_id large_stringlengths 24 24 | hash large_stringlengths 16 16 | vocab_hash large_stringlengths 16 16 | vocab_size int64 1 2.4M | model_type large_stringclasses 4
values | num_merges float64 0 4.41M ⌀ | has_normalizer bool 2
classes | has_pre_tokenizer bool 2
classes | has_post_processor bool 2
classes | has_decoder bool 2
classes | num_added_tokens int64 0 479k | normalizer_type large_stringclasses 11
values | pre_tokenizer_type large_stringclasses 10
values | decoder_type large_stringclasses 9
values | normalizer_types listlengths 0 11.3k ⌀ | pre_tokenizer_types listlengths 1 35 ⌀ | decoder_types listlengths 0 24 ⌀ | tokenizer_id int64 0 24.8k | id large_stringlengths 7 122 | author stringlengths 2 41 ⌀ | base_models dict | downloads int64 0 206M | downloads_all_time int64 0 2.81B | gated stringclasses 3
values | created_at timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-11-03 14:41:54 | last_modified timestamp[us, tz=UTC]date 2020-12-11 21:34:15 2026-03-29 10:14:05 ⌀ | library_name stringclasses 36
values | likes int64 0 13.1k | trending_score float64 0 47 | model_index stringlengths 30 911k ⌀ | pipeline_tag stringclasses 46
values | safetensors stringlengths 30 122 ⌀ | siblings listlengths 0 10k ⌀ | sha stringlengths 40 40 ⌀ | tags listlengths 2 1.82k ⌀ | gguf stringclasses 789
values | config stringlengths 2 53.7k ⌀ | transformers_info dict | card_data stringlengths 234 905k ⌀ | card stringlengths 0 638k ⌀ | spaces null | licenses listlengths 1 3 ⌀ | datasets listlengths 1 289 ⌀ | languages listlengths 1 1.81k ⌀ | safetensors_params float64 0 1,019B ⌀ | gguf_params float64 0 122B ⌀ | metrics listlengths 1 15 ⌀ | architectures listlengths 1 6 ⌀ | tasks listlengths 1 6 ⌀ | modalities listlengths 1 4 ⌀ | input_modalities listlengths 1 3 ⌀ | output_modalities listlengths 1 4 ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
63e162edabdffca8bf9118cc | f0b360fada45735d | 8cdfcc055eb9d121 | 32,101 | Unigram | null | true | true | true | true | 104 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 196 | Salesforce/blip2-flan-t5-xl | Salesforce | null | 74,364 | 2,897,339 | False | 2023-02-06T20:28:29Z | 2025-02-03T06:36:28Z | transformers | 91 | 0 | null | image-text-to-text | {"parameters": {"F32": 3942446592}, "total": 3942446592} | [
".gitattributes",
"README.md",
"added_tokens.json",
"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",
"pytorch_model-00001-of-00002.bin",
"pytorch... | 0eb0d3b46c14c1f8c7680bca2693baafdb90bb28 | [
"transformers",
"pytorch",
"safetensors",
"blip-2",
"visual-question-answering",
"vision",
"image-to-text",
"image-captioning",
"image-text-to-text",
"en",
"arxiv:2301.12597",
"arxiv:2210.11416",
"license:mit",
"region:us"
] | null | {"architectures": ["Blip2ForConditionalGeneration"], "model_type": "blip-2", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForVisualQuestionAnswering",
"custom_class": null,
"pipeline_tag": "visual-question-answering",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["vision", "image-to-text", "image-captioning", "visual-question-answering... | # BLIP-2, Flan T5-xl, pre-trained only
BLIP-2 model, leveraging [Flan T5-xl](https://huggingface.co/google/flan-t5-xl) (a large language model).
It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by L... | null | [
"mit"
] | null | [
"en"
] | 3,942,446,592 | null | null | [
"Blip2ForConditionalGeneration",
"AutoModelForVisualQuestionAnswering",
"blip-2"
] | [
"image-text-to-text",
"visual-question-answering",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text",
"logits"
] |
6274d198fea3c20ddea8fb5e | c88b36417e2a82ee | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 64 | qanastek/51-languages-classifier | qanastek | null | 47,423 | 2,898,208 | False | 2022-05-06T07:43:20Z | 2022-05-19T12:56:56Z | transformers | 35 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"optimizer.pt",
"pytorch_model.bin",
"rng_state.pth",
"scheduler.pt",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"test.py",
"tokenizer.json",
"tokenizer_config.json",
"trainer_state.json",
"training_args.bin"
] | 966ca1a15a30f218ad48561943f046d809d4ed26 | [
"transformers",
"pytorch",
"Transformers",
"text-classification",
"multi-class-classification",
"dataset:qanastek/MASSIVE",
"arxiv:1911.02116",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | null | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": ["qanastek/MASSIVE"], "eval_results": null, "language": null, "library_name": null, "license": "cc-by-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["Transformers", "text-classification", "multi-class-classification"... | **People Involved**
* [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1)
**Affiliations**
1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France.
## Model
XLM-Roberta : [https://huggingface.co/xlm-roberta-base](https://huggingface.co/xlm-roberta-base)
Paper : [U... | null | [
"cc-by-4.0"
] | [
"qanastek/MASSIVE"
] | null | null | null | null | [
"AutoModel"
] | [
null,
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
678e13bdda686d5964869153 | dae53fc866d79bd6 | 1b9fd2d5dbbc6e0d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 83 | deepseek-ai/DeepSeek-R1-Distill-Llama-70B | deepseek-ai | null | 119,886 | 4,305,502 | False | 2025-01-20T09:13:33Z | 2025-02-24T03:31:15Z | transformers | 757 | 3 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"figures/benchmark.jpg",
"generation_config.json",
"model-00001-of-000017.safetensors",
"model-00002-of-000017.safetensors",
"model-00003-of-000017.safetensors",
"model-00004-of-000017.safetensors",
"model-00005-of-000017.safetensors",
"... | b1c0b44b4369b597ad119a196caf79a9c40e141e | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2501.12948",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<\uff5cbegin\u2581of\u2581sentence\uff5c>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<\uff5cend\u258... | {
"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": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # DeepSeek-R1
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
</div>
<hr>
<div align="center... | null | [
"mit"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6465d98b9c627c78f8629e5d | dab37a375cc83085 | ade5e378ce727196 | 32,001 | BPE | 61,249 | true | false | true | true | 4 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 197 | TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ | TheBloke | {
"models": [
{
"_id": "646583b66ceebdc7fd9257e4",
"id": "QuixiAI/Wizard-Vicuna-7B-Uncensored"
}
],
"relation": "quantized"
} | 340 | 2,528,306 | False | 2023-05-18T07:53:47Z | 2023-09-27T12:44:19Z | transformers | 164 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"quantize_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json",
"trainer_state.json"
] | 6da0e84eb7dc072e39cd679c0bdbefca54f9d690 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"uncensored",
"en",
"dataset:ehartford/wizard_vicuna_70k_unfiltered",
"base_model:QuixiAI/Wizard-Vicuna-7B-Uncensored",
"base_model:quantized:QuixiAI/Wizard-Vicuna-7B-Uncensored",
"license:other",
"text-generation-inference",
"4-bit",
... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "cont... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "ehartford/Wizard-Vicuna-7B-Uncensored", "datasets": ["ehartford/wizard_vicuna_70k_unfiltered"], "eval_results": null, "language": ["en"], "library_name": null, "license": "other", "license_name": null, "license_link": null, "metrics": null, "model_name": "Wizard Vicuna 7B Uncensored", "pipeline_tag": nu... | <!-- 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 | [
"other"
] | [
"ehartford/wizard_vicuna_70k_unfiltered"
] | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f1802ea | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | sentence-transformers/paraphrase-distilroberta-base-v2 | sentence-transformers | null | 24,165 | 2,588,744 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:25:45Z | sentence-transformers | 11 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 82118400}, "total": 82118914} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"flax_model.msgpack",
"merges.txt",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
... | 1972a507973d92e15f4f7cd9457d9fcf43ea117f | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"onnx",
"safetensors",
"openvino",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaModel"], "model_type": "roberta", "tokenizer_config": {"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": t... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/paraphrase-distilroberta-base-v2
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 eas... | null | [
"apache-2.0"
] | null | null | 82,118,914 | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
646dfe3e40e741b1913a1ba4 | 3a732ab237c338af | c21f0239d1cf0f86 | 65,024 | BPE | 64,784 | false | true | false | true | 12 | null | Sequence | ByteLevel | null | [
"Punctuation",
"ByteLevel",
"Digits",
"Split"
] | [
"ByteLevel"
] | 45 | tiiuae/falcon-40b | tiiuae | null | 22,074 | 2,589,607 | False | 2023-05-24T12:08:30Z | 2024-08-09T07:48:37Z | transformers | 2,432 | 0 | null | text-generation | {"parameters": {"BF16": 41835970560}, "total": 41835970560} | [
".gitattributes",
"README.md",
"config.json",
"configuration_falcon.py",
"generation_config.json",
"model-00001-of-00009.safetensors",
"model-00002-of-00009.safetensors",
"model-00003-of-00009.safetensors",
"model-00004-of-00009.safetensors",
"model-00005-of-00009.safetensors",
"model-00006-of-0... | 05ab2ee8d6b593bdbab17d728de5c028a7a94d83 | [
"transformers",
"pytorch",
"safetensors",
"falcon",
"text-generation",
"custom_code",
"en",
"de",
"es",
"fr",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2205.14135",
"arxiv:1911.02150",
"arxiv:2101.00027",
"arxiv:2005.14165",
"arxiv:2104.09864",
"arxiv:2306.01116",
"license:apache-... | null | {"architectures": ["FalconForCausalLM"], "auto_map": {"AutoConfig": "configuration_falcon.FalconConfig", "AutoModel": "modeling_falcon.FalconModel", "AutoModelForSequenceClassification": "modeling_falcon.FalconForSequenceClassification", "AutoModelForTokenClassification": "modeling_falcon.FalconForTokenClassification",... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["tiiuae/falcon-refinedweb"], "eval_results": null, "language": ["en", "de", "es", "fr"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "inference": false} | # 🚀 Falcon-40B
**Falcon-40B is a 40B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 1,000B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. It is made available under the Apache 2.0 license.**
*Paper coming soon 😊... | null | [
"apache-2.0"
] | [
"tiiuae/falcon-refinedweb"
] | [
"en",
"de",
"es",
"fr"
] | 41,835,970,560 | null | null | [
"AutoModelForCausalLM",
"falcon",
"FalconForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6562f5ae6ef2a1d0f96bcbdc | c97a6495c5454551 | f26f356cd728caa8 | 49,408 | BPE | 48,894 | true | true | true | true | 2 | Sequence | Sequence | ByteLevel | [
"NFC",
"Replace",
"Lowercase"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 2 | LanguageBind/LanguageBind_Video_FT | LanguageBind | null | 5,379 | 2,542,830 | False | 2023-11-26T07:37:18Z | 2024-02-01T06:57:50Z | transformers | 7 | 0 | null | zero-shot-image-classification | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 13f52c20ce666a7d017bcd00522039f4ab034a66 | [
"transformers",
"pytorch",
"LanguageBindVideo",
"zero-shot-image-classification",
"arxiv:2310.01852",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["CLIPModel"], "model_type": "LanguageBindVideo", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<|startoftext|>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "no... | {
"auto_model": "AutoModelForZeroShotImageClassification",
"custom_class": null,
"pipeline_tag": "zero-shot-image-classification",
"processor": null
} | {"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} | <p align="center">
<img src="https://s11.ax1x.com/2024/02/01/pFMDAm9.png" width="250" style="margin-bottom: 0.2;"/>
<p>
<h2 align="center"> <a href="https://arxiv.org/pdf/2310.01852.pdf">【ICLR 2024 🔥】LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment</a></h2>
<h5 ... | null | [
"mit"
] | null | null | null | null | null | [
"AutoModelForZeroShotImageClassification",
"CLIPModel",
"LanguageBindVideo"
] | [
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
65aea26d308fc2952b0352b8 | 34093184d10f76a9 | 35b001f3a491f9f9 | 151,646 | BPE | 151,387 | true | true | true | true | 3 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 53 | Qwen/Qwen1.5-7B | Qwen | null | 115,399 | 2,905,660 | False | 2024-01-22T17:14:21Z | 2024-04-05T10:42:41Z | transformers | 56 | 0 | null | text-generation | {"parameters": {"BF16": 7721324544}, "total": 7721324544} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"special_tokens_map.j... | 831096e3a59a0789a541415da25ef195ceb802fe | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"pretrained",
"conversational",
"en",
"arxiv:2309.16609",
"license:other",
"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": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "other", "license_name": "tongyi-qianwen", "license_link": "https://huggingface.co/Qwen/Qwen1.5-7B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["pretr... | # Qwen1.5-7B
## 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, and an MoE m... | null | [
"other",
"tongyi-qianwen",
"https://huggingface.co/Qwen/Qwen1.5-7B/blob/main/LICENSE"
] | null | [
"en"
] | 7,721,324,544 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66e98a9fb099520781a9208d | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-32B-Instruct-AWQ | Qwen | {
"models": [
{
"_id": "66e902f34eeaced24a2787f8",
"id": "Qwen/Qwen2.5-32B-Instruct"
}
],
"relation": "quantized"
} | 1,746,335 | 8,547,599 | False | 2024-09-17T13:56:47Z | 2024-10-09T12:26:37Z | transformers | 96 | 1 | null | text-generation | {"parameters": {"I32": 31205621760, "F16": 1558254592}, "total": 32763876352} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00005.safetensors",
"model-00002-of-00005.safetensors",
"model-00003-of-00005.safetensors",
"model-00004-of-00005.safetensors",
"model-00005-of-00005.safetensors",
"model.safetensor... | 5c7cb76a268fc6cfbb9c4777eb24ba6e27f9ee6c | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"arxiv:2309.00071",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-32B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compati... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"bits": 4, "quant_method": "awq"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-32B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-32B-Instruct-AWQ/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_... | # Qwen2.5-32B-Instruct-AWQ
## 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 **more... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-32B-Instruct-AWQ/blob/main/LICENSE"
] | null | [
"en"
] | 32,763,876,352 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
658e776016227c7a2ddaeb50 | 6950482313cc6dd9 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 68 | maidalun1020/bce-embedding-base_v1 | maidalun1020 | null | 5,243 | 2,549,648 | False | 2023-12-29T07:38:08Z | 2025-07-22T05:13:53Z | sentence-transformers | 392 | 0 | null | feature-extraction | null | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"assets/Wechat.jpg",
"assets/rag_eval_multiple_domains_summary.jpg",
"config.json",
"config_sentence_transformers.json",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"sentencepiece.bpe.model",
"special_tokens_map.json... | 9c0d82af44af61abe171ffae23fde5740c0ec1a8 | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"zh",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["XLMRobertaModel"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "zh"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "feature-extraction", "tags": ["sentence-transformers", "feature-extraction", "sentence-s... | null | null | [
"apache-2.0"
] | null | [
"en",
"zh"
] | null | null | null | [
"XLMRobertaModel",
"AutoModel",
"xlm-roberta"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6748dc340fc71e443afddd9b | e87bd1cd4bfe35be | 7d900c9c71170270 | 257,153 | BPE | 580,604 | true | true | true | true | 1,370 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
"Fuse"
] | 198 | vidore/colpali-v1.2-hf | vidore | {
"models": [
{
"_id": "66c86b8d78bb509e7066282d",
"id": "vidore/colpaligemma-3b-pt-448-base"
}
],
"relation": "finetune"
} | 1,044 | 2,493,768 | False | 2024-11-28T21:10:12Z | 2025-04-16T09:26:30Z | transformers | 9 | 0 | null | visual-document-retrieval | {"parameters": {"BF16": 2924613488}, "total": 2924613488} | [
".gitattributes",
"README.md",
"config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"preprocessor_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 56b5c732913abf3501ba208562df7b8f4edd5861 | [
"transformers",
"safetensors",
"colpali",
"pretraining",
"visual-document-retrieval",
"en",
"dataset:vidore/colpali_train_set",
"arxiv:2004.12832",
"arxiv:2407.01449",
"arxiv:2106.09685",
"base_model:vidore/colpaligemma-3b-pt-448-base",
"base_model:finetune:vidore/colpaligemma-3b-pt-448-base",... | null | {"architectures": ["ColPaliForRetrieval"], "model_type": "colpali", "tokenizer_config": {"bos_token": "<bos>", "eos_token": "<eos>", "pad_token": "<pad>", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForPreTraining",
"custom_class": null,
"pipeline_tag": "pretraining",
"processor": "AutoProcessor"
} | {"base_model": ["vidore/colpaligemma-3b-pt-448-base"], "datasets": ["vidore/colpali_train_set"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "gemma", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "visual-document-retrieval", "ta... | null | null | [
"gemma"
] | [
"vidore/colpali_train_set"
] | [
"en"
] | 2,924,613,488 | null | null | [
"colpali",
"ColPaliForRetrieval",
"AutoModelForPreTraining"
] | [
"visual-document-retrieval",
"pretraining"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
65778ac63ceeb2f078e6c864 | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | TheBloke/Mistral-7B-Instruct-v0.2-GPTQ | TheBloke | {
"models": [
{
"_id": "65770c3426ef61bbf101d4da",
"id": "mistralai/Mistral-7B-Instruct-v0.2"
}
],
"relation": "quantized"
} | 16,300 | 2,736,536 | False | 2023-12-11T22:18:46Z | 2023-12-11T22:46:53Z | transformers | 55 | 0 | null | text-generation | {"parameters": {"I32": 6979321856, "BF16": 262410240, "F16": 1376256}, "total": 7243108352} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"quantize_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 7532d6bc89ef9300fb39d2d94ed4414ec534b72a | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"conversational",
"arxiv:2310.06825",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:quantized:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"text-generation-inference",
"4-bit",
"gptq",
"d... | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": null, "unk_token": "<unk>", "use_default_system_prompt": false, "chat_template": "{{ bos_token }}{% for message in mes... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "mistralai/Mistral-7B-Instruct-v0.2", "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": "Mistral 7B Instruct v0.2", "pipeline_tag": "text-generation", "tags": ["finetuned"], "... | <!-- 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 | null | 7,243,108,352 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6801849906babaea60ca8cb3 | 03b2b331a48c71c3 | b14664993aa88526 | 100,278 | BPE | 100,000 | false | true | false | true | 22 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 199 | allenai/OLMo-2-0425-1B | allenai | null | 531,804 | 4,255,412 | False | 2025-04-17T22:45:45Z | 2025-05-28T20:35:45Z | transformers | 72 | 0 | null | text-generation | {"parameters": {"F32": 1484916736}, "total": 1484916736} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | a1847dff35000b4271fa70afc5db10fd29fedbdf | [
"transformers",
"safetensors",
"olmo2",
"text-generation",
"en",
"arxiv:2501.00656",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Olmo2ForCausalLM"], "model_type": "olmo2", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|pad|>", "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": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ## Model Details
<img alt="OLMo Logo" src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/olmo2/olmo.png" width="242px" style="margin-left:'auto' margin-right:'auto' display:'block'">
# Model Card for OLMo 2 1B
We introduce OLMo 2 1B, the smallest model in the OLMo 2 family.
OLMo 2 was pre-trained... | null | [
"apache-2.0"
] | null | [
"en"
] | 1,484,916,736 | null | null | [
"AutoModelForCausalLM",
"olmo2",
"Olmo2ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67c85cdbebd06f51d80ca1c7 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Qwen/QwQ-32B | Qwen | {
"models": [
{
"_id": "66e6d09952865ca1e41c6566",
"id": "Qwen/Qwen2.5-32B"
}
],
"relation": "finetune"
} | 64,062 | 2,821,774 | False | 2025-03-05T14:16:59Z | 2025-03-11T12:15:48Z | transformers | 2,879 | 2 | null | text-generation | {"parameters": {"BF16": 32763876352}, "total": 32763876352} | [
".gitattributes",
"LICENSE",
"README.md",
"added_tokens.json",
"config.json",
"figures/benchmark.jpg",
"generation_config.json",
"merges.txt",
"model-00001-of-00014.safetensors",
"model-00002-of-00014.safetensors",
"model-00003-of-00014.safetensors",
"model-00004-of-00014.safetensors",
"mode... | 976055f8c83f394f35dbd3ab09a285a984907bd0 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"arxiv:2309.00071",
"arxiv:2412.15115",
"base_model:Qwen/Qwen2.5-32B",
"base_model:finetune:Qwen/Qwen2.5-32B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azu... | 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 {{- '' }}\n {%- endif %}\n {{... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-32B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/QWQ-32B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "... | # QwQ-32B
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
QwQ is the reasoning model of the Qwen series. Compared ... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/QWQ-32B/blob/main/LICENSE"
] | null | [
"en"
] | 32,763,876,352 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6523cbe254967a3a49e42c1f | ac6394f02c5030ba | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 200 | BAAI/llm-embedder | BAAI | null | 32,641 | 2,574,076 | False | 2023-10-09T09:46:10Z | 2023-11-14T10:11:55Z | transformers | 128 | 0 | null | feature-extraction | {"parameters": {"I64": 512, "F32": 109482240}, "total": 109482752} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | c3e8ac86ff4859012570d6b59341fe67b88e55ad | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | <h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Conta... | null | [
"mit"
] | null | null | 109,482,752 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
6341a241df7779d7886578bf | 7a4cc4a6090329ff | ab8f40e5a89244c4 | 250,680 | BPE | 250,434 | false | true | true | true | 4 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 31 | bigscience/bloomz-1b1 | bigscience | null | 909 | 2,464,190 | False | 2022-10-08T16:16:01Z | 2023-05-27T17:26:45Z | transformers | 33 | 0 | [{"name": "bloomz-1b1", "results": [{"task": {"type": "Coreference resolution"}, "dataset": {"type": "winogrande", "name": "Winogrande XL (xl)", "config": "xl", "split": "validation", "revision": "a80f460359d1e9a67c006011c94de42a8759430c"}, "metrics": [{"type": "Accuracy", "value": 52.33, "verified": false}]}, {"task":... | text-generation | {"parameters": {"F16": 1065314304}, "total": 1065314304} | [
".gitattributes",
"README.md",
"config.json",
"logs/logs/xp3capmixnewcodelonglossseq/main_log.txt",
"logs/tensorboard/xp3capmixnewcodelonglossseq/events.out.tfevents.1665131197.jean-zay-iam43.309312.0",
"logs/tensorboard/xp3capmixnewcodelonglossseq/events.out.tfevents.1665132163.jean-zay-iam10.2918616.0",... | 3c4e8ecc1fbeb3a127a9869dd5a2d0f34560577d | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bloom",
"text-generation",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
... | null | {"architectures": ["BloomForCausalLM"], "model_type": "bloom", "tokenizer_config": {"unk_token": "<unk>", "eos_token": "</s>", "bos_token": "<s>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"datasets": ["bigscience/xP3"], "language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh",... | 
# Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
7. [Citation](#citation)
# Model Summary
> We present BLOOMZ & mT0, a ... | null | [
"bigscience-bloom-rail-1.0"
] | [
"bigscience/xP3"
] | [
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
... | 1,065,314,304 | null | null | [
"AutoModelForCausalLM",
"BloomForCausalLM",
"bloom"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66f459de2613850478c73b9d | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | unsloth/Llama-3.2-3B-Instruct | unsloth | {
"models": [
{
"_id": "66eaef786865fea1324edb5d",
"id": "meta-llama/Llama-3.2-3B-Instruct"
}
],
"relation": "finetune"
} | 175,181 | 3,035,060 | False | 2024-09-25T18:43:42Z | 2025-06-02T21:40:12Z | transformers | 88 | 0 | null | text-generation | {"parameters": {"BF16": 3212749824}, "total": 3212749824} | [
".gitattributes",
"README.md",
"chat_template.jinja",
"config.json",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 006f5dcd1393c3add266de40994ba96225e9689d | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3",
"meta",
"facebook",
"unsloth",
"conversational",
"en",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"text-generation-inference",
"endpoints_... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|eot_id|>", "pad_token": "<|finetune_right_pad_id|>", "unk_token": null, "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- en... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Llama-3.2-3B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "llama3.2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["llama-3", "llama", "meta", "facebook", "u... | ## ***See [our collection](https://huggingface.co/collections/unsloth/llama-32-66f46afde4ca573864321a22) for all versions of Llama 3.2 including GGUF, 4-bit and original 16-bit formats.***
# Finetune Llama 3.2, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tesla T4 noteboo... | null | [
"llama3.2"
] | null | [
"en"
] | 3,212,749,824 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
669fd42bc95aa51e031be612 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | unsloth/Meta-Llama-3.1-8B-bnb-4bit | unsloth | {
"models": [
{
"_id": "66944f1fe0c5c2e493a804f5",
"id": "meta-llama/Llama-3.1-8B"
}
],
"relation": "quantized"
} | 54,957 | 2,626,466 | False | 2024-07-23T16:02:51Z | 2025-02-15T10:58:33Z | transformers | 110 | 0 | null | text-generation | {"parameters": {"F32": 486912, "BF16": 1050939392, "U8": 7197503052}, "total": 8248929356} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 70d37f62f4f475a5a42dbbd5b7aad38f420e0960 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3",
"meta",
"facebook",
"unsloth",
"en",
"arxiv:2204.05149",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:quantized:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"text-generation-inference",
"endpoints_compatible",
... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|end_of_text|>", "pad_token": "<|finetune_right_pad_id|>", "unk_token": null}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Meta-Llama-3.1-8B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["llama-3", "llama", "meta", "facebook", "unslo... | # Finetune Llama 3.2, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/D... | null | [
"llama3.1"
] | null | [
"en"
] | 8,248,929,356 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17a32b | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | dandelin/vilt-b32-finetuned-coco | dandelin | null | 196 | 2,432,230 | False | 2022-03-02T23:29:05Z | 2022-01-23T09:45:24Z | transformers | 1 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 2f3f7f3f62a3f4f429c26309256485bbd9b0e40a | [
"transformers",
"pytorch",
"vilt",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["ViltForImageAndTextRetrieval"], "model_type": "vilt", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "ViltForImageAndTextRetrieval",
"custom_class": null,
"pipeline_tag": null,
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # Vision-and-Language Transformer (ViLT), fine-tuned on COCO
Vision-and-Language Transformer (ViLT) model fine-tuned on [COCO](https://cocodataset.org/#home). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al.... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"ViltForImageAndTextRetrieval",
"vilt"
] | [
null
] | null | null | null |
66f36be6af8609e9ef8198e5 | b71dbf9d2b11078f | 69900c4d8b285a99 | 152,069 | BPE | 151,387 | true | true | true | true | 426 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 202 | allenai/Molmo-7B-D-0924 | allenai | {
"models": [
{
"_id": "665f181fb9e194a20c5e8d44",
"id": "Qwen/Qwen2-7B"
}
],
"relation": "finetune"
} | 27,670 | 2,545,682 | False | 2024-09-25T01:48:22Z | 2025-12-15T19:39:34Z | transformers | 565 | 0 | null | image-text-to-text | {"parameters": {"F32": 8021025280}, "total": 8021025280} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"config_molmo.py",
"generation_config.json",
"image_preprocessing_molmo.py",
"merges.txt",
"model-00001-of-00007.safetensors",
"model-00002-of-00007.safetensors",
"model-00003-of-00007.safetensors",
"model-00004-of-00007.safete... | cab33fb7f1a40091911f81165f8481920621948f | [
"transformers",
"safetensors",
"molmo",
"text-generation",
"multimodal",
"olmo",
"pixmo",
"image-text-to-text",
"conversational",
"custom_code",
"en",
"arxiv:2409.17146",
"base_model:Qwen/Qwen2-7B",
"base_model:finetune:Qwen/Qwen2-7B",
"license:apache-2.0",
"region:us"
] | null | {"architectures": ["MolmoForCausalLM"], "auto_map": {"AutoConfig": "config_molmo.MolmoConfig", "AutoModelForCausalLM": "modeling_molmo.MolmoForCausalLM"}, "model_type": "molmo", "tokenizer_config": {"bos_token": null, "chat_template": "{% for message in messages -%}\n {%- if (loop.index % 2 == 1 and message['rol... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_molmo.MolmoForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": ["openai/clip-vit-large-patch14-336", "Qwen/Qwen2-7B"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["mul... | <img src="molmo_logo.png" alt="Logo for the Molmo Project" style="width: auto; height: 50px;">
# Molmo 7B-D
Molmo is a family of open vision-language models developed by the Allen Institute for AI. Molmo models are trained on PixMo, a dataset of 1 million, highly-curated image-text pairs. It has state-of-the-art perf... | null | [
"apache-2.0"
] | null | [
"en"
] | 8,021,025,280 | null | null | [
"MolmoForCausalLM",
"modeling_molmo.MolmoForCausalLM",
"AutoModelForCausalLM",
"molmo"
] | [
"image-text-to-text",
"text-generation"
] | [
"text",
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
621ffdc136468d709f17ade8 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | facebook/dpr-ctx_encoder-multiset-base | facebook | null | 2,272 | 2,426,233 | False | 2022-03-02T23:29:05Z | 2022-12-21T15:19:57Z | transformers | 5 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | fdb3d46584386d2f20aa00724ae31cebc348d16b | [
"transformers",
"pytorch",
"tf",
"dpr",
"en",
"dataset:nq_open",
"arxiv:2004.04906",
"arxiv:1702.08734",
"arxiv:1910.09700",
"license:cc-by-nc-4.0",
"region:us"
] | null | {"architectures": ["DPRContextEncoder"], "model_type": "dpr", "tokenizer_config": {}} | {
"auto_model": "DPRContextEncoder",
"custom_class": null,
"pipeline_tag": null,
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["nq_open"], "eval_results": null, "language": "en", "library_name": null, "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["dpr"], "inference": false} | # `dpr-ctx_encoder-multiset-base`
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation-results)
- [Environmenta... | null | [
"cc-by-nc-4.0"
] | [
"nq_open"
] | [
"en"
] | null | null | null | [
"DPRContextEncoder",
"dpr"
] | [
null
] | null | null | null |
655d976bc166f4c0a7ab2235 | 2b80a37c48945822 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 153 | vikp/column_detector | vikp | null | 1,548 | 2,409,652 | False | 2023-11-22T05:53:47Z | 2023-12-22T05:55:14Z | transformers | 10 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 52292f530612e92a661f9705a6f020c234a7acf8 | [
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LayoutLMv3ForSequenceClassification"], "model_type": "layoutlmv3", "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": "AutoProcessor"
} | null | Detects number of columns in pdf page images. Based on layoutlmv3.
Used in [marker](https://github.com/VikParuchuri/marker). | null | null | null | null | null | null | null | [
"LayoutLMv3ForSequenceClassification",
"AutoModelForSequenceClassification",
"layoutlmv3"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
68a77049ad150b5dcf7e38a5 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | inference-net/Schematron-3B | inference-net | {
"models": [
{
"_id": "66eaef786865fea1324edb5d",
"id": "meta-llama/Llama-3.2-3B-Instruct"
}
],
"relation": "finetune"
} | 1,256 | 3,468,283 | False | 2025-08-21T19:15:21Z | 2026-02-05T03:08:50Z | transformers | 326 | 1 | null | text-generation | {"parameters": {"BF16": 3212749824}, "total": 3212749824} | [
".gitattributes",
"Banner.png",
"README.md",
"chat_template.jinja",
"config.json",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 01521d04bd5ff45fdef7a392116ffbf912375c82 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|eot_id|>", "pad_token": "<|finetune_right_pad_id|>"}, "chat_template_jinja": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Llama-3.2-3B-Instruct", "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "llama3.2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | null | null | [
"llama3.2"
] | null | null | 3,212,749,824 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17bab7 | 382b5a4eb98f0296 | d965e4a9140ecf72 | 21,128 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 33 | hfl/chinese-roberta-wwm-ext-large | hfl | null | 16,085 | 2,493,965 | False | 2022-03-02T23:29:05Z | 2022-03-01T09:15:16Z | transformers | 228 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | a25cc9e05974bd9687e528edd516f2cfdb3f5db9 | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["zh"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["bert"]} | # Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)*... | null | [
"apache-2.0"
] | null | [
"zh"
] | null | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f1802fb | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | sentence-transformers/stsb-distilbert-base | sentence-transformers | null | 993 | 2,416,443 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:23:22Z | sentence-transformers | 6 | 0 | null | sentence-similarity | {"parameters": {"F32": 66362880}, "total": 66362880} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/m... | a560fa5fec90547a51a4a41a392d4aef93b49f16 | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["DistilBertModel"], "model_type": "distilbert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/stsb-distilbert-base
This is a [sentence-transfor... | null | [
"apache-2.0"
] | null | null | 66,362,880 | null | null | [
"AutoModel",
"distilbert",
"DistilBertModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
62d6b65d67d25fd8948709de | 069271a785587f26 | 444cbd7556b7f193 | 57,525 | Unigram | null | true | true | true | true | 8 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 203 | naver-clova-ix/donut-base | naver-clova-ix | null | 228,460 | 3,341,140 | False | 2022-07-19T13:49:17Z | 2022-08-13T08:27:12Z | transformers | 252 | 0 | null | image-to-text | null | [
".gitattributes",
".gitignore",
"README.md",
"added_tokens.json",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | a959cf33c20e09215873e338299c900f57047c61 | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"donut",
"image-to-text",
"vision",
"arxiv:2111.15664",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["VisionEncoderDecoderModel"], "model_type": "vision-encoder-decoder", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": ... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["donut", "image-to-text", "vision"]} | # Donut (base-sized model, pre-trained only)
Donut model pre-trained-only. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
Disclaimer: The team releasing Donut di... | null | [
"mit"
] | null | null | null | null | null | [
"AutoModelForImageTextToText",
"vision-encoder-decoder",
"VisionEncoderDecoderModel"
] | [
"image-text-to-text",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
62c80da29333b7f133416f8d | 7903ca758156b287 | ffae8233e53a99e4 | 256,204 | BPE | 514,098 | true | true | true | true | 207 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 60 | facebook/nllb-200-distilled-1.3B | facebook | null | 245,721 | 3,375,265 | False | 2022-07-08T10:57:38Z | 2023-02-11T20:19:10Z | transformers | 181 | 0 | null | translation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 7be3e24664b38ce1cac29b8aeed6911aa0cf0576 | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"nllb",
"translation",
"ace",
"acm",
"acq",
"aeb",
"af",
"ajp",
"ak",
"als",
"am",
"apc",
"ar",
"ars",
"ary",
"arz",
"as",
"ast",
"awa",
"ayr",
"azb",
"azj",
"ba",
"bm",
"ban",
"be",
"bem",
"b... | null | {"architectures": ["M2M100ForConditionalGeneration"], "model_type": "m2m_100", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": "<pad>", "... | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["flores-200"], "eval_results": null, "language": ["ace", "acm", "acq", "aeb", "af", "ajp", "ak", "als", "am", "apc", "ar", "ars", "ary", "arz", "as", "ast", "awa", "ayr", "azb", "azj", "ba", "bm", "ban", "be", "bem", "bn", "bho", "bjn", "bo", "bs", "bug", "bg", "ca", "ceb", "cs", "cjk"... | # NLLB-200
This is the model card of NLLB-200's distilled 1.3B variant.
Here are the [metrics](https://tinyurl.com/nllb200densedst1bmetrics) for that particular checkpoint.
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorith... | null | [
"cc-by-nc-4.0"
] | [
"flores-200"
] | [
"ace",
"acm",
"acq",
"aeb",
"af",
"ajp",
"ak",
"als",
"am",
"apc",
"ar",
"ars",
"ary",
"arz",
"as",
"ast",
"awa",
"ayr",
"azb",
"azj",
"ba",
"bm",
"ban",
"be",
"bem",
"bn",
"bho",
"bjn",
"bo",
"bs",
"bug",
"bg",
"ca",
"ceb",
"cs",
"cjk",
"ckb",... | null | null | [
"bleu",
"spbleu",
"chrf++"
] | [
"AutoModelForSeq2SeqLM",
"m2m_100",
"M2M100ForConditionalGeneration"
] | [
"text2text-generation",
"translation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
666d79e53ae28e1bcf497877 | 34093184d10f76a9 | 35b001f3a491f9f9 | 151,646 | BPE | 151,387 | true | true | true | true | 3 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 53 | Alibaba-NLP/gte-Qwen2-7B-instruct | Alibaba-NLP | null | 69,976 | 2,774,751 | False | 2024-06-15T11:24:21Z | 2025-03-24T09:43:55Z | sentence-transformers | 478 | 1 | [{"name": "gte-qwen2-7B-instruct", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value"... | sentence-similarity | {"parameters": {"F32": 7612620288}, "total": 7612620288} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"added_tokens.json",
"config.json",
"config_sentence_transformers.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00007.safetensors",
"model-00002-of-00007.safetensors",
"model-00003-of-00007.safetensors",
"model-00004-of-0... | a8d08b36ada9cacfe34c4d6f80957772a025daf2 | [
"sentence-transformers",
"safetensors",
"qwen2",
"text-generation",
"mteb",
"transformers",
"Qwen2",
"sentence-similarity",
"custom_code",
"arxiv:2308.03281",
"license:apache-2.0",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen2ForCausalLM"], "auto_map": {"AutoModel": "modeling_qwen.Qwen2Model", "AutoModelForCausalLM": "modeling_qwen.Qwen2ForCausalLM", "AutoModelForSequenceClassification": "modeling_qwen.Qwen2ForSequenceClassification"}, "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"license": "apache-2.0", "tags": ["mteb", "sentence-transformers", "transformers", "Qwen2", "sentence-similarity"], "model-index": [{"name": "gte-qwen2-7B-instruct", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual"... | ## gte-Qwen2-7B-instruct
**gte-Qwen2-7B-instruct** is the latest model in the gte (General Text Embedding) model family that ranks **No.1** in both English and Chinese evaluations on the Massive Text Embedding Benchmark [MTEB benchmark](https://huggingface.co/spaces/mteb/leaderboard) (as of June 16, 2024).
Recently, ... | null | [
"apache-2.0"
] | null | null | 7,612,620,288 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"sentence-similarity",
"text-generation"
] | [
"text"
] | [
"text"
] | [
"logits",
"text"
] |
663894ee346b74994240bb2a | 5afd7d59d2aa5e01 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 125 | HuggingFaceFW/fineweb-edu-classifier | HuggingFaceFW | null | 7,449 | 2,433,035 | False | 2024-05-06T08:29:34Z | 2024-11-17T15:00:11Z | transformers | 210 | 1 | null | text-classification | {"parameters": {"F32": 109483009}, "total": 109483009} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"special_tokens_map.json",
"src/README.md",
"src/run_edu_bert.py",
"src/run_edu_bert.slurm",
"src/train_edu_bert.py",
"src/train_edu_bert.slurm",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"utils/prompt... | 284663cbb2dabf9bda30d8f8cc49601251ee1631 | [
"transformers",
"safetensors",
"bert",
"text-classification",
"en",
"dataset:HuggingFaceFW/fineweb-edu-llama3-annotations",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["HuggingFaceFW/fineweb-edu-llama3-annotations"], "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} | # FineWeb-Edu classifier
## Model summary
This is a classifier for judging the educational value of web pages. It was developed to filter and curate educational content from web datasets and was trained on 450k [annotations](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu-llama3-annotations) generated by [LL... | null | [
"apache-2.0"
] | [
"HuggingFaceFW/fineweb-edu-llama3-annotations"
] | [
"en"
] | 109,483,009 | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
655fd08a24e0d359c1ee4ac7 | bfe694f64f4a18f5 | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 22 | MoritzLaurer/deberta-v3-base-zeroshot-v1.1-all-33 | MoritzLaurer | null | 20,798 | 2,461,018 | False | 2023-11-23T22:22:02Z | 2024-06-03T10:27:59Z | transformers | 30 | 0 | null | zero-shot-classification | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"onnx/model.onnx",
"onnx/model_quantized.onnx",
"pytorch_model.bin",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | 613e8c52c33e2bc0677ada4ad760f693e5e0f581 | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"deberta-v2",
"text-classification",
"zero-shot-classification",
"en",
"arxiv:2312.17543",
"license:mit",
"endpoints_compatible",
"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"]} | # Model description: deberta-v3-base-zeroshot-v1.1-all-33
The model is designed for zero-shot classification with the Hugging Face pipeline.
The model can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text
(`entailment` vs. `not_entailment`).
This task format ... | null | [
"mit"
] | null | [
"en"
] | null | null | null | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
] | [
"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6464977106cd98685a9cc5ef | 8e42935d2dbeb45e | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 204 | deepset/deberta-v3-base-injection | deepset | {
"models": [
{
"_id": "621ffdc136468d709f17dee4",
"id": "microsoft/deberta-v3-base"
}
],
"relation": "finetune"
} | 13,997 | 2,496,903 | False | 2023-05-17T08:59:29Z | 2024-10-15T10:06:11Z | transformers | 43 | 0 | [{"name": "deberta-v3-base-injection", "results": []}] | text-classification | {"parameters": {"I64": 512, "F32": 184423682}, "total": 184424194} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"gitattributes.txt",
"gitignore.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | 80dda00d0b0d9a03917a7685e2ddbcd28e04dbb1 | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"en",
"de",
"dataset:deepset/prompt-injections",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"text-embeddings-inference",
"en... | null | {"architectures": ["DebertaV2ForSequenceClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "microsoft/deberta-v3-base", "datasets": ["deepset/prompt-injections"], "eval_results": [], "language": ["en", "de"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": "deberta-v3-base-injection", "pipeline_tag": null, "tags": ["gen... | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-base-injection
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/debe... | null | [
"mit"
] | [
"deepset/prompt-injections"
] | [
"en",
"de"
] | 184,424,194 | null | [
"accuracy"
] | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
64363705a9265d9aaf93e89a | 820723d44f714bed | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 73 | jitesh/emotion-english | jitesh | null | 116,094 | 3,093,381 | False | 2023-04-12T04:43:49Z | 2023-05-09T08:30:32Z | transformers | 10 | 0 | null | text-classification | {"parameters": {"I64": 514, "F32": 82133780}, "total": 82134294} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | e85b65daab78ec1426eb840a4f2acf657026f82e | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"emotion",
"20 classes",
"code",
"emotions",
"en",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-classification", "tags": ["emotion", "20 classes", "code", "emotions"], "widget": [{"text": "I'm... | # Emotion classification from 20 classes
## 20 Emotion labels
| id | label |
| --- | ---------- |
| 0 | anger |
| 1 | cheeky |
| 2 | confuse |
| 3 | curious |
| 4 | disgust |
| 5 | empathetic |
| 6 | energetic |
| 7 | fear |
| 8 | grumpy |
| 9 | guilty |
| 10 ... | null | [
"mit"
] | null | [
"en"
] | 82,134,294 | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f18123a | 4c16659bdaa50ce6 | 68d6bae570dda044 | 51,997 | BPE | 51,736 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 205 | ufal/robeczech-base | ufal | null | 1,645 | 2,371,183 | False | 2022-03-02T23:29:05Z | 2024-09-30T14:58:09Z | transformers | 15 | 0 | null | fill-mask | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"scripts-for-generating-v1.1/pt_fix.py",
"scripts-for-generating-v1.1/tf_fix.py",
"scripts-for-generating-v1.1/tokenizer_fix.py",
"special_tokens_map.json",
"tf_model.h5",
"tokeni... | 98c2170e7b81703dce391ed4c43ac31460a80b1c | [
"transformers",
"pytorch",
"tf",
"safetensors",
"roberta",
"fill-mask",
"RobeCzech",
"Czech",
"RoBERTa",
"ÚFAL",
"cs",
"arxiv:2105.11314",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta", "tokenizer_config": {"bos_token": "[CLS]", "eos_token": "[SEP]", "sep_token": "[SEP]", "cls_token": "[CLS]", "unk_token": "[UNK]", "pad_token": "[PAD]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "cs", "library_name": null, "license": "cc-by-nc-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["RobeCzech", "Czech", "RoBERTa", "\u00daFAL"]} | # Model Card for RobeCzech
## Version History
- **version 1.1**: Version 1.1 was released in Jan 2024, with a change to the
tokenizer described below; the model parameters were mostly kept the same, but
(a) the embeddings were enlarged (by copying suitable rows) to correspond to
the updated tokenizer, (b) the p... | null | [
"cc-by-nc-sa-4.0"
] | null | [
"cs"
] | null | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65e7b3c7f44c1b48be9ddc23 | 3a3a1da6c0c74b0d | 2f8d5857ae5fa330 | 50,277 | BPE | 50,009 | true | true | true | true | 25 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 206 | state-spaces/mamba-130m-hf | state-spaces | null | 203,999 | 3,083,568 | False | 2024-03-06T00:07:35Z | 2024-03-06T00:39:29Z | transformers | 68 | 0 | null | text-generation | {"parameters": {"F32": 129135360}, "total": 129135360} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"tokenizer.json",
"tokenizer_config.json"
] | 1e76775f628fbf1350fbe4dbb3d971ba64af25a1 | [
"transformers",
"safetensors",
"mamba",
"text-generation",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MambaForCausalLM"], "model_type": "mamba", "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": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": []} | # Mamba
<!-- Provide a quick summary of what the model is/does. -->
This repository contains the `transfromers` compatible `mamba-2.8b`. The checkpoints are untouched, but the full `config.json` and tokenizer are pushed to this repo.
# Usage
You need to install `transformers` from `main` until `transformers=4.39.0`... | null | null | null | null | 129,135,360 | null | null | [
"AutoModelForCausalLM",
"mamba",
"MambaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63eb0ba8e35a4dfbb8e88c68 | 58967ba68263f0aa | 2f8d5857ae5fa330 | 50,277 | BPE | 50,009 | true | true | true | true | 25 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 86 | EleutherAI/pythia-6.9b | EleutherAI | null | 50,237 | 2,488,172 | False | 2023-02-14T04:18:48Z | 2025-03-10T18:30:39Z | transformers | 59 | 0 | null | text-generation | {"parameters": {"F16": 6857302528, "U8": 134217728}, "total": 6991520256} | [
".gitattributes",
"README.md",
"config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"pytorch_model-00001-of-00002.bin",
"pytorch_model-00002-of-00002.bin",
"pytorch_model.bin.index.json",
"special_tokens_map.json",
"tokenizer.json... | c0e3eee36dc47af0c49f361c74cfe459c09f7f23 | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/pile",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"reg... | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["EleutherAI/pile"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["pytorch", "causal-lm", "pythia"]} | The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and ... | null | [
"apache-2.0"
] | [
"EleutherAI/pile"
] | [
"en"
] | 6,991,520,256 | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67b52d4a824d77f2bba8b0af | 63d0650f505ce8f9 | 0d1b0351a03fb052 | 200,029 | BPE | 199,742 | false | true | false | true | 12 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 207 | microsoft/Phi-4-mini-instruct | microsoft | null | 605,689 | 3,848,451 | False | 2025-02-19T01:00:58Z | 2025-12-10T20:24:40Z | transformers | 706 | 7 | null | text-generation | null | [
".gitattributes",
"CODE_OF_CONDUCT.md",
"LICENSE",
"NOTICE.md",
"README.md",
"SECURITY.md",
"added_tokens.json",
"config.json",
"configuration_phi3.py",
"data_summary_card.md",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
... | cfbefacb99257ffa30c83adab238a50856ac3083 | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"nlp",
"code",
"conversational",
"custom_code",
"multilingual",
"ar",
"zh",
"cs",
"da",
"nl",
"en",
"fi",
"fr",
"de",
"he",
"hu",
"it",
"ja",
"ko",
"no",
"pl",
"pt",
"ru",
"es",
"sv",
"th",
"tr",
... | null | {"architectures": ["Phi3ForCausalLM"], "auto_map": {"AutoConfig": "configuration_phi3.Phi3Config", "AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM", "AutoTokenizer": "Xenova/gpt-4o"}, "model_type": "phi3", "tokenizer_config": {"bos_token": "<|endoftext|>", "chat_template": "{% for message in messages %}{% if mes... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual", "ar", "zh", "cs", "da", "nl", "en", "fi", "fr", "de", "he", "hu", "it", "ja", "ko", "no", "pl", "pt", "ru", "es", "sv", "th", "tr", "uk"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": "ht... | null | null | [
"mit",
"https://huggingface.co/microsoft/Phi-4-mini-instruct/resolve/main/LICENSE"
] | null | [
"multilingual",
"ar",
"zh",
"cs",
"da",
"nl",
"en",
"fi",
"fr",
"de",
"he",
"hu",
"it",
"ja",
"ko",
"no",
"pl",
"pt",
"ru",
"es",
"sv",
"th",
"tr",
"uk"
] | null | null | null | [
"AutoModelForCausalLM",
"phi3",
"Phi3ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
669151297aa672eb898ffbe9 | 51d5ab0d0cb758dc | 35b001f3a491f9f9 | 151,646 | BPE | 151,387 | true | true | true | true | 3 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 208 | NovaSearch/stella_en_1.5B_v5 | NovaSearch | null | 30,915 | 2,475,260 | False | 2024-07-12T15:52:09Z | 2025-07-28T11:41:52Z | sentence-transformers | 261 | 0 | [{"name": "stella_en_1.5B_v5", "results": [{"dataset": {"config": "en", "name": "MTEB AmazonCounterfactualClassification (en)", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205", "split": "test", "type": "mteb/amazon_counterfactual"}, "metrics": [{"type": "accuracy", "value": 92.86567164179104, "verified": false},... | sentence-similarity | null | [
".gitattributes",
"1_Pooling/config.json",
"2_Dense/config.json",
"2_Dense/model.safetensors",
"2_Dense/pytorch_model.bin",
"2_Dense_1024/config.json",
"2_Dense_1024/model.safetensors",
"2_Dense_1024/pytorch_model.bin",
"2_Dense_2048/config.json",
"2_Dense_2048/model.safetensors",
"2_Dense_2048/... | 7817065102fd9e1b031fe874e910c01f40b2f001 | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"qwen2",
"text-generation",
"mteb",
"transformers",
"sentence-similarity",
"custom_code",
"arxiv:2412.19048",
"arxiv:2205.13147",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
... | null | {"architectures": ["Qwen2ForCausalLM"], "auto_map": {"AutoModel": "modeling_qwen.Qwen2Model", "AutoModelForCausalLM": "modeling_qwen.Qwen2ForCausalLM", "AutoModelForSequenceClassification": "modeling_qwen.Qwen2ForSequenceClassification"}, "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"license": "mit", "tags": ["mteb", "sentence-transformers", "transformers", "sentence-similarity"], "model-index": [{"name": "stella_en_1.5B_v5", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "en", "s... | null | null | [
"mit"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"sentence-similarity",
"text-generation"
] | [
"text"
] | [
"text"
] | [
"logits",
"text"
] |
63873fde23da90491eb8de37 | 2381945720c2ce4d | d1f3fcfcd5ab53f9 | 250,102 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 209 | timpal0l/mdeberta-v3-base-squad2 | timpal0l | {
"models": [
{
"_id": "621ffdc136468d709f17defb",
"id": "microsoft/mdeberta-v3-base"
}
],
"relation": "finetune"
} | 385,360 | 3,037,917 | False | 2022-11-30T11:34:54Z | 2024-11-26T17:14:50Z | transformers | 257 | 0 | null | question-answering | {"parameters": {"I64": 512, "F32": 278220290}, "total": 278220802} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 08d6e89c7a6557f967db2e1021f7f640483400ed | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"question-answering",
"deberta",
"deberta-v3",
"mdeberta",
"qa",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
... | null | {"architectures": ["DebertaV2ForQuestionAnswering"], "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": "AutoModelForQuestionAnswering",
"custom_class": null,
"pipeline_tag": "question-answering",
"processor": "AutoTokenizer"
} | {"base_model": ["microsoft/mdeberta-v3-base"], "datasets": ["squad_v2"], "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"... | ## This model can be used for Extractive QA
It has been finetuned for 3 epochs on [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/).
## Usage
```python
from transformers import pipeline
qa_model = pipeline("question-answering", "timpal0l/mdeberta-v3-base-squad2")
question = "Where do I live?"
context = "My name... | null | [
"mit"
] | [
"squad_v2"
] | [
"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",
"i... | 278,220,802 | null | null | [
"AutoModelForQuestionAnswering",
"deberta-v2",
"DebertaV2ForQuestionAnswering"
] | [
"question-answering"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65ac0c7c3e7c8dcbbe1c5ebb | 83bf28f8b6767a2f | ae88e903e2b563a1 | 50,295 | BPE | 50,000 | false | true | true | true | 39 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 85 | vikhyatk/moondream1 | vikhyatk | null | 9,904 | 2,399,747 | False | 2024-01-20T18:10:04Z | 2024-02-07T02:57:53Z | transformers | 487 | 0 | null | text-generation | {"parameters": {"F16": 1857482608}, "total": 1857482608} | [
".gitattributes",
"README.md",
"added_tokens.json",
"assets/demo-1.jpg",
"assets/demo-2.jpg",
"assets/demo-3.jpg",
"assets/demo-4.jpg",
"assets/demo-5.jpg",
"config.json",
"configuration_moondream.py",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002... | f6e9da68e8f1b78b8f3ee10905d56826db7a5802 | [
"transformers",
"pytorch",
"safetensors",
"moondream1",
"text-generation",
"custom_code",
"en",
"region:us"
] | null | {"architectures": ["Moondream"], "auto_map": {"AutoConfig": "configuration_moondream.MoondreamConfig", "AutoModelForCausalLM": "moondream.Moondream"}, "model_type": "moondream1", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "moondream.Moondream",
"pipeline_tag": "text-generation",
"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} | # 🌔 moondream1
1.6B parameter model built by [@vikhyatk](https://x.com/vikhyatk) using SigLIP, Phi-1.5 and the LLaVa training dataset.
The model is release for research purposes only, commercial use is not allowed.
Try it out on [Huggingface Spaces](https://huggingface.co/spaces/vikhyatk/moondream1)!
**Usage**
```... | null | null | null | [
"en"
] | 1,857,482,608 | null | null | [
"Moondream",
"AutoModelForCausalLM",
"moondream1",
"moondream.Moondream"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
664a15bb0e5395fb529b1258 | 6f6afefc9249e541 | 1f31c6d7208be2a2 | 32,045 | BPE | 61,249 | true | false | true | true | 48 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 105 | microsoft/Phi-3-vision-128k-instruct | microsoft | null | 92,365 | 2,591,122 | False | 2024-05-19T15:07:39Z | 2025-12-10T20:23:49Z | transformers | 970 | 0 | null | text-generation | null | [
".gitattributes",
"CODE_OF_CONDUCT.md",
"LICENSE",
"README.md",
"SECURITY.md",
"SUPPORT.md",
"config.json",
"configuration_phi3_v.py",
"data_summary_card.md",
"image_embedding_phi3_v.py",
"image_processing_phi3_v.py",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
... | ed2772fabe9dc9acd0caad54b62761d92520cc44 | [
"transformers",
"safetensors",
"phi3_v",
"text-generation",
"nlp",
"code",
"vision",
"conversational",
"custom_code",
"multilingual",
"license:mit",
"region:us"
] | null | {"architectures": ["Phi3VForCausalLM"], "auto_map": {"AutoConfig": "configuration_phi3_v.Phi3VConfig", "AutoModelForCausalLM": "modeling_phi3_v.Phi3VForCausalLM"}, "model_type": "phi3_v", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + mes... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_phi3_v.Phi3VForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual"], "library_name": null, "license": "mit", "license_name": null, "license_link": "https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generati... | 🎉 **Phi-3.5**: [[mini-instruct]](https://huggingface.co/microsoft/Phi-3.5-mini-instruct); [[MoE-instruct]](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) ; [[vision-instruct]](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)
## Model Summary
The Phi-3-Vision-128K-Instruct is a lightweight, state-of-... | null | [
"mit",
"https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/LICENSE"
] | null | [
"multilingual"
] | null | null | null | [
"modeling_phi3_v.Phi3VForCausalLM",
"AutoModelForCausalLM",
"Phi3VForCausalLM",
"phi3_v"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
680f0da2f99a20ddfc9a9f26 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen3-0.6B-Base | Qwen | null | 276,865 | 3,397,622 | False | 2025-04-28T05:09:54Z | 2025-07-26T03:45:42Z | transformers | 157 | 1 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | da87bfb608c14b7cf20ba1ce41287e8de496c0cd | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2505.09388",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call o... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": null} | null | null | [
"apache-2.0"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"Qwen3ForCausalLM",
"qwen3"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
669594ce854cb67318cc22a4 | 62ad3ad2b18d7994 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 81 | rafaelstankiewicz/setfit-article-classifier | rafaelstankiewicz | null | 3 | 3,419,433 | False | 2024-07-15T21:29:50Z | 2024-07-15T21:37:56Z | transformers | 0 | 0 | null | feature-extraction | {"parameters": {"F32": 22713216}, "total": 22713216} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"config_setfit.json",
"model.safetensors",
"model_head.pkl",
"modules.json",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab... | f596e06760fe64acdbf1ecad7ef488afaa23abdf | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | null | [
"apache-2.0"
] | null | null | 22,713,216 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] | |
62c097c33e7b8a5067d0f4b5 | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | tner/roberta-large-tweetner7-all | tner | null | 152,253 | 3,035,124 | False | 2022-07-02T19:08:51Z | 2022-09-27T15:29:57Z | transformers | 1 | 0 | [{"name": "tner/roberta-large-tweetner7-all", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "tner/tweetner7", "type": "tner/tweetner7", "args": "tner/tweetner7"}, "metrics": [{"name": "F1 (test_2021)", "type": "f1", "value": 0.6574551220340903, "verified": fa... | token-classification | null | [
".gitattributes",
"README.md",
"config.json",
"eval/metric.test_2020.json",
"eval/metric.test_2021.json",
"eval/metric_span.test_2020.json",
"eval/metric_span.test_2021.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"trainer_co... | e7fbeec91dc056794ba7853db79686dd9b0acf47 | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForTokenClassification"], "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": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | # tner/roberta-large-tweetner7-all
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_all` split).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter ... | null | null | [
"tner/tweetner7"
] | null | null | null | [
"f1",
"precision",
"recall"
] | [
"roberta",
"AutoModelForTokenClassification",
"RobertaForTokenClassification"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f1802e6 | 62ad3ad2b18d7994 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 81 | sentence-transformers/paraphrase-TinyBERT-L6-v2 | sentence-transformers | null | 6,990 | 2,333,169 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:36:53Z | sentence-transformers | 5 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 66955008}, "total": 66955520} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/m... | 68ed8e61dc7009b863c86c2ea2f748e864cd6b34 | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/paraphrase-TinyBERT-L6-v2
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 ... | null | [
"apache-2.0"
] | null | null | 66,955,520 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6340b0cd733f9eef46966d52 | 7386401a4b259520 | 9c72bc14f391c07c | 50,265 | BPE | 50,003 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 210 | ehsanaghaei/SecureBERT | ehsanaghaei | {
"models": [
{
"_id": "621ffdc036468d709f174350",
"id": "FacebookAI/roberta-base"
}
],
"relation": "finetune"
} | 12,996 | 2,331,299 | False | 2022-10-07T23:05:49Z | 2025-11-01T17:04:38Z | transformers | 65 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"optimizer.pt",
"pytorch_model.bin",
"rng_state.pth",
"scaler.pt",
"scheduler.pt",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"trainer_state.json",
"training_args.bin",
"vocab.json"
] | 3a47918dd874e5c769efd152b51c5756a953fb67 | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"cybersecurity",
"cyber threat intelligence",
"en",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"doi:10.57967/hf/0042",
"license:bigscience-openrail-m",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta", "tokenizer_config": {"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normaliz... | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": ["FacebookAI/roberta-base"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "bigscience-openrail-m", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "fill-mask", "tags": ["cybersecurity", "cyber threat intellig... | # SecureBERT: A Domain-Specific Language Model for Cybersecurity
**SecureBERT** is a RoBERTa-based, domain-specific language model trained on a large cybersecurity-focused corpus. It is designed to represent and understand cybersecurity text more effectively than general-purpose models.
[SecureBERT](https://link.... | null | [
"bigscience-openrail-m"
] | null | [
"en"
] | null | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6393e42025ab73b606913e31 | 5afd7d59d2aa5e01 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 125 | michelleli99/NSFW_text_classifier | michelleli99 | null | 78,092 | 2,947,174 | False | 2022-12-10T01:42:56Z | 2022-12-10T19:59:37Z | transformers | 108 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | 652bad24f49ec5b7dd218f5d273c7bbe815242ee | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"distilroberta",
"sentiment",
"NSFW",
"inappropriate",
"spam",
"twitter",
"reddit",
"en",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"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": ["distilroberta", "sentiment", "NSFW", "inappropriate", "spam", "twitter", "reddit"], "widg... | null | null | null | null | [
"en"
] | null | null | null | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
63c2ae00758e752d9226f63a | 3c1d23c6cee63b47 | befceb9191a2d18d | 50,358 | BPE | 50,000 | true | true | true | true | 102 | NFKC | ByteLevel | ByteLevel | [
"NFKC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 211 | eenzeenee/t5-base-korean-summarization | eenzeenee | null | 11,540 | 2,351,077 | False | 2023-01-14T13:28:32Z | 2023-05-21T03:49:27Z | transformers | 33 | 0 | null | summarization | {"parameters": {"F32": 275579136}, "total": 275579136} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | 80bead40c2fa1b0f01093cb2de84f801b0bb5fe6 | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"T5",
"summarization",
"ko",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<pad>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ko"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "summarization", "tags": ["T5"]} | # t5-base-korean-summarization
This is [T5](https://huggingface.co/docs/transformers/model_doc/t5) model for korean text summarization.
- Finetuned based on ['paust/pko-t5-base'](https://huggingface.co/paust/pko-t5-base) model.
- Finetuned with 3 datasets. Specifically, it is described below.
- [Korean Paper Su... | null | null | null | [
"ko"
] | 275,579,136 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"summarization"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64e78661d0964816be6a1d1b | 271640fddfc1bd19 | 704a2955bcffd2d4 | 32,016 | BPE | 61,260 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 212 | codellama/CodeLlama-7b-Instruct-hf | codellama | null | 239,267 | 2,672,841 | False | 2023-08-24T16:33:37Z | 2024-04-12T14:18:42Z | transformers | 255 | 0 | null | text-generation | {"parameters": {"BF16": 6738546688}, "total": 6738546688} | [
".gitattributes",
"LICENSE",
"README.md",
"USE_POLICY.md",
"config.json",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"pytorch_model-00001-of-00003.bin",
"pytorch_model-00002-of-00003.bin",
"pytorch_model-000... | 22cb240e0292b0b5ab4c17ccd97aa3a2f799cbed | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"llama-2",
"conversational",
"code",
"arxiv:2308.12950",
"license:llama2",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% f... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["code"], "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["llama-2"]} | # **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding.... | null | [
"llama2"
] | null | [
"code"
] | 6,738,546,688 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17a219 | bfe694f64f4a18f5 | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 22 | cross-encoder/nli-deberta-v3-large | cross-encoder | {
"models": [
{
"_id": "621ffdc136468d709f17dee5",
"id": "microsoft/deberta-v3-large"
}
],
"relation": "quantized"
} | 35,784 | 2,409,670 | False | 2022-03-02T23:29:05Z | 2025-04-15T07:48:35Z | sentence-transformers | 39 | 0 | null | zero-shot-classification | {"parameters": {"I64": 512, "F32": 435064835}, "total": 435065347} | [
".gitattributes",
"CESoftmaxAccuracyEvaluator_AllNLI-dev_results.csv",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/model_qint8_avx512.onnx",
"onnx/model_qint8_avx512_vnni.onnx",
"onnx/model_... | bab4bc7178836f731dcfd18c06ca9def0a137712 | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"deberta-v2",
"text-classification",
"transformers",
"zero-shot-classification",
"en",
"dataset:nyu-mll/multi_nli",
"dataset:stanfordnlp/snli",
"base_model:microsoft/deberta-v3-large",
"base_model:quantized:microsoft/deberta-v3-large... | null | {"architectures": ["DebertaV2ForSequenceClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": ["microsoft/deberta-v3-large"], "datasets": ["nyu-mll/multi_nli", "stanfordnlp/snli"], "eval_results": null, "language": "en", "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": null, "pipeline_tag": "zero-s... | null | null | [
"apache-2.0"
] | [
"nyu-mll/multi_nli",
"stanfordnlp/snli"
] | [
"en"
] | 435,065,347 | null | [
"accuracy"
] | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
] | [
"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62b9dcc60b85d500b9b38e46 | 2a959a9941b29bd9 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 213 | pszemraj/long-t5-tglobal-base-16384-book-summary | pszemraj | null | 375 | 2,278,255 | False | 2022-06-27T16:37:26Z | 2025-12-29T03:38:50Z | transformers | 136 | 0 | [{"name": "pszemraj/long-t5-tglobal-base-16384-book-summary", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "kmfoda/booksum", "type": "kmfoda/booksum", "config": "kmfoda--booksum", "split": "test"}, "metrics": [{"name": "ROUGE-1", "type": "rouge", "value": 36.4085, "verifi... | summarization | {"parameters": {"F32": 247587456}, "total": 247587456} | [
".gitattributes",
".gitignore",
"README.md",
"config.json",
"evals-outputs/GAUNTLET.md",
"long-t5-tglobal-base-16384-booksum-V6-partial-ft3-booksum_training_metadata.json",
"long-t5-tglobal-base-16384-booksum-V7.9-ft1-booksum_training_metadata.json",
"long-t5-tglobal-base-16384-booksum-V9-ft1-booksum_... | a40cddfd840d398653f37d98c384bfe56d677125 | [
"transformers",
"pytorch",
"rust",
"onnx",
"safetensors",
"longt5",
"text2text-generation",
"summarization",
"summary",
"booksum",
"long-document",
"long-form",
"dataset:kmfoda/booksum",
"arxiv:2112.07916",
"arxiv:2105.08209",
"doi:10.57967/hf/2078",
"license:apache-2.0",
"license:... | null | {"architectures": ["LongT5ForConditionalGeneration"], "model_type": "longt5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"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", "summary", "booksum", "long-document", "long-form"], "widget": [{"text": "large earthquakes along a given fault segment do not occur at random intervals because it takes time to accumulate the str... | # long-t5-tglobal-base-16384 + BookSum
<a href="https://colab.research.google.com/gist/pszemraj/d9a0495861776168fd5cdcd7731bc4ee/example-long-t5-tglobal-base-16384-book-summary.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
> [!IMPORTANT]
> As of [this discus... | null | [
"apache-2.0",
"bsd-3-clause"
] | [
"kmfoda/booksum"
] | null | 247,587,456 | null | [
"rouge"
] | [
"AutoModelForSeq2SeqLM",
"longt5",
"LongT5ForConditionalGeneration"
] | [
"text2text-generation",
"summarization"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17baa1 | 382b5a4eb98f0296 | d965e4a9140ecf72 | 21,128 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 33 | hfl/chinese-electra-180g-small-discriminator | hfl | null | 380 | 2,271,703 | False | 2022-03-02T23:29:05Z | 2022-11-25T02:46:56Z | transformers | 26 | 0 | null | null | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 826a243f3f387450ef8d70de9c3d0706d8d8e924 | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["ElectraForPreTraining"], "model_type": "electra", "tokenizer_config": {}} | {
"auto_model": "AutoModelForPreTraining",
"custom_class": null,
"pipeline_tag": "pretraining",
"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} | # This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accel... | null | [
"apache-2.0"
] | null | [
"zh"
] | null | null | null | [
"ElectraForPreTraining",
"AutoModelForPreTraining",
"electra"
] | [
"pretraining"
] | null | null | null |
652638bd2ceb2f426c4c4db8 | 5b8a6156b5b6e013 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 37 | TaylorAI/bge-micro-v2 | TaylorAI | null | 348,712 | 3,331,771 | False | 2023-10-11T05:55:09Z | 2024-06-06T22:44:08Z | sentence-transformers | 60 | 0 | [{"name": "bge_micro", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 67.7611940... | sentence-similarity | null | [
".gitattributes",
"1_Pooling/config.json",
"LICENSE",
"README.md",
"added_tokens.json",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_quantized.onnx",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens... | 3edf6d7de0faa426b09780416fe61009f26ae589 | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"license": "mit", "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "mteb"], "model-index": [{"name": "bge_micro", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "ty... | # bge-micro-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Distilled in a 2-step training process (bge-micro was step 1) from `BAAI/bge-small-en-v1.5`.
## Usage (Se... | null | [
"mit"
] | null | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6629fcae81280f9fda84a6d7 | bc75b1072bca086b | b48c4f1fb87798c9 | 32,038 | BPE | 61,249 | true | false | true | true | 41 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 136 | kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | kaitchup | null | 1,311,803 | 7,022,672 | False | 2024-04-25T06:48:14Z | 2024-04-25T06:49:29Z | transformers | 2 | 0 | null | text-generation | {"parameters": {"I32": 3623878656, "F16": 198216704}, "total": 3822095360} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 8d0c383a88f9b355bbc7ac0c36625fa7b9421e71 | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] | null | {"architectures": ["Phi3ForCausalLM"], "auto_map": {"AutoConfig": "microsoft/Phi-3-mini-4k-instruct--configuration_phi3.Phi3Config", "AutoModelForCausalLM": "microsoft/Phi-3-mini-4k-instruct--modeling_phi3.Phi3ForCausalLM"}, "model_type": "phi3", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_co... | {
"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": 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 | 3,822,095,360 | null | null | [
"AutoModelForCausalLM",
"phi3",
"Phi3ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
68134f29d882a15c6a3b8c5b | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Qwen/Qwen3-32B-AWQ | Qwen | {
"models": [
{
"_id": "680daa1b62e834e6fd9fdbdf",
"id": "Qwen/Qwen3-32B"
}
],
"relation": "quantized"
} | 786,357 | 3,927,527 | False | 2025-05-01T10:38:33Z | 2025-05-21T06:17:51Z | transformers | 130 | 0 | null | text-generation | {"parameters": {"I32": 31205621760, "BF16": 1556501504}, "total": 32762123264} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"tokenizer.json",
"... | 0499c3ac83fdef8810b907a23894ba91e95eddd8 | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2309.00071",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-32B",
"base_model:quantized:Qwen/Qwen3-32B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
... | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "quantization_config": {"bits": 4, "quant_method": "awq"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen3-32B", "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "ta... | # Qwen3-32B-AWQ
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Qwen3 Highlights
Qwen3 is the latest generation of large language ... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE"
] | null | null | 32,762,123,264 | null | null | [
"AutoModelForCausalLM",
"Qwen3ForCausalLM",
"qwen3"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63e4fe54a29375eaff26791c | 58967ba68263f0aa | 2f8d5857ae5fa330 | 50,277 | BPE | 50,009 | true | true | true | true | 25 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 86 | EleutherAI/pythia-1.4b | EleutherAI | null | 138,929 | 2,457,422 | False | 2023-02-09T14:08:20Z | 2023-07-09T16:01:57Z | transformers | 26 | 0 | null | text-generation | {"parameters": {"F16": 1414648192, "U8": 100663296}, "total": 1515311488} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | fedc38a16eea3bd36a96b906d78d11d2ce18ed79 | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/the_pile",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
... | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["EleutherAI/the_pile"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["pytorch", "causal-lm", "pythia"]} | The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and ... | null | [
"apache-2.0"
] | [
"EleutherAI/the_pile"
] | [
"en"
] | 1,515,311,488 | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64d2703c693f8cafb185b5a0 | a539070f46a63c93 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 110 | Kyle1668/ag-news-t5-large | Kyle1668 | null | 37 | 2,227,171 | False | 2023-08-08T16:41:32Z | 2024-02-07T18:41:03Z | transformers | 0 | 0 | null | null | {"parameters": {"F32": 737668096}, "total": 737668096} | [
".gitattributes",
"config.json",
"generation_config.json",
"model.safetensors",
"optimizer.pt",
"pytorch_model.bin",
"rng_state.pth",
"scheduler.pt",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json",
"trainer_state.json",
"training_args.bin"
] | c5c05ea7813be3e4923f1ca7437ef102bf8368ed | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | 737,668,096 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
672b1f9e956e6880fdb8c1e5 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-Coder-32B-Instruct | Qwen | {
"models": [
{
"_id": "672dec7762371d59e703f671",
"id": "Qwen/Qwen2.5-Coder-32B"
}
],
"relation": "finetune"
} | 925,966 | 4,949,297 | False | 2024-11-06T07:49:50Z | 2025-01-12T02:02:22Z | transformers | 1,997 | 2 | null | text-generation | {"parameters": {"BF16": 32763876352}, "total": 32763876352} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00014.safetensors",
"model-00002-of-00014.safetensors",
"model-00003-of-00014.safetensors",
"model-00004-of-00014.safetensors",
"model-00005-of-00014.safetensors",
"model-00006-of-0... | 381fc969f78efac66bc87ff7ddeadb7e73c218a7 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"codeqwen",
"chat",
"qwen",
"qwen-coder",
"conversational",
"en",
"arxiv:2409.12186",
"arxiv:2309.00071",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-Coder-32B",
"base_model:finetune:Qwen/Qwen2.5-Coder-32B",
"license... | 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-32B"], "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-32B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline... | # Qwen2.5-Coder-32B-Instruct
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
Qwen2.5-Coder is the latest series of ... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | 32,763,876,352 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63b6847eb24a3784b068b90d | 33d4191fa9a8568f | f5b8f8135e777964 | 32,000 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 214 | ku-nlp/deberta-v2-base-japanese | ku-nlp | null | 5,143 | 2,273,180 | False | 2023-01-05T08:04:14Z | 2023-05-12T14:13:03Z | transformers | 30 | 0 | null | fill-mask | {"parameters": {"I64": 512, "F32": 137030656}, "total": 137031168} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json"
] | 833680905d7c57dbc7914dd33707a9fe1b20c60d | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"fill-mask",
"deberta",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"dataset:oscar",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DebertaV2ForMaskedLM"], "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": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["wikipedia", "cc100", "oscar"], "eval_results": null, "language": "ja", "library_name": "transformers", "license": "cc-by-sa-4.0", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": null, "pipeline_tag": null, "tags": ["deberta", "deberta-v2", "fill-mask... | # Model Card for Japanese DeBERTa V2 base
## Model description
This is a Japanese DeBERTa V2 base model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR.
## How to use
You can use this model for masked language modeling as follows:
```python
from transformers imp... | null | [
"cc-by-sa-4.0"
] | [
"wikipedia",
"cc100",
"oscar"
] | [
"ja"
] | 137,031,168 | null | [
"accuracy"
] | [
"DebertaV2ForMaskedLM",
"AutoModelForMaskedLM",
"deberta-v2"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
629333d125ee4c63e1c85835 | 71140a663949f258 | 8a01c02d4d0a401c | 119,547 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 9 | facebook/mcontriever-msmarco | facebook | null | 18,301 | 2,264,810 | False | 2022-05-29T08:50:25Z | 2022-05-29T08:50:51Z | transformers | 9 | 0 | null | null | null | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 9ff6abed2c2fdf32bbbd8b4e98fb10160e317375 | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Contriever"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "Contriever",
"custom_class": null,
"pipeline_tag": null,
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | null | null | null | [
"Contriever",
"bert"
] | [
null
] | null | null | null |
683f090e77b41ac9fc98c51f | 883c2d6535a54097 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 215 | Qwen/Qwen3-Embedding-8B | Qwen | {
"models": [
{
"_id": "680f0c3e6eacf22a04ee1219",
"id": "Qwen/Qwen3-8B-Base"
}
],
"relation": "finetune"
} | 1,648,122 | 9,165,432 | False | 2025-06-03T14:39:10Z | 2025-07-07T09:02:21Z | sentence-transformers | 628 | 11 | null | feature-extraction | {"parameters": {"BF16": 7567295488}, "total": 7567295488} | [
".gitattributes",
"1_Pooling/config.json",
"LICENSE",
"README.md",
"config.json",
"config_sentence_transformers.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safet... | 1d8ad4ca9b3dd8059ad90a75d4983776a23d44af | [
"sentence-transformers",
"safetensors",
"qwen3",
"text-generation",
"transformers",
"sentence-similarity",
"feature-extraction",
"text-embeddings-inference",
"arxiv:2506.05176",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:finetune:Qwen/Qwen3-8B-Base",
"license:apache-2.0",
"endpoints_compat... | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Qwen/Qwen3-8B-Base"], "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["transformers", "sentence-transformers", "sentence-similarity", ... | null | null | [
"apache-2.0"
] | null | null | 7,567,295,488 | null | null | [
"AutoModelForCausalLM",
"Qwen3ForCausalLM",
"qwen3"
] | [
"sentence-similarity",
"feature-extraction",
"text-generation"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"text",
"embeddings"
] |
63c8209df7cd81d306d0edc6 | 41a000f2f9024404 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 13 | Hello-SimpleAI/chatgpt-detector-roberta | Hello-SimpleAI | null | 26,351 | 2,321,946 | False | 2023-01-18T16:38:53Z | 2023-01-19T11:03:04Z | transformers | 60 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | d2b342c61775d5dd0221808a79983ed3b86ffd86 | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"chatgpt",
"en",
"dataset:Hello-SimpleAI/HC3",
"arxiv:2301.07597",
"doi:10.57967/hf/1203",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["Hello-SimpleAI/HC3"], "eval_results": null, "language": ["en"], "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`
This model is trained on **the mix of full-text and splitted sentences** of `answer`s from [Hello-SimpleAI/HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3).
More details refer to [arxiv: 2301.07597](https://arxiv.org/abs/2301.07597) and Gtihub project... | null | null | [
"Hello-SimpleAI/HC3"
] | [
"en"
] | null | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62af2ce2a5bd7cef3efe4d2a | 191e06b04083279c | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 26 | d4data/biomedical-ner-all | d4data | null | 88,783 | 2,730,505 | False | 2022-06-19T14:04:18Z | 2023-07-02T07:28:28Z | transformers | 187 | 0 | null | token-classification | {"parameters": {"F32": 66427476}, "total": 66427476} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 015a4050c9ac99722e61c547aa9b4282bcbedc7f | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"token-classification",
"Token Classification",
"en",
"license:apache-2.0",
"co2_eq_emissions",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DistilBertForTokenClassification"], "model_type": "distilbert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["Token Classification"], "co2_eq_emissions": 0.0279399890043426, "widget": [{"te... | ## About the Model
An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased
- Dataset: Maccrobat https://figshare.com/articles/dataset/MACCROBAT2018/9764942
-... | null | [
"apache-2.0"
] | null | [
"en"
] | 66,427,476 | null | null | [
"AutoModelForTokenClassification",
"distilbert",
"DistilBertForTokenClassification"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
624f496dbcbeb0d535f47bcf | 58967ba68263f0aa | 2f8d5857ae5fa330 | 50,277 | BPE | 50,009 | true | true | true | true | 25 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 86 | EleutherAI/gpt-neox-20b | EleutherAI | null | 259,148 | 2,702,540 | False | 2022-04-07T20:28:29Z | 2024-01-31T20:30:35Z | transformers | 579 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model-00001-of-00046.safetensors",
"model-00002-of-00046.safetensors",
"model-00003-of-00046.safetensors",
"model-00004-of-00046.safetensors",
"model-00005-of-00046.safetensors",
"model-00006-of-00046.safetensors",
"model-00007-of-0004... | c292233c833e336628618a88a648727eb3dff0a7 | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"en",
"dataset:EleutherAI/pile",
"arxiv:2204.06745",
"arxiv:2101.00027",
"arxiv:2201.07311",
"arxiv:2104.09864",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azur... | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "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": ["EleutherAI/pile"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["pytorch", "causal-lm"]} | GPT-NeoX-20B is a 20 billion parameter autoregressive language model trained
on [the Pile](https://pile.eleuther.ai/) using the [GPT-NeoX
library](https://github.com/EleutherAI/gpt-neox). Its architecture intentionally
resembles that of GPT-3, and is almost identical to that of [GPT-J-
6B](https://huggingface.co/Ele... | null | [
"apache-2.0"
] | [
"EleutherAI/pile"
] | [
"en"
] | null | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17a223 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | cross-encoder/stsb-TinyBERT-L4 | cross-encoder | null | 12,800 | 2,240,311 | False | 2022-03-02T23:29:05Z | 2025-04-11T08:20:56Z | sentence-transformers | 7 | 0 | null | text-ranking | {"parameters": {"I64": 512, "F32": 14350561}, "total": 14351073} | [
".gitattributes",
"CECorrelationEvaluator_sts-dev_results.csv",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/model_qint8_a... | 1f478482145797a0cac02d6b9ab3c65c9d25fb1e | [
"sentence-transformers",
"pytorch",
"jax",
"onnx",
"safetensors",
"openvino",
"bert",
"text-classification",
"transformers",
"text-ranking",
"en",
"dataset:sentence-transformers/stsb",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["sentence-transformers/stsb"], "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"
] | [
"sentence-transformers/stsb"
] | [
"en"
] | 14,351,073 | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-ranking",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6448ee10ab5abd92785d0ded | 2012b52fa41ec860 | db117e9883c4d0ab | 50,257 | BPE | 50,000 | false | true | true | true | 1 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 113 | tiiuae/falcon-rw-1b | tiiuae | null | 11,415 | 2,215,794 | False | 2023-04-26T09:25:36Z | 2023-07-12T21:34:11Z | transformers | 118 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"configuration_falcon.py",
"generation_config.json",
"merges.txt",
"modeling_falcon.py",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | e4b9872bb803165eb22f0a867d4e6a64d34fce19 | [
"transformers",
"pytorch",
"falcon",
"text-generation",
"custom_code",
"en",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2306.01116",
"arxiv:2005.14165",
"arxiv:2108.12409",
"arxiv:2205.14135",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | {"architectures": ["FalconForCausalLM"], "auto_map": {"AutoConfig": "configuration_falcon.FalconConfig", "AutoModel": "modeling_falcon.FalconModel", "AutoModelForSequenceClassification": "modeling_falcon.FalconForSequenceClassification", "AutoModelForTokenClassification": "modeling_falcon.FalconForTokenClassification",... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["tiiuae/falcon-refinedweb"], "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, "inference": false} | # Falcon-RW-1B
**Falcon-RW-1B is a 1B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 350B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb). It is made available under the Apache 2.0 license.**
See the 📓 [paper on arXiv](https://arxiv.org/abs/230... | null | [
"apache-2.0"
] | [
"tiiuae/falcon-refinedweb"
] | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"falcon",
"FalconForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64f7d2fbb16a298a1ca41374 | e23b6bdbe047bb7e | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | Riiid/sheep-duck-llama-2 | Riiid | null | 855 | 2,176,168 | False | 2023-09-06T01:16:43Z | 2023-10-13T00:59:55Z | transformers | 35 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model-00001-of-00015.bin",
"pytorch_model-00002-of-00015.bin",
"pytorch_model-00003-of-00015.bin",
"pytorch_model-00004-of-00015.bin",
"pytorch_model-00005-of-00015.bin",
"pytorch_model-00006-of-00015.bin",
"pytorch... | 366f2bfd912e8f258541c0480a34eb973198286c | [
"transformers",
"pytorch",
"llama",
"text-generation",
"Riiid",
"llama-2",
"en",
"arxiv:2308.07317",
"arxiv:2306.02707",
"license:llama2",
"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": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["Riiid", "llama-2"], "thumbnail": "https://cdn-uploads.huggin... | # sheep-duck-llama-2
<img src = "https://cdn-uploads.huggingface.co/production/uploads/62fb1ef7e8c9c532aa7d19e4/NswB5XPkkOljeRh1xbMmR.png" width="30%" height="30%">
This is a finetuned model from llama-2-70b.
## Model Details
* **Developed by**: [Riiid](https://riiid.com/)
* **Backbone Model**: [LLaMA-2](https://gi... | null | [
"llama2"
] | null | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66eb4f0d15688c55abbacf3d | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit | unsloth | {
"models": [
{
"_id": "66e81c0ecfd2f50991047e92",
"id": "Qwen/Qwen2.5-0.5B-Instruct"
}
],
"relation": "quantized"
} | 10,706 | 2,196,556 | False | 2024-09-18T22:07:09Z | 2025-02-06T02:06:59Z | transformers | 4 | 0 | null | text-generation | {"parameters": {"F32": 67536, "BF16": 136206208, "U8": 369065966}, "total": 505339710} | [
".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"
] | 216d96b52c97a3c8294776df8477592f6828981c | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"conversational",
"en",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-0.5B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-0.5B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["unsloth", "transformers"]} | # Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a Qwen 2.5 (all model sizes) [free Google Colab Tesla T4 notebook](https://colab.research.google.com/drive/1Kose-ucXO1IBaZq5BvbwWieuubP7hxvQ?usp=sharing).
Also a [Qwen 2.5 conversational style notebook](https://colab.research.... | null | [
"apache-2.0"
] | null | [
"en"
] | 505,339,710 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
637f7262e0d5fd319959af32 | e68b0b111c49dac2 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 28 | nielsr/lilt-xlm-roberta-base | nielsr | null | 2,727 | 2,183,932 | False | 2022-11-24T13:32:18Z | 2023-05-17T07:40:48Z | transformers | 25 | 0 | null | feature-extraction | {"parameters": {"I64": 514, "F32": 284153024}, "total": 284153538} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 39325597c4aa6698a50323f444013287bae52e67 | [
"transformers",
"pytorch",
"safetensors",
"lilt",
"feature-extraction",
"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",
... | null | {"architectures": ["LiltModel"], "model_type": "lilt", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": "<pad>", "sep_token": "</s>", "unk... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"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... | # LiLT + XLM-RoBERTa-base
This model is created by combining the Language-Independent Layout Transformer (LiLT) with [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base), a multilingual RoBERTa model (trained on 100 languages).
This way, we have a LayoutLM-like model for 100 languages :) | null | [
"mit"
] | null | [
"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",
"i... | 284,153,538 | null | null | [
"LiltModel",
"AutoModel",
"lilt"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
66034ee449b942652c8763d5 | e2c43495aed6e9fb | e6ffdc5dd5f1fd86 | 256,000 | BPE | 580,604 | true | false | true | true | 217 | Replace | null | Sequence | [
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse"
] | 106 | google/gemma-1.1-2b-it | google | null | 81,392 | 2,458,545 | manual | 2024-03-26T22:40:36Z | 2024-06-27T14:09:52Z | transformers | 172 | 0 | null | text-generation | {"parameters": {"BF16": 2506172416}, "total": 2506172416} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | d750f5eceb83e978c09e2b3597c2a8784e381022 | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:2312.11805",
"arxiv:2009.03300",
"arxiv:1905.07830",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1905.10044",
"arxiv:1907.10641",
"arxiv:1811.00937",
"arxiv:1809.02789",
"arxiv:1911.01547",
"arxiv:17... | null | {"architectures": ["GemmaForCausalLM"], "model_type": "gemma", "tokenizer_config": {"bos_token": "<bos>", "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 %... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "gemma", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"messages": [{"role": "user", "content": "How does the brain... | null | null | [
"gemma"
] | null | null | 2,506,172,416 | null | null | [
"AutoModelForCausalLM",
"gemma",
"GemmaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
664e35e7498fa3a7dcda39ad | 278b7bd720762cfd | fadac82906efdc90 | 32,768 | BPE | 58,980 | false | true | true | true | 771 | null | Metaspace | Sequence | null | [
"Metaspace"
] | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 216 | unsloth/mistral-7b-v0.3-bnb-4bit | unsloth | {
"models": [
{
"_id": "664dc156dba1a2aeb958dc90",
"id": "mistralai/Mistral-7B-v0.3"
}
],
"relation": "quantized"
} | 390,334 | 4,035,912 | False | 2024-05-22T18:13:59Z | 2024-11-22T08:30:49Z | transformers | 22 | 0 | null | text-generation | {"parameters": {"F32": 486912, "BF16": 268701696, "U8": 7197503306}, "total": 7466691914} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer.model.v3",
"tokenizer_config.json"
] | cd23ad17d3fbbd4fce2ad35e61ae612e6052b4b5 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"unsloth",
"mistral-7b",
"en",
"base_model:mistralai/Mistral-7B-v0.3",
"base_model:quantized:mistralai/Mistral-7B-v0.3",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"deplo... | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "[control_768]", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "mistralai/Mistral-7B-v0.3", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["unsloth", "transformers", "mistral", "mistral-... | # Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
We have a Google Colab Tesla T4 notebook for Mistral v3 7b here: https://colab.research.google.com/drive/1_yNCks4BTD5zOnjozppphh5GzMFaMKq_?usp=sharing
For conversational ShareGPT style and using Mistral v3 Instruct: https://colab.research.... | null | [
"apache-2.0"
] | null | [
"en"
] | 7,466,691,914 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f180854 | 202d53a2dc42b584 | 02f3f31bfd3360f8 | 30,528 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 217 | squeezebert/squeezebert-uncased | squeezebert | null | 7,337 | 2,179,965 | False | 2022-03-02T23:29:05Z | 2020-12-11T22:02:17Z | transformers | 2 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"tokenizer.json",
"vocab.txt"
] | 7978b0c163f11850ec35d5cd541828159313ac41 | [
"transformers",
"pytorch",
"squeezebert",
"arxiv:2006.11316",
"arxiv:1904.00962",
"endpoints_compatible",
"region:us"
] | null | {"model_type": "squeezebert"} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | null | # SqueezeBERT pretrained model
This model, `squeezebert-uncased`, is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective.
SqueezeBERT was introduced in [this paper](https://arxiv.org/abs/2006.11316). This model is case-insensitive. The model ... | null | null | null | null | null | null | null | [
"squeezebert",
"AutoModel"
] | [
null
] | null | null | null |
668fc71968b20cd8e6e4d0c2 | 6949732424a8d297 | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 42 | ggml-org/stories15M_MOE | ggml-org | null | 507,351 | 3,746,203 | False | 2024-07-11T11:50:49Z | 2024-08-05T21:45:59Z | transformers | 5 | 0 | null | text-generation | {"parameters": {"F32": 6912, "F16": 36351648}, "total": 36358560} | [
".gitattributes",
".gitignore",
"README.md",
"config.json",
"data.txt",
"finetune.ipynb",
"generate_moe.ipynb",
"generation_config.json",
"model.safetensors",
"model.safetensors.index.json",
"moe_shakespeare15M.gguf",
"moe_shakespeare15M/checkpoint-400/README.md",
"moe_shakespeare15M/checkpo... | b6dd737497465570b5f5e962dbc9d9454ed1e0eb | [
"transformers",
"safetensors",
"gguf",
"mixtral",
"text-generation",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | {"total": 7817216, "architecture": "llama", "context_length": 256, "bos_token": "<s>", "eos_token": "</s>"} | {"architectures": ["MixtralForCausalLM"], "model_type": "mixtral", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": true, "rstr... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # stories15M_MOE
This model is [ModelCloud/tinyllama-15M-stories](https://huggingface.co/ModelCloud/tinyllama-15M-stories) repeated 4 times to make 4 experts.
The model is used for testing, not intended to be used in production (unless your product is some kind of bedtime story teller)
Weight of router is initialize... | null | [
"mit"
] | null | null | 36,358,560 | 7,817,216 | null | [
"mixtral",
"llama",
"MixtralForCausalLM",
"AutoModelForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65a639b3f2e68a41486faea7 | fc29ccd738d51f6b | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 1 | Qdrant/all-MiniLM-L6-v2-onnx | Qdrant | null | 566,730 | 3,332,376 | False | 2024-01-16T08:09:23Z | 2025-06-09T14:05:55Z | transformers | 6 | 0 | null | sentence-similarity | null | [
".gitattributes",
"README.md",
"config.json",
"model.onnx",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 5f1b8cd78bc4fb444dd171e59b18f3a3af89a079 | [
"transformers",
"onnx",
"bert",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": null} | ONNX port of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) for text classification and similarity searches.
### Usage
Here's an example of performing inference using the model with [FastEmbed](https://github.com/qdrant/fastembed).
```py
from fastembed import ... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
64e785cfdbec2317e6b3a213 | 271640fddfc1bd19 | 704a2955bcffd2d4 | 32,016 | BPE | 61,260 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 212 | codellama/CodeLlama-7b-hf | codellama | null | 90,145 | 2,359,415 | False | 2023-08-24T16:31:11Z | 2024-04-12T14:17:26Z | transformers | 375 | 0 | null | text-generation | {"parameters": {"BF16": 6738546688}, "total": 6738546688} | [
".gitattributes",
"LICENSE",
"README.md",
"USE_POLICY.md",
"config.json",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"pytorch_model-00001-of-00003.bin",
"pytorch_model-00002-of-00003.bin",
"pytorch_model-000... | 6c284d1468fe6c413cf56183e69b194dcfa27fe6 | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"llama-2",
"code",
"arxiv:2308.12950",
"license:llama2",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": true, "rstrip":... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["code"], "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["llama-2"]} | # **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the base 7B version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to ... | null | [
"llama2"
] | null | [
"code"
] | 6,738,546,688 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66eb48d0bd1051b7adace155 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | unsloth/Qwen2.5-7B-Instruct-bnb-4bit | unsloth | {
"models": [
{
"_id": "66e81cbcd683a3f4e5291bbf",
"id": "Qwen/Qwen2.5-7B-Instruct"
}
],
"relation": "quantized"
} | 183,730 | 2,520,723 | False | 2024-09-18T21:40:32Z | 2025-04-28T04:16:25Z | transformers | 20 | 0 | null | text-generation | {"parameters": {"F32": 451584, "BF16": 1090328064, "U8": 6729271260}, "total": 7820050908} | [
".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"
] | bdd404162d94997f390efbfa660eb3f21cbbc81d | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"qwen",
"conversational",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"arxiv:2309.00071",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"bas... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-7B-Instruct", "datasets": null, "eval_results": null, "language": ["zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": ... | # Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a Qwen 2.5 (all model sizes) [free Google Colab Tesla T4 notebook](https://colab.research.google.com/drive/1Kose-ucXO1IBaZq5BvbwWieuubP7hxvQ?usp=sharing).
Also a [Qwen 2.5 conversational style notebook](https://colab.resear... | null | [
"apache-2.0"
] | null | [
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara"
] | 7,820,050,908 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17a217 | b5328a3bc1751d46 | 664d254b6eeb0893 | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 218 | cross-encoder/nli-deberta-base | cross-encoder | {
"models": [
{
"_id": "621ffdc136468d709f17dedd",
"id": "microsoft/deberta-base"
}
],
"relation": "quantized"
} | 1,344 | 2,112,348 | False | 2022-03-02T23:29:05Z | 2025-04-15T07:59:34Z | sentence-transformers | 17 | 0 | null | zero-shot-classification | {"parameters": {"I64": 512, "F32": 139194627}, "total": 139195139} | [
".gitattributes",
"CESoftmaxAccuracyEvaluator_AllNLI-dev_results.csv",
"README.md",
"bpe_encoder.bin",
"config.json",
"merges.txt",
"model.safetensors",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
... | f375a3f8bdba5a9ea9489179fe2f7b7d69c00b76 | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"deberta",
"text-classification",
"transformers",
"zero-shot-classification",
"en",
"dataset:nyu-mll/multi_nli",
"dataset:stanfordnlp/snli",
"base_model:microsoft/deberta-base",
"base_model:quantized:microsoft/deberta-base",
"licen... | null | {"architectures": ["DebertaForSequenceClassification"], "model_type": "deberta", "tokenizer_config": {"unk_token": {"content": "[UNK]", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "[CLS]", "single_word": false, "lstrip": false, "rstrip": ... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": ["microsoft/deberta-base"], "datasets": ["nyu-mll/multi_nli", "stanfordnlp/snli"], "eval_results": null, "language": "en", "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": null, "pipeline_tag": "zero-shot-... | null | null | [
"apache-2.0"
] | [
"nyu-mll/multi_nli",
"stanfordnlp/snli"
] | [
"en"
] | 139,195,139 | null | [
"accuracy"
] | [
"DebertaForSequenceClassification",
"AutoModelForSequenceClassification",
"deberta"
] | [
"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66aa024d19bc636023bdbeba | 34093184d10f76a9 | 35b001f3a491f9f9 | 151,646 | BPE | 151,387 | true | true | true | true | 3 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 53 | Qwen/Qwen2-Audio-7B-Instruct | Qwen | null | 359,941 | 4,010,654 | False | 2024-07-31T09:22:21Z | 2025-01-12T02:05:48Z | transformers | 526 | 0 | null | audio-text-to-text | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00005.safetensors",
"model-00002-of-00005.safetensors",
"model-00003-of-00005.safetensors",
"model-00004-of-00005.safetensors",
"model-00005-of-00005.safetensors",
"model.safetensors.index.json"... | 0a095220c30b7b31434169c3086508ef3ea5bf0a | [
"transformers",
"safetensors",
"qwen2_audio",
"text2text-generation",
"chat",
"audio",
"audio-text-to-text",
"en",
"arxiv:2407.10759",
"arxiv:2311.07919",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen2AudioForConditionalGeneration"], "model_type": "qwen2_audio", "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_star... | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoProcessor"
} | {"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": ["chat", "audio", "audio-text-to-text"]} | # Qwen2-Audio-7B-Instruct
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
Qwen2-Audio is the new series of Qwen lar... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"AutoModelForSeq2SeqLM",
"qwen2_audio",
"Qwen2AudioForConditionalGeneration"
] | [
"text2text-generation",
"audio-text-to-text"
] | null | null | null |
650c6c13ced77e2bac9a07b8 | 78a7dfbfd6d9e88b | 65dc24499e5bba86 | 48,000 | BPE | 40,804 | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"NFKC",
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 219 | globis-university/deberta-v3-japanese-large | globis-university | null | 102 | 2,271,791 | False | 2023-09-21T16:15:15Z | 2024-07-05T05:50:06Z | transformers | 4 | 0 | null | token-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json"
] | 62c5ea786ab08d8ad5a33f64ff1e0808be62f7ba | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"ja",
"dataset:globis-university/aozorabunko-clean",
"dataset:oscar-corpus/OSCAR-2301",
"dataset:Wikipedia",
"dataset:WikiBooks",
"dataset:CC-100",
"dataset:allenai/c4",
"arxiv:2302.03169",
"license:cc-by-sa-4.0",
"endpoints_... | null | {"architectures": ["DebertaV2ForTokenClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["globis-university/aozorabunko-clean", "oscar-corpus/OSCAR-2301", "Wikipedia", "WikiBooks", "CC-100", "allenai/c4"], "eval_results": null, "language": ["ja"], "library_name": "transformers", "license": "cc-by-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_... | # What’s this?
日本語リソースで学習した [DeBERTa V3](https://huggingface.co/microsoft/deberta-v3-large) モデルです。
以下のような特徴を持ちます:
- 定評のある [DeBERTa V3](https://huggingface.co/microsoft/deberta-v3-large) を用いたモデル
- 日本語特化
- 推論時に形態素解析器を用いない
- 単語境界をある程度尊重する (`の都合上` や `の判定負けを喫し` のような複数語のトークンを生じさせない)
---
This is a model based on [DeBERTa V... | null | [
"cc-by-sa-4.0"
] | [
"globis-university/aozorabunko-clean",
"oscar-corpus/OSCAR-2301",
"Wikipedia",
"WikiBooks",
"CC-100",
"allenai/c4"
] | [
"ja"
] | null | null | null | [
"DebertaV2ForTokenClassification",
"AutoModelForTokenClassification",
"deberta-v2"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f180303 | da443ce982f05a4c | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 51 | sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens | sentence-transformers | null | 2,075 | 2,097,391 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:21:58Z | sentence-transformers | 8 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 278043648}, "total": 278044162} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/m... | 88b1aae5fcf203597dfbe40144aaed61833bc017 | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["XLMRobertaModel"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": ... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens
Thi... | null | [
"apache-2.0"
] | null | null | 278,044,162 | null | null | [
"XLMRobertaModel",
"AutoModel",
"xlm-roberta"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
65e1b60278cace616c3fb2ad | d49b8fd5deacd284 | 3aba7be11585fdda | 122,753 | BPE | 171,540 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 220 | katuni4ka/tiny-random-minicpm | katuni4ka | null | 2,044 | 2,088,236 | False | 2024-03-01T11:03:30Z | 2025-03-05T09:27:58Z | transformers | 1 | 0 | null | text-generation | {"parameters": {"F32": 80000640}, "total": 80000640} | [
".gitattributes",
"config.json",
"configuration_minicpm.py",
"generation_config.json",
"model.safetensors",
"modeling_minicpm.py",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | e05cb4f94ea9a34b1af07a809778b9f7dd35e3cb | [
"transformers",
"safetensors",
"minicpm",
"text-generation",
"conversational",
"custom_code",
"region:us"
] | null | {"architectures": ["MiniCPMForCausalLM"], "auto_map": {"AutoConfig": "configuration_minicpm.MiniCPMConfig", "AutoModel": "modeling_minicpm.MiniCPMModel", "AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM", "AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM", "AutoModelForSequenceClassification": "... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_minicpm.MiniCPMForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | null | null | null | null | null | null | 80,000,640 | null | null | [
"minicpm",
"AutoModelForCausalLM",
"modeling_minicpm.MiniCPMForCausalLM",
"MiniCPMForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66067651cf9d6fff26bb995a | c51c16a9b9888d90 | 89ddf9a6a388e623 | 92,544 | BPE | 165,112 | true | false | true | true | 1 | Sequence | null | Sequence | [
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 221 | katuni4ka/tiny-random-internlm2 | katuni4ka | null | 1,769 | 2,086,264 | False | 2024-03-29T08:05:37Z | 2024-03-29T08:06:20Z | transformers | 0 | 0 | null | feature-extraction | {"parameters": {"F32": 24052864}, "total": 24052864} | [
".gitattributes",
"config.json",
"configuration_internlm2.py",
"generation_config.json",
"model.safetensors",
"modeling_internlm2.py",
"special_tokens_map.json",
"tokenization_internlm2.py",
"tokenization_internlm2_fast.py",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | b72aedd8aa4f5a3e411c657d5d860330803d5c10 | [
"transformers",
"safetensors",
"internlm2",
"feature-extraction",
"custom_code",
"region:us"
] | null | {"architectures": ["InternLM2ForCausalLM"], "auto_map": {"AutoConfig": "configuration_internlm2.InternLM2Config", "AutoModel": "modeling_internlm2.InternLM2ForCausalLM", "AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM"}, "model_type": "internlm2", "tokenizer_config": {"bos_token": "<s>", "eos_token": "... | {
"auto_model": "AutoModel",
"custom_class": "modeling_internlm2.InternLM2ForCausalLM",
"pipeline_tag": "feature-extraction",
"processor": null
} | null | null | null | null | null | null | 24,052,864 | null | null | [
"InternLM2ForCausalLM",
"AutoModel",
"internlm2",
"modeling_internlm2.InternLM2ForCausalLM"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
63ee964c049599a8df94e360 | 52bad4bf7b21579a | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 39 | laion/clap-htsat-unfused | laion | null | 346,383 | 3,712,085 | False | 2023-02-16T20:47:08Z | 2023-04-24T14:39:57Z | transformers | 69 | 0 | null | feature-extraction | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 8fa0f1c6d0433df6e97c127f64b2a1d6c0dcda8a | [
"transformers",
"pytorch",
"clap",
"feature-extraction",
"arxiv:2211.06687",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["ClapModel"], "model_type": "clap", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # Model card for CLAP
Model card for CLAP: Contrastive Language-Audio Pretraining
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Citation]... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"clap",
"AutoModel",
"ClapModel"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
621ffdc036468d709f1770ab | 5c71c3514a95efd1 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 222 | Rakib/roberta-base-on-cuad | Rakib | null | 5,024 | 2,120,962 | False | 2022-03-02T23:29:04Z | 2023-01-18T12:18:53Z | transformers | 8 | 0 | null | question-answering | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"nbest_predictions.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
] | c0171973e286d120865dbeb4311af5af1b47d765 | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"legal-contract-review",
"cuad",
"en",
"dataset:cuad",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForQuestionAnswering"], "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": "AutoModelForQuestionAnswering",
"custom_class": null,
"pipeline_tag": "question-answering",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["cuad"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "question-answering", "tags": ["legal-contract-review", "roberta", "cuad"]} | # Model Card for roberta-base-on-cuad
# Model Details
## Model Description
- **Developed by:** Mohammed Rakib
- **Shared by [Optional]:** More information needed
- **Model type:** Question Answering
- **Language(s) (NLP):** en
- **License:** MIT
- **Related Models:**
- **Parent Model:** RoBERTa
- **Resources... | null | [
"mit"
] | [
"cuad"
] | [
"en"
] | null | null | null | [
"roberta",
"AutoModelForQuestionAnswering",
"RobertaForQuestionAnswering"
] | [
"question-answering"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17a83e | bbf489d4445ec8f5 | 291a9305acaae14c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 223 | dmis-lab/biosyn-sapbert-bc5cdr-disease | dmis-lab | null | 225 | 2,084,203 | False | 2022-03-02T23:29:05Z | 2021-10-25T14:46:40Z | transformers | 5 | 0 | null | feature-extraction | null | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"sparse_encoder.pk",
"sparse_weight.pt",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 53d4525fccf15663f19f0d0846c50286a0a01f1e | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
63c48d92a8a2d3a59eea23fb | 191e06b04083279c | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 26 | sadickam/sdgBERT | sadickam | null | 37,549 | 2,120,032 | False | 2023-01-15T23:34:42Z | 2026-02-05T07:28:10Z | transformers | 14 | 0 | null | text-classification | {"parameters": {"F32": 109494544}, "total": 109494544} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"onnx/config.json",
"onnx/model.onnx",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 520e28ca97f2882500d2dc14e34a668c8e3708ce | [
"transformers",
"onnx",
"safetensors",
"bert",
"text-classification",
"en",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["accuracy", "matthews_correlation"], "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"text": "Highway work zones create pot... | # sadickam/sdgBERT
<!-- Provide a quick summary of what the model is/does. -->
sgdBERT (previously named "sdg-classification-bert"), is an NLP model for classifying text with respect to the United Nations sustainable development goals (SDG).
![image](https://user-images.githubusercontent.com/73560591/216751462-ced48... | null | [
"mit"
] | null | [
"en"
] | 109,494,544 | null | [
"accuracy",
"matthews_correlation"
] | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
67a53c5611dd6db97223bb3a | 27a47e631d8f9d77 | 377ed779fd867883 | 128,257 | BPE | 280,147 | false | true | true | true | 257 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 224 | fixie-ai/ultravox-v0_5-llama-3_2-1b | fixie-ai | null | 778,346 | 4,495,717 | False | 2025-02-06T22:48:54Z | 2026-03-11T11:03:46Z | transformers | 73 | 0 | null | audio-text-to-text | {"parameters": {"BF16": 683118592}, "total": 683118592} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"preprocessor_config.json",
"processor_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"ultravox_config.py",
"ultravox_model.py",
"ultravox_pipeline.py",
"ultrav... | b95bec8ab291eeb04b5cd600dd473377f6b79026 | [
"transformers",
"safetensors",
"ultravox",
"feature-extraction",
"audio-text-to-text",
"custom_code",
"ar",
"be",
"bg",
"bn",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"fa",
"fi",
"fr",
"gl",
"hi",
"hu",
"it",
"ja",
"ka",
"lt",
"lv",
"mk",
"mr",
"n... | null | {"architectures": ["UltravoxModel"], "auto_map": {"AutoConfig": "ultravox_config.UltravoxConfig", "AutoModel": "ultravox_model.UltravoxModel", "AutoProcessor": "ultravox_processing.UltravoxProcessor"}, "model_type": "ultravox", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{... | {
"auto_model": "AutoModel",
"custom_class": "ultravox_model.UltravoxModel",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ar", "be", "bg", "bn", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "fi", "fr", "gl", "hi", "hu", "it", "ja", "ka", "lt", "lv", "mk", "mr", "nl", "pl", "pt", "ro", "ru", "sk", "sl", "sr", "sv", "sw", "ta", "th", "tr", "uk", "ur", "vi", ... | # Model Card for Ultravox
Ultravox is a multimodal Speech LLM built around a pretrained [Llama3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B) and [whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) backbone.
See https://ultravox.ai for the GitHub repo and more information.
... | null | [
"mit"
] | null | [
"ar",
"be",
"bg",
"bn",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"fa",
"fi",
"fr",
"gl",
"hi",
"hu",
"it",
"ja",
"ka",
"lt",
"lv",
"mk",
"mr",
"nl",
"pl",
"pt",
"ro",
"ru",
"sk",
"sl",
"sr",
"sv",
"sw",
"ta",
"th",
"tr",
"uk",
"ur"... | 683,118,592 | null | [
"bleu"
] | [
"AutoModel",
"UltravoxModel",
"ultravox",
"ultravox_model.UltravoxModel"
] | [
"feature-extraction",
"audio-text-to-text"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
621ffdc036468d709f1765b8 | 382b5a4eb98f0296 | d965e4a9140ecf72 | 21,128 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 33 | Langboat/mengzi-bert-base | Langboat | null | 37 | 2,066,371 | False | 2022-03-02T23:29:04Z | 2021-10-14T09:01:34Z | transformers | 40 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"tokenizer.json",
"vocab.txt"
] | a685cb1101fb1ea116e8432b2e14042194e4738b | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"zh",
"arxiv:2110.06696",
"doi:10.57967/hf/0023",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert"} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"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, "widget": [{"text": "\u751f\u6d3b\u7684\u771f\u8c1b\u662f[MASK]\u3002"}]} | # Mengzi-BERT base model (Chinese)
Pretrained model on 300G Chinese corpus. Masked language modeling(MLM), part-of-speech(POS) tagging and sentence order prediction(SOP) are used as training task.
[Mengzi: A lightweight yet Powerful Chinese Pre-trained Language Model](https://arxiv.org/abs/2110.06696)
## Usage
```p... | null | [
"apache-2.0"
] | null | [
"zh"
] | null | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
67882547eb36144551980fb3 | 7967e1fae39d98e0 | 75b5e47a4b00191e | 151,655 | BPE | 151,387 | true | true | true | true | 12 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 57 | allenai/olmOCR-7B-0225-preview | allenai | {
"models": [
{
"_id": "66cee7d175a88154443a4056",
"id": "Qwen/Qwen2-VL-7B-Instruct"
}
],
"relation": "finetune"
} | 58,330 | 2,145,107 | False | 2025-01-15T21:14:47Z | 2025-08-19T15:31:31Z | transformers | 699 | 0 | null | image-text-to-text | {"parameters": {"BF16": 8291375616}, "total": 8291375616} | [
".gitattributes",
"README.md",
"chat_template.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"preproces... | 6f102f3807c44450e4ea1ec3eae775e9e68927cb | [
"transformers",
"safetensors",
"qwen2_vl",
"image-text-to-text",
"conversational",
"en",
"dataset:allenai/olmOCR-mix-0225",
"base_model:Qwen/Qwen2-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"reg... | null | {"architectures": ["Qwen2VLForConditionalGeneration"], "model_type": "qwen2_vl", "processor_config": {"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpfu... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": ["Qwen/Qwen2-VL-7B-Instruct"], "datasets": ["allenai/olmOCR-mix-0225"], "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": null, "new_version": "a... | <img alt="olmOCR Logo" src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/olmocr/olmocr.png" width="242px" style="margin-left:'auto' margin-right:'auto' display:'block'">
# olmOCR-7B-0225-preview
This is a preview release of the olmOCR model that's fine tuned from Qwen2-VL-7B-Instruct using the
[o... | null | [
"apache-2.0"
] | [
"allenai/olmOCR-mix-0225"
] | [
"en"
] | 8,291,375,616 | null | null | [
"AutoModelForImageTextToText",
"qwen2_vl",
"Qwen2VLForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
664633f48dc3d2cea08a386d | bf9cff1d4dac26d5 | c09e4490a85b51e5 | 100,533 | BPE | 3,709 | false | true | false | true | 6 | null | Sequence | Sequence | null | [
"Digits",
"Punctuation"
] | [
"ByteFallback"
] | 225 | katuni4ka/tiny-random-xverse | katuni4ka | null | 2,210 | 2,058,755 | False | 2024-05-16T16:27:32Z | 2024-10-28T06:35:37Z | transformers | 0 | 0 | null | text-generation | {"parameters": {"F32": 6459056}, "total": 6459056} | [
".gitattributes",
"config.json",
"configuration_xverse.py",
"generation_config.json",
"model.safetensors",
"modeling_xverse.py",
"quantization.py",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 67619ef8ac7e345aa8136a565bdd05159d621594 | [
"transformers",
"safetensors",
"xverse",
"text-generation",
"custom_code",
"region:us"
] | null | {"architectures": ["XverseForCausalLM"], "auto_map": {"AutoConfig": "configuration_xverse.XverseConfig", "AutoModelForCausalLM": "modeling_xverse.XverseForCausalLM"}, "model_type": "xverse", "tokenizer_config": {}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_xverse.XverseForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | null | null | null | null | null | null | 6,459,056 | null | null | [
"AutoModelForCausalLM",
"XverseForCausalLM",
"modeling_xverse.XverseForCausalLM",
"xverse"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
664636b1f7fb9cc4f6df9ddb | d4e71c54037a4e15 | 4e07ae5db4d7b661 | 100,008 | BPE | 99,743 | false | true | true | true | 9 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 226 | katuni4ka/tiny-random-aquila2 | katuni4ka | null | 2,049 | 2,056,670 | False | 2024-05-16T16:39:13Z | 2024-05-16T16:40:21Z | transformers | 0 | 0 | null | text-generation | {"parameters": {"F32": 6425376}, "total": 6425376} | [
".gitattributes",
"added_tokens.json",
"config.json",
"configuration_aquila.py",
"generation_config.json",
"merges.txt",
"model.safetensors",
"modeling_aquila.py",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 4e667fca0047819dba742b1e15fd9e46d7d0da6b | [
"transformers",
"safetensors",
"aquila",
"text-generation",
"custom_code",
"region:us"
] | null | {"architectures": ["AquilaForCausalLM"], "auto_map": {"AutoConfig": "configuration_aquila.AquilaConfig", "AutoModelForCausalLM": "modeling_aquila.AquilaForCausalLM"}, "model_type": "aquila", "tokenizer_config": {"bos_token": "[CLS]", "eos_token": "</s>", "pad_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_aquila.AquilaForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | null | null | null | null | null | null | 6,425,376 | null | null | [
"AutoModelForCausalLM",
"modeling_aquila.AquilaForCausalLM",
"aquila",
"AquilaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6646f84b50bde9cab3b9230d | 246ef4ae7790e707 | 4e07ae5db4d7b661 | 100,008 | BPE | 99,743 | false | true | true | true | 9 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 227 | katuni4ka/tiny-random-aquilachat | katuni4ka | null | 2,046 | 2,055,680 | False | 2024-05-17T06:25:15Z | 2024-05-17T06:27:42Z | transformers | 0 | 0 | null | text-generation | {"parameters": {"F32": 6425392}, "total": 6425392} | [
".gitattributes",
"added_tokens.json",
"config.json",
"configuration_aquila.py",
"generation_config.json",
"merges.txt",
"model.safetensors",
"modeling_aquila.py",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | f5bed3527c58a7656721ba283711bb5fc21e2b3c | [
"transformers",
"safetensors",
"aquila",
"text-generation",
"custom_code",
"region:us"
] | null | {"architectures": ["AquilaForCausalLM"], "auto_map": {"AutoConfig": "configuration_aquila.AquilaConfig", "AutoModelForCausalLM": "modeling_aquila.AquilaForCausalLM"}, "model_type": "aquila", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_aquila.AquilaForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | null | null | null | null | null | null | 6,425,392 | null | null | [
"AutoModelForCausalLM",
"modeling_aquila.AquilaForCausalLM",
"aquila",
"AquilaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6615d61745336ca7744956a4 | a1524adaa43b439b | 3e62a24f3d0a884f | 32,003 | BPE | 58,980 | true | false | true | true | 6 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 228 | HuggingFaceM4/idefics2-8b | HuggingFaceM4 | null | 152,651 | 2,399,850 | False | 2024-04-09T23:58:15Z | 2024-10-14T08:24:00Z | transformers | 620 | 0 | null | image-text-to-text | {"parameters": {"F32": 8402768112}, "total": 8402768112} | [
".gitattributes",
"README.md",
"added_tokens.json",
"assets/Idefics2_flowchart.png",
"config.json",
"generation_config.json",
"model-00001-of-00007.safetensors",
"model-00002-of-00007.safetensors",
"model-00003-of-00007.safetensors",
"model-00004-of-00007.safetensors",
"model-00005-of-00007.safe... | 2c42686c57fe21cf0348c9ce1077d094b72e7698 | [
"transformers",
"safetensors",
"idefics2",
"image-text-to-text",
"multimodal",
"vision",
"en",
"dataset:HuggingFaceM4/OBELICS",
"dataset:laion/laion-coco",
"dataset:wikipedia",
"dataset:facebook/pmd",
"dataset:pixparse/idl-wds",
"dataset:pixparse/pdfa-eng-wds",
"dataset:wendlerc/RenderedTe... | null | {"architectures": ["Idefics2ForConditionalGeneration"], "model_type": "idefics2", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<unk>", "unk_token": "<unk>", "use_default_system_prompt": true}} | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": ["HuggingFaceM4/OBELICS", "laion/laion-coco", "wikipedia", "facebook/pmd", "pixparse/idl-wds", "pixparse/pdfa-eng-wds", "wendlerc/RenderedText", "HuggingFaceM4/the_cauldron", "teknium/OpenHermes-2.5", "GAIR/lima", "databricks/databricks-dolly-15k", "meta-math/MetaMathQA", "TIGER-Lab/Mat... | <p align="center">
<img src="https://huggingface.co/HuggingFaceM4/idefics-80b/resolve/main/assets/IDEFICS.png" alt="Idefics-Obelics logo" width="200" height="100">
</p>
> [!WARNING]
> Idefics2 will NOT work with `Transformers` version between 4.41.0 and 4.43.3 included. See the issue https://github.com/huggingface... | null | [
"apache-2.0"
] | [
"HuggingFaceM4/OBELICS",
"laion/laion-coco",
"wikipedia",
"facebook/pmd",
"pixparse/idl-wds",
"pixparse/pdfa-eng-wds",
"wendlerc/RenderedText",
"HuggingFaceM4/the_cauldron",
"teknium/OpenHermes-2.5",
"GAIR/lima",
"databricks/databricks-dolly-15k",
"meta-math/MetaMathQA",
"TIGER-Lab/MathInstr... | [
"en"
] | 8,402,768,112 | null | null | [
"AutoModelForImageTextToText",
"Idefics2ForConditionalGeneration",
"idefics2"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
62c8018851eb740327229910 | 7903ca758156b287 | ffae8233e53a99e4 | 256,204 | BPE | 514,098 | true | true | true | true | 207 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 60 | facebook/nllb-200-3.3B | facebook | null | 58,098 | 2,270,668 | False | 2022-07-08T10:06:00Z | 2023-02-11T20:19:13Z | transformers | 434 | 3 | null | translation | 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",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer... | 1a07f7d195896b2114afcb79b7b57ab512e7b43e | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"nllb",
"translation",
"ace",
"acm",
"acq",
"aeb",
"af",
"ajp",
"ak",
"als",
"am",
"apc",
"ar",
"ars",
"ary",
"arz",
"as",
"ast",
"awa",
"ayr",
"azb",
"azj",
"ba",
"bm",
"ban",
"be",
"bem",
"b... | null | {"architectures": ["M2M100ForConditionalGeneration"], "model_type": "m2m_100", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": "<pad>", "... | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["flores-200"], "eval_results": null, "language": ["ace", "acm", "acq", "aeb", "af", "ajp", "ak", "als", "am", "apc", "ar", "ars", "ary", "arz", "as", "ast", "awa", "ayr", "azb", "azj", "ba", "bm", "ban", "be", "bem", "bn", "bho", "bjn", "bo", "bs", "bug", "bg", "ca", "ceb", "cs", "cjk"... | # NLLB-200
This is the model card of NLLB-200's 3.3B variant.
Here are the [metrics](https://tinyurl.com/nllb200dense3bmetrics) for that particular checkpoint.
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and t... | null | [
"cc-by-nc-4.0"
] | [
"flores-200"
] | [
"ace",
"acm",
"acq",
"aeb",
"af",
"ajp",
"ak",
"als",
"am",
"apc",
"ar",
"ars",
"ary",
"arz",
"as",
"ast",
"awa",
"ayr",
"azb",
"azj",
"ba",
"bm",
"ban",
"be",
"bem",
"bn",
"bho",
"bjn",
"bo",
"bs",
"bug",
"bg",
"ca",
"ceb",
"cs",
"cjk",
"ckb",... | null | null | [
"bleu",
"spbleu",
"chrf++"
] | [
"AutoModelForSeq2SeqLM",
"m2m_100",
"M2M100ForConditionalGeneration"
] | [
"text2text-generation",
"translation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66f457d83f2542b81204929f | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | mlx-community/Llama-3.2-3B-Instruct-4bit | mlx-community | {
"models": [
{
"_id": "66f4970900d2de868719bfe2",
"id": "mlx-community/Llama-3.2-3B-Instruct"
}
],
"relation": "quantized"
} | 48,334 | 2,236,621 | False | 2024-09-25T18:35:04Z | 2025-03-05T02:59:08Z | transformers | 41 | 0 | null | text-generation | {"parameters": {"F16": 100568064, "U32": 401571840}, "total": 502139904} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 7f0dc925e0d0afb0322d96f9255cfddf2ba5636e | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"mlx",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"base_model:mlx-community/Llama-3.2-3B-Instruct",
"base_model:quantized:mlx-community/Llama-3.2-3B-Instr... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4}, "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 ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "mlx-community/Llama-3.2-3B-Instruct", "datasets": null, "eval_results": null, "language": ["en", "de", "fr", "it", "pt", "hi", "es", "th"], "library_name": "transformers", "license": "llama3.2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-genera... | # mlx-community/Llama-3.2-3B-Instruct-4bit
The Model [mlx-community/Llama-3.2-3B-Instruct-4bit](https://huggingface.co/mlx-community/Llama-3.2-3B-Instruct-4bit) was
converted to MLX format from [mlx-community/Llama-3.2-3B-Instruct](https://huggingface.co/mlx-community/Llama-3.2-3B-Instruct)
using mlx-lm version **0.21... | null | [
"llama3.2"
] | null | [
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th"
] | 502,139,904 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6583331a4f6ed39deedd833e | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | ealvaradob/bert-finetuned-phishing | ealvaradob | {
"models": [
{
"_id": "621ffdc036468d709f17433e",
"id": "google-bert/bert-large-uncased"
}
],
"relation": "finetune"
} | 5,355 | 2,050,991 | False | 2023-12-20T18:31:54Z | 2024-02-07T05:11:47Z | transformers | 30 | 0 | [{"name": "bert-finetuned-phishing", "results": []}] | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"gitattributes",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | fa8fb73a007174c410ab7160d4e4c6e6b8d998d4 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"phishing",
"BERT",
"en",
"dataset:ealvaradob/phishing-dataset",
"base_model:google-bert/bert-large-uncased",
"base_model:finetune:google-bert/bert-large-uncased",
"license:apache-2.0",
"text-embeddings-infer... | 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": "bert-large-uncased", "datasets": ["ealvaradob/phishing-dataset"], "eval_results": [], "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["accuracy", "precision", "recall"], "model_name": "bert-finetuned-phishing", "pipeline_tag": "t... | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT FINETUNED ON PHISHING DETECTION
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large... | null | [
"apache-2.0"
] | [
"ealvaradob/phishing-dataset"
] | [
"en"
] | null | null | [
"accuracy",
"precision",
"recall"
] | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc036468d709f17432f | a3d54d6c1d5c62b4 | f87d7bfce4fd9617 | 30,000 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Replace",
"Replace",
"NFKD",
"StripAccents",
"Lowercase",
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 12 | albert/albert-xxlarge-v2 | albert | null | 2,681 | 2,036,251 | False | 2022-03-02T23:29:04Z | 2024-02-19T11:02:09Z | transformers | 21 | 0 | null | fill-mask | {"parameters": {"F32": 223180256}, "total": 223180256} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"rust_model.ot",
"spiece.model",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"with-prefix-tf_model.h5"
] | 97d3e58863d3a41dc581882f73b34d110b18f1f8 | [
"transformers",
"pytorch",
"tf",
"rust",
"safetensors",
"albert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["AlbertForMaskedLM"], "model_type": "albert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["bookcorpus", "wikipedia"], "eval_results": null, "language": "en", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["exbert"]} | # ALBERT XXLarge v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not ma... | null | [
"apache-2.0"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | 223,180,256 | null | null | [
"albert",
"AutoModelForMaskedLM",
"AlbertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
629e14ba6daf7662067d4ec2 | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | cardiffnlp/tweet-topic-21-multi | cardiffnlp | null | 1,859 | 2,039,591 | False | 2022-06-06T14:52:42Z | 2023-05-28T04:56:09Z | transformers | 68 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 8b1867b06d8756ccc132f58b5a99666c22d1a4e7 | [
"transformers",
"pytorch",
"tf",
"roberta",
"text-classification",
"en",
"dataset:cardiffnlp/tweet_topic_multi",
"arxiv:2209.09824",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": fa... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["cardiffnlp/tweet_topic_multi"], "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["f1", "accuracy"], "model_name": null, "pipeline_tag": "text-classification", "tags": null, "widget": [{"text": "It i... | # tweet-topic-21-multi
This model is based on a [TimeLMs](https://github.com/cardiffnlp/timelms) language model trained on ~124M tweets from January 2018 to December 2021 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m)), and finetuned for multi-label topic classification on a corpus of 11... | null | [
"mit"
] | [
"cardiffnlp/tweet_topic_multi"
] | [
"en"
] | null | null | [
"f1",
"accuracy"
] | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65302d76800ac851b9aea2ba | 6a4a07acf6212099 | 625319e7b610da21 | 128,001 | Unigram | null | true | true | true | true | 5 | NFKC | Metaspace | Metaspace | [
"NFKC"
] | [
"Metaspace"
] | [
"Metaspace"
] | 229 | sdadas/mmlw-retrieval-roberta-large | sdadas | null | 217,686 | 3,066,970 | False | 2023-10-18T19:09:42Z | 2026-02-12T11:05:46Z | sentence-transformers | 14 | 0 | null | sentence-similarity | {"parameters": {"F16": 434961408}, "total": 434961408} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"added_tokens.json",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"uni... | 2b7408433ce38eecf58be7089591722c6f295edf | [
"sentence-transformers",
"pytorch",
"safetensors",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"information-retrieval",
"pl",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaModel"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "pl", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similari... | <h1 align="center">MMLW-retrieval-roberta-large</h1>
MMLW (muszę mieć lepszą wiadomość) are neural text encoders for Polish.
This model is optimized for information retrieval tasks. It can transform queries and passages to 1024 dimensional vectors.
The model was developed using a two-step procedure:
- In the first s... | null | [
"apache-2.0"
] | null | [
"pl"
] | 434,961,408 | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
669b777af1356d2d96d410d3 | 1f077a9a0005c931 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 230 | Alibaba-NLP/gte-multilingual-reranker-base | Alibaba-NLP | null | 265,947 | 3,224,377 | False | 2024-07-20T08:38:18Z | 2025-07-05T01:38:02Z | sentence-transformers | 175 | 1 | null | text-ranking | {"parameters": {"F16": 305959681}, "total": 305959681} | [
".gitattributes",
"README.md",
"config.json",
"images/mgte-reranker.png",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 8215cf04918ba6f7b6a62bb44238ce2953d8831c | [
"sentence-transformers",
"safetensors",
"new",
"text-classification",
"transformers",
"text-embeddings-inference",
"text-ranking",
"custom_code",
"af",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"eu",
"fa",
"fi",
"... | null | {"architectures": ["NewForSequenceClassification"], "auto_map": {"AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig", "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM", "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel", "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.N... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
"pipeline_tag": "text-classification",
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["af", "ar", "az", "be", "bg", "bn", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "eu", "fa", "fi", "fr", "gl", "gu", "he", "hi", "hr", "ht", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ky", "lo", "lt",... | null | null | [
"apache-2.0"
] | null | [
"af",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"gl",
"gu",
"he",
"hi",
"hr",
"ht",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ky",
"lo... | 305,959,681 | null | null | [
"Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
"new",
"NewForSequenceClassification",
"AutoModelForSequenceClassification"
] | [
"text-ranking",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17b61f | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | google/electra-large-discriminator | google | null | 32,316 | 2,067,668 | False | 2022-03-02T23:29:05Z | 2024-02-29T10:20:21Z | transformers | 16 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | c13c3df7efadc2162f42588bd28eb4e187d602a5 | [
"transformers",
"pytorch",
"tf",
"jax",
"electra",
"pretraining",
"en",
"arxiv:1406.2661",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["ElectraForPreTraining"], "model_type": "electra", "tokenizer_config": {}} | {
"auto_model": "AutoModelForPreTraining",
"custom_class": null,
"pipeline_tag": "pretraining",
"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, "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | ## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" inp... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"ElectraForPreTraining",
"AutoModelForPreTraining",
"electra"
] | [
"pretraining"
] | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.