensive-lang-detection-tr | TURKCELL | null | 792 | 348,008 | False | 2024-01-30T12:37:15Z | 2024-01-30T15:09:44Z | transformers | 8 | 0 | null | text-classification | {"parameters": {"F32": 184346882}, "total": 184346882} | [
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} | {"base_model": null, "datasets": ["offenseval2020_tr"], "eval_results": null, "language": "tr", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["accuracy", "f1", "precision", "recall"], "model_name": null, "pipeline_tag": null, "tags": ["bert", "turkish", "text-classifica... | Offensive Language Detection For Turkish Language
## Model Description
This model has been fine-tuned using [dbmdz/bert-base-turkish-128k-uncased](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) model with the [OffensEval 2020](https://huggingface.co/datasets/offenseval2020_tr) dataset.
The offenseval-tr ... | null | [
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"tr"
] | 184,346,882 | null | [
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] | [
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] |
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"ByteLevel"
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] | 186 | ybelkada/opt-125m-gptq-4bit | ybelkada | null | 419 | 286,292 | False | 2023-07-31T17:27:21Z | 2023-07-31T17:27:38Z | transformers | 0 | 0 | null | text-generation | null | [
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} | null | null | null | null | null | null | null | null | null | [
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"NFD",
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"StripAccents",
"Replace",
"Replace"
] | [
"Split"
] | null | 613 | dicta-il/dictabert-joint | dicta-il | null | 3,793 | 303,415 | False | 2024-01-10T15:27:50Z | 2025-04-17T14:41:50Z | transformers | 5 | 0 | null | feature-extraction | {"parameters": {"F32": 186012926}, "total": 186012926} | [
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] | ae559caa8bf1be8c84658926d18b2737a5a62eb2 | [
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"arxiv:2403.06970",
"license:cc-by-4.0",
"text-embeddings-inference",
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] | null | {"architectures": ["BertForJointParsing"], "auto_map": {"AutoModel": "BertForJointParsing.BertForJointParsing"}, "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": "BertForJointParsing.BertForJointParsing",
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["he"], "library_name": null, "license": "cc-by-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "inference": false} | # DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew
State-of-the-art language model for Hebrew, released [here](https://arxiv.org/abs/2403.06970).
This is the fine-tuned model for the joint parsing of the following tasks:
- Prefix Segmentation
- Morphological Disabmgiuation
- Lexicographical Analysis (Lemm... | null | [
"cc-by-4.0"
] | null | [
"he"
] | 186,012,926 | null | null | [
"BertForJointParsing.BertForJointParsing",
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] | [
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"text"
] | [
"embeddings"
] |
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"ByteLevel"
] | [
"ByteLevel"
] | 113 | Salesforce/codegen-350M-nl | Salesforce | null | 725 | 284,279 | False | 2022-04-11T15:19:18Z | 2025-01-31T21:26:50Z | transformers | 5 | 0 | null | text-generation | null | [
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"deploy:azure",
"region:us"
] | null | {"architectures": ["CodeGenForCausalLM"], "model_type": "codegen", "tokenizer_config": {"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "bsd-3-clause", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # CodeGen (CodeGen-NL 350M)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savare... | null | [
"bsd-3-clause"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"codegen",
"CodeGenForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc036468d709f17747b | b94e6835580cdd2f | ca977325cb8d31d5 | 52,001 | BPE | 51,739 | false | true | true | true | 1 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 614 | Sentdex/GPyT | Sentdex | null | 33 | 281,495 | False | 2022-03-02T23:29:04Z | 2023-03-31T15:29:31Z | transformers | 22 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 20e2d51eb11c057932c19aa68a7129aae5f798d3 | [
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"Code",
"GPyT",
"code generator",
"code",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "tokenizer_config": {"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": fals... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "code", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["Code", "GPyT", "code generator"]} | GPyT is a GPT2 model trained from scratch (not fine tuned) on Python code from Github. Overall, it was ~80GB of pure Python code, the current GPyT model is a mere 2 epochs through this data, so it may benefit greatly from continued training and/or fine-tuning.
Newlines are replaced by `<N>`
Input to the model is code... | null | [
"mit"
] | null | [
"code"
] | null | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17fe4e | 7da867c453b32c82 | bf93ff275a8f4783 | 30,000 | BPE | 28,995 | true | true | true | true | 5 | NFKC | Metaspace | Metaspace | [
"NFKC"
] | [
"Metaspace"
] | [
"Metaspace"
] | 615 | robot-test/dummy-tokenizer-fast-with-model-config | robot-test | null | 5,896 | 306,232 | False | 2022-03-02T23:29:05Z | 2021-05-31T15:40:58Z | transformers | 0 | 0 | null | null | null | [
".gitattributes",
"config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 8a27784052ebf721c837afe249050f6460c6a587 | [
"transformers",
"albert",
"endpoints_compatible",
"region:us"
] | null | {"model_type": "albert", "tokenizer_config": {"bos_token": {"content": "[CLS]", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "[SEP]", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "sep... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | null | null | null | null | null | null | null | null | null | [
"albert",
"AutoModel"
] | [
null
] | null | null | null |
659dd31304b93eb6db8a08e0 | 099efed77b535bd4 | c81b23e1dcb12302 | 50,280 | BPE | 50,009 | true | true | true | true | 28 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 616 | allenai/OLMo-7B | allenai | null | 1,560 | 293,739 | False | 2024-01-09T23:13:23Z | 2025-10-09T19:01:29Z | transformers | 650 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"configuration_olmo.py",
"model.safetensors",
"modeling_olmo.py",
"pytorch_model.bin",
"requirements.txt",
"revisions.txt",
"special_tokens_map.json",
"tokenization_olmo_fast.py",
"tokenizer.json",
"tokenizer_config.json"
] | 0f605aa2bf7f9585bcedfa9b419326f15b453ddd | [
"transformers",
"pytorch",
"safetensors",
"hf_olmo",
"text-generation",
"custom_code",
"en",
"dataset:allenai/dolma",
"arxiv:2402.00838",
"arxiv:2302.13971",
"license:apache-2.0",
"region:us"
] | null | {"architectures": ["OLMoForCausalLM"], "model_type": "hf_olmo", "auto_map": {"AutoConfig": "configuration_olmo.OLMoConfig", "AutoModelForCausalLM": "modeling_olmo.OLMoForCausalLM"}, "tokenizer_config": {"eos_token": "<|endoftext|>", "pad_token": "<|padding|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_olmo.OLMoForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": ["allenai/dolma"], "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} | <img src="https://allenai.org/olmo/olmo-7b-animation.gif" alt="OLMo Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Model Card for OLMo 7B
<!-- Provide a quick summary of what the model is/does. -->
**For transformers versions v4.40.0 or newer, we suggest using [OLMo 7B HF](http... | null | [
"apache-2.0"
] | [
"allenai/dolma"
] | [
"en"
] | null | null | null | [
"modeling_olmo.OLMoForCausalLM",
"AutoModelForCausalLM",
"hf_olmo",
"OLMoForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17da23 | 086036148c53e78f | d965e4a9140ecf72 | 21,128 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 14 | luhua/chinese_pretrain_mrc_roberta_wwm_ext_large | luhua | null | 397 | 282,306 | False | 2022-03-02T23:29:05Z | 2021-06-12T02:53:16Z | transformers | 86 | 0 | null | question-answering | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"tokenizer.json",
"vocab.txt"
] | 71a61139397cbb5fd773d8b8b72282a3387ff130 | [
"transformers",
"pytorch",
"bert",
"question-answering",
"zh",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForQuestionAnswering"], "model_type": "bert"} | {
"auto_model": "AutoModelForQuestionAnswering",
"custom_class": null,
"pipeline_tag": "question-answering",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["zh"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ## Chinese MRC roberta_wwm_ext_large
* 使用大量中文MRC数据训练的roberta_wwm_ext_large模型，详情可查看：https://github.com/basketballandlearn/MRC_Competition_Dureader
* 此库发布的再训练模型，在 阅读理解/分类 等任务上均有大幅提高<br/>
（已有多位小伙伴在Dureader-2021等多个比赛中取得**top5**的成绩😁）
| 模型/数据集 | Dureader-2021 | tencentmedical |
| --------... | null | [
"apache-2.0"
] | null | [
"zh"
] | null | null | null | [
"AutoModelForQuestionAnswering",
"bert",
"BertForQuestionAnswering"
] | [
"question-answering"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6374e7b3627b7ad976ce2a8d | a2eba66f56003a7a | 830fa437e6f588f3 | 50,000 | BPE | 49,721 | true | true | false | true | 23 | NFKC | Sequence | ByteLevel | [
"NFKC"
] | [
"Split",
"Digits",
"Split",
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 439 | facebook/galactica-1.3b | facebook | null | 2,234 | 289,335 | False | 2022-11-16T13:37:55Z | 2023-01-24T17:20:39Z | transformers | 75 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | f711c69357d598defb703ddce93c5d7f7bc6e6da | [
"transformers",
"pytorch",
"opt",
"text-generation",
"galactica",
"arxiv:1810.03993",
"license:cc-by-nc-4.0",
"text-generation-inference",
"deploy:azure",
"region:us"
] | null | {"architectures": ["OPTForCausalLM"], "model_type": "opt", "tokenizer_config": {}} | {
"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": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["galactica"], "widget": [{"text": "The Transformer architecture [START_REF]"}, {... | 
# GALACTICA 1.3B (base)
Model card from the original [repo](https://github.com/paperswithcode/galai/blob/main/docs/model_card.md)
Following [Mitchell et al. (2018)](https://arxiv.org/abs/1810.03993), this model c... | null | [
"cc-by-nc-4.0"
] | null | null | null | null | null | [
"OPTForCausalLM",
"AutoModelForCausalLM",
"opt"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64dc9a1ca3433b23e7ee4bd5 | 46874ac0bd0e2129 | ade5e378ce727196 | 32,001 | BPE | 61,249 | true | false | true | true | 4 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 617 | Voicelab/trurl-2-7b | Voicelab | null | 2,070 | 287,792 | False | 2023-08-16T09:42:52Z | 2023-11-23T12:56:38Z | transformers | 16 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"pytorch_model-00001-of-00002.bin",
"pytorch_model-00002-of-00002.bin",
"pytorch_model.bin.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | f391c4c8e6547a42d485244dc9315a63fb019c20 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"voicelab",
"llama-2",
"trurl",
"trurl-2",
"en",
"pl",
"text-generation-inference",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": false, "rstrip... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "pl"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["voicelab", "pytorch", "llama-2", "trurl", "trurl-2"], "inference": f... | <img src="https://public.3.basecamp.com/p/rs5XqmAuF1iEuW6U7nMHcZeY/upload/download/VL-NLP-short.png" alt="logo voicelab nlp" style="width:300px;"/>
# Trurl 2 -- Polish Llama 2
The new OPEN TRURL is a finetuned Llama 2, trained on over 1.7b tokens (970k conversational **Polish** and **English** samples) with a large ... | null | null | null | [
"en",
"pl"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6777ab8fc34e5d1135657b99 | 2234f4af1c73a279 | c83500ba5c20e7ca | 128,258 | BPE | 280,147 | false | true | true | true | 258 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 618 | thkim0305/llama3.2_3B_vl | thkim0305 | null | 102 | 292,277 | False | 2025-01-03T09:19:11Z | 2025-01-07T04:19:51Z | transformers | 0 | 0 | null | image-text-to-text | {"parameters": {"BF16": 3528852480}, "total": 3528852480} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"preprocessor_config.json",
"processor_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.js... | 3d0c4920d1be5afa54df7c325f194d1828536a2d | [
"transformers",
"safetensors",
"llava",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlavaForConditionalGeneration"], "model_type": "llava", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_i... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": []} | # Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated... | null | null | null | null | 3,528,852,480 | null | null | [
"AutoModelForImageTextToText",
"LlavaForConditionalGeneration",
"llava"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
640a0d9ec16a90d02ff1f433 | da5e05d13100ae6f | b5947e8ca3a6d83c | 50,282 | BPE | 50,009 | true | true | true | true | 30 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 619 | OpenAssistant/oasst-sft-1-pythia-12b | OpenAssistant | null | 1,013 | 284,945 | False | 2023-03-09T16:47:26Z | 2023-03-11T14:25:14Z | transformers | 278 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model-00001-of-00003.bin",
"pytorch_model-00002-of-00003.bin",
"pytorch_model-00003-of-00003.bin",
"pytorch_model.bin.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 293df535fe7711a5726987fc2f17dfc87de452a1 | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"sft",
"en",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["sft"], "widget": [{"text": "<|prompter|>What is a meme, and what's... | # Open-Assistant SFT-1 12B Model
This is the first iteration English supervised-fine-tuning (SFT) model of
the [Open-Assistant](https://github.com/LAION-AI/Open-Assistant) project.
It is based on a Pythia 12B that was fine-tuned on ~22k human demonstrations
of assistant conversations collected through the
[https:... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6659b8b93114a8eca3bcb84a | 915ff9eff5cd57bb | df29491fd7f36c31 | 151,647 | BPE | 151,387 | true | true | true | true | 4 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 620 | numind/NuExtract-tiny | numind | {
"models": [
{
"_id": "65ae98125c9e7ad7bbdef8aa",
"id": "Qwen/Qwen1.5-0.5B"
}
],
"relation": "finetune"
} | 19,271 | 359,410 | False | 2024-05-31T11:47:05Z | 2024-10-17T15:28:37Z | transformers | 42 | 0 | null | text-generation | {"parameters": {"F32": 463987712}, "total": 463987712} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 41bb93b81b226dd7fc2b216f6029a41541f7246a | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:finetune:Qwen/Qwen1.5-0.5B",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen1.5-0.5B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "new_version": "numind/NuExtract-v1.5"} | > ⚠️ **_NOTE:_** This model is out-dated. Find the updated version [here](https://huggingface.co/numind/NuExtract-tiny-v1.5)
>
# Structure Extraction Model by NuMind 🔥
NuExtract_tiny is a version of [Qwen1.5-0.5](https://huggingface.co/Qwen/Qwen1.5-0.5B), fine-tuned on a private high-quality synthetic dataset for i... | null | [
"mit"
] | null | [
"en"
] | 463,987,712 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66aa1f5d58cff488d9880842 | fba7124a9a9e8160 | ad2d62bc66603fb6 | 128,031 | Unigram | null | true | true | true | true | 35 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 621 | relik-ie/relik-reader-deberta-v3-small-re-wikipedia | relik-ie | null | 945 | 326,663 | False | 2024-07-31T11:26:21Z | 2024-08-08T12:45:33Z | transformers | 0 | 0 | null | feature-extraction | {"parameters": {"F32": 146452230}, "total": 146452230} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"configuration_relik.py",
"model.safetensors",
"modeling_relik.py",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json"
] | 9ed9a981de5f88f4df6ff1640a2c02d205eb174b | [
"transformers",
"safetensors",
"relik-reader",
"feature-extraction",
"custom_code",
"en",
"region:us"
] | null | {"architectures": ["RelikReaderREModel"], "auto_map": {"AutoConfig": "configuration_relik.RelikReaderConfig", "AutoModel": "modeling_relik.RelikReaderREModel"}, "model_type": "relik-reader", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD... | {
"auto_model": "AutoModel",
"custom_class": "modeling_relik.RelikReaderREModel",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"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": null, "tags": null} | <div align="center">
<img src="https://github.com/SapienzaNLP/relik/blob/main/relik.png?raw=true" height="150">
<img src="https://github.com/SapienzaNLP/relik/blob/main/Sapienza_Babelscape.png?raw=true" height="50">
</div>
<div align="center">
<h1>Retrieve, Read and LinK: Fast and Accurate Entity Linking and Rel... | null | null | null | [
"en"
] | 146,452,230 | null | null | [
"modeling_relik.RelikReaderREModel",
"AutoModel",
"relik-reader",
"RelikReaderREModel"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
66940a2d6a04ddccda769b70 | 82cf3d0f50c70c6d | a0b6201332bae429 | 128,257 | BPE | 280,147 | false | true | true | true | 257 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 622 | royokong/e5-v | royokong | null | 16,361 | 358,719 | False | 2024-07-14T17:26:05Z | 2025-12-10T03:54:59Z | transformers | 31 | 0 | null | image-text-to-text | {"parameters": {"F16": 8355276800}, "total": 8355276800} | [
".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",
"preprocessor_config.json",
"specia... | 3580c62cf6a56d7b033f8a528c66ef1385705996 | [
"transformers",
"safetensors",
"llava_next",
"image-text-to-text",
"conversational",
"arxiv:2407.12580",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlavaNextForConditionalGeneration"], "model_type": "llava_next", "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['c... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": []} | # [E5-V: Universal Embeddings with Multimodal Large Language Models](https://arxiv.org/abs/2407.12580)
E5-V is fine-tuned based on lmms-lab/llama3-llava-next-8b.
## Overview
We propose a framework, called E5-V, to adpat MLLMs for achieving multimodal embeddings. E5-V effectively bridges the modality gap between diffe... | null | null | null | null | 8,355,276,800 | null | null | [
"AutoModelForImageTextToText",
"LlavaNextForConditionalGeneration",
"llava_next"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
6545c8eff7dd43aaa97e3d22 | e23b6bdbe047bb7e | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T | TinyLlama | null | 92 | 279,724 | False | 2023-11-04T04:30:39Z | 2024-02-03T18:33:08Z | transformers | 61 | 0 | null | text-generation | {"parameters": {"F32": 1100048384}, "total": 1100048384} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 19a81efa07bf28ec81dc4de327776fa00e34cf3f | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": false, "rstrip... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["cerebras/SlimPajama-627B", "bigcode/starcoderdata"], "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} | <div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-0... | null | [
"apache-2.0"
] | [
"cerebras/SlimPajama-627B",
"bigcode/starcoderdata"
] | [
"en"
] | 1,100,048,384 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6233157a84dcd3a04f9ff69e | d861518721af2524 | a15e15d2e27a76ba | 98,818 | BPE | 98,611 | false | true | false | false | 3 | null | ByteLevel | null | null | [
"ByteLevel"
] | null | 623 | navervision/KELIP | navervision | null | 496 | 280,445 | False | 2022-03-17T11:03:22Z | 2022-03-17T11:04:13Z | transformers | 6 | 0 | null | null | null | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 027d7a67da81f4d2c092f296c47e6e33344dfede | [
"transformers",
"pytorch",
"kelip",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["KelipModel"], "model_type": "kelip", "tokenizer_config": {}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | null | null | null | null | null | null | null | null | null | [
"AutoModel",
"kelip",
"KelipModel"
] | [
null
] | null | null | null |
6601543e298176eb214912ed | 58967ba68263f0aa | 2f8d5857ae5fa330 | 50,277 | BPE | 50,009 | true | true | true | true | 25 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 86 | echarlaix/tiny-mpt-random-remote-code | echarlaix | null | 2,116 | 283,704 | False | 2024-03-25T10:38:54Z | 2024-03-25T10:55:43Z | transformers | 0 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"adapt_tokenizer.py",
"attention.py",
"blocks.py",
"config.json",
"configuration_mpt.py",
"custom_embedding.py",
"flash_attn_triton.py",
"generation_config.json",
"hf_prefixlm_converter.py",
"meta_init_context.py",
"modeling_mpt.py",
"norm.py",
"param_init_... | 85e64794e74a6fb2e71e7055c7e0188ccdd32905 | [
"transformers",
"pytorch",
"mpt",
"text-generation",
"custom_code",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["MPTForCausalLM"], "auto_map": {"AutoConfig": "configuration_mpt.MPTConfig", "AutoModelForCausalLM": "modeling_mpt.MPTForCausalLM"}, "model_type": "mpt", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_mpt.MPTForCausalLM",
"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} | null | null | [
"apache-2.0"
] | null | null | null | null | null | [
"modeling_mpt.MPTForCausalLM",
"AutoModelForCausalLM",
"mpt",
"MPTForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63c82155a0c5c8cfa84d9fc1 | 0885bc0974e83fc4 | d965e4a9140ecf72 | 21,128 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 624 | Hello-SimpleAI/chatgpt-detector-roberta-chinese | Hello-SimpleAI | null | 1,192 | 280,646 | False | 2023-01-18T16:41:57Z | 2023-01-19T11:02:48Z | transformers | 25 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 2f0f5f2af59af169a3f236c14ffad0fa7d6f072b | [
"transformers",
"pytorch",
"bert",
"text-classification",
"chatgpt",
"zh",
"dataset:Hello-SimpleAI/HC3-Chinese",
"arxiv:2301.07597",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["Hello-SimpleAI/HC3-Chinese"], "eval_results": null, "language": ["zh"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-classification", "tags": ["chatgpt"]} | # Model Card for `Hello-SimpleAI/chatgpt-detector-roberta-chinese`
This model is trained on **the mix of full-text and splitted sentences** of `answer`s from [Hello-SimpleAI/HC3-Chinese](https://huggingface.co/datasets/Hello-SimpleAI/HC3-Chinese).
More details refer to [arxiv: 2301.07597](https://arxiv.org/abs/2301.0... | null | null | [
"Hello-SimpleAI/HC3-Chinese"
] | [
"zh"
] | null | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65441824276a59f143ebfe1e | 09574be15ef6a4ca | 081b543d8f95004e | 32,002 | BPE | 58,980 | true | false | true | true | 5 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 625 | TheBloke/OpenHermes-2.5-Mistral-7B-AWQ | TheBloke | {
"models": [
{
"_id": "653ec25781277ed9683d246f",
"id": "teknium/OpenHermes-2.5-Mistral-7B"
}
],
"relation": "quantized"
} | 24,641 | 341,535 | False | 2023-11-02T21:44:04Z | 2023-11-09T18:16:14Z | transformers | 21 | 0 | [{"name": "OpenHermes-2-Mistral-7B", "results": []}] | text-generation | {"parameters": {"I32": 6979321856, "F16": 262426624}, "total": 7241748480} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
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"model.safetensors",
"quant_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | a0e39e3a0ca6767307293819acb7c38e7a18cd31 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"en",
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"base_model:quantized:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"text-generation-... | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "quantization_config": {"quant_method": "awq", "bits": 4}, "tokenizer_config": {"bos_token": "<s>", "eos_token": "<|im_end|>", "pad_token": null, "unk_token": "<unk>", "use_default_system_prompt": true}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "teknium/OpenHermes-2.5-Mistral-7B", "datasets": null, "eval_results": [], "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": "OpenHermes-2-Mistral-7B", "pipeline_tag": null, "tags": ["mistral", "instruct", "finetu... | <!-- markdownlint-disable MD041 -->
<!-- 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:... | null | [
"apache-2.0"
] | null | [
"en"
] | 7,241,748,480 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
678e40a07967054e64d4c6b7 | 36935258f89e8e9e | 83d37c6e8ee56b77 | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 315 | unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit | unsloth | {
"models": [
{
"_id": "678e11922b39b4ed1381531b",
"id": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
}
],
"relation": "quantized"
} | 7,488 | 310,383 | False | 2025-01-20T12:25:04Z | 2025-02-14T23:54:38Z | transformers | 17 | 0 | null | text-generation | {"parameters": {"F32": 121432, "BF16": 598619648, "U8": 1215357928}, "total": 1814099008} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | c93b549ceae3f8137b567af565c2b204b3045091 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"deepseek",
"qwen",
"unsloth",
"conversational",
"en",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"license:apache-2.0",
"text-generation-inference",
"en... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": "<\uff5cbegin\u2581of\u2581sentence\uff5c>", "chat_template": "{% if not add_generation_prompt is defined %}{% set add_ge... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["deepseek", "qwen", "qwen2", "u... | ## ***See [our collection](https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5) for versions of Deepseek-R1 including GGUF and original formats.***
# Finetune LLMs 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) he... | null | [
"apache-2.0"
] | null | [
"en"
] | 1,814,099,008 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
642489fc6a04b6dd12409852 | 716852fad96cc799 | 64541a3d0db00c39 | 1,024 | BPE | 767 | false | true | true | true | 1 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 599 | hf-tiny-model-private/tiny-random-GPT2LMHeadModel | hf-tiny-model-private | null | 1,706 | 282,092 | False | 2023-03-29T18:57:00Z | 2023-03-29T18:57:04Z | transformers | 0 | 0 | null | text-generation | null | [
".gitattributes",
"config.json",
"generation_config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 253dae1977f5ecf0d3709305feb4e6c3690f3c32 | [
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized"... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | null | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6695aabcd4ca2767b9cb39ac | bafc5e0c20be7cd8 | 82d3db801e5d7f68 | 49,152 | BPE | 48,900 | false | true | false | true | 17 | null | Sequence | ByteLevel | null | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 115 | HuggingFaceTB/SmolLM-1.7B-Instruct | HuggingFaceTB | {
"models": [
{
"_id": "6693f5313774cc5e5c1d68ce",
"id": "HuggingFaceTB/SmolLM-1.7B"
}
],
"relation": "quantized"
} | 13,810 | 314,111 | False | 2024-07-15T23:03:24Z | 2024-08-18T16:38:59Z | transformers | 117 | 0 | null | text-generation | {"parameters": {"BF16": 1711376384}, "total": 1711376384} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"onnx/model.onnx",
"onnx/model.onnx_data",
"onnx/model_bnb4.onnx",
"onnx/model_fp16.onnx",
"onnx/model_fp16.onnx_data",
"onnx/model_int8.onnx",
"onnx/model_q4.onnx",
"onnx/model_q4f16... | 69f49d9c36434d6a3f319dadc5bd3b812752b98b | [
"transformers",
"onnx",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"conversational",
"en",
"dataset:Magpie-Align/Magpie-Pro-300K-Filtered",
"dataset:bigcode/self-oss-instruct-sc2-exec-filter-50k",
"dataset:teknium/OpenHermes-2.5",
"dataset:HuggingFaceTB/... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|im_start|>", "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "HuggingFaceTB/SmolLM-1.7B", "datasets": ["Magpie-Align/Magpie-Pro-300K-Filtered", "bigcode/self-oss-instruct-sc2-exec-filter-50k", "teknium/OpenHermes-2.5", "HuggingFaceTB/everyday-conversations-llama3.1-2k"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2... | # SmolLM-1.7B-Instruct
<center>
<img src="https://huggingface.co/datasets/HuggingFaceTB/images/resolve/main/banner_smol.png" alt="SmolLM" width="1100" height="600">
</center>
## Model Summary
SmolLM is a series of small language models available in three sizes: 135M, 360M, and 1.7B parameters.
These models ar... | null | [
"apache-2.0"
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"Magpie-Align/Magpie-Pro-300K-Filtered",
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621ffdc136468d709f17b8f2 | 6ad670d2742d0236 | 5f06a539f7067498 | 50,400 | BPE | 50,000 | false | true | true | true | 144 | null | ByteLevel | ByteLevel | null | [
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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": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["pytorch", "causal-lm"]} | # Lit-6B - A Large Fine-tuned Model For Fictional Storytelling
Lit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.
## Model Description
The model used for fine-tuning is [GPT-J](https://github.com... | null | [
"mit"
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<br>
<img src="https://github.com/ymcui/MacBERT/raw/master/pics/banner.png" width="500"/>
<br>
</p>
<p align="center">
<a href="https://github.com/ymcui/MacBERT/blob/master/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/ymcui/MacBERT.svg?color=blue&styl... | null | [
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should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-mnli-amazon-query-shopping
This model is a fine-tuned version of [nlptown/bert-base-multilingua... | null | [
"apache-2.0"
] | null | null | null | null | [
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"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "other", "license_name": "tongyi-qianwen", "license_link": "https://huggingface.co/Qwen/Qwen2-72B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["pretra... | # Qwen2-72B
## Introduction
Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the 72B Qwen2 base language model.
Compar... | null | [
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<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[🏠Ho... | null | [
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] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
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# sentence-transformers/stsb-bert-base
This is a [sentence-transformers](... | null | [
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] | 19502f1e215844f7e48bd48473f86932486d3441 | [
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"image-text-matching",
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"endpoints_compatible",
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] | null | {"architectures": ["BlipForImageTextRetrieval"], "model_type": "blip", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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Model card for BLIP trained on image-text matching - large architecture (with ViT large backbone) trained on COCO dataset.
| ![BLIP.gif](https://cdn-uploads.huggingface.co/production/uploads/1670928184033-62441d... | null | [
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"e... | 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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*Train used 1x H100 SXM for like a total of 24 Hours over multiple runs.*
Support me here if you're interested:
<br>Ko-fi: https://ko-fi.com/sao10k
<br> *wink* Euryale v2?
If not, that's fine too. Feedback would ... | null | [
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],
"relation": "finetune"
} | 145,541 | 610,780 | False | 2024-08-29T08:52:39Z | 2025-08-22T05:44:45Z | transformers | 32 | 0 | null | text-generation | {"parameters": {"BF16": 1050939392, "F8_E4M3": 6979321856}, "total": 8030261248} | [
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] | 42d9515ebd69eea3a87351d079c671c3c5ff0a31 | [
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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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} | {"base_model": ["meta-llama/Llama-3.1-8B-Instruct"], "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": null} | # Model Overview
## Description:
The NVIDIA Llama 3.1 8B Instruct FP8 model is the quantized version of the Meta's Llama 3.1 8B Instruct model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check [here](https://huggingface.co/meta-llama/Meta-Ll... | null | [
"llama3.1"
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You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/v... | null | [
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] | 12 | albert/albert-xlarge-v1 | albert | null | 690 | 273,677 | False | 2022-03-02T23:29:04Z | 2024-02-19T11:01:28Z | transformers | 4 | 0 | null | fill-mask | {"parameters": {"F32": 59047392}, "total": 59047392} | [
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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 | [
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# Academic Trurl 2 -- Polish Llama 2
The Academic TRURL is a finetuned Llama 2, trained on over 1.7b tokens (855k conversational **Polish** and **English** samples) with... | null | null | null | [
"en",
"pl"
] | null | null | null | [
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This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on 6 different nli datasets. The model will predict a score between 0 (not... | null | [
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"deploy:azure... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"chat_template": "{%- if tools %}\n{{- '<|system|>\\n' }}\n{%- if messages[0]['role'] == 'system' %}\n{{- messages[0]['content'] }}\n{%- set remaining_messages = messages[1:] %}\n{%- else %}\n{%- set remaining_messages = messages %}\n{%... | {
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<img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt="drawing" width="500"/>
</div>
# Falcon3-3B-Instruct
**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters.
**Falc... | null | [
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] | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>"}} | {
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ko"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["pytorch", "causal-lm"]} | # Polyglot-Ko-12.8B
## Model Description
Polyglot-Ko is a series of large-scale Korean autoregressive language models made by the EleutherAI polyglot team.
| Hyperparameter | Value |
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["text-embedding", "embeddings", "information-retrieval", "be... | # LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine... | 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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GODEL is a large-scale pre-trained model for goal-directed dialogs. It is parameterized with a Transformer-based encoder-decoder model and trained for response generation grounded in external text, which allows more effective fine-tuning on dialog tasks tha... | null | [
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] | null | null | null | null | null | [
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"text-generation-inference",
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] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized"... | {
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["conversational"]} | # DialoGPT model that talks like Elon Musk
Trained on Twitter tweets by Elon Musk. This model will spew meaningless shit about 40% of the time.
2nd version. This time trained with 8 epochs instead of 4 [1st version](https://huggingface.co/Pi3141/DialoGPT-medium-elon) | null | null | null | null | null | null | null | [
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] | 635 | openbmb/MiniCPM3-4B | openbmb | null | 13,322 | 327,794 | False | 2024-09-03T12:47:22Z | 2025-02-27T12:09:50Z | transformers | 419 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
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"generation_config.json",
"modeling_minicpm.py",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | d6b14ddaefdb11c624dd75c3c779549bc90b08cb | [
"transformers",
"pytorch",
"minicpm3",
"text-generation",
"conversational",
"custom_code",
"zh",
"en",
"arxiv:2404.06395",
"license:apache-2.0",
"region:us"
] | null | {"architectures": ["MiniCPM3ForCausalLM"], "auto_map": {"AutoConfig": "configuration_minicpm.MiniCPM3Config", "AutoModel": "modeling_minicpm.MiniCPM3Model", "AutoModelForCausalLM": "modeling_minicpm.MiniCPM3ForCausalLM", "AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPM3ForCausalLM", "AutoModelForSequenceClassificatio... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_minicpm.MiniCPM3ForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["zh", "en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": null} | <div align="center">
<img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img>
</div>
<p align="center">
<a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">MiniCPM Repo</a> |
<a href="https://arxiv.org/abs/2404.06395" target="_blank">MiniCPM Paper</a> |
... | null | [
"apache-2.0"
] | null | [
"zh",
"en"
] | null | null | null | [
"MiniCPM3ForCausalLM",
"modeling_minicpm.MiniCPM3ForCausalLM",
"AutoModelForCausalLM",
"minicpm3"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6535e536ae42162a1e0f8523 | bcdced20559a6ff4 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 265 | Falconsai/medical_summarization | Falconsai | null | 3,638 | 282,252 | False | 2023-10-23T03:15:02Z | 2024-01-20T12:48:04Z | transformers | 146 | 0 | null | summarization | {"parameters": {"F32": 60506624}, "total": 60506624} | [
".gitattributes",
"README.md",
"config.json",
"coreml/text2text-generation/decoder_float32_model.mlpackage/Data/com.apple.CoreML/model.mlmodel",
"coreml/text2text-generation/decoder_float32_model.mlpackage/Data/com.apple.CoreML/weights/weight.bin",
"coreml/text2text-generation/decoder_float32_model.mlpack... | 28df52e1c1a37ae90145951ca5d684aa58ec830b | [
"transformers",
"pytorch",
"coreml",
"safetensors",
"t5",
"text2text-generation",
"medical",
"summarization",
"en",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "summarization", "tags": ["medical"], "widget": [{"text": "the need for magnetic resonance imag... | # Model Card: T5 Large for Medical Text Summarization
## Model Description
The **T5 Large for Medical Text Summarization** is a specialized variant of the T5 transformer model, fine-tuned for the task of summarizing medical text. This model is designed to generate concise and coherent summaries of medical documents, ... | null | [
"apache-2.0"
] | null | [
"en"
] | 60,506,624 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"summarization"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64fd41d131a82e0d406377a3 | 83bf28f8b6767a2f | ae88e903e2b563a1 | 50,295 | BPE | 50,000 | false | true | true | true | 39 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 85 | microsoft/phi-1 | microsoft | null | 5,682 | 296,345 | False | 2023-09-10T04:10:57Z | 2025-11-24T16:59:07Z | transformers | 218 | 0 | null | text-generation | {"parameters": {"F16": 1418270720}, "total": 1418270720} | [
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"LICENSE",
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"generation_config.json",
"merges.txt",
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"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vo... | d4c0adcb065e84e00ca814e35cba3012ea9841ab | [
"transformers",
"safetensors",
"phi",
"text-generation",
"code",
"en",
"arxiv:2306.11644",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["PhiForCausalLM"], "model_type": "phi", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "mit", "license_name": null, "license_link": "https://huggingface.co/microsoft/phi-1/resolve/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["code"]} | ## Model Summary
The language model Phi-1 is a Transformer with 1.3 billion parameters, specialized for basic Python coding. Its training involved a variety of data sources, including subsets of Python codes from [The Stack v1.2](https://huggingface.co/datasets/bigcode/the-stack), Q&A content from [StackOverflow](http... | null | [
"mit",
"https://huggingface.co/microsoft/phi-1/resolve/main/LICENSE"
] | null | [
"en"
] | 1,418,270,720 | null | null | [
"AutoModelForCausalLM",
"phi",
"PhiForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
668755f56328de3e95b84748 | 83de6816f09fa474 | ad0a62d6e3740ce9 | 151,343 | BPE | 318,088 | false | true | true | true | 14 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 636 | zai-org/codegeex4-all-9b | zai-org | null | 8,366 | 286,230 | False | 2024-07-05T02:09:57Z | 2024-07-18T12:45:18Z | transformers | 265 | 0 | null | text-generation | {"parameters": {"BF16": 9399951392}, "total": 9399951392} | [
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... | 6ee90cf42fbd24807825b5ff6bed9830a5a4cfb2 | [
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"thudm",
"text-generation",
"custom_code",
"zh",
"en",
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"region:us"
] | null | {"architectures": ["ChatGLMModel"], "auto_map": {"AutoConfig": "configuration_chatglm.ChatGLMConfig", "AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration", "AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration", "AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration", ... | {
"auto_model": "AutoModel",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["zh", "en"], "library_name": null, "license": "other", "license_name": "codegeex4", "license_link": "https://huggingface.co/THUDM/codegeex4-all-9b/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags":... | null | null | [
"other",
"codegeex4",
"https://huggingface.co/THUDM/codegeex4-all-9b/blob/main/LICENSE"
] | null | [
"zh",
"en"
] | 9,399,951,392 | null | null | [
"AutoModel",
"chatglm",
"ChatGLMModel"
] | [
null,
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6611f275e94d9046b75da8e9 | 191e06b04083279c | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 26 | eliasalbouzidi/distilbert-nsfw-text-classifier | eliasalbouzidi | {
"models": [
{
"_id": "621ffdc036468d709f174347",
"id": "distilbert/distilbert-base-uncased"
}
],
"relation": "finetune"
} | 4,203 | 297,756 | False | 2024-04-07T01:10:13Z | 2025-05-16T21:36:46Z | transformers | 23 | 0 | [{"name": "NSFW-Safe-Dataset", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "NSFW-Safe-Dataset", "type": "."}, "metrics": [{"name": "F1", "type": "f1", "value": 0.974, "verified": false}, {"name": "Accuracy", "type": "accuracy", "value": 0.98, "verified": fals... | text-classification | {"parameters": {"F32": 66955010}, "total": 66955010} | [
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"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | c0abb7a10abea1f50528885e9e38d51cca935516 | [
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"distilbert",
"text-classification",
"Transformers",
" PyTorch",
"safety",
"innapropriate",
"DiffGuard",
"en",
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"arxiv:2412.00064",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/dis... | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "distilbert-base-uncased", "datasets": ["eliasalbouzidi/NSFW-Safe-Dataset"], "language": ["en"], "license": "apache-2.0", "metrics": ["f1", "accuracy", "precision", "recall"], "pipeline_tag": "text-classification", "tags": ["Transformers", " PyTorch", "safety", "innapropriate", "distilbert", "DiffGuard"]... | # Model Card
<!-- Provide a quick summary of what the model is/does. -->
This model is designed to categorize text into two classes: "safe", or "nsfw" (not safe for work), which makes it suitable for content moderation and filtering applications.
The model was trained using a dataset containing 190,000 labeled text ... | null | [
"apache-2.0"
] | [
"eliasalbouzidi/NSFW-Safe-Dataset"
] | [
"en"
] | 66,955,010 | null | [
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"precision",
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] | [
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"distilbert",
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] | [
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] | [
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] | [
"text"
] | [
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621ffdc136468d709f17a21e | 5d253ce26c8dd572 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 572 | cross-encoder/qnli-distilroberta-base | cross-encoder | {
"models": [
{
"_id": "621ffdc036468d709f174349",
"id": "distilbert/distilroberta-base"
}
],
"relation": "quantized"
} | 3,820 | 281,454 | False | 2022-03-02T23:29:05Z | 2025-04-11T08:54:34Z | sentence-transformers | 0 | 0 | null | text-ranking | {"parameters": {"I64": 514, "F32": 82119169}, "total": 82119683} | [
".gitattributes",
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"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
... | 7dd04ee0a6040c06fb381ad7edcb8585f4d937fd | [
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"base_model:quantized:distilbert/distilroberta-base",
"license:apache-2.0"... | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": ["distilbert/distilroberta-base"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-ranking", "tags": ["transformers"]} | null | null | [
"apache-2.0"
] | null | [
"en"
] | 82,119,683 | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-ranking",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6679cb1ff5984f44d43b098a | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | gokaygokay/Florence-2-SD3-Captioner | gokaygokay | null | 15,500 | 291,187 | False | 2024-06-24T19:38:07Z | 2024-09-18T01:18:49Z | transformers | 41 | 0 | null | image-text-to-text | {"parameters": {"F32": 270957657}, "total": 270957657} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"configuration_florence2.py",
"merges.txt",
"model.safetensors",
"modeling_florence2.py",
"preprocessor_config.json",
"processing_florence2.py",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 178fd612533079793c33ede7245b29d23db307b5 | [
"transformers",
"safetensors",
"florence2",
"image-text-to-text",
"art",
"custom_code",
"en",
"dataset:google/docci",
"dataset:google/imageinwords",
"dataset:ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Florence2ForConditionalGeneration"], "auto_map": {"AutoConfig": "configuration_florence2.Florence2Config", "AutoModelForCausalLM": "modeling_florence2.Florence2ForConditionalGeneration"}, "model_type": "florence2", "tokenizer_config": {}} | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": ["google/docci", "google/imageinwords", "ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "... | ```
pip install -q datasets flash_attn timm einops
```
```python
from transformers import AutoModelForCausalLM, AutoProcessor, AutoConfig
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModelForCausalLM.from_pretrained("gokaygokay/Florence-2-SD3-Captioner", trust_remote... | null | [
"apache-2.0"
] | [
"google/docci",
"google/imageinwords",
"ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions"
] | [
"en"
] | 270,957,657 | null | null | [
"florence2",
"AutoModelForImageTextToText",
"Florence2ForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
6744927dc16228e54d8adb7b | b49e169598db4d83 | 0cdc2927ce4cf866 | 32,002 | BPE | 61,249 | true | false | true | true | 5 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 74 | trl-internal-testing/tiny-LlavaForConditionalGeneration | trl-internal-testing | null | 104,548 | 785,588 | False | 2024-11-25T15:06:37Z | 2025-12-19T19:13:23Z | transformers | 0 | 0 | null | image-text-to-text | {"parameters": {"F16": 2423984}, "total": 2423984} | [
".gitattributes",
"README.md",
"added_tokens.json",
"chat_template.jinja",
"chat_template.json",
"config.json",
"generation_config.json",
"model.safetensors",
"preprocessor_config.json",
"processor_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_con... | 4dd249b19366cb0772b01613d1ab37cd039b2aa1 | [
"transformers",
"safetensors",
"llava",
"image-text-to-text",
"trl",
"conversational",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlavaForConditionalGeneration"], "model_type": "llava", "processor_config": {"chat_template": "{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalt... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["trl"]} | # Tiny LlavaForConditionalGeneration
This is a minimal model built for unit tests in the [TRL](https://github.com/huggingface/trl) library. | null | null | null | null | 2,423,984 | null | null | [
"AutoModelForImageTextToText",
"LlavaForConditionalGeneration",
"llava"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
669599bda73ba402a36355e4 | bafc5e0c20be7cd8 | 82d3db801e5d7f68 | 49,152 | BPE | 48,900 | false | true | false | true | 17 | null | Sequence | ByteLevel | null | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 115 | HuggingFaceTB/SmolLM-360M-Instruct | HuggingFaceTB | {
"models": [
{
"_id": "669447b5fc25100d1ebbfbab",
"id": "HuggingFaceTB/SmolLM-360M"
}
],
"relation": "quantized"
} | 9,898 | 326,595 | False | 2024-07-15T21:50:53Z | 2024-08-18T16:33:51Z | transformers | 84 | 0 | null | text-generation | {"parameters": {"BF16": 361821120}, "total": 361821120} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
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"onnx/model_q4.onnx",
"onnx/model_q4f16.onnx",
"onnx/model_quantized.onnx",
"onnx/model_uint... | 73b7144f76331266f5f45d5642fd8da653583b13 | [
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"dataset:bigcode/self-oss-instruct-sc2-exec-filter-50k",
"dataset:teknium/OpenHermes-2.5",
"dataset:HuggingFaceTB/... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|im_start|>", "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "HuggingFaceTB/SmolLM-360M", "datasets": ["Magpie-Align/Magpie-Pro-300K-Filtered", "bigcode/self-oss-instruct-sc2-exec-filter-50k", "teknium/OpenHermes-2.5", "HuggingFaceTB/everyday-conversations-llama3.1-2k"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2... | # SmolLM-360M-Instruct
<center>
<img src="https://huggingface.co/datasets/HuggingFaceTB/images/resolve/main/banner_smol.png" alt="SmolLM" width="1100" height="600">
</center>
## Model Summary
Chat with the model at: https://huggingface.co/spaces/HuggingFaceTB/instant-smol
SmolLM is a series of language models a... | null | [
"apache-2.0"
] | [
"Magpie-Align/Magpie-Pro-300K-Filtered",
"bigcode/self-oss-instruct-sc2-exec-filter-50k",
"teknium/OpenHermes-2.5",
"HuggingFaceTB/everyday-conversations-llama3.1-2k"
] | [
"en"
] | 361,821,120 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66eb50118ed4a19697c13511 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | mlx-community/Qwen2.5-Coder-7B-Instruct-4bit | mlx-community | {
"models": [
{
"_id": "66e81d2494457ccb513ef9c9",
"id": "Qwen/Qwen2.5-Coder-7B"
}
],
"relation": "finetune"
} | 4,090 | 274,665 | False | 2024-09-18T22:11:29Z | 2024-09-18T22:14:51Z | transformers | 10 | 1 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"model.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 019cc73c45c770444708a6dd8690c66243cc5c80 | [
"transformers",
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"text-generation",
"code",
"codeqwen",
"chat",
"qwen",
"qwen-coder",
"mlx",
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"base_model:finetune:Qwen/Qwen2.5-Coder-7B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_c... | 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-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-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_t... | # mlx-community/Qwen2.5-Coder-7B-Instruct-4bit
The Model [mlx-community/Qwen2.5-Coder-7B-Instruct-4bit](https://huggingface.co/mlx-community/Qwen2.5-Coder-7B-Instruct-4bit) was converted to MLX format from [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) using mlx-lm version **0.... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17ae9f | 0becab10e3734e80 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 58 | facebook/xlm-roberta-xxl | facebook | null | 8,976 | 300,910 | False | 2022-03-02T23:29:05Z | 2022-08-08T07:19:25Z | transformers | 17 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 03e0fb540c3c9afd4bdda0072e7cb82d2eafd060 | [
"transformers",
"pytorch",
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"multilingual",
"af",
"am",
"ar",
"as",
"az",
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"br",
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"ca",
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"da",
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"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
... | null | {"architectures": ["XLMRobertaXLForMaskedLM"], "model_type": "xlm-roberta-xl", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "no... | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "j... | # XLM-RoBERTa-XL (xxlarge-sized model)
XLM-RoBERTa-XL model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anan... | null | [
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] | null | [
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"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
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"i... | null | null | null | [
"AutoModelForMaskedLM",
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"xlm-roberta-xl"
] | [
"fill-mask"
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] | [
"logits"
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68885298e5d97c69f3491dd9 | 0fbdb3abe17d0d5f | b02c1908f37c4055 | 151,365 | BPE | 318,088 | false | true | true | true | 36 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 320 | cyankiwi/GLM-4.5-Air-AWQ-4bit | cyankiwi | {
"models": [
{
"_id": "687c61c324649ecb26a748f0",
"id": "zai-org/GLM-4.5-Air"
}
],
"relation": "quantized"
} | 3,793 | 1,386,895 | False | 2025-07-29T04:48:24Z | 2026-01-13T14:18:17Z | transformers | 28 | 0 | null | text-generation | {"parameters": {"I64": 34920, "F32": 5760, "I32": 13070499840, "BF16": 5555871744}, "total": 18626406504} | [
".gitattributes",
"README.md",
"chat_template.jinja",
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"model-00004-of-00013.safetensors",
"model-00005-of-00013.safetensors",
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"transformers",
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"base_model:quantized:zai-org/GLM-4.5-Air",
"license:mit",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] | null | {"architectures": ["Glm4MoeForCausalLM"], "model_type": "glm4_moe", "quantization_config": {"quant_method": "compressed-tensors"}, "tokenizer_config": {"eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>"}, "chat_template_jinja": "[gMASK]<sop>\n{%- if tools -%}\n<|system|>\n# Tools\n\nYou may call one or more fun... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "zai-org/GLM-4.5-Air", "datasets": null, "eval_results": null, "language": ["en", "zh"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": null} | null | null | [
"mit"
] | null | [
"en",
"zh"
] | 18,626,406,504 | null | null | [
"AutoModelForCausalLM",
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"glm4_moe"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6344c2236e988c6a79300eaf | 04f5fbc309982132 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 259 | MingZhong/unieval-dialog | MingZhong | null | 1,430 | 271,824 | False | 2022-10-11T01:08:51Z | 2022-10-14T01:09:17Z | transformers | 4 | 0 | null | null | null | [
".gitattributes",
"README.md",
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"optimizer.pt",
"pytorch_model.bin",
"rng_state_0.pth",
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"scheduler.pt",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | 7e6521dab47bfc2bba18ccdfb1661529fda2a6c5 | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2210.07197",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | null | Pre-trained evaluator in EMNLP 2022 paper
*[Towards a Unified Multi-Dimensional Evaluator for Text Generation](https://arxiv.org/abs/2210.07197)*
## Introduction
**Multi-dimensional evaluation** is the dominant paradigm for human evaluation in Natural Language Generation (NLG), i.e., evaluating the generated text fr... | null | null | null | null | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
621ffdc136468d709f17b05d | 729fbde64e945265 | c906e3d2c0e399fb | 50,105 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 238 | fergusq/finbert-finnsentiment | fergusq | null | 11,945 | 324,043 | False | 2022-03-02T23:29:05Z | 2023-03-24T11:14:28Z | transformers | 2 | 0 | null | text-classification | {"parameters": {"I64": 512, "F32": 124524291}, "total": 124524803} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 836116f364844201a6df01ffb970107f575620d2 | [
"transformers",
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"safetensors",
"bert",
"text-classification",
"fi",
"arxiv:2012.02613",
"license:cc-by-4.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "fi", "library_name": null, "license": "cc-by-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # FinBERT fine-tuned with the FinnSentiment dataset
This is a FinBERT model fine-tuned with the [FinnSentiment dataset](https://arxiv.org/pdf/2012.02613.pdf). 90% of sentences were used for training and 10% for evaluation.
## Evaluation results
|Metric|Score|
|--|--|
|Accuracy|0.8639028475711893|
|F1-score|0.8643024... | null | [
"cc-by-4.0"
] | null | [
"fi"
] | 124,524,803 | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6629f70a4f9d8e75f2caa0cd | 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-262k | gradientai | null | 1,824 | 270,075 | False | 2024-04-25T06:24:10Z | 2024-10-28T20:45:40Z | transformers | 261 | 0 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
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# Llama-3 8B Gradient Instruct 262k
Join our custom agent and long context (262k-1M+) waitlist: https://forms.gle/L6TDY7dozx8TuoUv7
Gradient incorporates your data to deploy autonomous as... | null | [
"llama3"
] | null | [
"en"
] | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
660fd8339ff4d088f583ef48 | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | lex-hue/Delexa-7b | lex-hue | null | 15,270 | 333,643 | False | 2024-04-05T10:53:39Z | 2024-05-10T17:27:32Z | transformers | 19 | 0 | [{"name": "Delexa-7b", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 68, "name": "normalized acc... | text-generation | {"parameters": {"F16": 7241732096}, "total": 7241732096} | [
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... | 93916358b007d33ce58baf3f02e480bd90532be6 | [
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"license:apache-2.0",
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"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"language": ["en", "de"], "license": "apache-2.0", "inference": true, "model-index": [{"name": "Delexa-7b", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_... | # Model Card
### Model Name: Delexa-7b
#### Overview:
**Purpose:** Delexa-7b is our newest large language model designed for general-purpose language tasks. It's currently under development, with ongoing improvements and testing.
**Status:** Active development and refinement. More comprehensive evaluation result... | null | [
"apache-2.0"
] | null | [
"en",
"de"
] | 7,241,732,096 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17e003 | 191e06b04083279c | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 26 | ml6team/bert-base-uncased-city-country-ner | ml6team | null | 3,132 | 279,172 | False | 2022-03-02T23:29:05Z | 2022-07-01T07:27:25Z | transformers | 16 | 0 | null | token-classification | null | [
".gitattributes",
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"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
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] | e38e683af1174120b192661dbdcbc2358fe56964 | [
"transformers",
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"tf",
"bert",
"token-classification",
"address-NER",
"NER",
"bert-base-uncased",
"en",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForTokenClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": ["Precision", "Recall", "F1 Score"], "model_name": null, "pipeline_tag": null, "tags": ["token-classification", "address-NER", "NER", "bert-base-... | ## City-Country-NER
A `bert-base-uncased` model finetuned on a custom dataset to detect `Country` and `City` names from a given sentence.
### Custom Dataset
We weakly supervised the [Ultra-Fine Entity Typing](https://www.cs.utexas.edu/~eunsol/html_pages/open_entity.html) dataset to include the `City` and `Country` i... | null | null | null | [
"en"
] | null | null | [
"Precision",
"Recall",
"F1 Score"
] | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66ebd4243843d7d5b56903af | 623874f716f0fe83 | 8687636a64918cd7 | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 637 | Zyphra/Zamba2-1.2B-instruct | Zyphra | {
"models": [
{
"_id": "66bfdcc4934e2f077568a24a",
"id": "Zyphra/Zamba2-1.2B"
}
],
"relation": "finetune"
} | 85,970 | 580,658 | False | 2024-09-19T07:35:00Z | 2025-02-07T02:04:48Z | transformers | 30 | 0 | null | text-generation | {"parameters": {"F32": 1215062272, "BF16": 2432}, "total": 1215064704} | [
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"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
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"tokenizer_config.json"
] | c06b789753995762b3c11b3eeb39e38634f897e9 | [
"transformers",
"safetensors",
"zamba2",
"text-generation",
"conversational",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:BAAI/Infinity-Instruct",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"dataset:Intel/orca_dpo_pairs",
"dataset:argilla/OpenHermesPreferences",
"base_model:Zyphra/Zam... | null | {"architectures": ["Zamba2ForCausalLM"], "model_type": "zamba2", "tokenizer_config": {"bos_token": "<|im_start|>", "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Zyphra/Zamba2-1.2B"], "datasets": ["HuggingFaceH4/ultrachat_200k", "BAAI/Infinity-Instruct", "HuggingFaceH4/ultrafeedback_binarized", "Intel/orca_dpo_pairs", "argilla/OpenHermesPreferences"], "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name"... | # Model Card for Zamba2-1.2B
Zamba2-1.2B-instruct is obtained from Zamba2-1.2B by fine-tuning on instruction-following and chat datasets. Specifically:
1. SFT of the base [Zamba2-1.2B](https://huggingface.co/Zyphra/Zamba2-1.2B) model on [ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) an... | null | [
"apache-2.0"
] | [
"HuggingFaceH4/ultrachat_200k",
"BAAI/Infinity-Instruct",
"HuggingFaceH4/ultrafeedback_binarized",
"Intel/orca_dpo_pairs",
"argilla/OpenHermesPreferences"
] | null | 1,215,064,704 | null | null | [
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"zamba2"
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"text-generation"
] | [
"text"
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"text"
] | [
"text"
] |
644e8d55cf72e60a5b7c3c88 | 25a9a87c9f9394e9 | 101408923f784238 | 64,000 | WordPiece | null | true | true | true | true | 8 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 638 | medmediani/Arabic-KW-Mdel | medmediani | null | 46 | 263,706 | False | 2023-04-30T15:46:29Z | 2023-04-30T20:11:21Z | sentence-transformers | 5 | 0 | null | sentence-similarity | null | [
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"sentence-similarity",
"transformers",
"text-embeddings-inference",
"endpoints_compatible",
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] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
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"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "tr... | # {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when y... | null | null | null | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f1812ea | f0b30459222b62d4 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 36 | valurank/distilroberta-bias | valurank | null | 3,348 | 304,467 | False | 2022-03-02T23:29:05Z | 2022-06-08T20:44:39Z | transformers | 12 | 0 | null | text-classification | null | [
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"pytorch_model.bin",
"special_tokens_map.json",
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"tokenizer_config.json",
"training_args.bin",
"vocab.json"
] | c1e4a2773522c3acc929a7b2c9af2b7e4137b96d | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:valurank/wikirev-bias",
"license:other",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["valurank/wikirev-bias"], "eval_results": null, "language": "en", "library_name": null, "license": "other", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # DistilROBERTA fine-tuned for bias detection
This model is based on [distilroberta-base](https://huggingface.co/distilroberta-base) pretrained weights, with a classification head fine-tuned to classify text into 2 categories (neutral, biased).
## Training data
The dataset used to fine-tune the model is [wikirev-bias... | null | [
"other"
] | [
"valurank/wikirev-bias"
] | [
"en"
] | null | null | null | [
"roberta",
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] | [
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621ffdc136468d709f1814a2 | a5cf9094a13a7ef2 | f766a15c782f2300 | 66,050 | BPE | 64,000 | false | true | true | true | 1 | null | WhitespaceSplit | BPEDecoder | null | [
"WhitespaceSplit"
] | [
"BPEDecoder"
] | 80 | vinai/bertweet-covid19-base-cased | vinai | null | 113 | 263,187 | False | 2022-03-02T23:29:05Z | 2024-08-20T03:58:22Z | transformers | 2 | 0 | null | fill-mask | null | [
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"flax_model.msgpack",
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"tokenization_bertweet_fast.py",
"tokenizer.json",
"vocab.txt"
] | ccfa24adfebac857b9a7d0304d55e547fd69945d | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta"} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets
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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"roberta",
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"RobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
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"text"
] | [
"logits"
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64cb5e611f8a37e3ed3e927a | e23b6bdbe047bb7e | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
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"Replace",
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] | 46 | TheBloke/vicuna-13B-v1.5-16K-GPTQ | TheBloke | {
"models": [
{
"_id": "64c93822f3d2a59a4331ee94",
"id": "lmsys/vicuna-13b-v1.5-16k"
}
],
"relation": "quantized"
} | 13 | 262,981 | False | 2023-08-03T07:59:29Z | 2023-09-27T12:45:17Z | transformers | 33 | 0 | null | text-generation | {"parameters": {"I32": 12687769600, "F16": 330224640}, "total": 13017994240} | [
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"license:llama2",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "con... | {
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"pipeline_tag": "text-generation",
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} | {"base_model": "lmsys/vicuna-13b-v1.5-16k", "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": "Vicuna 13B v1.5 16K", "pipeline_tag": null, "tags": null, "inference": false, "model_creator": "lms... | <!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<d... | null | [
"llama2"
] | null | null | 13,017,994,240 | null | null | [
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"llama",
"LlamaForCausalLM"
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659269d3b72f4ce63b886695 | e23b6bdbe047bb7e | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | unsloth/tinyllama-bnb-4bit | unsloth | null | 39,341 | 335,085 | False | 2024-01-01T07:29:23Z | 2024-09-03T03:45:39Z | transformers | 12 | 0 | null | text-generation | {"parameters": {"F32": 101024, "BF16": 131164160, "U8": 999214784}, "total": 1130479968} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
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] | c6cc31241054c2fe9b4c804f398faffe1254728f | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"tinyllama",
"en",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<unk>", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["unsloth", "transformers", "tinyllama"]} | # Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
Directly quantized 4bit model with `bitsandbytes`.
We have a Google Colab Tesla T4 notebook for TinyLlama with 4096 max sequence length RoPE Scaling here: https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharin... | null | [
"apache-2.0"
] | null | [
"en"
] | 1,130,479,968 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
679bc96de2b409a8d381be48 | d944255e7d785801 | 2f8c3374367f63f8 | 131,072 | BPE | 269,443 | false | true | true | true | 1,000 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 568 | stelterlab/Mistral-Small-24B-Instruct-2501-AWQ | stelterlab | {
"models": [
{
"_id": "6798dbe54a10be7109f954f2",
"id": "mistralai/Mistral-Small-24B-Instruct-2501"
}
],
"relation": "quantized"
} | 408,326 | 955,861 | False | 2025-01-30T18:48:13Z | 2025-03-30T19:38:57Z | vllm | 27 | 0 | null | text-generation | {"parameters": {"I32": 22229811200, "F16": 1342592000}, "total": 23572403200} | [
".gitattributes",
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"params.json",
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"tekken.json",
"tokenizer.json",
"toke... | cbda099649a0188dd888d44f0e4964d8d982dc9a | [
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"en",
"fr",
"de",
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"base_model:mistralai/Mistral-Small-24B-Instruct-2501",
"base_model:quantized:mistralai/Mistral-Small-24B-Instruct-2501",
"license:ap... | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "quantization_config": {"bits": 4, "quant_method": "awq"}, "tokenizer_config": {"bos_token": "<s>", "chat_template": "{%- set today = strftime_now(\"%Y-%m-%d\") %}\n{%- set default_system_message = \"You are Mistral Small 3, a Large Language Model (LLM)... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "mistralai/Mistral-Small-24B-Instruct-2501", "datasets": null, "eval_results": null, "language": ["en", "fr", "de", "es", "it", "pt", "zh", "ja", "ru", "ko"], "library_name": "vllm", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": ... | null | null | [
"apache-2.0"
] | null | [
"en",
"fr",
"de",
"es",
"it",
"pt",
"zh",
"ja",
"ru",
"ko"
] | 23,572,403,200 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
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621ffdc136468d709f17ae09 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | facebook/muppet-roberta-large | facebook | null | 24 | 261,550 | False | 2022-03-02T23:29:05Z | 2021-06-28T21:44:41Z | transformers | 13 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"tokenizer.json",
"vocab.json"
] | 87df24857474bf92dc6789bf1e5a8d73bc7510cb | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2101.11038",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta"} | {
"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": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["exbert"]} | # Muppet: Massive Multi-task Representations with Pre-Finetuning
# RoBERTa large model
This is a Massive Multi-task Pre-finetuned version of Roberta large. It was introduced in
[this paper](https://arxiv.org/abs/2101.11038). The model improves over roberta-base in a wide range of GLUE, QA tasks (details can be fou... | null | [
"mit"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | null | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17cb21 | 1f62b51786dcb798 | a845301e9ec0d81f | 30,073 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 639 | jegormeister/bert-base-dutch-cased-snli | jegormeister | null | 14 | 267,474 | False | 2022-03-02T23:29:05Z | 2021-08-16T09:10:25Z | sentence-transformers | 4 | 0 | null | sentence-similarity | null | [
".gitattributes",
".ipynb_checkpoints/README-checkpoint.md",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"voca... | d507d8dc67f5c40103aeebfd8552595ad806484d | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "tr... | # bert-base-dutch-cased-snli
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becom... | null | null | null | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
651b895d3da74cb63e37f6da | dccbb7b59eac010e | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 159 | MoritzLaurer/deberta-v3-large-zeroshot-v1 | MoritzLaurer | null | 1,897 | 267,577 | False | 2023-10-03T03:24:13Z | 2023-11-29T19:30:53Z | transformers | 19 | 0 | null | zero-shot-classification | {"parameters": {"F16": 435063810}, "total": 435063810} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json"
] | 3e887081f452e35b19c2105be4e9c5d1cd010679 | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"zero-shot-classification",
"en",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DebertaV2ForSequenceClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "zero-shot-classification", "tags": ["text-classification", "zero-shot-classification"]} | # deberta-v3-large-zeroshot-v1
## Model description
The model is designed for zero-shot classification with the Hugging Face pipeline.
The model should be substantially better at zero-shot classification than my other zero-shot models on the
Hugging Face hub: https://huggingface.co/MoritzLaurer.
The model can do one ... | null | [
"mit"
] | null | [
"en"
] | 435,063,810 | null | null | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
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"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66001291298176eb21eaf759 | ac35e3244a1fc1f8 | 5ff1c70e91becad4 | 49,152 | BPE | 48,872 | false | true | false | true | 38 | null | Sequence | ByteLevel | null | [
"Digits",
"ByteLevel"
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"ByteLevel"
] | 121 | TechxGenus/starcoder2-15b-instruct-GPTQ | TechxGenus | null | 9,362 | 269,759 | False | 2024-03-24T11:46:25Z | 2024-03-24T15:54:29Z | transformers | 2 | 0 | null | text-generation | {"parameters": {"I32": 15351152640, "F16": 606736384}, "total": 15957889024} | [
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"config.json",
"merges.txt",
"model.safetensors",
"quantize_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | e9c0a36ea94a3a64ff0ea2659fa96c3bdb0b2bc7 | [
"transformers",
"safetensors",
"starcoder2",
"text-generation",
"code",
"license:bigcode-openrail-m",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] | null | {"architectures": ["Starcoder2ForCausalLM"], "model_type": "starcoder2", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "bigcode-openrail-m", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["code", "starcoder2"]} | <p align="center">
<img width="300px" alt="starcoder2-instruct" src="https://huggingface.co/TechxGenus/starcoder2-15b-instruct/resolve/main/starcoder2-instruct.jpg">
</p>
GPTQ quantized version of starcoder2-15b-instruct model.
---
### starcoder2-instruct
We've fine-tuned starcoder2-15b with an additional 0.7 billi... | null | [
"bigcode-openrail-m"
] | null | null | 15,957,889,024 | null | null | [
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"text-generation"
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"text"
] | [
"text"
] |
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"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | Jiqing/tiny-random-tvp | Jiqing | null | 1,539 | 270,933 | False | 2023-10-10T06:07:52Z | 2023-11-09T06:21:17Z | transformers | 0 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"tokenizer.json",
"vocab.txt"
] | 72f400fdf8408f2ad259bbac5477f41526c43021 | [
"transformers",
"pytorch",
"tvp",
"license:unknown",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["TvpForVideoGrounding"], "model_type": "tvp"} | {
"auto_model": "TvpForVideoGrounding",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "unknown", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | null | null | [
"unknown"
] | null | null | null | null | null | [
"TvpForVideoGrounding",
"tvp"
] | [
null
] | null | null | null |
621ffdc136468d709f17f0ea | c373ed05ad808e9b | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 519 | pszemraj/led-base-book-summary | pszemraj | null | 1,195 | 263,867 | False | 2022-03-02T23:29:05Z | 2025-12-29T03:38:08Z | transformers | 64 | 0 | [{"name": "pszemraj/led-base-book-summary", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "kmfoda/booksum", "type": "kmfoda/booksum", "config": "kmfoda--booksum", "split": "test"}, "metrics": [{"type": "rouge", "value": 33.4536, "name": "ROUGE-1", "verified": true, "verify... | summarization | {"parameters": {"F32": 161894745}, "total": 161894745} | [
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] | 842ba9376965acac7e72cab2161ba21f5830be70 | [
"transformers",
"pytorch",
"safetensors",
"led",
"text2text-generation",
"summarization",
"summary",
"longformer",
"booksum",
"long-document",
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"dataset:kmfoda/booksum",
"license:apache-2.0",
"license:bsd-3-clause",
"model-index",
"endpoints_compatible",
"deploy:azure",
... | null | {"architectures": ["LEDForConditionalGeneration"], "model_type": "led", "tokenizer_config": {"bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "norm... | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"datasets": ["kmfoda/booksum"], "license": ["apache-2.0", "bsd-3-clause"], "metrics": ["rouge"], "tags": ["summarization", "led", "summary", "longformer", "booksum", "long-document", "long-form"], "widget": [{"text": "large earthquakes along a given fault segment do not occur at random intervals because it takes time ... | # LED-Based Summarization Model: Condensing Long and Technical Information
<a href="https://colab.research.google.com/gist/pszemraj/36950064ca76161d9d258e5cdbfa6833/led-base-demo-token-batching.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
The Longformer Enco... | null | [
"apache-2.0",
"bsd-3-clause"
] | [
"kmfoda/booksum"
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"LEDForConditionalGeneration"
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"text2text-generation",
"summarization"
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"text"
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"text"
] | [
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6656a3f432e7ebaf4e4906f4 | 34df1b8cec23ffbb | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 640 | Linq-AI-Research/Linq-Embed-Mistral | Linq-AI-Research | null | 29,547 | 345,297 | False | 2024-05-29T03:41:40Z | 2024-06-05T12:50:34Z | sentence-transformers | 140 | 0 | [{"name": "Linq-Embed-Mistral", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 8... | feature-extraction | {"parameters": {"F16": 7110660096}, "total": 7110660096} | [
".gitattributes",
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"mo... | 0c1a0b0589177079acc552433cad51d7c9132379 | [
"sentence-transformers",
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"arxiv:2210.07316",
"arxiv:2310.06825",
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"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azu... | null | {"architectures": ["MistralModel"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "</s>", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"language": ["en"], "license": "cc-by-nc-4.0", "tags": ["mteb", "transformers", "sentence-transformers"], "model-index": [{"name": "Linq-Embed-Mistral", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "... | <h1 align="center">Linq-AI-Research/Linq-Embed-Mistral</h1>
**Linq-Embed-Mistral**
Linq-Embed-Mistral has been developed by building upon the foundations of the [E5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) and [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) mode... | null | [
"cc-by-nc-4.0"
] | null | [
"en"
] | 7,110,660,096 | null | null | [
"AutoModel",
"mistral",
"MistralModel"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
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"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
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"models": [
{
"_id": "66e81cefd1b1391042d0e47e",
"id": "Qwen/Qwen2.5-72B-Instruct"
}
],
"relation": "quantized"
} | 59,699 | 401,666 | False | 2024-09-17T12:53:17Z | 2024-10-09T12:27:51Z | transformers | 43 | 0 | null | text-generation | {"parameters": {"F16": 2493554688, "I32": 70464307200}, "total": 72957861888} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
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"model-00005-of-00011.safetensors",
"model-00006-of-0... | da6e9d45661b91e02782f4ae2c6bb39c4a5b4821 | [
"transformers",
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"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"arxiv:2309.00071",
"arxiv:2407.10671",
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"base_model:quantized:Qwen/Qwen2.5-72B-Instruct",
"license:other",
"text-generation-inference",
"endpoints_compatible",... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "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'] }}\n... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-72B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": "qwen", "license_link": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct-GPTQ-Int4/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeli... | # Qwen2.5-72B-Instruct-GPTQ-Int4
## Introduction
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly ... | null | [
"other",
"qwen",
"https://huggingface.co/Qwen/Qwen2.5-72B-Instruct-GPTQ-Int4/blob/main/LICENSE"
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"text"
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] | 199 | allenai/OLMo-2-0325-32B | allenai | null | 9,542 | 286,197 | False | 2025-02-23T00:36:43Z | 2025-04-29T18:51:42Z | transformers | 65 | 0 | null | text-generation | {"parameters": {"F32": 32234279936}, "total": 32234279936} | [
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"text"
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"NFC"
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"Split",
"ByteLevel"
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"ByteLevel"
] | 53 | Qwen/Qwen1.5-14B-Chat-AWQ | Qwen | null | 147 | 259,969 | False | 2024-02-03T06:58:56Z | 2024-04-30T07:23:04Z | transformers | 23 | 0 | null | text-generation | {"parameters": {"I32": 12609126400, "F16": 1558164480}, "total": 14167290880} | [
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"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "other", "license_name": "tongyi-qianwen", "license_link": "https://huggingface.co/Qwen/Qwen1.5-14B-Chat-AWQ/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags... | # Qwen1.5-14B-Chat-AWQ
## Introduction
Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
* 8 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B and 72B dense models, a... | null | [
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"ByteLevel"
] | 358 | dengcao/GLM-4.1V-9B-Thinking-AWQ | dengcao | null | 405 | 983,879 | False | 2025-07-03T13:21:52Z | 2025-07-07T18:30:52Z | transformers | 1 | 0 | null | image-text-to-text | {"parameters": {"I32": 9070182400, "F16": 1287082496}, "total": 10357264896} | [
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"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": ["ZhipuAI/GLM-4.1V-9B-Thinking"], "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["glm4v", "AWQ", "vLLM"], "base_mode... | null | null | [
"mit"
] | null | null | 10,357,264,896 | null | null | [
"AutoModelForImageTextToText",
"glm4v",
"Glm4vForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
65c518990f5fdbda745cf3dd | 3ffa542dec2b91a4 | d3a5589134b292ff | 250,100 | Unigram | null | true | true | true | true | 3 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Strip",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 641 | CohereLabs/aya-101 | CohereLabs | null | 2,320 | 275,945 | False | 2024-02-08T18:08:25Z | 2025-09-10T19:53:29Z | transformers | 661 | 0 | null | null | {"parameters": {"F32": 12921057280}, "total": 12921057280} | [
".gitattributes",
"README.md",
"assets/aya-fig1.png",
"aya-fig1.png",
"config.json",
"generation_config.json",
"model-00001-of-00011.safetensors",
"model-00002-of-00011.safetensors",
"model-00003-of-00011.safetensors",
"model-00004-of-00011.safetensors",
"model-00005-of-00011.safetensors",
"mo... | e7dad472b9de8a30a00cf08a05c19003bf59028d | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"afr",
"amh",
"ara",
"aze",
"bel",
"ben",
"bul",
"cat",
"ceb",
"ces",
"cym",
"dan",
"deu",
"ell",
"eng",
"epo",
"est",
"eus",
"fin",
"fil",
"fra",
"fry",
"gla",
"gle",
"glg",
"guj",
"hat",
"hau... | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["CohereLabs/xP3x", "CohereLabs/aya_dataset", "CohereLabs/aya_collection", "DataProvenanceInitiative/Commercially-Verified-Licenses", "CohereLabs/aya_evaluation_suite"], "eval_results": null, "language": ["afr", "amh", "ara", "aze", "bel", "ben", "bul", "cat", "ceb", "ces", "cym", "dan"... | null | null | [
"apache-2.0"
] | [
"CohereLabs/xP3x",
"CohereLabs/aya_dataset",
"CohereLabs/aya_collection",
"DataProvenanceInitiative/Commercially-Verified-Licenses",
"CohereLabs/aya_evaluation_suite"
] | [
"afr",
"amh",
"ara",
"aze",
"bel",
"ben",
"bul",
"cat",
"ceb",
"ces",
"cym",
"dan",
"deu",
"ell",
"eng",
"epo",
"est",
"eus",
"fin",
"fil",
"fra",
"fry",
"gla",
"gle",
"glg",
"guj",
"hat",
"hau",
"heb",
"hin",
"hun",
"hye",
"ibo",
"ind",
"isl",
"... | 12,921,057,280 | null | [
"accuracy",
"bleu"
] | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
621ffdc136468d709f180348 | cca50eb633c0b220 | ffbe9a95309d8eb7 | 173,347 | WordPiece | null | true | true | true | true | 1 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 642 | setu4993/smaller-LaBSE | setu4993 | null | 678 | 262,116 | False | 2022-03-02T23:29:05Z | 2023-10-19T06:24:02Z | transformers | 13 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 219171840}, "total": 219172352} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 47a6065e7d3c2c2d36e4146c9c0201cbf6f74711 | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"feature-extraction",
"sentence_embedding",
"multilingual",
"google",
"sentence-similarity",
"labse",
"ar",
"de",
"en",
"es",
"fr",
"it",
"ja",
"ko",
"nl",
"pl",
"pt",
"ru",
"th",
"tr",
"zh",
"datase... | 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": ["CommonCrawl", "Wikipedia"], "eval_results": null, "language": ["ar", "de", "en", "es", "fr", "it", "ja", "ko", "nl", "pl", "pt", "ru", "th", "tr", "zh"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pi... | # LaBSE
## Model description
Smaller Language-agnostic BERT Sentence Encoder (LaBSE) is a BERT-based model distilled from the [original LaBSE model](https://huggingface.co/setu4993/LaBSE) to 15 languages (from the original 109 languages) using the techniques described in the paper ['Load What You Need: Smaller Versio... | null | [
"apache-2.0"
] | [
"CommonCrawl",
"Wikipedia"
] | [
"ar",
"de",
"en",
"es",
"fr",
"it",
"ja",
"ko",
"nl",
"pl",
"pt",
"ru",
"th",
"tr",
"zh"
] | 219,172,352 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
65cba4559a4ee02bbb997e89 | 3ac4c49f275b821b | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 643 | prometheus-eval/prometheus-7b-v2.0 | prometheus-eval | null | 61,597 | 406,411 | False | 2024-02-13T17:18:13Z | 2024-11-29T16:56:42Z | transformers | 102 | 0 | null | text-generation | {"parameters": {"BF16": 7241732096}, "total": 7241732096} | [
".gitattributes",
"README.md",
"config.json",
"finegrained_eval.JPG",
"mergekit_config.yml",
"model-00001-of-00008.safetensors",
"model-00002-of-00008.safetensors",
"model-00003-of-00008.safetensors",
"model-00004-of-00008.safetensors",
"model-00005-of-00008.safetensors",
"model-00006-of-00008.s... | 66ffb1fc20beebfb60a3964a957d9011723116c5 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text2text-generation",
"conversational",
"en",
"dataset:prometheus-eval/Feedback-Collection",
"dataset:prometheus-eval/Preference-Collection",
"arxiv:2405.01535",
"arxiv:2310.08491",
"license:apache-2.0",
"text-generation-inferenc... | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["prometheus-eval/Feedback-Collection", "prometheus-eval/Preference-Collection"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["pearsonr", "spearmanr", "kendall-tau", "accuracy"... | ## Links for Reference
- **Homepage: In Progress**
- **Repository:https://github.com/prometheus-eval/prometheus-eval**
- **Paper:https://arxiv.org/abs/2405.01535**
- **Point of Contact:seungone@cmu.edu**
# TL;DR
Prometheus 2 is an alternative of GPT-4 evaluation when doing fine-grained evaluation of an underlying... | null | [
"apache-2.0"
] | [
"prometheus-eval/Feedback-Collection",
"prometheus-eval/Preference-Collection"
] | [
"en"
] | 7,241,732,096 | null | [
"pearsonr",
"spearmanr",
"kendall-tau",
"accuracy"
] | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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