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<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
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This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese-paraphrase.
It maps sentences to a 768 dimensional dense vector space and can be used for tasks
like sentence embeddings, text matching or semantic search.
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CompactBioBERT is a distilled version of the [BioBERT](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2?text=The+goal+of+life+is+%5BMASK%5D.) model which is distilled for 100k training steps using a total batch size of 192 on the PubMed dataset.
# Distillation Procedure
This model has the s... | null | [
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<p style="margin-bottom: 0; margin-top: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/gemma-3-67d12b7e8816ec6efa7e4e5b">our collection</a> for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em>Unsloth's <a href="htt... | null | [
"gemma"
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"en"
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This model is the pretrained version of `Pix2Struct`, use this model for fine-tuning purposes only.
# Table of Contents
0. [TL;DR](#TL;DR)
1... | null | [
"apache-2.0"
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Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model `Vit-B-16Plus` can be retrieved via instructions found on `mlfoundations` [open_clip reposit... | null | null | null | [
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": ["mit"], "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["llama-2", "self-instruct", "distillation", "synthetic instruction"]} | # Model Card: Nous-Hermes-Llama2-13b
Compute provided by our project sponsor Redmond AI, thank you! Follow RedmondAI on Twitter @RedmondAI.
## Model Description
Nous-Hermes-Llama2-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Tekniu... | null | [
"mit"
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"en"
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"text-generation-inference",
"endpoints_compatible",
"deploy:azure",... | 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 and harmless... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-32B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/QwQ-32B-Preview/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": nul... | # QwQ-32B-Preview
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
**QwQ-32B-Preview** is an experimental research m... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/QwQ-32B-Preview/blob/main/LICENSE"
] | null | [
"en"
] | 32,763,876,352 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
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"text-generation"
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"text"
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66510ea233fc744153da38d3 | 479e0f2ad833455b | edc89d2336a1a9fc | 90 | WordPiece | null | true | true | true | false | 5 | Sequence | Sequence | null | [
"Lowercase"
] | [
"Split",
"Whitespace"
] | null | 310 | adibvafa/CodonTransformer | adibvafa | null | 40,010 | 1,741,435 | False | 2024-05-24T22:03:14Z | 2025-10-31T23:16:03Z | transformers | 8 | 0 | null | token-classification | {"parameters": {"F32": 89579610}, "total": 89579610} | [
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"token-classification",
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] | null | {"architectures": ["BigBirdForMaskedLM"], "model_type": "big_bird", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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**CodonTransformer** is the ultimate tool for codon optimization, transforming protein sequences into optimized DNA sequences specific for your target organisms. Whether you are a researcher or a practitioner in genetic engineerin... | null | [
"apache-2.0"
] | null | null | 89,579,610 | null | null | [
"big_bird",
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"BigBirdForMaskedLM"
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"text"
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674c45b4810801f2fa142dc3 | 4f91da2c156973cc | 0a1438da55eb2e11 | 102,400 | BPE | 101,782 | true | true | true | true | 362 | NFKC | Sequence | ByteLevel | [
"NFKC"
] | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 285 | LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct | LGAI-EXAONE | null | 66,699 | 1,689,684 | False | 2024-12-01T11:17:08Z | 2026-02-06T06:14:59Z | transformers | 184 | 0 | null | text-generation | {"parameters": {"F32": 2405327360}, "total": 2405327360} | [
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"LICENSE",
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"modeling_exaone.py",
"special... | ccce25bd39c141fe053e0bc75818a8f5fe962802 | [
"transformers",
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"lg-ai",
"exaone-3.5",
"conversational",
"custom_code",
"en",
"ko",
"arxiv:2412.04862",
"license:other",
"region:us"
] | null | {"architectures": ["ExaoneForCausalLM"], "auto_map": {"AutoConfig": "configuration_exaone.ExaoneConfig", "AutoModelForCausalLM": "modeling_exaone.ExaoneForCausalLM", "AutoModelForSequenceClassification": "modeling_exaone.ExaoneForSequenceClassification"}, "model_type": "exaone", "tokenizer_config": {"bos_token": "[BOS]... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_exaone.ExaoneForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "ko"], "library_name": "transformers", "license": "other", "license_name": "exaone", "license_link": "LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["lg-ai", "exaone", "exaone-3.5"]} | <p align="center">
<img src="assets/EXAONE_Symbol+BI_3d.png", width="300", style="margin: 40 auto;">
<br>
# EXAONE-3.5-2.4B-Instruct
## Introduction
We introduce EXAONE 3.5, a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released ... | null | [
"other",
"exaone",
"LICENSE"
] | null | [
"en",
"ko"
] | 2,405,327,360 | null | null | [
"AutoModelForCausalLM",
"exaone",
"modeling_exaone.ExaoneForCausalLM",
"ExaoneForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17d28b | 6b4bbb15a5aacab6 | dc945455af795596 | 32,000 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 107 | klue/roberta-large | klue | null | 16,363 | 1,357,017 | False | 2022-03-02T23:29:05Z | 2023-06-12T12:29:57Z | transformers | 62 | 0 | null | fill-mask | {"parameters": {"I64": 514, "F32": 336690432}, "total": 336690946} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 28d911204e9022eda172571ca8cc61eaffd942f7 | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"korean",
"klue",
"ko",
"arxiv:2105.09680",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForMaskedLM"], "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": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "ko", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["korean", "klue"], "mask_token": "[MASK]", "widget": [{"text": "\ub300\ud55c\ubbfc\uad6d\u... | # KLUE RoBERTa large
Pretrained RoBERTa Model on Korean Language. See [Github](https://github.com/KLUE-benchmark/KLUE) and [Paper](https://arxiv.org/abs/2105.09680) for more details.
## How to use
_NOTE:_ Use `BertTokenizer` instead of RobertaTokenizer. (`AutoTokenizer` will load `BertTokenizer`)
```python
from tra... | null | null | null | [
"ko"
] | 336,690,946 | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6557cbff029b03128ba80c18 | 664779cf89c4e6a9 | e6aa9b619ddb63c3 | 61,056 | BPE | 60,795 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 311 | jinaai/jina-embeddings-v2-base-code | jinaai | null | 367,326 | 2,089,383 | False | 2023-11-17T20:24:31Z | 2025-01-06T16:28:09Z | sentence-transformers | 136 | 0 | null | feature-extraction | {"parameters": {"F16": 160869120}, "total": 160869120} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_fp16.onnx",
"onnx/model_quantized.onnx",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokeni... | 516f4baf13dec4ddddda8631e019b5737c8bc250 | [
"sentence-transformers",
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"onnx",
"safetensors",
"bert",
"fill-mask",
"feature-extraction",
"sentence-similarity",
"mteb",
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"custom_code",
"en",
"dataset:allenai/c4",
"arxiv:2108.12409",
"arxiv:2310.19923",
"license:apache-2.0",
"text-emb... | null | {"architectures": ["JinaBertForMaskedLM"], "auto_map": {"AutoConfig": "jinaai/jina-bert-v2-qk-post-norm--configuration_bert.JinaBertConfig", "AutoModel": "jinaai/jina-bert-v2-qk-post-norm--modeling_bert.JinaBertModel", "AutoModelForMaskedLM": "jinaai/jina-bert-v2-qk-post-norm--modeling_bert.JinaBertForMaskedLM", "AutoM... | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": "jinaai/jina-bert-v2-qk-post-norm--modeling_bert.JinaBertForMaskedLM",
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["allenai/c4"], "eval_results": null, "language": "en", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "m... | <!-- TODO: add evaluation results here -->
<br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>The text embedding set trained by <a href="https://jina.a... | null | [
"apache-2.0"
] | [
"allenai/c4"
] | [
"en"
] | 160,869,120 | null | null | [
"JinaBertForMaskedLM",
"AutoModelForMaskedLM",
"bert",
"jinaai/jina-bert-v2-qk-post-norm--modeling_bert.JinaBertForMaskedLM"
] | [
"fill-mask",
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
65c0f283272604d95a0a7b54 | c60cd9fbaec937fa | 704a2955bcffd2d4 | 32,016 | BPE | 61,260 | true | false | true | true | 7 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 312 | defog/sqlcoder-7b-2 | defog | null | 92,257 | 1,548,882 | False | 2024-02-05T14:36:51Z | 2024-02-12T14:06:11Z | transformers | 423 | 0 | null | text-generation | {"parameters": {"F16": 6738546688}, "total": 6738546688} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
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"predictions.npy",
"speci... | 7e5b6f7981c0aa7d143f6bec6fa26625bdfcbe66 | [
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"gguf",
"llama",
"text-generation",
"license:cc-by-sa-4.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | {"total": 6738546688, "architecture": "llama", "context_length": 16384, "bos_token": "<s>", "eos_token": "</s>"} | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": null, "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "cc-by-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": null} | # Update notice
The model weights were updated at 7 AM UTC on Feb 7, 2024. The new model weights lead to a much more performant model – particularly for joins.
If you downloaded the model before that, please redownload the weights for best performance.
# Model Card for SQLCoder-7B-2
A capable large language model fo... | null | [
"cc-by-sa-4.0"
] | null | null | 6,738,546,688 | 6,738,546,688 | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
664c2acf1f1974992186bda9 | 451ee55e88313ef2 | 4815e8c432d72506 | 100,280 | BPE | 100,000 | false | true | false | true | 24 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 313 | katuni4ka/tiny-random-dbrx | katuni4ka | null | 1,249 | 1,242,042 | False | 2024-05-21T05:02:07Z | 2024-07-02T10:35:08Z | transformers | 0 | 0 | null | text-generation | {"parameters": {"F32": 1612456}, "total": 1612456} | [
".gitattributes",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | f2bffd7fc3e6bc8bb318259f29dab3db37061c10 | [
"transformers",
"safetensors",
"dbrx",
"text-generation",
"conversational",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DbrxForCausalLM"], "model_type": "dbrx", "tokenizer_config": {"bos_token": "<|endoftext|>", "chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif 'system' not in messages[0]['role'] %}{% set loop_messa... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | 1,612,456 | null | null | [
"AutoModelForCausalLM",
"DbrxForCausalLM",
"dbrx"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17b5d6 | 69d9860d38b5c798 | cf7a052aa6fec7ad | 30,000 | BPE | 15,977 | true | true | false | true | 229 | Sequence | Metaspace | Metaspace | [
"NFKC",
"BertNormalizer"
] | [
"Metaspace"
] | [
"Metaspace"
] | 282 | gogamza/kobart-summarization | gogamza | null | 22,771 | 1,373,077 | False | 2022-03-02T23:29:05Z | 2023-06-12T23:54:06Z | transformers | 18 | 0 | null | null | {"parameters": {"F32": 123889968}, "total": 123889968} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"summ.png",
"tokenizer.json",
"vocab.json"
] | 31f181b155a0ad74bd93bd90ee04310ff72691f4 | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"ko",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BartForConditionalGeneration"], "model_type": "bart"} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "ko", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["bart"]} | # Korean News Summarization Model
## Demo
https://huggingface.co/spaces/gogamza/kobart-summarization
## How to use
```python
import torch
from transformers import PreTrainedTokenizerFast
from transformers import BartForConditionalGeneration
tokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-summar... | null | [
"mit"
] | null | [
"ko"
] | 123,889,968 | null | null | [
"bart",
"BartForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
672e6a70d433c6b122f8bef2 | 5b8a6156b5b6e013 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 37 | OrcaDB/gte-base-en-v1.5 | OrcaDB | null | 376,064 | 2,834,666 | False | 2024-11-08T19:45:52Z | 2024-11-08T22:34:51Z | transformers | 0 | 0 | [{"name": "gte-base-en-v1.5", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 74.... | sentence-similarity | {"parameters": {"F32": 136776192}, "total": 136776192} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"configuration.py",
"model.safetensors",
"modules.json",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 6a9b641cb627be442996ce096a996ced2bdd81ea | [
"transformers",
"safetensors",
"new",
"feature-extraction",
"sentence-transformers",
"gte",
"mteb",
"transformers.js",
"sentence-similarity",
"custom_code",
"en",
"arxiv:2407.19669",
"arxiv:2308.03281",
"license:apache-2.0",
"model-index",
"text-embeddings-inference",
"endpoints_comp... | null | {"architectures": ["NewModel"], "auto_map": {"AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig", "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel", "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM", "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice"... | {
"auto_model": "AutoModel",
"custom_class": "Alibaba-NLP/new-impl--modeling.NewModel",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"language": ["en"], "library_name": "transformers", "license": "apache-2.0", "tags": ["sentence-transformers", "gte", "mteb", "transformers.js", "sentence-similarity"], "model-index": [{"name": "gte-base-en-v1.5", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassifica... | <!-- **English** | [中文](./README_zh.md) -->
# gte-base-en-v1.5
We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance.
The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/new... | null | [
"apache-2.0"
] | null | [
"en"
] | 136,776,192 | null | null | [
"new",
"NewModel",
"AutoModel",
"Alibaba-NLP/new-impl--modeling.NewModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
651f4c0d063e54a50cb307b9 | d614fcc5cba7a712 | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 314 | mesolitica/llama2-embedding-1b-8k | mesolitica | null | 179,920 | 2,113,741 | False | 2023-10-05T23:51:41Z | 2023-10-10T22:43:18Z | transformers | 2 | 0 | null | feature-extraction | {"parameters": {"F32": 946902528}, "total": 946902528} | [
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"llama",
"feature-extraction",
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] | null | {"architectures": ["LlamaModelEmbedding"], "auto_map": {"AutoModel": "modeling.LlamaModelEmbedding"}, "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "c... | {
"auto_model": "AutoModel",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ms"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # 1B 32768 context length Llama2 on Malaysian text embedding task
Trained on truncated 8k context length, but infer able to scale up to 32k context length.
README at https://github.com/mesolitica/llama2-embedding#finetune
WandB, https://wandb.ai/mesolitica/llama2-embedding-1b?workspace=user-husein-mesolitica
## how... | null | null | null | [
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} | {"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... | > Update @ 2024.05.20: Re-Upload RoPE fixed model
> 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... | null | [
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**Model Summary:**
Granite-Embedding-30m-English is a 30M parameter dense bi-encoder 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 comb... | null | [
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653cbb4b7bd6a97439e7d29e | 16e7c4472bd9e4b6 | 2244742552034edc | 32,022 | BPE | 31,757 | true | true | true | true | 22 | Sequence | Sequence | ByteLevel | [] | [
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<img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https... | null | [
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678e64ff327f1b984b57053f | 36935258f89e8e9e | 83d37c6e8ee56b77 | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
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# Finetune LLMs 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) he... | null | [
"apache-2.0"
] | null | [
"en"
] | 33,741,233,138 | null | null | [
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68ea05fb43df37d95ad2491d | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
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"ByteLevel"
] | [
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] | 47 | Qwen/Qwen3-VL-8B-Instruct | Qwen | null | 4,649,140 | 20,365,833 | False | 2025-10-11T07:23:39Z | 2025-10-15T16:16:59Z | transformers | 837 | 12 | null | image-text-to-text | {"parameters": {"BF16": 8767123696}, "total": 8767123696} | [
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67536c1f97148ba00aaf3f65 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
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} | 12,989 | 1,250,302 | False | 2024-12-06T21:26:55Z | 2025-11-25T05:55:43Z | transformers | 51 | 1 | null | text-generation | {"parameters": {"BF16": 70553706496}, "total": 70553706496} | [
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"text... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|eot_id|>", "pad_token": "<|finetune_right_pad_id|>", "unk_token": null, "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- en... | {
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} | {"base_model": "meta-llama/Llama-3.3-70B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "llama3.3", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["llama-3", "llama", "meta", "facebook", "... | ## ***See [our collection](https://huggingface.co/collections/unsloth/llama-33-all-versions-67535d7d994794b9d7cf5e9f) for all versions of Llama 3.3 including GGUF, 4-bit and original 16-bit formats.***
# Finetune Llama 3.3, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tes... | null | [
"llama3.3"
] | null | [
"en"
] | 70,553,706,496 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65008ee13587d3ebfd9ccc30 | 55aaeb49a5b6e7ea | 1587e0c2902ccd67 | 30 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 316 | GleghornLab/SYNTERACT | GleghornLab | null | 20 | 1,212,328 | False | 2023-09-12T16:16:33Z | 2025-02-11T18:05:49Z | transformers | 6 | 0 | null | text-classification | null | [
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] | 0a52e5ef92dfc77ed21a63087060129bf7cb5dc8 | [
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"bert",
"text-classification",
"protein language model",
"biology",
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"dataset:Negatome",
"license:cc-by-nc-4.0",
"text-embeddings-inference",
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] | 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",
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} | {"base_model": null, "datasets": ["BIOGRID", "Negatome"], "eval_results": null, "language": null, "library_name": "transformers", "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-classification", "tags": ["protein language model", "biology... | [SYNTERACT 2.0](https://huggingface.co/Synthyra/SYNTERACT2) is coming soon, please stay tuned!
<img src="https://cdn-uploads.huggingface.co/production/uploads/62f2bd3bdb7cbd214b658c48/Ro4uhQDurP-x7IHJj11xa.png" width="350">
## Model description
SYNTERACT (SYNThetic data-driven protein-protein intERACtion Transforme... | null | [
"cc-by-nc-4.0"
] | [
"BIOGRID",
"Negatome"
] | null | null | null | null | [
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"AutoModelForSequenceClassification"
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"text"
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"Prepend",
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"Replace",
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] | 74 | katuni4ka/tiny-random-llava | katuni4ka | null | 1,875 | 1,233,881 | False | 2024-11-07T07:43:31Z | 2025-02-06T15:56:30Z | transformers | 0 | 0 | null | image-text-to-text | {"parameters": {"F32": 1054490}, "total": 1054490} | [
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<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated... | null | null | null | null | 1,054,490 | null | null | [
"AutoModelForImageTextToText",
"LlavaForConditionalGeneration",
"llava"
] | [
"image-text-to-text"
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"multimodal"
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"text",
"image"
] | [
"text"
] |
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"ByteLevel"
] | [
"ByteLevel"
] | 6 | allenai/longformer-large-4096-finetuned-triviaqa | allenai | null | 1,084 | 1,201,301 | False | 2022-03-02T23:29:05Z | 2022-10-03T22:04:43Z | transformers | 7 | 0 | null | question-answering | null | [
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"README.md",
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"pytorch",
"tf",
"longformer",
"question-answering",
"en",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["LongformerForQuestionAnswering"], "model_type": "longformer"} | {
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"en"
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"AutoModelForQuestionAnswering",
"LongformerForQuestionAnswering",
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"text"
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"text"
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6357dab02cc607c05427c271 | 820723d44f714bed | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 73 | cardiffnlp/tweet-topic-latest-multi | cardiffnlp | null | 73 | 1,197,340 | False | 2022-10-25T12:46:40Z | 2024-03-13T20:59:48Z | transformers | 13 | 0 | null | text-classification | null | [
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"text-classification",
"arxiv:2209.09824",
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] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | null | -----------------------|---------------------|----------------------------|--------------------------|
| 1: business_&_entrepreneurs | 6: film_tv_&_video | 11: music | 16: sports |
| 2: celebrity_&_pop_culture | 7: fitness_&_health | 12: news_&_social_concern | 17: travel_&_adventure ... | null | null | null | null | null | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
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"text"
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"logits"
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665f181fb9e194a20c5e8d44 | 34093184d10f76a9 | 35b001f3a491f9f9 | 151,646 | BPE | 151,387 | true | true | true | true | 3 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 53 | Qwen/Qwen2-7B | Qwen | null | 63,057 | 1,490,511 | False | 2024-06-04T13:35:27Z | 2024-06-06T14:41:44Z | transformers | 171 | 0 | null | text-generation | {"parameters": {"BF16": 7615616512}, "total": 7615616512} | [
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"README.md",
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"model.safetensors.index.json",
"tokenizer.json",
"... | 453ed1575b739b5b03ce3758b23befdb0967f40e | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"pretrained",
"conversational",
"en",
"license:apache-2.0",
"eval-results",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["pretrained"]} | # Qwen2-7B
## Introduction
Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the 7B Qwen2 base language model.
Compared... | null | [
"apache-2.0"
] | null | [
"en"
] | 7,615,616,512 | null | null | [
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629abd5753a72d997d447741 | ff184e6969536b02 | 5d0543b90d3ada56 | 32,004 | Unigram | null | true | true | true | true | 7 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 318 | yanekyuk/camembert-keyword-extractor | yanekyuk | null | 426 | 1,188,219 | False | 2022-06-04T02:03:03Z | 2022-06-04T10:28:45Z | transformers | 13 | 0 | [{"name": "camembert-keyword-extractor", "results": []}] | token-classification | null | [
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"pipeline_tag": "token-classification",
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} | {"base_model": null, "datasets": null, "eval_results": [], "language": ["fr"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["precision", "recall", "accuracy", "f1"], "model_name": "camembert-keyword-extractor", "pipeline_tag": null, "tags": ["generated_from_trainer"], ... | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# camembert-keyword-extractor
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an u... | null | [
"mit"
] | null | [
"fr"
] | null | null | [
"precision",
"recall",
"accuracy",
"f1"
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"CamembertForTokenClassification",
"AutoModelForTokenClassification",
"camembert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
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"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 319 | mixedbread-ai/deepset-mxbai-embed-de-large-v1 | mixedbread-ai | null | 114,836 | 2,344,375 | False | 2024-07-12T06:45:32Z | 2025-03-24T14:42:47Z | sentence-transformers | 58 | 0 | null | feature-extraction | {"parameters": {"F16": 487066624}, "total": 487066624} | [
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] | null | [
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] | 487,066,624 | null | null | [
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"Split",
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"id": "meta-llama/Llama-3.1-8B-Instruct"
}
],
"relation": "quantized"
} | 45,639 | 1,330,230 | False | 2024-07-26T18:58:13Z | 2026-02-13T18:22:29Z | transformers | 30 | 0 | null | text-generation | {"parameters": {"I32": 6979321856, "F16": 1052315648}, "total": 8031637504} | [
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Meta-Llama-3.1-8B-Instruct-quantized.w4a16
<img src="https://www.redhat.com/rhdc/managed-files/Catalog-Validated_model_0.png" alt="Model Icon" width="40" style="margin: 0; padding: 0;" />
</h1>
<a href="https://www.redhat.com/en/products/ai/v... | null | [
"llama3.1",
"llama3.1",
"https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE"
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"en",
"de",
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"it",
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"hi",
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] | 8,031,637,504 | null | null | [
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"text"
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67a7ae157dcbef67ccb4e3f6 | 7ec8239dd3bcce7b | 46a701b36ce8cd6c | 49,280 | BPE | 48,900 | false | true | false | true | 145 | null | Sequence | ByteLevel | null | [
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} | 112,210 | 1,864,858 | False | 2025-02-08T19:18:45Z | 2025-04-08T07:25:13Z | transformers | 309 | 1 | null | image-text-to-text | null | [
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] | 320 | zai-org/GLM-4.5V-FP8 | zai-org | {
"models": [
{
"_id": "687c617b24649ecb26a74106",
"id": "zai-org/GLM-4.5-Air-Base"
}
],
"relation": "quantized"
} | 8,397 | 1,233,588 | False | 2025-08-10T13:41:24Z | 2025-10-25T13:20:44Z | transformers | 42 | 0 | null | image-text-to-text | null | [
".gitattributes",
"README.md",
"chat_template.jinja",
"config.json",
"generation_config.json",
"model-00001-of-00046.safetensors",
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"model-00004-of-00046.safetensors",
"model-00005-of-00046.safetensors",
"model-00006-of-00046... | 3ca028eac7af91c53109dcfa865e5c8da7b1faf3 | [
"transformers",
"safetensors",
"glm4v_moe",
"image-text-to-text",
"conversational",
"zh",
"en",
"arxiv:2507.01006",
"base_model:zai-org/GLM-4.5-Air-Base",
"base_model:quantized:zai-org/GLM-4.5-Air-Base",
"license:mit",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] | null | {"architectures": ["Glm4vMoeForConditionalGeneration"], "model_type": "glm4v_moe", "quantization_config": {"quant_method": "compressed-tensors"}, "tokenizer_config": {"eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>"}, "chat_template_jinja": "[gMASK]<sop>\n{%- if tools -%}\n<|system|>\n# Tools\n\nYou may call ... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": ["zai-org/GLM-4.5-Air-Base"], "datasets": null, "eval_results": null, "language": ["zh", "en"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": null} | # GLM-4.5V-FP8
<div align="center">
<img src=https://raw.githubusercontent.com/zai-org/GLM-V/refs/heads/main/resources/logo.svg width="40%"/>
</div>
<p align="center">
👋 Join our <a href="https://discord.com/invite/8cnQKdAprg" target="_blank">Discord</a> communities.
<br>
📖 Check out the <a href="https:/... | null | [
"mit"
] | null | [
"zh",
"en"
] | null | null | null | [
"AutoModelForImageTextToText",
"Glm4vMoeForConditionalGeneration",
"glm4v_moe"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
670543056dc61e9193430c7f | 7f7d5d53da5c6a4e | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 321 | jinaai/jina-clip-v2 | jinaai | {
"models": [
{
"_id": "661556d7a5849fc8a24811cc",
"id": "jinaai/xlm-roberta-flash-implementation"
}
],
"relation": "quantized"
} | 35,875 | 1,579,124 | False | 2024-10-08T14:34:45Z | 2025-04-28T09:08:11Z | transformers | 328 | 0 | null | feature-extraction | {"parameters": {"F16": 865278476}, "total": 865278476} | [
".gitattributes",
"README.md",
"config.json",
"config_sentence_transformers.json",
"custom_st.py",
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"modules.json",
"onnx/model.onnx",
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"onnx/model_fp16.onnx",
"onnx/model_int8.onnx",
"onnx/model_q4.onnx",
"onnx/model_q4f16... | 344d954da76eb8ad47a7aaff42d012e30c15b8fe | [
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"multi-modal",
"crossmodal",
"cross-modal",
"mteb",
"clip-benchmark",
"vidore",
"sentence-transformers",
"trans... | null | {"architectures": ["JinaCLIPModel"], "auto_map": {"AutoConfig": "jinaai/jina-clip-implementation--configuration_clip.JinaCLIPConfig", "AutoModel": "jinaai/jina-clip-implementation--modeling_clip.JinaCLIPModel"}, "model_type": "jina_clip", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>",... | {
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"custom_class": "jinaai/jina-clip-implementation--modeling_clip.JinaCLIPModel",
"pipeline_tag": "feature-extraction",
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} | {"base_model": ["jinaai/xlm-roberta-flash-implementation"], "datasets": null, "eval_results": null, "language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi",... | <br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>The embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
<p align="cent... | null | [
"cc-by-nc-4.0"
] | null | [
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"az",
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"hr",
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653bb81bea8b6b5fe3a67097 | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
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] | null | [
"Replace",
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"Fuse",
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] | 88 | TheBloke/zephyr-7B-beta-AWQ | TheBloke | {
"models": [
{
"_id": "653a4cbd66081db6fc588b21",
"id": "HuggingFaceH4/zephyr-7b-beta"
}
],
"relation": "quantized"
} | 1,941 | 1,179,449 | False | 2023-10-27T13:16:11Z | 2023-11-09T18:16:40Z | transformers | 39 | 0 | [{"name": "zephyr-7b-beta", "results": []}] | text-generation | {"parameters": {"I32": 6979321856, "F16": 262410240}, "total": 7241732096} | [
".gitattributes",
"README.md",
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"tokenizer.model",
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"train_results.json"
] | ad7867b7a09d5504c86cc015d2d3a73904d488dd | [
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"text-generation",
"generated_from_trainer",
"conversational",
"en",
"dataset:HuggingFaceH4/ultrachat_200k",
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"arxiv:2305.18290",
"arxiv:2310.16944",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_mod... | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "quantization_config": {"quant_method": "awq", "bits": 4}, "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "HuggingFaceH4/zephyr-7b-beta", "datasets": ["HuggingFaceH4/ultrachat_200k", "HuggingFaceH4/ultrafeedback_binarized"], "eval_results": [], "language": ["en"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": "zephyr-7b-beta", "pipeline_tag... | <!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content:... | null | [
"mit"
] | [
"HuggingFaceH4/ultrachat_200k",
"HuggingFaceH4/ultrafeedback_binarized"
] | [
"en"
] | 7,241,732,096 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66a5de99a28bc058db44d78f | ef388cf39ab360f2 | ef713e28c43f9b50 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 322 | NousResearch/Hermes-3-Llama-3.1-8B | NousResearch | {
"models": [
{
"_id": "66944f1fe0c5c2e493a804f5",
"id": "meta-llama/Llama-3.1-8B"
}
],
"relation": "finetune"
} | 161,863 | 2,667,390 | False | 2024-07-28T06:00:57Z | 2024-09-08T07:39:55Z | transformers | 400 | 2 | [{"name": "Hermes-3-Llama-3.1-70B", "results": []}] | 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",
"... | 896ea440e5a9e6070e3d8a2774daf2b481ab425b | [
"transformers",
"safetensors",
"llama",
"text-generation",
"Llama-3",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"function calling",
"json mode",
"axolotl",
"roleplaying",
"chat",
"conversational",
"en",
"arxiv:2408.11857",
"base_model:meta-llam... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": [{"name": "default", "template": "{{bos_token}}{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Meta-Llama-3.1-8B", "datasets": null, "eval_results": [], "language": ["en"], "library_name": null, "license": "llama3", "license_name": null, "license_link": null, "metrics": null, "model_name": "Hermes-3-Llama-3.1-70B", "pipeline_tag": null, "tags": ["Llama-3", "instruct", "finetune", "chat... | # Hermes 3 - Llama-3.1 8B
## Model Description
Hermes 3 is the latest version of our flagship Hermes series of LLMs by Nous Research.
For more details on new capabilities, training results, and ... | null | [
"llama3"
] | null | [
"en"
] | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65a63770bcc380edddda13af | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | Qdrant/bge-base-en-v1.5-onnx-Q | Qdrant | {
"models": [
{
"_id": "64ff2c767a4a6ae49afa72b5",
"id": "BAAI/bge-base-en-v1.5"
}
],
"relation": "quantized"
} | 38,891 | 1,355,366 | False | 2024-01-16T07:59:44Z | 2024-09-12T18:12:31Z | transformers | 2 | 0 | null | sentence-similarity | null | [
".gitattributes",
"README.md",
"config.json",
"model_optimized.onnx",
"ort_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 738cad1c108e2f23649db9e44b2eab988626493b | [
"transformers",
"onnx",
"bert",
"feature-extraction",
"sentence-similarity",
"base_model:BAAI/bge-base-en-v1.5",
"base_model:quantized:BAAI/bge-base-en-v1.5",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": ["BAAI/bge-base-en-v1.5"], "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": null} | Quantized ONNX port of [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) for text classification and similarity searches.
### Usage
Here's an example of performing inference using the model with [FastEmbed](https://github.com/qdrant/fastembed).
```py
from fastembed import TextEmbedding
documents... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f1794dd | eb52febf08b1f46a | 046224ca498cefbf | 64,000 | WordPiece | null | true | true | true | true | 36 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 323 | aubmindlab/bert-base-arabertv2 | aubmindlab | null | 43,460 | 1,429,452 | False | 2022-03-02T23:29:05Z | 2023-08-03T12:32:06Z | transformers | 42 | 0 | null | fill-mask | {"parameters": {"I64": 512, "F32": 135851010}, "total": 135851522} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tf1_model.tar.gz",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 97522efce17efa33036ac619802d5cec238dcad9 | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"ar",
"dataset:wikipedia",
"dataset:Osian",
"dataset:1.5B-Arabic-Corpus",
"dataset:oscar-arabic-unshuffled",
"arxiv:2003.00104",
"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": ["wikipedia", "Osian", "1.5B-Arabic-Corpus", "oscar-arabic-unshuffled"], "eval_results": null, "language": "ar", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"text"... | # AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png" width="100" align="left"/>
**AraBERT** is an Arabic pretrained lanaguage model based on [Google's BERT architechture](https://github.com/google-research/bert). ... | null | null | [
"wikipedia",
"Osian",
"1.5B-Arabic-Corpus",
"oscar-arabic-unshuffled"
] | [
"ar"
] | 135,851,522 | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66bed43adc1c1a0600f8e5cc | 86f7385b380f33a8 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 268 | dangvantuan/french-document-embedding | dangvantuan | null | 4,756 | 1,191,015 | False | 2024-08-16T04:23:22Z | 2024-11-21T16:10:44Z | sentence-transformers | 13 | 0 | [{"name": "7eff199d41ff669fad99d83cad9249c393c3f14b", "results": [{"task": {"type": "Clustering"}, "dataset": {"type": "lyon-nlp/alloprof", "name": "MTEB AlloProfClusteringP2P", "config": "default", "split": "test", "revision": "392ba3f5bcc8c51f578786c1fc3dae648662cb9b"}, "metrics": [{"type": "v_measure", "value": 59.6... | sentence-similarity | {"parameters": {"F32": 305368320}, "total": 305368320} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"configuration.py",
"model.safetensors",
"modeling.py",
"modules.json",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 4f99da7d847d33af7a89107de805f1af881c5018 | [
"sentence-transformers",
"safetensors",
"Bilingual",
"feature-extraction",
"sentence-similarity",
"transformers",
"french",
"english",
"sentence-embedding",
"mteb",
"custom_code",
"fr",
"en",
"arxiv:1705.00652",
"arxiv:2402.16829",
"arxiv:1908.10084",
"arxiv:2407.19669",
"arxiv:230... | null | {"architectures": ["BilingualModel"], "auto_map": {"AutoConfig": "dangvantuan/bilingual_impl--configuration.BilingualConfig", "AutoModel": "dangvantuan/bilingual_impl--modelling.BilingualModel", "AutoModelForMaskedLM": "dangvantuan/bilingual_impl--modelling.BilingualForMaskedLM", "AutoModelForMultipleChoice": "dangvant... | {
"auto_model": "AutoModel",
"custom_class": "dangvantuan/bilingual_impl--modelling.BilingualModel",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"language": ["fr", "en"], "library_name": "sentence-transformers", "license": "apache-2.0", "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "french", "english", "sentence-embedding", "mteb"], "model-index": [{"name": "7eff199d41ff669... | ## Model Description:
[**french-document-embedding**](https://huggingface.co/dangvantuan/french-document-embedding) is an embedding model for documents in the French-English language, with a context length of up to 8096 tokens. This model is a specialized text-embedding model trained specifically for the French-English... | null | [
"apache-2.0"
] | null | [
"fr",
"en"
] | 305,368,320 | null | null | [
"BilingualModel",
"AutoModel",
"dangvantuan/bilingual_impl--modelling.BilingualModel",
"Bilingual"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f177aa7 | ef01695f81049c46 | fd18c2fff55752e6 | 60,000 | BPE | 59,789 | true | true | true | true | 2 | NFKC | ByteLevel | ByteLevel | [
"NFKC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 324 | VietAI/gpt-neo-1.3B-vietnamese-news | VietAI | null | 493 | 1,169,319 | False | 2022-03-02T23:29:05Z | 2022-08-07T14:32:07Z | transformers | 30 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"tokenizer.json"
] | 1be2f0c2e4193b525166f1286df874a0cadb0813 | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"causal-lm",
"gpt",
"vi",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPTNeoForCausalLM"], "model_type": "gpt_neo"} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["vi"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["pytorch", "causal-lm", "gpt"]} | # GPT-Neo 1.3B on Vietnamese News
Details will be available soon.
For more information, please contact anhduongng.1001@gmail.com (Dương) / imthanhlv@gmail.com (Thành) / nguyenvulebinh@gmail.com (Bình).
### How to use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.f... | null | null | null | [
"vi"
] | null | null | null | [
"gpt_neo",
"AutoModelForCausalLM",
"GPTNeoForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
62977f7e65c0fc78a71ffafd | b6a49271ca7583d0 | dc945455af795596 | 32,000 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 76 | BM-K/KoSimCSE-roberta-multitask | BM-K | null | 26,556 | 1,284,071 | False | 2022-06-01T15:02:22Z | 2023-03-24T00:48:07Z | transformers | 28 | 0 | null | feature-extraction | {"parameters": {"I64": 514, "F32": 110618112}, "total": 110618626} | [
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"model.safetensors",
"pytorch_model.bin",
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] | c83e4efdf7ee6647e62498ce8738e4efe10e5def | [
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"roberta",
"feature-extraction",
"korean",
"ko",
"text-embeddings-inference",
"endpoints_compatible",
"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": null, "tags": ["korean"]} | https://github.com/BM-K/Sentence-Embedding-is-all-you-need
# Korean-Sentence-Embedding
🍭 Korean sentence embedding repository. You can download the pre-trained models and inference right away, also it provides environments where individuals can train models.
## Quick tour
```python
import torch
from transformers imp... | null | null | null | [
"ko"
] | 110,618,626 | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
66e97bb2d89bf05ecbba3705 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-32B-Instruct-GPTQ-Int8 | Qwen | {
"models": [
{
"_id": "66e902f34eeaced24a2787f8",
"id": "Qwen/Qwen2.5-32B-Instruct"
}
],
"relation": "quantized"
} | 114,651 | 2,175,799 | False | 2024-09-17T12:53:06Z | 2024-10-09T12:27:35Z | transformers | 14 | 0 | null | text-generation | {"parameters": {"I32": 31205621760, "F16": 1558254592}, "total": 32763876352} | [
".gitattributes",
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"endpoints_compati... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"bits": 8, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n... | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-32B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-32B-Instruct-GPTQ-Int8/blob/main/LICENSE", "metrics": null, "model_name": null, "pip... | # Qwen2.5-32B-Instruct-GPTQ-Int8
## Introduction
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly ... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-32B-Instruct-GPTQ-Int8/blob/main/LICENSE"
] | null | [
"en"
] | 32,763,876,352 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63c6b20d2f651b6762844faf | 518863659fc64ad5 | c8d09f35df89046b | 54,343 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 325 | nlp04/korean_sentiment_analysis_kcelectra | nlp04 | null | 4,085 | 1,175,185 | False | 2023-01-17T14:34:53Z | 2023-01-18T03:26:01Z | transformers | 9 | 0 | [{"name": "korean_sentiment_analysis_kcelectra", "results": []}] | text-classification | null | [
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"electra",
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"generated_from_trainer",
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] | null | {"architectures": ["ElectraForSequenceClassification"], "model_type": "electra", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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} | {"base_model": null, "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": "korean_sentiment_analysis_kcelectra", "pipeline_tag": null, "tags": ["generated_from_trainer"]} | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# korean_sentiment_analysis_kcelectra
This model is a fine-tuned version of [beomi/KcELECTRA-base-v2022](https://huggingface.co/beo... | null | [
"mit"
] | null | null | null | null | [
"accuracy"
] | [
"ElectraForSequenceClassification",
"AutoModelForSequenceClassification",
"electra"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
657dc463c7cb69069a64c743 | 191e06b04083279c | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 26 | nisuga/food_type_classification_model | nisuga | {
"models": [
{
"_id": "621ffdc036468d709f174347",
"id": "distilbert/distilbert-base-uncased"
}
],
"relation": "finetune"
} | 14 | 1,157,858 | False | 2023-12-16T15:38:11Z | 2024-01-31T01:31:05Z | transformers | 6 | 0 | [{"name": "food_type_classification_model", "results": []}] | text-classification | {"parameters": {"F32": 66955010}, "total": 66955010} | [
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"runs/Dec16_15-43-02_1516d1f2c21f/events.out.tfevents.1702741383.... | 83dbab8e86cb9cbc77d44e496dabfdbf3b177a74 | [
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"text-embeddings-inference",
"endpoints_compatible",
"re... | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "distilbert-base-uncased", "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": "food_type_classification_model", "pipeline_tag": null, "tags": ["generated_from_trainer"]} | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# food_type_classification_model
Easily classify any food product into Plant-based(**PLANT_BASED**) or Animal-based(**ANIMAL_BASED*... | null | [
"apache-2.0"
] | null | null | 66,955,010 | null | [
"accuracy"
] | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65225c69d65824bda7a58263 | 5b0c1a1193420318 | 98eb4ad3932ce4a1 | 32,100 | BPE | 31,740 | false | true | true | true | 102 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 326 | mesolitica/sentiment-analysis-nanot5-small-malaysian-cased | mesolitica | null | 16,196 | 1,229,742 | False | 2023-10-08T07:38:17Z | 2023-10-08T07:38:48Z | transformers | 0 | 0 | null | text-classification | {"parameters": {"F32": 41623811}, "total": 41623811} | [
".gitattributes",
"added_tokens.json",
"config.json",
"merges.txt",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
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"vocab.json"
] | 83c27565b162b115d7deae8758032101fe4e3dde | [
"transformers",
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"t5",
"text-classification",
"endpoints_compatible",
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"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | 41,623,811 | null | null | [
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"text"
] | [
"logits"
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65955dccd8a2cd7d9751a227 | c88b36417e2a82ee | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 64 | EvanD/xlm-roberta-base-romanian-ner-ronec | EvanD | null | 268 | 1,156,507 | False | 2024-01-03T13:14:52Z | 2024-01-03T14:04:31Z | transformers | 5 | 0 | null | token-classification | null | [
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"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
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] | 5d713008d94ebe9eaa701d8c4f66a345f294e9a8 | [
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"xlm-roberta",
"token-classification",
"named-entity-recognition",
"sequence-tagger-model",
"ro",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["XLMRobertaForTokenClassification"], "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": "<pa... | {
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"pipeline_tag": "token-classification",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ro"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "token-classification", "tags": ["named-entity-recognition", "sequence-tagger-model"], "widget": [{"tex... | xlm-roberta model trained on [ronec](https://github.com/dumitrescustefan/ronec) dataset, performing 95 f1-Macro on test set.
| Test metric | Results |
|------------------------|--------------------------|
| test_f1_mac_ronec | 0.9547659158706665 |
| test_loss_ronec | 0.16371206... | null | null | null | [
"ro"
] | null | null | null | [
"XLMRobertaForTokenClassification",
"AutoModelForTokenClassification",
"xlm-roberta"
] | [
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] | [
"text"
] | [
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621ffdc136468d709f17b9bc | 941cf5a08e689a1e | cde222976aea8b42 | 32,000 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 327 | hatmimoha/arabic-ner | hatmimoha | null | 891,711 | 2,173,101 | False | 2022-03-02T23:29:05Z | 2025-04-10T10:07:00Z | transformers | 22 | 0 | null | token-classification | {"parameters": {"I64": 512, "F32": 110041363}, "total": 110041875} | [
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"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
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] | 1ee89ac0c790943207d781e41fef8621991def35 | [
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"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"token-classification",
"ar",
"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",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "ar", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # Arabic Named Entity Recognition Model
<a href='https://ko-fi.com/Y8Y71D98TC' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
Pretrained BERT-based ([arabic-bert-base](https://huggingface.co/asafaya... | null | null | null | [
"ar"
] | 110,041,875 | null | null | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f1798b2 | aac5e3c3f00c5960 | 95d2d7fbe5c7eda2 | 30,000 | BPE | 29,776 | false | true | false | false | 5 | null | ByteLevel | null | null | [
"ByteLevel"
] | null | 328 | beomi/KcELECTRA-base | beomi | null | 1,844 | 1,193,584 | False | 2022-03-02T23:29:05Z | 2024-05-23T13:26:43Z | transformers | 43 | 0 | null | null | {"parameters": {"I64": 512, "F32": 109082113}, "total": 109082625} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 681201a097b8120e6dd2a2e63a3e13d338736f88 | [
"transformers",
"pytorch",
"safetensors",
"electra",
"pretraining",
"korean",
"ko",
"en",
"doi:10.57967/hf/0017",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["ElectraForPreTraining"], "model_type": "electra", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForPreTraining",
"custom_class": null,
"pipeline_tag": "pretraining",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ko", "en"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["electra", "korean"]} | # KcELECTRA: Korean comments ELECTRA
** Updates on 2022.10.08 **
- KcELECTRA-base-v2022 (구 v2022-dev) 모델 이름이 변경되었습니다. --> KcELECTRA-base 레포의 `v2022`로 통합되었습니다.
- 위 모델의 세부 스코어를 추가하였습니다.
- 기존 KcELECTRA-base(v2021) 대비 대부분의 downstream task에서 ~1%p 수준의 성능 향상이 있습니다.
---
공개된 한국어 Transformer 계열 모델들은 대부분 한국어 위키, 뉴스 기사, 책 등 잘 ... | null | [
"mit"
] | null | [
"ko",
"en"
] | 109,082,625 | null | null | [
"ElectraForPreTraining",
"AutoModelForPreTraining",
"electra"
] | [
"pretraining"
] | null | null | null |
659a5e051b4a26041af869ac | bc2c6b1ccadaf507 | d1e0dcc971dc8303 | 32,002 | BPE | 58,980 | true | false | true | true | 5 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 329 | openchat/openchat-3.5-0106 | openchat | {
"models": [
{
"_id": "650aedb6238a644cb93a52c3",
"id": "mistralai/Mistral-7B-v0.1"
}
],
"relation": "finetune"
} | 12,880 | 1,219,324 | False | 2024-01-07T08:17:09Z | 2024-05-18T18:14:51Z | transformers | 359 | 0 | null | text-generation | null | [
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"endpoint... | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{{ bos_token }}{% for message in messages %}{{ 'GPT4 Correct ' + message['role'].title() + ': ' + message['content'] + '<|end_of_turn|>'}}{% endfor %}{% if add_generation_prompt %}{{ 'GPT4 Corre... | {
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"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "mistralai/Mistral-7B-v0.1", "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["openchat", "mistral", "C-RLFT"]} | <div align="center">
<img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%">
<h1>Advancing Open-source Language Models with Mixed-Quality Data</h1>
</div>
<p align="center" style="margin-top: 0px;">
<a href="https://openchat.team">
<img src="https://github... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
68b1a84678d0bd542fdbc5a7 | 4415e988eddf3bf9 | 53206b798001acfe | 200,029 | BPE | 446,189 | false | true | true | true | 31 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 330 | OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF | OpenGVLab | {
"models": [
{
"_id": "674022eb1b99fe8e53fde088",
"id": "OpenGVLab/InternViT-300M-448px-V2_5"
},
{
"_id": "68913539bd3d0a833438591d",
"id": "openai/gpt-oss-20b"
}
],
"relation": "merge"
} | 683,145 | 5,093,582 | False | 2025-08-29T13:16:54Z | 2025-09-08T09:54:41Z | transformers | 9 | 0 | null | image-text-to-text | {"parameters": {"BF16": 21232768704}, "total": 21232768704} | [
".gitattributes",
"README.md",
"chat_template.jinja",
"config.json",
"examples/image1.jpg",
"examples/image2.jpg",
"examples/red-panda.mp4",
"generation_config.json",
"model-00001-of-00009.safetensors",
"model-00002-of-00009.safetensors",
"model-00003-of-00009.safetensors",
"model-00004-of-000... | 49b11e8d5e9d4c822de52dee190f95c988009559 | [
"transformers",
"safetensors",
"internvl",
"image-text-to-text",
"custom_code",
"conversational",
"multilingual",
"dataset:OpenGVLab/MMPR-v1.2",
"dataset:OpenGVLab/MMPR-Tiny",
"arxiv:2312.14238",
"arxiv:2404.16821",
"arxiv:2412.05271",
"arxiv:2411.10442",
"arxiv:2504.10479",
"arxiv:2508.... | null | {"architectures": ["InternVLForConditionalGeneration"], "model_type": "internvl", "tokenizer_config": {"bos_token": "<|startoftext|>", "eos_token": "<|return|>", "pad_token": "<|endoftext|>"}, "chat_template_jinja": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n'}}{% if message['content'] is str... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": ["OpenGVLab/InternViT-300M-448px-V2_5", "openai/gpt-oss-20b"], "datasets": ["OpenGVLab/MMPR-v1.2", "OpenGVLab/MMPR-Tiny"], "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name"... | # InternVL3_5-GPT-OSS-20B-A4B-Preview
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271) [\[📜 InternVL2.5-MPO\]](https... | null | [
"apache-2.0"
] | [
"OpenGVLab/MMPR-v1.2",
"OpenGVLab/MMPR-Tiny"
] | [
"multilingual"
] | 21,232,768,704 | null | null | [
"internvl",
"InternVLForConditionalGeneration",
"AutoModelForImageTextToText"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
687c61c324649ecb26a748f0 | 0fbdb3abe17d0d5f | b02c1908f37c4055 | 151,365 | BPE | 318,088 | false | true | true | true | 36 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 320 | zai-org/GLM-4.5-Air | zai-org | null | 406,257 | 2,723,104 | False | 2025-07-20T03:25:55Z | 2025-08-11T13:25:37Z | transformers | 592 | 4 | null | text-generation | {"parameters": {"BF16": 110468818944, "F32": 5888}, "total": 110468824832} | [
".gitattributes",
"README.md",
"chat_template.jinja",
"config.json",
"generation_config.json",
"model-00001-of-00047.safetensors",
"model-00002-of-00047.safetensors",
"model-00003-of-00047.safetensors",
"model-00004-of-00047.safetensors",
"model-00005-of-00047.safetensors",
"model-00006-of-00047... | a24ceef6ce4f3536971efe9b778bdaa1bab18daa | [
"transformers",
"safetensors",
"glm4_moe",
"text-generation",
"conversational",
"en",
"zh",
"arxiv:2508.06471",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Glm4MoeForCausalLM"], "model_type": "glm4_moe", "tokenizer_config": {"eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>"}, "chat_template_jinja": "[gMASK]<sop>\n{%- if tools -%}\n<|system|>\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "zh"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": null} | # GLM-4.5-Air
<div align="center">
<img src=https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/logo.svg width="15%"/>
</div>
<p align="center">
👋 Join our <a href="https://discord.gg/QR7SARHRxK" target="_blank">Discord</a> community.
<br>
📖 Check out the GLM-4.5 <a href="https://... | null | [
"mit"
] | null | [
"en",
"zh"
] | 110,468,824,832 | null | null | [
"AutoModelForCausalLM",
"Glm4MoeForCausalLM",
"glm4_moe"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f1806b9 | 9a40a34059a31270 | 95d40fb9794d58d0 | 20,000 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 331 | snunlp/KR-FinBert-SC | snunlp | null | 79,476 | 1,500,946 | False | 2022-03-02T23:29:05Z | 2022-04-28T05:07:18Z | transformers | 37 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"images/KR-FinBert.png",
"images/readme.MD",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | f8586286cc3161fb648e9fee09a456069fd846d0 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"ko",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ko"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # KR-FinBert & KR-FinBert-SC
Much progress has been made in the NLP (Natural Language Processing) field, with numerous studies showing that domain adaptation using small-scale corpus and fine-tuning with labeled data is effective for overall performance improvement.
we proposed KR-FinBert for the financial domain by ... | null | null | null | [
"ko"
] | null | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
63c539019fa5d07c2c2f5c2c | dd8ef53ef5a0f56a | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 90 | wajidlinux99/gibberish-text-detector | wajidlinux99 | null | 36,511 | 1,305,783 | False | 2023-01-16T11:46:09Z | 2024-10-07T08:34:52Z | transformers | 6 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"git/HEAD",
"git/config",
"git/description",
"git/hooks/applypatch-msg.sample",
"git/hooks/commit-msg.sample",
"git/hooks/fsmonitor-watchman.sample",
"git/hooks/post-update.sample",
"git/hooks/pre-applypatch.sample",
"git/hooks/pre-commit.sample",
... | 81168d3b24a242063ca8225bcc9de95bef4dc198 | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"text",
"nlp",
"correction",
"en",
"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": null, "eval_results": null, "language": ["en"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-classification", "tags": ["text", "nlp", "correction"]} | # Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 492513457
- CO2 Emissions (in grams): 5.527544460835904
## Validation Metrics
- Loss: 0.07609463483095169
- Accuracy: 0.9735624586913417
- Macro F1: 0.9736173135739408
- Micro F1: 0.9735624586913417
- Weighted F1: 0.9736173135739408... | null | null | null | [
"en"
] | null | null | null | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
669fbfba1631541594c89063 | 21feada71fab2765 | 12ebeebff692ebb1 | 131,072 | BPE | 269,443 | false | true | true | true | 1,000 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 245 | casperhansen/mistral-nemo-instruct-2407-awq | casperhansen | {
"models": [
{
"_id": "6697fed91020a9a4cdcca032",
"id": "mistralai/Mistral-Nemo-Instruct-2407"
}
],
"relation": "quantized"
} | 124,152 | 3,245,894 | False | 2024-07-23T14:35:38Z | 2024-09-27T07:14:03Z | transformers | 12 | 0 | null | text-generation | {"parameters": {"I32": 10905190400, "F16": 1342592000}, "total": 12247782400} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | c83b6438e13051ad1c0f5683635705ee83bb8772 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"base_model:mistralai/Mistral-Nemo-Instruct-2407",
"base_model:quantized:mistralai/Mistral-Nemo-Instruct-2407",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "quantization_config": {"bits": 4, "quant_method": "awq"}, "tokenizer_config": {"bos_token": "<s>", "chat_template": "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["mistralai/Mistral-Nemo-Instruct-2407"], "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | null | null | null | null | 12,247,782,400 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] | |
655b1febab0644b531bf30ec | e23b6bdbe047bb7e | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | TinyLlama/TinyLlama-1.1B-Chat-v0.6 | TinyLlama | null | 5,577 | 1,162,838 | False | 2023-11-20T08:59:23Z | 2023-11-20T11:22:36Z | transformers | 110 | 0 | null | text-generation | {"parameters": {"BF16": 1100048384}, "total": 1100048384} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"ggml-model-q4_0.gguf",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | bf9ae1c8bf026667e6f810768de259bb4a7f4777 | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"conversational",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:... | {"total": 1100048384, "architecture": "llama", "context_length": 2048, "bos_token": "<s>", "eos_token": "</s>"} | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content']... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["cerebras/SlimPajama-627B", "bigcode/starcoderdata", "OpenAssistant/oasst_top1_2023-08-25"], "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, "... | <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",
"OpenAssistant/oasst_top1_2023-08-25"
] | [
"en"
] | 1,100,048,384 | 1,100,048,384 | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
672b1f925963af3fe1693e28 | 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 | Qwen | {
"models": [
{
"_id": "672dec6a60ce5208843522f3",
"id": "Qwen/Qwen2.5-Coder-3B"
}
],
"relation": "finetune"
} | 128,046 | 1,714,320 | False | 2024-11-06T07:49:38Z | 2025-01-12T02:04:23Z | transformers | 96 | 0 | null | text-generation | {"parameters": {"BF16": 3085938688}, "total": 3085938688} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 488639f1ff808d1d3d0ba301aef8c11461451ec5 | [
"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",
"base_model:finetune:Qwen/Qwen2.5-Coder-3B",
"license:other",
"text-generat... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Aliba... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Qwen/Qwen2.5-Coder-3B"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": "qwen-research", "license_link": "https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipe... | # Qwen2.5-Coder-3B-Instruct
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
Qwen2.5-Coder is the latest series of C... | null | [
"other",
"qwen-research",
"https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | 3,085,938,688 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
659ed57a6132d4a1d3e4ff2e | bfe694f64f4a18f5 | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 22 | MoritzLaurer/deberta-v3-xsmall-zeroshot-v1.1-all-33 | MoritzLaurer | {
"models": [
{
"_id": "621ffdc136468d709f17dee7",
"id": "microsoft/deberta-v3-xsmall"
}
],
"relation": "quantized"
} | 26,499 | 1,410,548 | False | 2024-01-10T17:35:54Z | 2024-05-07T14:42:09Z | transformers | 8 | 0 | null | zero-shot-classification | {"parameters": {"F16": 70830722}, "total": 70830722} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"onnx/model.onnx",
"onnx/model_quantized.onnx",
"pytorch_model.bin",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | 262ae02f29173eec1c250f90804dc7edc677dcff | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"deberta-v2",
"text-classification",
"zero-shot-classification",
"en",
"arxiv:2312.17543",
"base_model:microsoft/deberta-v3-xsmall",
"base_model:quantized:microsoft/deberta-v3-xsmall",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["DebertaV2ForSequenceClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "microsoft/deberta-v3-xsmall", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "zero-shot-classification", "tags": ["text-classification", "zero-s... | # deberta-v3-xsmall-zeroshot-v1.1-all-33
This model was fine-tuned using the same pipeline as described in
the model card for [MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33](https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33)
and in this [paper](https://arxiv.org/pdf/2312.17543.pdf).
Th... | null | [
"mit"
] | null | [
"en"
] | 70,830,722 | null | null | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
] | [
"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
63178819aea37e1333dcfed1 | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | impira/layoutlm-invoices | impira | null | 1,688 | 1,132,874 | False | 2022-09-06T17:49:13Z | 2023-03-25T20:21:25Z | transformers | 224 | 0 | null | document-question-answering | {"parameters": {"I64": 514, "F32": 127794436}, "total": 127794950} | [
".gitattributes",
"README.md",
"after.png",
"before.png",
"config.json",
"merges.txt",
"model.safetensors",
"pyproject.toml",
"pytorch_model.bin",
"setup.cfg",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | fdf56765f8bdf236b2bd45d23347066475aa730a | [
"transformers",
"pytorch",
"safetensors",
"layoutlm",
"document-question-answering",
"pdf",
"invoices",
"en",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LayoutLMForQuestionAnswering"], "model_type": "layoutlm", "tokenizer_config": {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
"auto_model": "AutoModelForDocumentQuestionAnswering",
"custom_class": null,
"pipeline_tag": "document-question-answering",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": "cc-by-nc-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "document-question-answering", "tags": ["layoutlm", "document-question-answering", "pdf", "i... | # LayoutLM for Invoices
This is a fine-tuned version of the multi-modal [LayoutLM](https://aka.ms/layoutlm) model for the task of question answering on invoices and other documents. It has been fine-tuned on a proprietary dataset of
invoices as well as both [SQuAD2.0](https://huggingface.co/datasets/squad_v2) and [Doc... | null | [
"cc-by-nc-sa-4.0"
] | null | [
"en"
] | 127,794,950 | null | null | [
"AutoModelForDocumentQuestionAnswering",
"layoutlm",
"LayoutLMForQuestionAnswering"
] | [
"document-question-answering"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
621ffdc136468d709f180347 | b9c4d5f4e9446fec | 851f4e82f0ea6be0 | 501,153 | WordPiece | null | true | true | true | true | 1 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 280 | setu4993/LaBSE | setu4993 | null | 12,476 | 1,182,629 | False | 2022-03-02T23:29:05Z | 2023-10-19T06:23:16Z | transformers | 54 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 470926848}, "total": 470927360} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 5afa72968dfe068421082cbc30411073b15a6693 | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"feature-extraction",
"sentence_embedding",
"multilingual",
"google",
"sentence-similarity",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"bo",
"bs",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"... | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["CommonCrawl", "Wikipedia"], "eval_results": null, "language": ["af", "am", "ar", "as", "az", "be", "bg", "bn", "bo", "bs", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "he", "hi", "hmn", "hr... | # LaBSE
## Model description
Language-agnostic BERT Sentence Encoder (LaBSE) is a BERT-based model trained for sentence embedding for 109 languages. The pre-training process combines masked language modeling with translation language modeling. The model is useful for getting multilingual sentence embeddings and for b... | null | [
"apache-2.0"
] | [
"CommonCrawl",
"Wikipedia"
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"af",
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"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"haw",
"he",
"hi",
"hmn",
"hr",
"ht",
"hu",
"... | 470,927,360 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6331ae0aacb6472115ad951e | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | neulab/codebert-cpp | neulab | null | 1,208 | 1,132,157 | False | 2022-09-26T13:50:02Z | 2023-02-27T20:56:25Z | transformers | 11 | 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"
] | 64ee3c96fa26fdef5a8e84341113e946f05fff98 | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2302.05527",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "cls_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstri... | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | null | This is a `microsoft/codebert-base-mlm` model, trained for 1,000,000 steps (with `batch_size=32`) on **C++** code from the `codeparrot/github-code-clean` dataset, on the masked-language-modeling task.
It is intended to be used in CodeBERTScore: [https://github.com/neulab/code-bert-score](https://github.com/neulab/cod... | null | null | null | null | null | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
651bf46cca84a54d9cf6e50a | 592e444e2a382b7f | 0000e6c6a03fba1a | 32,003 | BPE | 61,249 | true | false | true | true | 6 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 332 | TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ | TheBloke | {
"models": [
{
"_id": "65203de7578e7da0d71891e5",
"id": "TinyLlama/TinyLlama-1.1B-Chat-v0.3"
}
],
"relation": "quantized"
} | 393,363 | 2,516,767 | False | 2023-10-03T11:01:00Z | 2023-10-03T11:07:41Z | transformers | 9 | 0 | null | text-generation | {"parameters": {"I32": 968884224, "F16": 131570688}, "total": 1100454912} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"model.safetensors",
"quantize_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 05835c52707fff57cecd16a364de2bc65c9bf102 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.3",
"base_model:quantized:TinyLlama/TinyLlama-1.1B-Chat-v0.3",
"licens... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": null, "unk_token": "<unk>", "use_default_system_prompt": true}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "PY007/TinyLlama-1.1B-Chat-v0.3", "datasets": ["cerebras/SlimPajama-627B", "bigcode/starcoderdata", "OpenAssistant/oasst_top1_2023-08-25"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": "... | <!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<d... | null | [
"apache-2.0"
] | [
"cerebras/SlimPajama-627B",
"bigcode/starcoderdata",
"OpenAssistant/oasst_top1_2023-08-25"
] | [
"en"
] | 1,100,454,912 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63d9ae914d48d0f19a1a6f6f | dccbb7b59eac010e | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 159 | OpenAssistant/reward-model-deberta-v3-large-v2 | OpenAssistant | null | 14,648 | 1,172,642 | False | 2023-02-01T00:13:05Z | 2023-02-01T00:55:05Z | transformers | 244 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | c355404efa9ad2ad069f3a197cae0523c14244fc | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"reward-model",
"reward_model",
"RLHF",
"en",
"dataset:openai/summarize_from_feedback",
"dataset:openai/webgpt_comparisons",
"dataset:Dahoas/instruct-synthetic-prompt-responses",
"dataset:Anthropic/hh-rlhf",
"license:mit",
"te... | null | {"architectures": ["DebertaV2ForSequenceClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["openai/summarize_from_feedback", "openai/webgpt_comparisons", "Dahoas/instruct-synthetic-prompt-responses", "Anthropic/hh-rlhf"], "eval_results": null, "language": ["en"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["accuracy"], "mod... | # Reward model trained from human feedback
Reward model (RM) trained to predict which generated answer is better judged by a human, given a question.
RM are useful in these domain:
- QA model evaluation
- serves as reward score in RLHF
- detect potential toxic response via ranking
All models are train on these d... | null | [
"mit"
] | [
"openai/summarize_from_feedback",
"openai/webgpt_comparisons",
"Dahoas/instruct-synthetic-prompt-responses",
"Anthropic/hh-rlhf"
] | [
"en"
] | null | null | [
"accuracy"
] | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66e34021e883eafa66141ad8 | fbcce7b4e4637640 | d1f3fcfcd5ab53f9 | 250,102 | Unigram | null | true | true | true | true | 105 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 333 | iiiorg/piiranha-v1-detect-personal-information | iiiorg | {
"models": [
{
"_id": "621ffdc136468d709f17defb",
"id": "microsoft/mdeberta-v3-base"
}
],
"relation": "finetune"
} | 27,908 | 1,250,026 | False | 2024-09-12T19:25:21Z | 2025-11-01T04:01:28Z | transformers | 235 | 0 | [{"name": "piiranha-1", "results": []}] | token-classification | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | 255acde67a2f34cf452eb42e365b24d2957352fc | [
"transformers",
"safetensors",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"pii",
"privacy",
"personaldata",
"redaction",
"piidetection",
"en",
"it",
"fr",
"de",
"nl",
"es",
"dataset:ai4privacy/pii-masking-400k",
"base_model:microsoft/mdeberta-v3-base",
"base_... | null | {"architectures": ["DebertaV2ForTokenClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": "microsoft/mdeberta-v3-base", "datasets": ["ai4privacy/pii-masking-400k"], "eval_results": [], "language": ["en", "it", "fr", "de", "nl", "es"], "library_name": "transformers", "license": "cc-by-nc-nd-4.0", "license_name": null, "license_link": null, "metrics": ["precision", "recall", "f1", "accuracy"], ... | # Piiranha-v1: Protect your personal information!
<a target="_blank" href="https://colab.research.google.com/github/williamgao1729/piiranha-quickstart/blob/main/piiranha_quickstart%20(1).ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
Piiranha (cc-by-nc-nd-4.0 l... | null | [
"cc-by-nc-nd-4.0"
] | [
"ai4privacy/pii-masking-400k"
] | [
"en",
"it",
"fr",
"de",
"nl",
"es"
] | null | null | [
"precision",
"recall",
"f1",
"accuracy"
] | [
"DebertaV2ForTokenClassification",
"AutoModelForTokenClassification",
"deberta-v2"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
659bedb0e1604bf9960dc52f | 7a9379003b337737 | 9ff9438e6447a43c | 32,000 | Unigram | null | true | true | true | true | 3 | Sequence | Metaspace | Metaspace | [
"Lowercase",
"Replace",
"Replace",
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 27 | google/siglip-base-patch16-512 | google | null | 4,457 | 1,206,668 | False | 2024-01-08T12:42:24Z | 2024-09-26T08:21:49Z | transformers | 30 | 0 | null | zero-shot-image-classification | {"parameters": {"F32": 203791874}, "total": 203791874} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"preprocessor_config.json",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | 753a949581523b60257d93e18391e8c27f72eb22 | [
"transformers",
"safetensors",
"siglip",
"zero-shot-image-classification",
"vision",
"arxiv:2303.15343",
"arxiv:2209.06794",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["SiglipModel"], "model_type": "siglip", "tokenizer_config": {"eos_token": "</s>", "pad_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForZeroShotImageClassification",
"custom_class": null,
"pipeline_tag": "zero-shot-image-classification",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["vision"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_image... | # SigLIP (base-sized model)
SigLIP model pre-trained on WebLi at resolution 512x512. It was introduced in the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Zhai et al. and first released in [this repository](https://github.com/google-research/big_vision).
Disclaimer: The t... | null | [
"apache-2.0"
] | null | null | 203,791,874 | null | null | [
"AutoModelForZeroShotImageClassification",
"siglip",
"SiglipModel"
] | [
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
659e117c01805191e5f9cb5f | bcedfbbaedf61cc5 | 94507c3d22ac2826 | 60,516 | BPE | 39,382 | true | true | true | false | 5 | Sequence | Whitespace | null | [
"NFC",
"Lowercase"
] | [
"Whitespace"
] | null | 334 | jinaai/jina-embeddings-v2-base-zh | jinaai | null | 25,875 | 1,755,178 | False | 2024-01-10T03:39:40Z | 2025-01-06T16:25:19Z | sentence-transformers | 246 | 0 | [{"name": "jina-embeddings-v2-base-zh", "results": [{"task": {"type": "STS"}, "dataset": {"type": "C-MTEB/AFQMC", "name": "MTEB AFQMC", "config": "default", "split": "validation", "revision": "None"}, "metrics": [{"type": "cos_sim_pearson", "value": 48.51403119231363, "verified": false}, {"type": "cos_sim_spearman", "v... | feature-extraction | {"parameters": {"F16": 160813824}, "total": 160813824} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"merges.txt",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_fp16.onnx",
"onnx/model_quantized.onnx",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_... | c1ff9086a89a1123d7b5eff58055a665db4fb4b9 | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"transformers",
"transformers.js",
"custom_code",
"en",
"zh",
"arxiv:2108.12409",
"arxiv:2402.17016",
"license:apache-2.0",
"model-index",
"text-embeddings-infere... | null | {"architectures": ["JinaBertModel"], "auto_map": {"AutoConfig": "jinaai/jina-bert-implementation--configuration_bert.JinaBertConfig", "AutoModel": "jinaai/jina-bert-implementation--modeling_bert.JinaBertModel", "AutoModelForMaskedLM": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForMaskedLM", "AutoModelForQu... | {
"auto_model": "AutoModel",
"custom_class": "jinaai/jina-bert-implementation--modeling_bert.JinaBertModel",
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"language": ["en", "zh"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb", "transformers", "transformers.js"], "inference": false, "model-index": [{"name": "jina-embeddings-v2-base-zh", "results": [{"task": {"type": "STS"}, "dataset": {"name": "MTEB AFQMC"... | <!-- TODO: add evaluation results here -->
<br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>The text embedding set trained by <a href="https://jina.a... | null | [
"apache-2.0"
] | null | [
"en",
"zh"
] | 160,813,824 | null | null | [
"JinaBertModel",
"AutoModel",
"bert",
"jinaai/jina-bert-implementation--modeling_bert.JinaBertModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f17e1ff | 45e3382ab5d17989 | 646d85bf22994df3 | 50,002 | BPE | 114,917 | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 335 | moussaKam/barthez-orangesum-abstract | moussaKam | null | 1,243 | 1,118,881 | False | 2022-03-02T23:29:05Z | 2021-11-15T13:03:03Z | transformers | 7 | 0 | null | summarization | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"tokenizer.json"
] | 2f4969c2f16bf27aaddb87bf9b862ccead48135b | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"summarization",
"bart",
"fr",
"arxiv:2010.12321",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MBartForConditionalGeneration"], "model_type": "mbart"} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["fr"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["summarization", "bart"], "widget": [{"text": "Citant les pr\u00e9occupations de... | ### Barthez model finetuned on orangeSum (abstract generation)
finetuning: examples/seq2seq (as of Feb 08 2021)
paper: https://arxiv.org/abs/2010.12321 \
github: https://github.com/moussaKam/BARThez
```
@article{eddine2020barthez,
title={BARThez: a Skilled Pretrained French Sequence-to-Sequence Model},
author={E... | null | [
"apache-2.0"
] | null | [
"fr"
] | null | null | null | [
"AutoModelForSeq2SeqLM",
"MBartForConditionalGeneration",
"mbart"
] | [
"text2text-generation",
"summarization"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65f1226f271175e4414e4394 | 5afd7d59d2aa5e01 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 125 | Qdrant/Splade_PP_en_v1 | Qdrant | null | 75,817 | 1,505,380 | False | 2024-03-13T03:50:07Z | 2024-07-15T12:55:11Z | transformers | 6 | 0 | null | sentence-similarity | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.onnx",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | efcd182bc7eb351e81a9445752d4388c2bab500b | [
"transformers",
"onnx",
"bert",
"fill-mask",
"sentence-similarity",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": null} | ONNX port of [prithivida/Splade_PP_en_v1](https://huggingface.co/prithivida/Splade_PP_en_v1) for text classification and similarity searches.
### Usage
Here's an example of performing inference using the model with [FastEmbed](https://github.com/qdrant/fastembed).
```py
from fastembed import SparseTextEmbedding
doc... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask",
"sentence-similarity"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66216595bbe70ad73f9d4b21 | 6e6a83dc4b556c95 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 111 | IlyaGusev/saiga_llama3_8b | IlyaGusev | null | 391,543 | 3,012,095 | False | 2024-04-18T18:25:25Z | 2024-07-04T07:34:28Z | transformers | 137 | 0 | 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",
"... | 5bb9917bdb85340549662ebb62c8e522037ff3f3 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ru",
"dataset:IlyaGusev/saiga_scored",
"doi:10.57967/hf/2368",
"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": ["IlyaGusev/saiga_scored"], "eval_results": null, "language": ["ru"], "library_name": null, "license": "other", "license_name": "llama3", "license_link": "https://llama.meta.com/llama3/license/", "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # Saiga/Llama3 8B, Russian Llama-3-based chatbot
Based on [Llama-3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
Llama.cpp version: [link](https://huggingface.co/IlyaGusev/saiga_llama3_8b_gguf)
Colab: [link](https://colab.research.google.com/drive/1qxgIPymzW6_H6s_wwXu3lknkkYM45Db4)
## Pr... | null | [
"other",
"llama3",
"https://llama.meta.com/llama3/license/"
] | [
"IlyaGusev/saiga_scored"
] | [
"ru"
] | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6848029d9a16b13edd1a3de4 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | nanonets/Nanonets-OCR-s | nanonets | {
"models": [
{
"_id": "6795ff8f7534713f94918fc5",
"id": "Qwen/Qwen2.5-VL-3B-Instruct"
}
],
"relation": "finetune"
} | 64,163 | 1,355,238 | False | 2025-06-10T10:02:05Z | 2025-06-20T19:15:33Z | transformers | 1,589 | 4 | null | image-text-to-text | {"parameters": {"BF16": 3754622976}, "total": 3754622976} | [
".gitattributes",
"Modelfile",
"README.md",
"added_tokens.json",
"chat_template.jinja",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"preprocessor_config.json",
"special_tokens_m... | 3baad182cc87c65a1861f0c30357d3467e978172 | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"OCR",
"pdf2markdown",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen2_5_VLForConditionalGeneration"], "model_type": "qwen2_5_vl", "tokenizer_config": {"bos_token": null, "eos_token": "<|im_end|>", "pad_token": "<|endoftext|>", "unk_token": null}, "chat_template_jinja": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for me... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": ["Qwen/Qwen2.5-VL-3B-Instruct"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["OCR", "pdf2markdown"]} | Nanonets-OCR-s by [Nanonets](https://nanonets.com) is a powerful, state-of-the-art image-to-markdown OCR model that goes far beyond traditional text extraction. It transforms documents into structured markdown with intelligent content recognition and semantic tagging, making it ideal for downstream processing by Large ... | null | null | null | [
"en"
] | 3,754,622,976 | null | null | [
"qwen2_5_vl",
"AutoModelForImageTextToText",
"Qwen2_5_VLForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
65148287463c4fd767f76cb6 | 5385e084548307e9 | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 156 | TheBloke/Mistral-7B-Instruct-v0.1-AWQ | TheBloke | {
"models": [
{
"_id": "65143cd8e31c0e2e3df713e5",
"id": "mistralai/Mistral-7B-Instruct-v0.1"
}
],
"relation": "quantized"
} | 561 | 1,107,258 | False | 2023-09-27T19:29:11Z | 2023-11-09T18:17:58Z | transformers | 38 | 0 | null | text-generation | {"parameters": {"I32": 6979321856, "BF16": 262410240}, "total": 7241732096} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"quant_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | b2f7c152209c12057c3a0d77b2c01a1def7d594f | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:quantized:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"text-generation-inference",
"4-bit",
"awq",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "quantization_config": {"quant_method": "awq", "bits": 4}, "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": null, "unk_token": "<unk>", "use_default_system_prompt": true}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "mistralai/Mistral-7B-Instruct-v0.1", "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": "Mistral 7B Instruct v0.1", "pipeline_tag": "text-generation", "tags": ["finetuned"], "... | <!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<d... | null | [
"apache-2.0"
] | null | null | 7,241,732,096 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6284d0fc36e75229107b782a | 0aecada12d973b0f | 6bd050bccd5c4e23 | 32,158 | Unigram | null | true | true | true | true | 161 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 336 | ibm-research/qcpg-sentences | ibm-research | null | 19 | 1,102,278 | False | 2022-05-18T10:57:00Z | 2023-02-06T21:11:45Z | transformers | 17 | 0 | null | null | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | 2313b99e0544837171c9883a423d3faec854abf5 | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text-2-text-generation",
"augmentation",
"paraphrase",
"paraphrasing",
"arxiv:2203.10940",
"arxiv:1910.09700",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"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": ["text-2-text-generation", "t5", "augmentation", "paraphrase", "paraphrasing"]} | # Model Card for qcpg-sentences
# Model Details
# Quality Controlled Paraphrase Generation (ACL 2022)
> Paraphrase generation has been widely used in various downstream tasks. Most tasks benefit mainly from high quality paraphrases, namely those that are semantically similar to, yet linguistically diverse from, th... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
680daa4ac41c05ba341b67d1 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Qwen/Qwen3-235B-A22B | Qwen | null | 597,542 | 3,471,721 | False | 2025-04-27T03:53:46Z | 2025-07-26T03:45:13Z | transformers | 1,082 | 2 | null | text-generation | {"parameters": {"BF16": 235093634560}, "total": 235093634560} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00118.safetensors",
"model-00002-of-00118.safetensors",
"model-00003-of-00118.safetensors",
"model-00004-of-00118.safetensors",
"model-00005-of-00118.safetensors",
"model-00006-of-0... | 8efa61729e24bd65b1d152b5ab5409052aa80e65 | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"arxiv:2309.00071",
"arxiv:2505.09388",
"license:apache-2.0",
"eval-results",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-235B-A22B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": n... | null | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-235B-A22B/blob/main/LICENSE"
] | null | null | 235,093,634,560 | null | null | [
"qwen3_moe",
"AutoModelForCausalLM",
"Qwen3MoeForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
659bf3a302c9b87978721208 | 7a9379003b337737 | 9ff9438e6447a43c | 32,000 | Unigram | null | true | true | true | true | 3 | Sequence | Metaspace | Metaspace | [
"Lowercase",
"Replace",
"Replace",
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 27 | google/siglip-large-patch16-384 | google | null | 15,972 | 1,191,302 | False | 2024-01-08T13:07:47Z | 2024-09-26T08:21:56Z | transformers | 11 | 0 | null | zero-shot-image-classification | {"parameters": {"F32": 652478466}, "total": 652478466} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"preprocessor_config.json",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | ce005573a40965dfd21fd937fbdeeebf2439fc35 | [
"transformers",
"safetensors",
"siglip",
"zero-shot-image-classification",
"vision",
"arxiv:2303.15343",
"arxiv:2209.06794",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["SiglipModel"], "model_type": "siglip", "tokenizer_config": {"eos_token": "</s>", "pad_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForZeroShotImageClassification",
"custom_class": null,
"pipeline_tag": "zero-shot-image-classification",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["vision"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_image... | # SigLIP (large-sized model)
SigLIP model pre-trained on WebLi at resolution 384x384. It was introduced in the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Zhai et al. and first released in [this repository](https://github.com/google-research/big_vision).
Disclaimer: The ... | null | [
"apache-2.0"
] | null | null | 652,478,466 | null | null | [
"AutoModelForZeroShotImageClassification",
"siglip",
"SiglipModel"
] | [
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
621ffdc136468d709f1802c5 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | sentence-transformers/msmarco-distilbert-base-v3 | sentence-transformers | null | 5,159 | 1,210,652 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:31:19Z | sentence-transformers | 4 | 0 | null | sentence-similarity | {"parameters": {"F32": 66362880}, "total": 66362880} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/m... | 18b4d6067a6ad5c5ca35be50f208fc848116d173 | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["DistilBertModel"], "model_type": "distilbert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/msmarco-distilbert-base-v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when... | null | [
"apache-2.0"
] | null | null | 66,362,880 | null | null | [
"AutoModel",
"distilbert",
"DistilBertModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
656231f7ed2bca599ead7d1f | bc2c6b1ccadaf507 | d1e0dcc971dc8303 | 32,002 | BPE | 58,980 | true | false | true | true | 5 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 329 | berkeley-nest/Starling-LM-7B-alpha | berkeley-nest | null | 3,952 | 1,101,465 | False | 2023-11-25T17:42:15Z | 2024-03-20T04:19:58Z | transformers | 559 | 0 | null | text-generation | {"parameters": {"BF16": 7241748480}, "total": 7241748480} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"model-00001-of-00003.safetensors",
"model-00002-of-00003.safetensors",
"model-00003-of-00003.safetensors",
"model.safetensors.index.json",
"openchat.json",
"special_tokens_map.json",
"tokenizer.json",... | 1dddf3b95bc1391f6307299eb1c162c194bde9bd | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"reward model",
"RLHF",
"RLAIF",
"conversational",
"en",
"dataset:berkeley-nest/Nectar",
"arxiv:2306.02231",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{{ bos_token }}{% for message in messages %}{{ 'GPT4 Correct ' + message['role'].title() + ': ' + message['content'] + '<|end_of_turn|>'}}{% endfor %}{% if add_generation_prompt %}{{ 'GPT4 Corre... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["berkeley-nest/Nectar"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["reward model", "RLHF", "RLAIF"]} | # Starling-LM-7B-alpha
<!-- Provide a quick summary of what the model is/does. -->
- **Developed by:** Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu and Jiantao Jiao.
- **Model type:** Language Model finetuned with RLHF / RLAIF
- **License:** Apache-2.0 license under the condition that the model is not use... | null | [
"apache-2.0"
] | [
"berkeley-nest/Nectar"
] | [
"en"
] | 7,241,748,480 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 337 | pythainlp/thainer-corpus-v2-base-model | pythainlp | null | 53,266 | 1,278,927 | False | 2023-03-22T18:03:03Z | 2023-03-23T07:31:21Z | transformers | 15 | 0 | null | token-classification | {"parameters": {"I64": 512, "F32": 104681508}, "total": 104682020} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 007695a6dde6f05009a65c272ac7660713d657fb | [
"transformers",
"pytorch",
"safetensors",
"camembert",
"token-classification",
"th",
"dataset:pythainlp/thainer-corpus-v2",
"license:cc-by-4.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["CamembertForTokenClassification"], "model_type": "camembert", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": "<pad>"... | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["pythainlp/thainer-corpus-v2"], "eval_results": null, "language": ["th"], "library_name": null, "license": "cc-by-4.0", "license_name": null, "license_link": null, "metrics": ["f1"], "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"text": "\u0e09\u0e31\u0e19\u0e0a\u... | This is a Named Entity Recognition model that trained with [Thai NER v2.0 Corpus](https://huggingface.co/datasets/pythainlp/thainer-corpus-v2)
Training script and split data: [https://zenodo.org/record/7761354](https://zenodo.org/record/7761354)
The model was trained by [WangchanBERTa base model](https://huggingface.... | null | [
"cc-by-4.0"
] | [
"pythainlp/thainer-corpus-v2"
] | [
"th"
] | 104,682,020 | null | [
"f1"
] | [
"CamembertForTokenClassification",
"AutoModelForTokenClassification",
"camembert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
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"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 52 | microsoft/Phi-3.5-MoE-instruct | microsoft | null | 94,818 | 1,573,158 | False | 2024-08-17T21:34:19Z | 2025-12-10T20:23:23Z | transformers | 571 | 0 | null | text-generation | null | [
".gitattributes",
"CODE_OF_CONDUCT.md",
"LICENSE",
"NOTICE.md",
"README.md",
"SECURITY.md",
"added_tokens.json",
"config.json",
"configuration_phimoe.py",
"data_summary_card.md",
"generation_config.json",
"model-00001-of-00017.safetensors",
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"model-00003-... | 43688451b462a3351d8580625ebe1931adb3986d | [
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"custom_code",
"multilingual",
"arxiv:2404.14219",
"arxiv:2407.13833",
"arxiv:2403.06412",
"license:mit",
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] | null | {"architectures": ["PhiMoEForCausalLM"], "auto_map": {"AutoConfig": "configuration_phimoe.PhiMoEConfig", "AutoModelForCausalLM": "modeling_phimoe.PhiMoEForCausalLM"}, "model_type": "phimoe", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}{% if message['role'] == 'system' and mes... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_phimoe.PhiMoEForCausalLM",
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": "https://huggingface.co/microsoft/Phi-3.5-MoE-instruct/resolve/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-gene... | ## Model Summary
Phi-3.5-MoE is a lightweight, state-of-the-art open model built upon datasets used for Phi-3 - synthetic data and filtered publicly available documents - with a focus on very high-quality, reasoning dense data. The model supports multilingual and comes with 128K context length (in tokens). The model u... | null | [
"mit",
"https://huggingface.co/microsoft/Phi-3.5-MoE-instruct/resolve/main/LICENSE"
] | null | [
"multilingual"
] | null | null | null | [
"AutoModelForCausalLM",
"PhiMoEForCausalLM",
"modeling_phimoe.PhiMoEForCausalLM",
"phimoe"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17e8f6 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large | nreimers | null | 73,818 | 1,434,548 | False | 2022-03-02T23:29:05Z | 2021-06-20T19:02:12Z | transformers | 1 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 37519953d888723fe745ea10a1438d8c20a3800f | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"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"
} | null | # MiniLMv2
This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm) | null | null | null | null | null | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
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640ba456037296e6738cf731 | 58967ba68263f0aa | 2f8d5857ae5fa330 | 50,277 | BPE | 50,009 | true | true | true | true | 25 | NFC | ByteLevel | ByteLevel | [
"NFC"
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"ByteLevel"
] | [
"ByteLevel"
] | 86 | EleutherAI/pythia-1b | EleutherAI | null | 79,916 | 1,276,101 | False | 2023-03-10T21:42:46Z | 2023-07-09T16:05:58Z | transformers | 44 | 0 | null | text-generation | {"parameters": {"F16": 1011782144, "U8": 67108864}, "total": 1078891008} | [
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"arxiv:2201.07311",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"... | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["the_pile"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["pytorch", "causal-lm", "pythia"]} | The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and ... | null | [
"apache-2.0"
] | [
"the_pile"
] | [
"en"
] | 1,078,891,008 | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
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621ffdc036468d709f175150 | c017a029b62d722b | 94eb381d2d7a0200 | 593 | BPE | 0 | false | true | true | true | 7 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
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"ByteLevel"
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".gitattributes",
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"added_tokens.json",
"config.json",
"merges.txt",
"pytorch_model.bin",
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"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
] | b65d0a6af3156071d9519e867d695aa265bb393f | [
"transformers",
"pytorch",
"roberta",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForRegression"], "model_type": "roberta", "tokenizer_config": {"unk_token": {"content": "[UNK]", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normal... | {
"auto_model": "RobertaForRegression",
"custom_class": null,
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["roberta"]} | # Model Card for ChemBERTa-10M-MTR
# Model Details
## Model Description
More information needed
- **Developed by:** DeepChem
- **Shared by [Optional]:** DeepChem
- **Model type:** Token Classification
- **Language(s) (NLP):** More information needed
- **License:** More information needed
- **Parent Model:** [... | null | null | null | null | null | null | null | [
"roberta",
"RobertaForRegression"
] | [
null
] | null | null | null |
6281ce66eeb15579946c6d22 | fdad161c812d0e6a | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
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] | 3300cd14e2494afbddfda6146eb17bd7dad40885 | [
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"Accounting",
"Finance",
"Business",
"ESG",
"CSR Reports",
"Financial News",
"Earnings Call Transcripts",
"Sustainability",
"Corporate governance",
"eng",
"license:apache-2.... | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta"} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["eng"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["text-classification", "Sentiment", "RoBERTa", "Financial Statements", "Account... | <!DOCTYPE html>
<html>
<body>
<h1><b>Financial-RoBERTa</b></h1>
<p><b>Financial-RoBERTa</b> is a pre-trained NLP model to analyze sentiment of financial text including:</p>
<ul style="PADDING-LEFT: 40px">
<li>Financial Statements,</li>
<li>Earnings Announcements,</li>
<li>Earnings Call Transcripts,</li>
<li>Co... | null | [
"apache-2.0"
] | null | [
"eng"
] | null | null | null | [
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"Replace",
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] | 46 | codellama/CodeLlama-34b-Instruct-hf | codellama | null | 111,999 | 1,279,275 | False | 2023-08-24T16:58:22Z | 2024-04-12T14:20:11Z | transformers | 304 | 0 | null | text-generation | {"parameters": {"BF16": 33743970304}, "total": 33743970304} | [
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"code",
"arxiv:2308.12950",
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"endpoints_compatible",
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"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% f... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["code"], "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["llama-2"]} | # **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 34B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding... | null | [
"llama2"
] | null | [
"code"
] | 33,743,970,304 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63b70fbb0d21367c0f64952d | 5f880b9c043b9814 | 84fb34b2e1636fc5 | 30,527 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 339 | nikcheerla/nooks-amd-detection-v2-full | nikcheerla | null | 3 | 3 | False | 2023-01-05T17:58:19Z | 2023-01-05T17:58:27Z | sentence-transformers | 0 | 0 | null | sentence-similarity | null | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
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"config_sentence_transformers.json",
"model_head.pkl",
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"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
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"sentence-transformers",
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"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"text-embeddings-inference",
"endpoints_compatible",
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] | null | {"architectures": ["MPNetModel"], "model_type": "mpnet", "tokenizer_config": {"bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, ... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "tr... | # {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when y... | null | null | null | null | null | null | null | [
"MPNetModel",
"AutoModel",
"mpnet"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
657494e1d0ed8f5761070faa | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | Intel/neural-chat-7b-v3-3 | Intel | {
"models": [
{
"_id": "65531bd06a12e47a509523fe",
"id": "Intel/neural-chat-7b-v3-1"
}
],
"relation": "finetune"
} | 5,083 | 1,206,174 | False | 2023-12-09T16:25:05Z | 2024-11-11T05:17:37Z | transformers | 80 | 0 | [{"name": "neural-chat-7b-v3-3", "results": [{"task": {"type": "Large Language Model", "name": "Large Language Model"}, "dataset": {"name": "meta-math/MetaMathQA", "type": "meta-math/MetaMathQA"}, "metrics": [{"type": "ARC (25-shot)", "value": 66.89, "name": "ARC (25-shot)", "verified": false}, {"type": "HellaSwag (10-... | text-generation | {"parameters": {"F16": 7241732096}, "total": 7241732096} | [
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"added_tokens.json",
"config.json",
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"pytorch_model-00001-of-00002.bin",
"pytorch_model-00002-of-00002.bin",
"pytorch_model.bin.index.js... | 7506dfc5fb325a8a8e0c4f9a6a001671833e5b8e | [
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"text-generation",
"LLMs",
"math",
"Intel",
"conversational",
"arxiv:2309.12284",
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"license:apache-2.0",
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"text-generation-inference"... | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": null, "unk_token": "<unk>", "use_default_system_prompt": true, "chat_template": "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Intel/neural-chat-7b-v3-1", "license": "apache-2.0", "tags": ["LLMs", "mistral", "math", "Intel"], "model-index": [{"name": "neural-chat-7b-v3-3", "results": [{"task": {"type": "Large Language Model", "name": "Large Language Model"}, "dataset": {"name": "meta-math/MetaMathQA", "type": "meta-math/MetaMat... | ## Model Details: Neural-Chat-v3-3
This model is a fine-tuned 7B parameter LLM on the Intel Gaudi 2 processor from the [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1) on the [meta-math/MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) dataset. The model was aligned using t... | null | [
"apache-2.0"
] | null | null | 7,241,732,096 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66290a68c4d750e61fc63b87 | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | TitanML/tiny-mixtral | TitanML | null | 93,918 | 1,408,565 | False | 2024-04-24T13:34:32Z | 2024-07-04T15:45:20Z | transformers | 2 | 0 | null | text-generation | {"parameters": {"F32": 246961152}, "total": 246961152} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 40b934a7efa1bf6732ba19ed7a97956c201fd775 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MixtralForCausalLM"], "model_type": "mixtral", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": null, "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | null | This is a tiny-random mixtral, useful for testing and CI/CD pipelines. It is not trained at all, and not suitable for inferencing in any application. | null | null | null | null | 246,961,152 | null | null | [
"mixtral",
"MixtralForCausalLM",
"AutoModelForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
663a4864bdcfe1472b15580d | f5e7f943c396c76b | ddde944c7b357600 | 32,011 | BPE | 61,249 | true | false | true | true | 14 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 52 | microsoft/Phi-3-medium-128k-instruct | microsoft | null | 4,887 | 1,243,402 | False | 2024-05-07T15:27:32Z | 2025-12-10T20:19:35Z | transformers | 387 | 0 | null | text-generation | null | [
".gitattributes",
"CODE_OF_CONDUCT.md",
"LICENSE",
"NOTICE.md",
"README.md",
"SECURITY.md",
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"data_summary_card.md",
"generation_config.json",
"model-00001-of-00006.safetensors",
"model-00002-of-00006.safetensors",
"model-00003-of... | a088b37c71d441ab6d862bb3fcfe6165b3014702 | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"nlp",
"code",
"conversational",
"custom_code",
"multilingual",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Phi3ForCausalLM"], "auto_map": {"AutoConfig": "configuration_phi3.Phi3Config", "AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"}, "model_type": "phi3", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual"], "library_name": null, "license": "mit", "license_name": null, "license_link": "https://huggingface.co/microsoft/Phi-3-medium-128k-instruct/resolve/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generati... | 🎉 **Phi-3.5**: [[mini-instruct]](https://huggingface.co/microsoft/Phi-3.5-mini-instruct); [[MoE-instruct]](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) ; [[vision-instruct]](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)
## Model Summary
The Phi-3-Medium-128K-Instruct is a 14B parameters, lightw... | null | [
"mit",
"https://huggingface.co/microsoft/Phi-3-medium-128k-instruct/resolve/main/LICENSE"
] | null | [
"multilingual"
] | null | null | null | [
"AutoModelForCausalLM",
"phi3",
"Phi3ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
680f8dbc517048583de82f94 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Qwen/Qwen3-30B-A3B-FP8 | Qwen | {
"models": [
{
"_id": "680da7c9aa6c98cb395660ac",
"id": "Qwen/Qwen3-30B-A3B"
}
],
"relation": "quantized"
} | 65,010 | 1,317,184 | False | 2025-04-28T14:16:28Z | 2025-07-26T03:46:17Z | transformers | 83 | 1 | null | text-generation | null | [
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] | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "quantization_config": {"quant_method": "fp8"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n... | {
"auto_model": "AutoModelForCausalLM",
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} | {"base_model": ["Qwen/Qwen3-30B-A3B"], "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-30B-A3B-FP8/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-ge... | null | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-30B-A3B-FP8/blob/main/LICENSE"
] | null | null | null | null | null | [
"qwen3_moe",
"AutoModelForCausalLM",
"Qwen3MoeForCausalLM"
] | [
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] | [
"text"
] | [
"text"
] |
66cfe00769a28ebc9e5f76ee | 9ce802ff75cac6bf | 6b3612a55f8124d9 | 151,657 | BPE | 151,387 | true | true | true | true | 14 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 340 | Qwen/Qwen2-VL-7B-Instruct-AWQ | Qwen | {
"models": [
{
"_id": "66cee7d175a88154443a4056",
"id": "Qwen/Qwen2-VL-7B-Instruct"
}
],
"relation": "quantized"
} | 1,631,766 | 3,704,944 | False | 2024-08-29T02:42:15Z | 2024-09-25T12:32:39Z | transformers | 49 | 0 | null | image-text-to-text | {"parameters": {"I32": 6525288448, "F16": 1766087168}, "total": 8291375616} | [
".gitattributes",
"LICENSE",
"README.md",
"added_tokens.json",
"chat_template.json",
"config.json",
"generation_config.json",
"merges.txt",
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"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"preprocessor_config.json",
"special_tokens_map.... | 6ec2560b0afc3a618d4acc9b8e2967d1642f463d | [
"transformers",
"safetensors",
"qwen2_vl",
"image-text-to-text",
"multimodal",
"conversational",
"en",
"arxiv:2409.12191",
"arxiv:2308.12966",
"base_model:Qwen/Qwen2-VL-7B-Instruct",
"base_model:quantized:Qwen/Qwen2-VL-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpo... | null | {"architectures": ["Qwen2VLForConditionalGeneration"], "model_type": "qwen2_vl", "quantization_config": {"bits": 4, "quant_method": "awq"}, "processor_config": {"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": "Qwen/Qwen2-VL-7B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["multimodal"]} | # Qwen2-VL-7B-Instruct-AWQ
## Introduction
We're excited to unveil **Qwen2-VL**, the latest iteration of our Qwen-VL model, representing nearly a year of innovation.
### What’s New in Qwen2-VL?
#### Key Enhancements:
* **SoTA understanding of images of various resolution & ratio**: Qwen2-VL achieves state-of-the-... | null | [
"apache-2.0"
] | null | [
"en"
] | 8,291,375,616 | null | null | [
"AutoModelForImageTextToText",
"qwen2_vl",
"Qwen2VLForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
68a686808e8db90f8998697a | 9baf32eaba6e2bf5 | 581b243d041ef3f5 | 128,815 | BPE | 127,741 | true | true | true | true | 818 | Sequence | Sequence | ByteLevel | [] | [
"Split",
"Split",
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 341 | deepseek-ai/DeepSeek-V3.1 | deepseek-ai | {
"models": [
{
"_id": "68a44b5c81d6dff37afe329f",
"id": "deepseek-ai/DeepSeek-V3.1-Base"
}
],
"relation": "quantized"
} | 153,389 | 1,580,641 | False | 2025-08-21T02:37:52Z | 2025-09-05T11:30:15Z | transformers | 819 | 0 | null | text-generation | null | [
".gitattributes",
"LICENSE",
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"assets/chat_template.jinja",
"assets/code_agent_trajectory.html",
"assets/search_python_tool_trajectory.html",
"assets/search_tool_trajectory.html",
"config.json",
"configuration_deepseek.py",
"generation_config.json",
"model-00001-of-000163.safetensors... | c0781d039fb7a1ba2abc4add0bdc293e92d2b8db | [
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"base_model:quantized:deepseek-ai/DeepSeek-V3.1-Base",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"fp8",... | null | {"architectures": ["DeepseekV3ForCausalLM"], "auto_map": {"AutoConfig": "configuration_deepseek.DeepseekV3Config", "AutoModel": "modeling_deepseek.DeepseekV3Model", "AutoModelForCausalLM": "modeling_deepseek.DeepseekV3ForCausalLM"}, "model_type": "deepseek_v3", "quantization_config": {"quant_method": "fp8"}, "tokenizer... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["deepseek-ai/DeepSeek-V3.1-Base"], "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # DeepSeek-V3.1
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
</div>
<hr>
<div align="cen... | null | [
"mit"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"deepseek_v3",
"DeepseekV3ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
688b7f628b83e9d315761485 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-4bit | lmstudio-community | {
"models": [
{
"_id": "688b1597e5e83e19d1b3238a",
"id": "Qwen/Qwen3-Coder-30B-A3B-Instruct"
}
],
"relation": "quantized"
} | 197,979 | 1,820,312 | False | 2025-07-31T14:36:18Z | 2025-07-31T14:37:10Z | transformers | 16 | 1 | null | text-generation | {"parameters": {"BF16": 954333184, "U32": 3816488960}, "total": 30532122624} | [
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"license:apache-2.0",
"endpoints_compatible",
"4-bit",
"region:us"
] | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "quantization_config": {"bits": 4}, "tokenizer_config": {"bos_token": null, "eos_token": "<|im_end|>", "pad_token": "<|endoftext|>", "unk_token": null, "chat_template": "{% macro render_item_list(item_list, tag_name='required') %}\n {%- if item_li... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
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} | {"base_model": "Qwen/Qwen3-Coder-30B-A3B-Instruct", "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, ... | null | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE"
] | null | null | 30,532,122,624 | null | null | [
"qwen3_moe",
"AutoModelForCausalLM",
"Qwen3MoeForCausalLM"
] | [
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"text"
] | [
"text"
] |
66f951dca90151b7d0463397 | 14a8cb12ce5dee8f | 8a01c02d4d0a401c | 119,547 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 87 | Gherman/bert-base-NER-Russian | Gherman | {
"models": [
{
"_id": "621ffdc036468d709f174336",
"id": "google-bert/bert-base-multilingual-cased"
}
],
"relation": "finetune"
} | 293,662 | 1,890,873 | False | 2024-09-29T13:10:52Z | 2025-12-16T14:24:12Z | transformers | 21 | 0 | null | token-classification | {"parameters": {"F32": 177288994}, "total": 177288994} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | fc6b2c5a2c5d7c82da7416f7fd9c055159bbb984 | [
"transformers",
"safetensors",
"bert",
"token-classification",
"ru",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForTokenClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": ["google-bert/bert-base-multilingual-cased"], "datasets": null, "eval_results": null, "language": ["ru"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "token-classification", "tags": null} | # Russian Named Entity Recognition Model
## Model description
This model is a fine-tuned version of `bert-base-multilingual-cased` for Named Entity Recognition (NER) in Russian text. It can identify various entity types such as person names, locations, and organizations using the BIOLU tagging format.
## Intended us... | null | [
"mit"
] | null | [
"ru"
] | 177,288,994 | null | null | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66eb40ba5818c1b134a567b5 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | unsloth/Qwen2.5-0.5B-Instruct | unsloth | {
"models": [
{
"_id": "66e81c0ecfd2f50991047e92",
"id": "Qwen/Qwen2.5-0.5B-Instruct"
}
],
"relation": "finetune"
} | 30,685 | 1,200,752 | False | 2024-09-18T21:06:02Z | 2025-02-06T02:04:10Z | transformers | 12 | 0 | null | text-generation | {"parameters": {"BF16": 494032768}, "total": 494032768} | [
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"README.md",
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"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | ae616882a38b36759fc46ac3fd6769498833b913 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"conversational",
"en",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
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"license:apache-2.0",
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"endpoints_compatible",
"deploy:az... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Aliba... | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-0.5B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["unsloth", "transformers"]} | # Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a Qwen 2.5 (all model sizes) [free Google Colab Tesla T4 notebook](https://colab.research.google.com/drive/1Kose-ucXO1IBaZq5BvbwWieuubP7hxvQ?usp=sharing).
Also a [Qwen 2.5 conversational style notebook](https://colab.research.... | null | [
"apache-2.0"
] | null | [
"en"
] | 494,032,768 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
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"id": "google/gemma-3n-E4B-it"
}
],
"relation": "quantized"
} | 136,401 | 1,635,562 | False | 2025-07-01T21:29:25Z | 2025-07-21T15:52:20Z | transformers | 1 | 0 | null | image-text-to-text | null | [
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] | [
"vision",
"multimodal"
] | [
"text",
"audio",
"image"
] | [
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] |
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"Split",
"ByteLevel"
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"ByteLevel"
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{
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}
],
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} | 1,092,049 | 2,453,821 | False | 2025-07-08T10:11:45Z | 2025-09-10T12:28:11Z | transformers | 920 | 7 | null | text-generation | {"parameters": {"BF16": 3075098624}, "total": 3075098624} | [
".gitattributes",
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"config.json",
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"deploy:azur... | null | {"architectures": ["SmolLM3ForCausalLM"], "model_type": "smollm3", "tokenizer_config": {"bos_token": null, "eos_token": "<|im_end|>", "pad_token": "<|im_end|>"}, "chat_template_jinja": "{# \u2500\u2500\u2500\u2500\u2500 defaults \u2500\u2500\u2500\u2500\u2500 #}\n{%- if enable_thinking is not defined -%}\n{%- set enabl... | {
"auto_model": "AutoModelForCausalLM",
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"processor": "AutoTokenizer"
} | {"base_model": ["HuggingFaceTB/SmolLM3-3B-Base"], "datasets": null, "eval_results": null, "language": ["en", "fr", "es", "it", "pt", "zh", "ar", "ru"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ... | # SmolLM3
## Table of Contents
1. [Model Summary](#model-summary)
2. [How to use](#how-to-use)
3. [Evaluation](#evaluation)
4. [Training](#training)
5. [Limitations](#limitations)
6. [License](#l... | null | [
"apache-2.0"
] | null | [
"en",
"fr",
"es",
"it",
"pt",
"zh",
"ar",
"ru"
] | 3,075,098,624 | null | null | [
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"text"
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"text"
] | [
"text"
] |
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"Split",
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"ByteLevel"
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"models": [
{
"_id": "678e12d6308aaf716869ea0e",
"id": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B"
}
],
"relation": "quantized"
} | 20,287 | 1,193,201 | False | 2025-01-20T15:12:24Z | 2025-07-18T13:05:54Z | transformers | 36 | 0 | null | text-generation | {"parameters": {"F32": 475344, "BF16": 1227100160, "U8": 7015836196}, "total": 8243411700} | [
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"tokenizer_config.json"
] | 99d24119ec73dd12a306bf48ccc69bfe7a343848 | [
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"base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
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"text-generation-inferenc... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": "<\uff5cbegin\u2581of\u2581sentence\uff5c>", "eos_token": "<\uff5cend\u2581of\u2581sentence\uff5c>", "pad_token": "<|fine... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
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} | {"base_model": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["deepseek", "unsloth", "transforme... | null | null | [
"llama3.1"
] | null | [
"en"
] | 8,243,411,700 | null | null | [
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"LlamaForCausalLM"
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6523bd962d3ba46ccd378b5a | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
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"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | HuggingFaceH4/zephyr-7b-alpha | HuggingFaceH4 | {
"models": [
{
"_id": "650aedb6238a644cb93a52c3",
"id": "mistralai/Mistral-7B-v0.1"
}
],
"relation": "finetune"
} | 4,201 | 1,055,009 | False | 2023-10-09T08:45:10Z | 2024-10-16T11:53:22Z | transformers | 1,120 | 0 | [{"name": "zephyr-7b-alpha", "results": []}] | text-generation | {"parameters": {"BF16": 7241732096}, "total": 7241732096} | [
".gitattributes",
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"base_model:mistralai/... | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['conte... | {
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should probably proofread and complete it, then remove this comment. -->
<img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="marg... | null | [
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] | [
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] | [
"ByteLevel"
] | 236 | deepseek-ai/DeepSeek-V2-Lite-Chat | deepseek-ai | null | 606,676 | 2,550,852 | False | 2024-05-15T05:34:09Z | 2024-06-25T08:36:27Z | transformers | 135 | 0 | null | text-generation | {"parameters": {"BF16": 15706484224}, "total": 15706484224} | [
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] | null | {"architectures": ["DeepseekV2ForCausalLM"], "auto_map": {"AutoConfig": "configuration_deepseek.DeepseekV2Config", "AutoModel": "modeling_deepseek.DeepseekV2Model", "AutoModelForCausalLM": "modeling_deepseek.DeepseekV2ForCausalLM"}, "model_type": "deepseek_v2", "tokenizer_config": {"bos_token": {"__type": "AddedToken",... | {
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<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V2" />
</div>
<hr>
<div align="center" style="line-... | null | [
"other",
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] | [
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] | [
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] |
62fbf03be44837de5447aaff | 076f306869b6e17b | 7871d57f101d75a3 | 144 | Unigram | null | false | false | true | true | 119 | null | null | Metaspace | null | null | [
"Metaspace"
] | 345 | ElnaggarLab/ankh-base | ElnaggarLab | null | 68,874 | 1,457,474 | False | 2022-08-16T19:30:03Z | 2023-12-18T12:54:09Z | transformers | 12 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | d99cb6b966530dfc2ae96bc69d9255c2a07308b0 | [
"transformers",
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"t5",
"text2text-generation",
"biology",
"protein",
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"protein embedding",
"doi:10.57967/hf/0276",
"license:cc-by-nc-sa-4.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "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": ["biology", "protein", "protein language model", "protein embedding"]} | null | [
"cc-by-nc-sa-4.0"
] | null | null | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
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