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Play with the model on the [LLaVA OneVision Chat](https://llava-onevision.lmms-lab.com/).
## Table of Contents
1. [Model Summary](##model-summary)
2. [Use](##use)
3. [Limitations](##limitations)
4. [Training](##training)
5. [License... | null | [
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ZySec-7B, stands as a pivotal innovation for security professionals, leveraging the advanced capabilities of HuggingFace's Zephyr language model series. This AI model is crafted to be an omnipresent cybersecurity ally, offering on-demand, expert guidance in cybersecurity issues. Picture ZySec-7B as an ever-... | null | [
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- Problem type: Multi-class Classification
- Model ID: 940131041
- CO2 Emissions (in grams): 0.03330651014155927
## Validation Metrics
- Loss: 0.3505457043647766
- Accuracy: 0.9263261296660118
- Macro F1: 0.9268371013605569
- Micro F1: 0.9263261296660118
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should probably proofread and complete it, then remove this comment. -->
# sapbert-from-pubmedbert-squad2
This model is a fine-tuned version of [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://hugg... | null | null | [
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] | null | {"architectures": ["DPRQuestionEncoder"], "model_type": "dpr", "tokenizer_config": {}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["vblagoje/lfqa"], "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ## Introduction
The question encoder model based on [DPRQuestionEncoder](https://huggingface.co/docs/transformers/master/en/model_doc/dpr#transformers.DPRQuestionEncoder) architecture. It uses the transformer's pooler outputs as question representations. See [blog post](https://towardsdatascience.com/long-form-qa-beyon... | null | [
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Lynx is an open-source hallucination evaluation model. Patronus-Lynx-8B-Instruct-v1.1 was trained on a mix of datasets including CovidQA, PubmedQA, DROP, RAGTruth.
The datasets contain a mix of hand-annotated and synthetic data. The maximum sequence length is 128000 tokens.
## Model Detail... | null | [
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### Usage
Here's an example of performing inference using the model with [FastEmbed](https://github.... | null | [
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],
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<!-- header start -->
<!-- 200823 -->
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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"apache-2.0"
] | null | [
"en"
] | 141,306,627 | null | null | [
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"text"
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] |
68780a389b793690a76ea35e | b3310d158ab6e0dc | 66be3297e0980489 | 28,895 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 409 | OpenMed/OpenMed-NER-OrganismDetect-PubMed-v2-109M | OpenMed | null | 12,746 | 243,769 | False | 2025-07-16T20:23:20Z | 2025-08-05T09:39:39Z | transformers | 0 | 0 | null | token-classification | {"parameters": {"BF16": 108893955}, "total": 108893955} | [
".gitattributes",
"README.md",
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"model.safetensors",
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"special_tokens_map.json",
"test_results.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | c553f5f78042c5ff98f5f0b7832e048539a2e1a1 | [
"transformers",
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"named-entity-recognition",
"biomedical-nlp",
"species-recognition",
"taxonomy",
"organism-identification",
"biodiversity",
"species",
"en",
"arxiv:2508.01630",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForTokenClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForTokenClassification",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["token-classification", "named-entity-recognition", "biomedical-nlp", "transform... | null | null | [
"apache-2.0"
] | null | [
"en"
] | 108,893,955 | null | null | [
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687815a9df827e82a9a860e5 | 6a67cbd79bab1e59 | cf1efbe71ef28fc9 | 50,368 | BPE | 50,009 | true | true | true | true | 116 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 395 | OpenMed/OpenMed-NER-BloodCancerDetect-ModernClinical-149M | OpenMed | null | 12,552 | 243,447 | False | 2025-07-16T21:12:09Z | 2025-08-05T09:43:11Z | transformers | 0 | 0 | null | token-classification | {"parameters": {"BF16": 149607171}, "total": 149607171} | [
".gitattributes",
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] | ddca214d2d3335df42600e76edcc4c05cd7485bc | [
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"leukemia",
"hematology",
"cancer",
"clinical-medicine",
"cl",
"en",
"arxiv:2508.01630",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["ModernBertForTokenClassification"], "model_type": "modernbert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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687813c74a0209cc20db286a | 87ec02145bad27f5 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 412 | OpenMed/OpenMed-NER-AnatomyDetect-SnowMed-568M | OpenMed | null | 12,580 | 243,345 | False | 2025-07-16T21:04:07Z | 2025-08-05T09:42:41Z | transformers | 0 | 0 | null | token-classification | {"parameters": {"BF16": 566708227}, "total": 566708227} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
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"sentencepiece.bpe.model",
"special_tokens_map.json",
"test_results.json",
"tokenizer.json",
"tokenizer_config.json"
] | f897b09e87f654461082cb7d18e52acb988aa0bf | [
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"named-entity-recognition",
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"anatomical-entity-recognition",
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"anatomy",
"healthcare",
"en",
"arxiv:2508.01630",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["XLMRobertaForTokenClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["token-classification", "named-entity-recognition", "biomedical-nlp", "transform... | null | null | [
"apache-2.0"
] | null | [
"en"
] | 566,708,227 | null | null | [
"XLMRobertaForTokenClassification",
"AutoModelForTokenClassification",
"xlm-roberta"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17f1f5 | 30094a1eabdbef67 | db117e9883c4d0ab | 50,257 | BPE | 50,000 | false | true | true | true | 1 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 4 | r3dhummingbird/DialoGPT-medium-joshua | r3dhummingbird | null | 25 | 187,727 | False | 2022-03-02T23:29:05Z | 2023-03-17T16:58:25Z | transformers | 17 | 0 | null | text-generation | {"parameters": {"F32": 354823168, "U8": 25165824}, "total": 379988992} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | f5c1f7e44e3cbd35f3127b6d95cf807cdcf50094 | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "tokenizer_config": {"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": fals... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["conversational"], "thumbnail": "https://raw.githubusercontent.com/RuolinZheng08/twewy-di... | # DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script ... | null | [
"mit"
] | null | null | 379,988,992 | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6878166539688a8e33f13084 | 6a67cbd79bab1e59 | cf1efbe71ef28fc9 | 50,368 | BPE | 50,009 | true | true | true | true | 116 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 395 | OpenMed/OpenMed-NER-BloodCancerDetect-ModernClinical-395M | OpenMed | null | 12,425 | 243,193 | False | 2025-07-16T21:15:17Z | 2025-08-05T09:43:26Z | transformers | 0 | 0 | null | token-classification | {"parameters": {"BF16": 395834371}, "total": 395834371} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"openmed_vs_sota_grouped_bars.png",
"special_tokens_map.json",
"test_results.json",
"tokenizer.json",
"tokenizer_config.json"
] | 20d5b88fe0b6f4e7da71b89d0ae4071b0abf0bdb | [
"transformers",
"safetensors",
"modernbert",
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"named-entity-recognition",
"biomedical-nlp",
"leukemia",
"hematology",
"cancer",
"clinical-medicine",
"cl",
"en",
"arxiv:2508.01630",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["ModernBertForTokenClassification"], "model_type": "modernbert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["token-classification", "named-entity-recognition", "biomedical-nlp", "transform... | null | null | [
"apache-2.0"
] | null | [
"en"
] | 395,834,371 | null | null | [
"AutoModelForTokenClassification",
"ModernBertForTokenClassification",
"modernbert"
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"token-classification"
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"text"
] | [
"text"
] | [
"logits"
] |
63e2617f8d8cda7a87e2c005 | 8e4e571045f91525 | 0b1ca067fdcf74b8 | 50,266 | BPE | 50,000 | false | true | true | true | 3 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 82 | Salesforce/blip2-opt-6.7b | Salesforce | null | 34,679 | 333,181 | False | 2023-02-07T14:34:39Z | 2025-02-03T06:36:09Z | transformers | 80 | 0 | null | image-text-to-text | {"parameters": {"F32": 7752869376}, "total": 7752869376} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"preprocess... | d19ab3496c47af64cbc5ce94b214898910481cd1 | [
"transformers",
"pytorch",
"safetensors",
"blip-2",
"visual-question-answering",
"vision",
"image-to-text",
"image-captioning",
"image-text-to-text",
"en",
"arxiv:2301.12597",
"license:mit",
"region:us"
] | null | {"architectures": ["Blip2ForConditionalGeneration"], "model_type": "blip-2", "tokenizer_config": {"bos_token": "</s>", "eos_token": "</s>", "pad_token": "<pad>", "unk_token": "</s>"}} | {
"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, OPT-6.7b, pre-trained only
BLIP-2 model, leveraging [OPT-6.7b](https://huggingface.co/facebook/opt-6.7b) (a large language model with 6.7 billion parameters).
It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.... | null | [
"mit"
] | null | [
"en"
] | 7,752,869,376 | null | null | [
"Blip2ForConditionalGeneration",
"AutoModelForVisualQuestionAnswering",
"blip-2"
] | [
"image-text-to-text",
"visual-question-answering",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text",
"logits"
] |
636920b1d322a76e1ea3cf61 | ea197a153967759f | 1fcd51277d003941 | 50,000 | BPE | 48,016 | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | BPEDecoder | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"BPEDecoder"
] | 515 | clarin-pl/FastPDN | clarin-pl | null | 4,571 | 212,628 | False | 2022-11-07T15:13:53Z | 2023-05-26T08:40:16Z | transformers | 10 | 0 | null | token-classification | {"parameters": {"I64": 514, "F32": 123979173}, "total": 123979687} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 0fcce46a197decff51f11a327a6509b9a5e4c95f | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"ner",
"pl",
"dataset:clarin-pl/kpwr-ner",
"license:cc-by-4.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForTokenClassification"], "model_type": "bert", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["clarin-pl/kpwr-ner"], "eval_results": null, "language": "pl", "library_name": null, "license": "cc-by-4.0", "license_name": null, "license_link": null, "metrics": ["f1", "accuracy", "precision", "recall"], "model_name": null, "pipeline_tag": null, "tags": ["ner"], "widget": [{"text": ... | # FastPDN
FastPolDeepNer is model for Named Entity Recognition, designed for easy use, training and configuration. The forerunner of this project is [PolDeepNer2](https://gitlab.clarin-pl.eu/information-extraction/poldeepner2). The model implements a pipeline consisting of data processing and training using: hydra, py... | null | [
"cc-by-4.0"
] | [
"clarin-pl/kpwr-ner"
] | [
"pl"
] | 123,979,687 | null | [
"f1",
"accuracy",
"precision",
"recall"
] | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6533828631e73689f3e9035a | 2b848dc3d13f83c1 | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 740 | webbigdata/ALMA-7B-Ja-V2 | webbigdata | null | 782 | 191,514 | False | 2023-10-21T07:49:26Z | 2024-11-03T05:03:36Z | transformers | 20 | 0 | null | text-generation | {"parameters": {"BF16": 6738415616}, "total": 6738415616} | [
".gitattributes",
"1085",
"README.md",
"added_tokens.json",
"config.json",
"generation_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.bi... | 715f4158737d771a746314670b93e94f39c4eecd | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"ja",
"en",
"de",
"is",
"zh",
"cs",
"arxiv:2309.11674",
"license:llama2",
"text-generation-inference",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<unk>", "unk_token": "<unk>", "use_default_system_prompt": true}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ja", "en", "de", "is", "zh", "cs"], "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "inference": false} | # New Translation model released.
[C3TR-Adapter](https://huggingface.co/webbigdata/C3TR-Adapter) is the QLoRA adapter for google/gemma-7b.
Despite the 4-bit quantization, the memory GPU requirement has increased to 8.1 GB.
However, it is possible to run it with the free version of Colab and the performance is much ... | null | [
"llama2"
] | null | [
"ja",
"en",
"de",
"is",
"zh",
"cs"
] | 6,738,415,616 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
664ac5c0c88927e6ddc48323 | 6e6a83dc4b556c95 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 111 | failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 | failspy | null | 10,007 | 235,942 | False | 2024-05-20T03:38:40Z | 2024-05-30T12:39:03Z | transformers | 62 | 1 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"... | 85a25be002841fe738a5267b6806473f36f86715 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:llama3",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|e... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "llama3", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # Llama-3-8B-Instruct-abliterated-v3 Model Card
[My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb)
This is [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-... | null | [
"llama3"
] | null | null | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65bd4ea854ab5eb7b67fb402 | 83c1b42ea7d45c79 | b1199e553042ca3b | 100,016 | BPE | 99,757 | true | true | true | true | 16 | Sequence | Sequence | ByteLevel | [] | [
"Split",
"Split",
"Split",
"Split",
"Split",
"Digits",
"ByteLevel"
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## Natural-SQL-7B is a model with very strong performance in Text-to-SQL instructions, has an excellent understanding of complex questions, and outperforms models of the same size in its space.
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We have a free Google Colab Tesla T4 notebook for Mistral Nemo 12b here: https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing
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} | null | [DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization](https://arxiv.org/abs/2109.02492).
## Introduction
DialogLED is a pre-trained model for long dialogue understanding and summarization. It builds on the Longformer-Encoder-Decoder (LED) architecture and uses window-based denoising as the pr... | null | null | null | null | null | null | null | [
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["token-classification", "named-entity-recognition", "biomedical-nlp", "transform... | null | null | [
"apache-2.0"
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62880ebf138c31f57a575a3b | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | arampacha/roberta-tiny | arampacha | null | 2,299 | 196,522 | False | 2022-05-20T21:57:19Z | 2022-05-20T22:07:50Z | transformers | 2 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | f55131409ab048f087bf793ad4009b8a463e8a7b | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | null | # roberta-tiny
Tiny untrained model for testing purposes | null | null | null | null | null | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62489157d43f923e55535854 | 6a321bbfacbb8909 | 30c0a02ad4ef2b27 | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 742 | UrukHan/t5-russian-summarization | UrukHan | null | 1,916 | 193,440 | False | 2022-04-02T18:09:27Z | 2023-04-05T10:11:59Z | transformers | 45 | 1 | [{"name": "t5-russian-summarization", "results": []}] | null | {"parameters": {"F32": 222903552}, "total": 222903552} | [
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"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",
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"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": "UrukHan/wav2vec2-russian", "eval_results": [], "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": "t5-russian-summarization", "pipeline_tag": null, "tags": ["generated_from_trainer"], "widget": [{"text": "... | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
---
# t5-russian-summarization
---
модель для исправление текста из распознаного аудио. моя модлеь для распознования аудио https://... | null | null | [
"UrukHan/wav2vec2-russian"
] | null | 222,903,552 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
621ffdc136468d709f179878 | 85b7f3e69e8fd98f | d8d37bdb4858cc2b | 50,265 | BPE | 50,020 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 743 | benjamin/roberta-base-wechsel-german | benjamin | null | 1,279 | 242,139 | False | 2022-03-02T23:29:05Z | 2023-05-30T09:55:22Z | transformers | 7 | 0 | null | fill-mask | {"parameters": {"I64": 514, "F32": 124697433}, "total": 124697947} | [
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"README.md",
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"merges.txt",
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"pytorch_model.bin",
"rng_state_0.pth",
"rng_state_1.pth",
"rng_state_2.pth",
"rng_state_3.pth",
"rng_state_4.pth",
"rng_state_5.pth",
"rng_state_6.pth",
"rng_state_7.pth",
"scheduler.pt",
... | 80fedfc29bd6057f1dc40cd47a7e2327e0afc5bb | [
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"fill-mask",
"de",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForMaskedLM"], "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": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": f... | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "de", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # roberta-base-wechsel-german
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: https://github.com/CPJKU/wechsel
And the paper here: https://aclanthology.org/2022.naacl-main.293/
## Performance
### RoBERTa
| Mod... | null | [
"mit"
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#### Model description
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} | {"datasets": ["tner/mit_movie_trivia"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/roberta-large-mit-movie-trivia", "results": [{"task":... | # tner/roberta-large-mit-movie-trivia
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/mit_movie_trivia](https://huggingface.co/datasets/tner/mit_movie_trivia) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter sea... | null | null | [
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] | null | null | null | [
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should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None ... | null | [
"apache-2.0"
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] | null | {"architectures": ["DistilBertForMaskedLM"], "model_type": "distilbert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "sentence-transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sent... | # multi-qa-distilbert-dot-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search,... | null | null | null | [
"en"
] | 66,362,880 | null | null | [
"distilbert",
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] | [
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] | [
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] | [
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"ByteLevel"
] | [
"ByteLevel"
] | 746 | OpenAssistant/oasst-rm-2.1-pythia-1.4b-epoch-2.5 | OpenAssistant | null | 1,792 | 188,621 | False | 2023-04-11T11:14:25Z | 2023-05-24T14:10:00Z | transformers | 31 | 0 | null | null | null | [
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"endpoints_compatible",
"region:us"
] | null | {"architectures": ["GPTNeoXRewardModel"], "model_type": "gpt_neox_reward_model", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"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} | # Pythia 1.4B Based Reward Model
- base model: [andreaskoepf/pythia-1.4b-gpt4all-pretrain](https://huggingface.co/andreaskoepf/pythia-1.4b-gpt4all-pretrain)
- wandb: https://wandb.ai/open-assistant/reward-model/runs/kadgqj65
- checkpoint: 10k steps
Compute was generously provided by [Stability AI](https://stability.... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"AutoModel",
"GPTNeoXRewardModel",
"gpt_neox_reward_model"
] | [
null
] | null | null | null |
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"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 747 | worachot-n/sentiment-V2-newdataset | worachot-n | null | 1 | 13 | False | 2024-06-23T20:33:27Z | 2024-06-23T20:33:46Z | transformers | 0 | 0 | null | text-classification | {"parameters": {"F32": 105246723}, "total": 105246723} | [
".gitattributes",
"config.json",
"model.safetensors",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | d3bdcdd87623bc77604081cee0eebd70fad98fc3 | [
"transformers",
"safetensors",
"camembert",
"text-classification",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["CamembertForSequenceClassification"], "model_type": "camembert", "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"
} | null | null | null | null | null | null | 105,246,723 | null | null | [
"AutoModelForSequenceClassification",
"camembert",
"CamembertForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
624d065d1e057afa296129ba | 0060aa4b889b944c | 3565054b8b174bb6 | 50,518 | BPE | 50,001 | false | true | true | true | 258 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 528 | facebook/incoder-6B | facebook | null | 216 | 186,323 | False | 2022-04-06T03:17:49Z | 2023-01-24T17:06:34Z | transformers | 80 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model.bin",
"pytorch_model.bin.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 18c1a34c6f3c34f07b7a39b1ca4d07777fb9465c | [
"transformers",
"pytorch",
"xglm",
"text-generation",
"code",
"python",
"javascript",
"arxiv:2204.05999",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["XGLMForCausalLM"], "model_type": "xglm", "tokenizer_config": {"bos_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["code", "python", "javascript"]} | # InCoder 6B
A 6B parameter decoder-only Transformer model trained on code using a causal-masked objective, which allows inserting/infilling code as well as standard left-to-right generation.
The model was trained on public open-source repositories with a permissive, non-copyleft, license (Apache 2.0, MIT, BSD-2 or B... | null | [
"cc-by-nc-4.0"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"xglm",
"XGLMForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66273226875a989520b947c1 | cb3f886e13ad7505 | 1f1d26e73b02d1f3 | 49,152 | BPE | 48,891 | false | true | false | true | 19 | null | Sequence | ByteLevel | null | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 70 | ibm-granite/granite-3b-code-base-2k | ibm-granite | null | 37,820 | 460,620 | False | 2024-04-23T03:59:34Z | 2024-09-02T05:39:26Z | transformers | 38 | 0 | [{"name": "granite-3b-code-base-2k", "results": [{"task": {"type": "text-generation"}, "dataset": {"type": "mbpp", "name": "MBPP"}, "metrics": [{"name": "pass@1", "type": "pass@1", "value": 36, "veriefied": false, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"type": "evalplus/mbppplus", "name... | text-generation | {"parameters": {"BF16": 3482503680}, "total": 3482503680} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
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"special_tokens_map.json",
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"tokenizer_config.json"
] | 70ebbeca304e202a08616555f31fdd41de6cb752 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"granite",
"dataset:codeparrot/github-code-clean",
"dataset:bigcode/starcoderdata",
"dataset:open-web-math/open-web-math",
"dataset:math-ai/StackMathQA",
"arxiv:2405.04324",
"license:apache-2.0",
"model-index",
"text-genera... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "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"
} | {"datasets": ["codeparrot/github-code-clean", "bigcode/starcoderdata", "open-web-math/open-web-math", "math-ai/StackMathQA"], "library_name": "transformers", "license": "apache-2.0", "metrics": ["code_eval"], "pipeline_tag": "text-generation", "tags": ["code", "granite"], "inference": false, "model-index": [{"name": "g... | 
# Granite-3B-Code-Base-2K
## Model Summary
**Granite-3B-Code-Base-2K** is a decoder-only code model designed for code generative tasks (e.g., code generation, code explanation, code fixing, etc.). It... | null | [
"apache-2.0"
] | [
"codeparrot/github-code-clean",
"bigcode/starcoderdata",
"open-web-math/open-web-math",
"math-ai/StackMathQA"
] | null | 3,482,503,680 | null | [
"code_eval"
] | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67324d3ec74a3af1ae38cd63 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | mlx-community/Qwen2.5-Coder-3B-Instruct-4bit | mlx-community | {
"models": [
{
"_id": "672b1f925963af3fe1693e28",
"id": "Qwen/Qwen2.5-Coder-3B-Instruct"
}
],
"relation": "quantized"
} | 2,619 | 189,777 | False | 2024-11-11T18:30:22Z | 2024-11-11T23:37:29Z | transformers | 1 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"model.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 3dd939c621c08e5753d5b89f35a2642cd83b98ca | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"codeqwen",
"chat",
"qwen",
"qwen-coder",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-Coder-3B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-Coder-3B-Instruct",
"license:other",
"text-generation-inference",
... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"bits": 4}, "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 ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-Coder-3B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": "qwen-research", "license_link": "https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null... | # mlx-community/Qwen2.5-Coder-3B-Instruct-4bit
The Model [mlx-community/Qwen2.5-Coder-3B-Instruct-4bit](https://huggingface.co/mlx-community/Qwen2.5-Coder-3B-Instruct-4bit) was converted to MLX format from [Qwen/Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct) using mlx-lm version **0.... | null | [
"other",
"qwen-research",
"https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f1792cf | 8885c44cb83ed94c | 8a01c02d4d0a401c | 119,547 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 748 | citizenlab/distilbert-base-multilingual-cased-toxicity | citizenlab | null | 19,392 | 256,019 | False | 2022-03-02T23:29:05Z | 2022-12-02T13:50:14Z | transformers | 22 | 0 | null | text-classification | null | [
".gitattributes",
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"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
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] | b4532a8b095d1886a7b5dff818331ecc88a855ae | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"nl",
"fr",
"pt",
"it",
"es",
"de",
"da",
"pl",
"af",
"dataset:jigsaw_toxicity_pred",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["jigsaw_toxicity_pred"], "eval_results": null, "language": ["en", "nl", "fr", "pt", "it", "es", "de", "da", "pl", "af"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": ["F1 Accuracy"], "model_name": null, "pipeline_tag": null, "tags": null... | # citizenlab/distilbert-base-multilingual-cased-toxicity
This is multilingual Distil-Bert model sequence classifier trained based on [JIGSAW Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) dataset.
## How to use it
```python
from transformers import pip... | null | null | [
"jigsaw_toxicity_pred"
] | [
"en",
"nl",
"fr",
"pt",
"it",
"es",
"de",
"da",
"pl",
"af"
] | null | null | [
"F1 Accuracy"
] | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
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"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 418 | OpenMed/OpenMed-NER-ChemicalDetect-BioClinical-108M | OpenMed | null | 14 | 222,301 | False | 2025-07-16T20:44:33Z | 2025-08-05T09:41:14Z | transformers | 0 | 0 | null | token-classification | {"parameters": {"BF16": 107721987}, "total": 107721987} | [
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] | e891a568b358cb5487e43a855d36c79a1c19af0d | [
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"chemistry",
"chem",
"en",
"arxiv:2508.01630",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForTokenClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForTokenClassification",
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"pipeline_tag": "token-classification",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["token-classification", "named-entity-recognition", "biomedical-nlp", "transform... | null | null | [
"apache-2.0"
] | null | [
"en"
] | 107,721,987 | null | null | [
"AutoModelForTokenClassification",
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"bert"
] | [
"token-classification"
] | [
"text"
] | [
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] | [
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"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 291 | Silly-Machine/TuPy-Bert-Large-Binary-Classifier | Silly-Machine | {
"models": [
{
"_id": "621ffdc136468d709f17e62a",
"id": "neuralmind/bert-large-portuguese-cased"
}
],
"relation": "finetune"
} | 18 | 184,695 | False | 2023-12-28T18:46:11Z | 2023-12-29T14:28:24Z | transformers | 1 | 0 | [{"name": "Yi-34B", "results": [{"task": {"type": "text-classfication"}, "dataset": {"name": "TuPyE-Dataset", "type": "Silly-Machine/TuPyE-Dataset"}, "metrics": [{"type": "accuracy", "value": 0.907, "name": "Accuracy", "verified": false}, {"type": "f1", "value": 0.903, "name": "F1-score", "verified": false}, {"type": "... | text-classification | {"parameters": {"F32": 334398466}, "total": 334398466} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 10df29f2462178d88619640865ca083321752303 | [
"transformers",
"safetensors",
"bert",
"text-classification",
"pt",
"dataset:Silly-Machine/TuPyE-Dataset",
"base_model:neuralmind/bert-large-portuguese-cased",
"base_model:finetune:neuralmind/bert-large-portuguese-cased",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_co... | 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": "neuralmind/bert-large-portuguese-cased", "datasets": ["Silly-Machine/TuPyE-Dataset"], "language": ["pt"], "license": "mit", "pipeline_tag": "text-classification", "widget": [{"text": "Bom dia, flor do dia!!"}], "model-index": [{"name": "Yi-34B", "results": [{"task": {"type": "text-classfication"}, "data... | ## Introduction
TuPy-Bert-Large-Binary-Classifier is a fine-tuned BERT model designed specifically for binary classification of hate speech in Portuguese.
Derived from the [BERTimbau base](https://huggingface.co/neuralmind/bert-large-portuguese-cased),
TuPy-Bert-Large-Binary-Classifier is a refined solution for add... | null | [
"mit"
] | [
"Silly-Machine/TuPyE-Dataset"
] | [
"pt"
] | 334,398,466 | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
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"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | sentence-transformers/facebook-dpr-ctx_encoder-multiset-base | sentence-transformers | null | 216 | 191,296 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:19:59Z | sentence-transformers | 6 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 109482240}, "total": 109482752} | [
".gitattributes",
"1_Pooling/config.json",
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"config.json",
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"model.safetensors",
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"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... | 2993763396662ccedc92937527292fa6074cf875 | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/facebook-dpr-ctx_encoder-multiset-base
This is a port of the [DPR Model](https://github.com/facebookresearch/DPR) to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic... | null | [
"apache-2.0"
] | null | null | 109,482,752 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
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"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | h2oai/h2ogpt-4096-llama2-70b-chat | h2oai | null | 136 | 185,147 | False | 2023-08-09T17:19:18Z | 2023-08-24T18:35:23Z | transformers | 21 | 0 | null | text-generation | {"parameters": {"F16": 68976648192, "F32": 5120}, "total": 68976653312} | [
".gitattributes",
"LICENSE.txt",
"MODEL_CARD.md",
"README.md",
"Responsible-Use-Guide.pdf",
"USE_POLICY.md",
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... | 2b035aa41adcd583d5c7a67aad93623c2e79317d | [
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"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": false, "rstrip... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["facebook", "meta", "pytorch", "llama", "llama-2", "h2ogpt"], "inferenc... | h2oGPT clone of [Meta's Llama 2 70B Chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf).
Try it live on our [h2oGPT demo](https://gpt.h2o.ai) with side-by-side LLM comparisons and private document chat!
See how it compares to other models on our [LLM Leaderboard](https://evalgpt.ai/)!
See more at [H2O.ai](h... | null | [
"llama2"
] | null | [
"en"
] | 68,976,653,312 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
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"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f180623 | 466acf8e536af208 | 31ed7686ae4ceb62 | 51,200 | BPE | 21,226 | true | true | false | true | 375 | Sequence | Sequence | Metaspace | [
"NFKC",
"BertNormalizer"
] | [
"Metaspace"
] | [
"Metaspace"
] | 749 | skt/ko-gpt-trinity-1.2B-v0.5 | skt | null | 2,114 | 189,821 | False | 2022-03-02T23:29:05Z | 2025-09-27T09:50:49Z | transformers | 52 | 0 | null | text-generation | {"parameters": {"F32": 1162556160, "U8": 25165824}, "total": 1187721984} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 2c9a93e78a61a52de058b66246f6acbd7b7c33b0 | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"gpt3",
"ko",
"license:cc-by-nc-sa-4.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "tokenizer_config": {}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "ko", "library_name": null, "license": "cc-by-nc-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["gpt3"]} | # Ko-GPT-Trinity 1.2B (v0.5)
## Model Description
Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture. Ko-GPT-Trinity refers to the class of models, while 1.2B represents the number of parameters of this particular pre-trained model.
### Model date
May 2021
#... | null | [
"cc-by-nc-sa-4.0"
] | null | [
"ko"
] | 1,187,721,984 | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
68a677062fc955d300dc18f9 | d9abafc9e84f6e37 | 298fb2136b26cee9 | 131,072 | BPE | 269,443 | false | true | true | true | 1,000 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 482 | nvidia/NVIDIA-Nemotron-Nano-12B-v2 | nvidia | {
"models": [
{
"_id": "689d3c56e7b7e599336b0fca",
"id": "nvidia/NVIDIA-Nemotron-Nano-12B-v2-Base"
}
],
"relation": "finetune"
} | 26,930 | 380,795 | False | 2025-08-21T01:31:50Z | 2025-11-25T00:50:59Z | transformers | 160 | 3 | null | text-generation | null | [
".gitattributes",
"README.md",
"acc-vs-budget.png",
"bias.md",
"config.json",
"configuration_nemotron_h.py",
"explainability.md",
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"model-00002-of-00006.safetensors",
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"model-00004-of-00006.safetens... | f428df0ec725fed457b89cfca54dc26500fb88c1 | [
"transformers",
"safetensors",
"nvidia",
"pytorch",
"text-generation",
"conversational",
"en",
"es",
"fr",
"de",
"it",
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"dataset:nvidia/Nemotron-Post-Training-Dataset-v1",
"dataset:nvidia/Nemotron-Post-Training-Dataset-v2",
"dataset:nvidia/Nemotron-Pretraining-Dataset-sample",
"da... | null | {"tokenizer_config": {"bos_token": "<s>", "chat_template": "{%- set ns = namespace(enable_thinking=true) %}{%- for message in messages -%}{%- set content = message['content'] -%}{%- if message['role'] == 'user' or message['role'] == 'system' -%}{%- if '/think' in content -%}{%- set ns.enable_thinking = true -%}{%- elif... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": ["nvidia/NVIDIA-Nemotron-Nano-12B-v2-Base"], "datasets": ["nvidia/Nemotron-Post-Training-Dataset-v1", "nvidia/Nemotron-Post-Training-Dataset-v2", "nvidia/Nemotron-Pretraining-Dataset-sample", "nvidia/Nemotron-CC-v2", "nvidia/Nemotron-CC-Math-v1", "nvidia/Nemotron-Pretraining-SFT-v1"], "eval_results": nul... | # NVIDIA-Nemotron-Nano-12B-v2
**Model Developer:** NVIDIA Corporation
**Model Dates:**
June 2025 \- August 2025
**Data Freshness:**
September 2024
The pretraining data has a cutoff date of September 2024.
## Model Overview
NVIDIA-Nemotron-Nano-12B-v2 is a large language model (LLM) trained from scratch by NVIDI... | null | [
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"nvidia-open-model-license",
"https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/"
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] | [
"en",
"es",
"fr",
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"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-Coder-14B | Qwen | {
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{
"_id": "66e6d08a5c06b7719cebd8ec",
"id": "Qwen/Qwen2.5-14B"
}
],
"relation": "finetune"
} | 23,726 | 267,313 | False | 2024-11-08T10:48:15Z | 2024-11-18T12:54:29Z | transformers | 68 | 0 | null | text-generation | {"parameters": {"BF16": 14770033664}, "total": 14770033664} | [
".gitattributes",
"LICENSE",
"README.md",
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"generation_config.json",
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"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"qwen",
"qwen-coder",
"codeqwen",
"conversational",
"en",
"arxiv:2409.12186",
"arxiv:2309.00071",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-14B",
"base_model:finetune:Qwen/Qwen2.5-14B",
"license:apache-2.0",
"text-... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Qwen/Qwen2.5-14B"], "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-14B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-ge... | # Qwen2.5-Coder-14B
## Introduction
Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings t... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-Coder-14B/blob/main/LICENSE"
] | null | [
"en"
] | 14,770,033,664 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
624444215c844bfbab8373a0 | 259a91b8aac6aa3c | be6dfd2a9a8f4151 | 30,522 | WordPiece | null | true | true | true | true | 5 | Sequence | Whitespace | WordPiece | [
"NFD",
"Lowercase",
"StripAccents"
] | [
"Whitespace"
] | [
"WordPiece"
] | 750 | batterydata/bde-cner-batteryonlybert-uncased-base | batterydata | null | 112 | 2,341,379 | False | 2022-03-30T11:50:57Z | 2022-05-31T15:12:29Z | transformers | 2 | 0 | null | token-classification | null | [
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"deploy:azure",
"region:us"
] | null | {"architectures": ["BertLstmCrfForTokenClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForTokenClassification",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | null | null | [
"mit"
] | null | null | null | null | null | [
"AutoModelForTokenClassification",
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6406b3d583b59abcddefab03 | b203dd1893a71a89 | d965e4a9140ecf72 | 21,128 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
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"BertPreTokenizer"
] | [
"WordPiece"
] | 266 | GanymedeNil/text2vec-base-chinese | GanymedeNil | null | 255 | 185,906 | False | 2023-03-07T03:47:33Z | 2024-06-25T03:13:25Z | transformers | 22 | 0 | null | sentence-similarity | null | [
".gitattributes",
"README.md",
"config.json",
"eval_results.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | f13ec0b6396814e1352f3d30fe80bb7079625777 | [
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"sentence-similarity",
"zh",
"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",
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"pipeline_tag": "feature-extraction",
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} | {"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": "sentence-similarity", "tags": ["text2vec", "feature-extraction", "sentence-similarity", "trans... | Based on the derivative model of https://huggingface.co/shibing624/text2vec-base-chinese, replace MacBERT with LERT, and keep other training conditions unchanged。
## News
2024-06-25 [text2vec-base-chinese](https://huggingface.co/GanymedeNil/text2vec-base-chinese-onnx) onnxruntime version. | null | [
"apache-2.0"
] | null | [
"zh"
] | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6627192a846c2a66a7ab0ecc | 6e6a83dc4b556c95 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 111 | beomi/Llama-3-Open-Ko-8B-Instruct-preview | beomi | null | 638 | 187,805 | False | 2024-04-23T02:12:58Z | 2024-05-02T01:49:20Z | transformers | 61 | 0 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
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"model-00002-of-00004.safetensors",
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"special_tokens_map.json",
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"... | d8c93440c5c0426f0127e2baf822ce5b60fa3a73 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"llama-3-ko",
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"en",
"ko",
"arxiv:2310.04799",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|e... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "ko"], "library_name": null, "license": "other", "license_name": "llama3", "license_link": "LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "llama... | ## Llama-3-Open-Ko-8B-Instruct-preview
> Update @ 2024.05.01: Pre-Release [Llama-3-KoEn-8B](https://huggingface.co/beomi/Llama-3-KoEn-8B-preview) model & [Llama-3-KoEn-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-KoEn-8B-Instruct-preview)
> Update @ 2024.04.24: Release [Llama-3-Open-Ko-8B model](https://... | null | [
"other",
"llama3",
"LICENSE"
] | null | [
"en",
"ko"
] | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6505b8949310ce8c4011a6cd | 46874ac0bd0e2129 | ade5e378ce727196 | 32,001 | BPE | 61,249 | true | false | true | true | 4 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 617 | TinyLlama/TinyLlama-1.1B-Chat-v0.1 | TinyLlama | null | 2,524 | 196,108 | False | 2023-09-16T14:15:48Z | 2023-09-26T10:38:09Z | transformers | 56 | 0 | null | text-generation | {"parameters": {"F32": 1100052480}, "total": 1100052480} | [
".gitattributes",
"README.md",
"added_tokens.json",
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"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
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"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:timdettmers/openassistant-guanaco",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:u... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": false, "rstrip... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["cerebras/SlimPajama-627B", "bigcode/starcoderdata", "timdettmers/openassistant-guanaco"], "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, "ta... | <div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-0... | null | [
"apache-2.0"
] | [
"cerebras/SlimPajama-627B",
"bigcode/starcoderdata",
"timdettmers/openassistant-guanaco"
] | [
"en"
] | 1,100,052,480 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6254b750777c5f644a4067c1 | 83bf28f8b6767a2f | ae88e903e2b563a1 | 50,295 | BPE | 50,000 | false | true | true | true | 39 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 85 | Salesforce/codegen-2B-mono | Salesforce | null | 2,136 | 189,102 | False | 2022-04-11T23:18:40Z | 2025-01-31T20:26:18Z | transformers | 21 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
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"vocab.json"
] | 0b1d0b33f26b1416f66f0ecf07cf6a29438c95ea | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"license:bsd-3-clause",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["CodeGenForCausalLM"], "model_type": "codegen", "tokenizer_config": {"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "bsd-3-clause", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # CodeGen (CodeGen-Mono 2B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savare... | null | [
"bsd-3-clause"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"codegen",
"CodeGenForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
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"NFC"
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"models": [
{
"_id": "66e98dd5899bdb384bd953b4",
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}
],
"relation": "quantized"
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"... | 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/Qwen2.5-1.5B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-AWQ/blob/main/LICENSE", "metrics": null, "model_name": null, "pipelin... | # Qwen2.5-1.5B-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 **mor... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-AWQ/blob/main/LICENSE"
] | null | [
"en"
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} | 5,413 | 212,958 | False | 2024-07-23T16:19:45Z | 2024-11-22T07:22:02Z | transformers | 32 | 0 | null | text-generation | null | [
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} | {"base_model": "meta-llama/Meta-Llama-3.1-70B-Instruct", "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", "faceboo... | # Finetune Llama 3.1, 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 | [
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"en"
] | null | null | null | [
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] | [
"BertPreTokenizer"
] | [
"WordPiece"
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} | null | Labels are based on Plutchik's model of emotions and may be combined:
 | null | null | null | null | null | null | null | [
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] | f0e0995eba801096ed04cb87931d96a8316871af | [
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] | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
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} | {"base_model": null, "datasets": ["togethercomputer/RedPajama-Data-1T", "OpenAssistant/oasst1", "databricks/databricks-dolly-15k"], "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": nu... | # RedPajama-INCITE-Chat-3B-v1
RedPajama-INCITE-Chat-3B-v1 was developed by Together and leaders from the open-source AI community including Ontocord.ai, ETH DS3Lab, AAI CERC, Université de Montréal, MILA - Québec AI Institute, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research gr... | null | [
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"en"
] | null | null | null | [
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"WhitespaceSplit",
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] | 64 | ERCDiDip/langdetect | ERCDiDip | null | 28 | 182,015 | False | 2022-11-25T08:29:03Z | 2023-03-27T15:02:39Z | transformers | 12 | 0 | null | text-classification | {"parameters": {"I64": 514, "F32": 278075177}, "total": 278075691} | [
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] | null | {"architectures": ["XLMRobertaForSequenceClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": "... | {
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"pipeline_tag": "text-classification",
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} | {"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, "tag": "text-classification", "widget": [{"text": "Inde dit salt du dun degelikes bi... | # XLM-RoBERTa (base) language-detection model (modern and medieval)
This model is a fine-tuned version of xlm-roberta-base on the [monasterium.net](https://www.icar-us.eu/en/cooperation/online-portals/monasterium-net/) dataset.
## Model description
On the top of this XLM-RoBERTa transformer model is a classification... | null | [
"mit"
] | null | null | 278,075,691 | null | null | [
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<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<d... | null | [
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"ByteLevel"
] | [
"ByteLevel"
] | 62 | sentence-transformers/nli-roberta-base-v2 | sentence-transformers | null | 1,315 | 188,084 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:27:33Z | sentence-transformers | 1 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 124645632}, "total": 124646146} | [
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... | f83f701a097e6fa75b956a3e601dde649ea818cc | [
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/nli-roberta-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 easy when you ha... | null | [
"apache-2.0"
] | null | null | 124,646,146 | null | null | [
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"AutoModel",
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"multimodal"
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"text"
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"ByteLevel"
] | [
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] | 751 | somosnlp-hackathon-2022/paraphrase-spanish-distilroberta | somosnlp-hackathon-2022 | null | 3,198 | 216,571 | False | 2022-03-30T17:58:23Z | 2022-04-02T18:33:17Z | sentence-transformers | 15 | 0 | null | sentence-similarity | null | [
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] | null | {"architectures": ["RobertaModel"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
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} | {"base_model": null, "datasets": ["hackathon-pln-es/parallel-sentences"], "eval_results": null, "language": ["es"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-ex... | # paraphrase-spanish-distilroberta
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.
We follow a **teacher-student** transfer learning approach to train an `bertin-roberta-... | null | null | [
"hackathon-pln-es/parallel-sentences"
] | [
"es"
] | null | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6750bb8c06ed9d6c74ba5c7a | 0becab10e3734e80 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 58 | ibm-granite/granite-embedding-107m-multilingual | ibm-granite | null | 34,426 | 294,648 | False | 2024-12-04T20:29:00Z | 2025-08-19T01:34:26Z | transformers | 48 | 0 | [{"name": "ibm-granite/granite-embedding-107m-multilingual", "results": [{"dataset": {"config": "en-ext", "name": "MTEB AmazonCounterfactualClassification (en-ext)", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205", "split": "test", "type": "mteb/amazon_counterfactual"}, "metrics": [{"type": "accuracy", "value": ... | sentence-similarity | {"parameters": {"BF16": 106994304}, "total": 106994304} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"model.onnx",
"model.safetensors",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | d6cffd338414d6a1c1f5decfad5fec62eebc90d5 | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"xlm-roberta",
"feature-extraction",
"language",
"granite",
"embeddings",
"multilingual",
"mteb",
"sentence-similarity",
"en",
"ar",
"cs",
"de",
"es",
"fr",
"it",
"ja",
"ko",
"nl",
"pt",
"zh",
"arxiv:2502.20204",
"ar... | 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"
} | {"language": ["en", "ar", "cs", "de", "es", "fr", "it", "ja", "ko", "nl", "pt", "zh"], "library_name": "transformers", "license": "apache-2.0", "pipeline_tag": "sentence-similarity", "tags": ["language", "granite", "embeddings", "multilingual", "mteb"], "model-index": [{"name": "ibm-granite/granite-embedding-107m-multi... | # Granite-Embedding-107m-multilingual
**Model Summary:**
Granite-Embedding-107M-Multilingual is a 107M parameter dense biencoder embedding model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 384 and is trained using a combinat... | null | [
"apache-2.0"
] | null | [
"en",
"ar",
"cs",
"de",
"es",
"fr",
"it",
"ja",
"ko",
"nl",
"pt",
"zh"
] | 106,994,304 | null | null | [
"XLMRobertaModel",
"AutoModel",
"xlm-roberta"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
62624bad02cd5952e012af2c | 191e06b04083279c | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 26 | sbcBI/sentiment_analysis | sbcBI | null | 9 | 181,229 | False | 2022-04-22T06:31:09Z | 2022-04-22T06:42:07Z | transformers | 6 | 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"
] | 2e9e3afe68478a6168a11adb6c6f1b741e00ae83 | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"exbert",
"en",
"dataset:Confidential",
"arxiv:1810.04805",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["Confidential"], "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"]} | # BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
b... | null | [
"apache-2.0"
] | [
"Confidential"
] | [
"en"
] | null | null | null | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6530f0f5fa9d8c83e68a45f1 | 60a6084d56838ff5 | 8b9ce24db12c4669 | 128,000 | BPE | 127,743 | false | true | true | true | 24 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 752 | LumiOpen/Poro-34B | LumiOpen | null | 1,652 | 191,093 | False | 2023-10-19T09:03:49Z | 2025-06-12T08:19:23Z | transformers | 118 | 0 | null | text-generation | {"parameters": {"BF16": 34216949760}, "total": 34216949760} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"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-00014.safetensors",
"model-0... | 14d8824c28d782fcd1cd9579ac06644f60e62450 | [
"transformers",
"safetensors",
"bloom",
"text-generation",
"fi",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:mc4",
"dataset:allenai/dolma",
"arxiv:2404.01856",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure... | null | {"architectures": ["BloomForCausalLM"], "model_type": "bloom", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["cerebras/SlimPajama-627B", "bigcode/starcoderdata", "mc4", "allenai/dolma"], "eval_results": null, "language": ["fi", "en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-... | <div align="center">
<img src="./poro-logo.png" width="200px">
</div>
# Poro 34B Model Card
Poro is a 34B parameter decoder-only transformer pretrained on Finnish, English and code. It was trained on 1 trillion tokens. Poro is a fully open source model and is made available under the Apache 2.0 License.
Poro was cr... | null | [
"apache-2.0"
] | [
"cerebras/SlimPajama-627B",
"bigcode/starcoderdata",
"mc4",
"allenai/dolma"
] | [
"fi",
"en"
] | 34,216,949,760 | null | null | [
"AutoModelForCausalLM",
"BloomForCausalLM",
"bloom"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6341ce77dd8853dc7e377d7f | 7a4cc4a6090329ff | ab8f40e5a89244c4 | 250,680 | BPE | 250,434 | false | true | true | true | 4 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 31 | bigscience/bloomz-1b7 | bigscience | null | 839 | 191,542 | False | 2022-10-08T19:24:39Z | 2023-05-27T17:26:25Z | transformers | 23 | 0 | [{"name": "bloomz-1b7", "results": [{"task": {"type": "Coreference resolution"}, "dataset": {"type": "winogrande", "name": "Winogrande XL (xl)", "config": "xl", "split": "validation", "revision": "a80f460359d1e9a67c006011c94de42a8759430c"}, "metrics": [{"type": "Accuracy", "value": 51.14, "verified": false}]}, {"task":... | text-generation | {"parameters": {"F16": 1722408960}, "total": 1722408960} | [
".gitattributes",
"README.md",
"config.json",
"logs/logs/xp3capmixnewcodelonglossseq/main_log.txt",
"logs/tensorboard/xp3capmixnewcodelonglossseq/events.out.tfevents.1665054289.jean-zay-iam31.826594.0",
"logs/tensorboard/xp3capmixnewcodelonglossseq/events.out.tfevents.1665054988.jean-zay-iam31.827552.0",
... | 4ea0141c75b2b96e17bc41f0d52c5368c879a281 | [
"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",
... | 1,722,408,960 | null | null | [
"AutoModelForCausalLM",
"BloomForCausalLM",
"bloom"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17cbd3 | b6a49271ca7583d0 | dc945455af795596 | 32,000 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 76 | jhgan/ko-sroberta-nli | jhgan | null | 943 | 188,621 | False | 2022-03-02T23:29:05Z | 2022-08-16T12:45:56Z | sentence-transformers | 9 | 0 | null | sentence-similarity | null | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"eval/similarity_evaluation_sts-dev_results.csv",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"similarity_evaluation_sts-test_results.csv",
"special_tokens_map.json",... | c4e15f24df2aceadfc931e2a57094726b2409861 | [
"sentence-transformers",
"pytorch",
"tf",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"ko",
"arxiv:2004.03289",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaModel"], "model_type": "roberta", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "bos_token": "[CLS]", "eos_token": "[SEP]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"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": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "tr... | # ko-sroberta-nli
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy whe... | null | null | null | [
"ko"
] | null | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
672f598e75014b0627f47bfc | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-Coder-3B-Instruct-AWQ | Qwen | {
"models": [
{
"_id": "672b1f925963af3fe1693e28",
"id": "Qwen/Qwen2.5-Coder-3B-Instruct"
}
],
"relation": "quantized"
} | 7,735 | 227,505 | False | 2024-11-09T12:46:06Z | 2024-11-18T12:53:55Z | transformers | 5 | 0 | null | text-generation | {"parameters": {"I32": 2774532096, "F16": 622571520}, "total": 3397103616} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 5d2659311fbbae1ec6ccc83e0fae9778bb469494 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"codeqwen",
"chat",
"qwen",
"qwen-coder",
"conversational",
"en",
"arxiv:2409.12186",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-Coder-3B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-Coder-3B-Instruct",
"license:othe... | 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-Coder-3B-Instruct"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": "qwen-research", "license_link": "https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": nu... | # Qwen2.5-Coder-3B-Instruct-AWQ
## Introduction
Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Co... | null | [
"other",
"qwen-research",
"https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | 3,397,103,616 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
62d2ec9b26213de379a359ee | fa3eb8b5ab2b6741 | 0bb73d739c8c3372 | 31,102 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 753 | dlicari/Italian-Legal-BERT | dlicari | null | 1,369 | 185,209 | False | 2022-07-16T16:51:39Z | 2023-08-28T17:54:43Z | transformers | 45 | 1 | null | fill-mask | {"parameters": {"I64": 512, "F32": 110729318}, "total": 110729830} | [
".gitattributes",
"ITALIAN_LEGAL_BERT.jpg",
"README.md",
"abbreviazioni.csv",
"config.json",
"model.safetensors",
"ner_it_legalbert.cfg",
"pytorch_model.bin",
"semantic_text_similarity.jpg",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.t... | 1dfd1e43de31ecdbb5c075d0bac6c89ca951c486 | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"it",
"license:afl-3.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "it", "library_name": null, "license": "afl-3.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"text": "Il [MASK] ha chiesto revocarsi l'obbligo di pagamento"}]} | <img src="https://huggingface.co/dlicari/Italian-Legal-BERT/resolve/main/ITALIAN_LEGAL_BERT.jpg" width="600"/>
<h1> ITALIAN-LEGAL-BERT:A pre-trained Transformer Language Model for Italian Law </h1>
ITALIAN-LEGAL-BERT is based on <a href="https://huggingface.co/dbmdz/bert-base-italian-xxl-cased">bert-base-italian-xxl... | null | [
"afl-3.0"
] | null | [
"it"
] | 110,729,830 | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
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