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The model is intended to be used for Q&A task, given the question & context, the model would attempt to infer the answer text, answer span & confidence score.<br>
Model is encoder-only (deepset/roberta-base-squad2) with QuestionAnswering LM Head, fine-tuned on SQUADx dataset with **exact_match:** ... | null | [
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# OLMo-2-1124-7B-Instruct
## NOTE: 1/3/2025 UPDATE:
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<p style="margin-top: 0;margin-bottom: 0;">
<em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
</p>
<div style="display: flex; gap: 5px; align-items: center; ">
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"id": "Qwen/Qwen2.5-VL-3B-Instruct"
}
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"relation": "finetune"
} | 5,155 | 256,330 | False | 2025-01-31T08:59:20Z | 2025-05-12T08:29:23Z | transformers | 5 | 0 | null | image-text-to-text | null | [
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<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
In the past five months since Qwen2-VL’s r... | null | [
"qwen-research",
"https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE"
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"en"
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"safetensors",
"xlm-roberta",
"token-classification",
"named-entity-recognition",
"biomedical-nlp",
"disease-entity-recognition",
"medical-diagnosis",
"ncbi",
"pathology",
"disease",
"en",
"arxiv:2508.01630",
"license:apache-2.0",
"endpoints_compatible",
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] | null | {"architectures": ["XLMRobertaForTokenClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["token-classification", "named-entity-recognition", "biomedical-nlp", "transform... | # 🧬 [OpenMed-NER-PathologyDetect-BigMed-278M](https://huggingface.co/OpenMed/OpenMed-NER-PathologyDetect-BigMed-278M)
**Specialized model for Disease Entity Recognition - Disease entities from the NCBI dataset**
[](https://opensource.org/licenses/... | null | [
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The context/passage encoder model based on [DPRContextEncoder](https://huggingface.co/docs/transformers/master/en/model_doc/dpr#transformers.DPRContextEncoder) architecture. It uses the transformer's pooler outputs as context/passage representations. See [blog post](https://towardsdatascience.com/long-f... | null | [
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<!-- 200823 -->
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CodeParrot 🦜 is a GPT-2 model (1.5B parameters) trained to generate Python code. After the initial training and release of v1.0 we trained the model some more and released v1.1 (see below for details).
## Usage
You can load the CodeParrot model and tokenizer directly in `transformers`:
```Python
fr... | null | null | [
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<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 model trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
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BioLinkBERT-large model pretrained on [PubMed](https://pubmed.ncbi.nlm.nih.gov/) abstracts along with citation link information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available... | null | [
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"transformers",
"safetensors",
"roberta",
"token-classification",
"named-entity-recognition",
"biomedical-nlp",
"disease-entity-recognition",
"medical-diagnosis",
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... | null | {"architectures": ["RobertaForTokenClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForTokenClassification",
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"pipeline_tag": "token-classification",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["token-classification", "named-entity-recognition", "biomedical-nlp", "transform... | # 🧬 [OpenMed-NER-DiseaseDetect-SuperMedical-355M](https://huggingface.co/OpenMed/OpenMed-NER-DiseaseDetect-SuperMedical-355M)
**Specialized model for Disease Entity Recognition - Disease entities from the BC5CDR dataset**
[](https://opensource.org... | null | [
"apache-2.0"
] | null | [
"en"
] | 354,313,219 | null | null | [
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628afc58c204ff147828d44d | 2012b52fa41ec860 | db117e9883c4d0ab | 50,257 | BPE | 50,000 | false | true | true | true | 1 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 113 | IDEA-CCNL/Wenzhong-GPT2-110M | IDEA-CCNL | null | 4,903 | 212,359 | False | 2022-05-23T03:15:36Z | 2023-05-25T09:48:34Z | transformers | 28 | 0 | null | text-generation | {"parameters": {"F16": 137058816}, "total": 137058816} | [
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"model.safetensors",
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"tokenizer.json",
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] | 2d61cfe8803b5281f361f2dbda67ad169fed32da | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"generate",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "tokenizer_config": {"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["zh"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["generate", "gpt2"], "inference": {"parameters": {"temperature": 0.7, "top_p": 0... | # Wenzhong-GPT2-110M
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理NLG任务，中文版的GPT2-Small。
Focused on handling NLG tasks, Chinese GPT2-Small.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Ser... | null | [
"apache-2.0"
] | null | [
"zh"
] | 137,058,816 | null | null | [
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"id": "swiss-ai/Apertus-70B-2509"
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} | 4,930 | 247,673 | False | 2025-09-01T16:03:48Z | 2025-11-14T11:00:18Z | transformers | 183 | 0 | null | text-generation | null | [
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"chat_template": "{% if messages[0]['role'] == 'system' %}{% set user_index = 1 %}{% else %}{% set user_index = 0 %}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != ((loop.index0 + user_index) % 2 == 0) %}{{ ... | {
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} | {"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"], "widget": [{"example_title": "Hello world (Python)", "mes... | # **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding... | null | [
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<\uff5cbegin\u2581of\u2581sentence\uff5c>", "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message }}{% end... | {
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["decompile", "binary"]} | ### 1. Introduction of LLM4Decompile
LLM4Decompile aims to decompile x86 assembly instructions into C. The newly released V1.5 series are trained with a larger dataset (15B tokens) and a maximum token length of 4,096, with remarkable performance (up to 100% improvement) compared to the previous model.
- **Github Repo... | null | [
"mit"
] | null | null | 6,740,512,768 | null | null | [
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"LlamaForCausalLM"
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} | null | null | null | null | null | null | null | null | null | [
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6787e813e6289f021f87ec0c | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
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] | null | {"architectures": ["LlamaForSequenceClassification"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|eot_id|>"}, "chat_template_jinja": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message... | {
"auto_model": "AutoModelForSequenceClassification",
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"pipeline_tag": "text-classification",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["trl"]} | # Tiny LlamaForSequenceClassification
This is a minimal model built for unit tests in the [TRL](https://github.com/huggingface/trl) library. | null | null | null | null | 2,079,840 | null | null | [
"LlamaForSequenceClassification",
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"AutoModelForSequenceClassification"
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] | 0 | sentence-transformers/bert-base-nli-max-tokens | sentence-transformers | null | 3,538 | 207,636 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:35:09Z | sentence-transformers | 3 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 109482240}, "total": 109482752} | [
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"sentence-similarity",
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] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/bert-base-nli-max-tokens
This is a [sentence-trans... | null | [
"apache-2.0"
] | null | null | 109,482,752 | null | null | [
"BertModel",
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"ByteLevel"
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] | 250 | DTAI-KULeuven/robbert-v2-dutch-sentiment | DTAI-KULeuven | null | 746 | 206,599 | False | 2022-05-30T16:53:44Z | 2022-06-29T13:11:28Z | transformers | 8 | 0 | [{"name": "robbert-v2-dutch-sentiment", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "dbrd", "type": "sentiment-analysis", "split": "test"}, "metrics": [{"name": "Accuracy", "type": "accuracy", "value": 0.93325, "verified": false}]}]}] | text-classification | null | [
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] | bb4e1466d94f15534e792fc6870040e024000432 | [
"transformers",
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"text-classification",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"nl",
"dataset:dbrd",
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"model-index",
"text-embeddings-inference",
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"deploy:azure",
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] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": fa... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
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} | {"datasets": ["dbrd"], "language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT"], "widget": [{"text": "Ik erken dat dit een boek is, daarmee is alles gezegd."}, {"text": "Prachtig verhaal, heel mooi verteld en een verrassend einde... Een topper!"}], "thumbnail": "https://github.com/iPieter/... | <p align="center">
<img src="https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo_with_name.png" alt="RobBERT: A Dutch RoBERTa-based Language Model" width="75%">
</p>
# RobBERT finetuned for sentiment analysis on DBRD
This is a finetuned model based on [RobBERT (v2)](https://huggingface.co/pdelobelle/... | null | [
"mit"
] | [
"dbrd"
] | [
"nl"
] | null | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65f2cc4ad265d013d74bc39e | 96bc65dfdb5816e0 | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 715 | ministral/Ministral-3b-instruct | ministral | null | 4,403 | 342,169 | False | 2024-03-14T10:07:06Z | 2024-03-21T14:39:28Z | transformers | 83 | 1 | null | text-generation | {"parameters": {"BF16": 3315716096}, "total": 3315716096} | [
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] | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{bos_token + message['role'] + '\n' + message['content'] + eos_token ... | {
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### Model Description
<!-- Provide a longer summary of what this model is. -->
Ministral is a series of language model, build with same architecture as the famous Mistral model, but with less si... | null | [
"apache-2.0"
] | null | [
"en"
] | 3,315,716,096 | null | null | [
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] | [
"text"
] | [
"text"
] |
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"roberta",
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] | 293 | Bingsu/clip-vit-large-patch14-ko | Bingsu | null | 1,121 | 223,847 | False | 2022-10-11T01:55:47Z | 2022-11-18T02:13:00Z | transformers | 17 | 0 | null | zero-shot-image-classification | {"parameters": {"F32": 427616512, "I64": 334}, "total": 427616846} | [
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"tf",
"safetensors",
"clip",
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"ko",
"arxiv:2004.09813",
"doi:10.57967/hf/1616",
"license:mit",
"endpoints_compatible",
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] | null | {"architectures": ["CLIPModel"], "model_type": "clip", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<|startoftext|>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized": tr... | {
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"custom_class": null,
"pipeline_tag": "zero-shot-image-classification",
"processor": "AutoProcessor"
} | {"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": null, "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/mai... | # clip-vit-large-patch14-ko
Korean CLIP model trained by [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813)
[Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813)로 학습된 한국어 CLIP 모델입니다.
훈련 코드... | null | [
"mit"
] | null | [
"ko"
] | 427,616,846 | null | null | [
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] | [
"multimodal"
] | [
"text",
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] | [
"logits"
] |
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"models": [
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"id": "valurank/distilroberta-bias"
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],
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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>"}} | {
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} | {"base_model": "valurank/distilroberta-bias", "datasets": ["valurank/wikirev-bias"], "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": ["bias", "distilroberta"], "... | # ONNX version of valurank/distilroberta-bias
**This model is a conversion of [valurank/distilroberta-bias](https://huggingface.co/valurank/distilroberta-bias) to ONNX** format. It is designed to detect biases in text using the distilled version of the RoBERTa model. The model was converted to ONNX using the [🤗 Optim... | null | null | [
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] | [
"en"
] | null | null | null | [
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] | null | {"architectures": ["Gemma2ForCausalLM"], "model_type": "gemma2", "tokenizer_config": {"bos_token": "<bos>", "chat_template": "{{ '<bos>' }}{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message i... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"language": ["ar", "en"], "library_name": "transformers", "license": "gemma", "pipeline_tag": "text-generation", "tags": ["conversational"], "extra_gated_button_content": "Acknowledge license", "model-index": [{"name": "SILMA-9B-Instruct-v1.0", "results": [{"task": {"type": "text-generation"}, "dataset": {"name": "Ara... | # SILMA AI
SILMA.AI is a leading Generative AI startup dedicated to empowering Arabic speakers with state-of-the-art AI solutions.
## 🚀 Our Flagship Model: SILMA 1.0 🚀
* **SILMA 1.0** was the **TOP-RANKED** open-weights Arabic LLM (Until February 2025) with an impressive **9 billion parameter size**, surpassing ... | null | [
"gemma"
] | null | [
"ar",
"en"
] | 9,241,705,984 | null | null | [
"AutoModelForCausalLM",
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] | [
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] | [
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] | [
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"auto_model": "AutoModelForCausalLM",
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<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discor... | null | [
"other",
"deepseek",
"https://github.com/deepseek-ai/DeepSeek-Math/blob/main/LICENSE-MODEL"
] | null | null | null | null | null | [
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] | [
"text"
] | [
"text"
] |
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"NFC"
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] | null | {"architectures": ["MPTForCausalLM"], "auto_map": {"AutoConfig": "configuration_mpt.MPTConfig", "AutoModelForCausalLM": "modeling_mpt.MPTForCausalLM"}, "model_type": "mpt", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
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"custom_class": "modeling_mpt.MPTForCausalLM",
"pipeline_tag": "text-generation",
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} | null | null | null | null | null | null | null | null | null | [
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] | 636c707430a5509c80b1aa51d05c127ed339a975 | [
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"phi3",
"phi",
"conversational",
"en",
"license:mit",
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] | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{'<|system|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'user' %}{{'<|user|>\n' + message['content'] + '<... | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["unsloth", "transformers", "phi3", "phi"]} | ## Reminder to use the dev version Transformers:
`pip install git+https://github.com/huggingface/transformers.git`
# Finetune Phi-3, Llama 3, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
Directly quantized 4bit model with `bitsandbytes`. We Mistralfied the model to ensure it could be used on many pl... | null | [
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] | null | [
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] | 3,821,079,552 | null | null | [
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] | 412 | OpenMed/OpenMed-NER-ProteinDetect-MultiMed-568M | OpenMed | null | 90,909 | 529,587 | False | 2025-07-16T19:23:56Z | 2025-08-05T09:35:12Z | transformers | 2 | 0 | null | token-classification | {"parameters": {"BF16": 566716427}, "total": 566716427} | [
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"biochemistry",
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"protein",
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"protein_enum",
"protein_familiy_or_group",
"protein_variant",... | null | {"architectures": ["XLMRobertaForTokenClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
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"apache-2.0"
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684318f0b2a5963f6235c66b | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | tomaarsen/Qwen3-Reranker-0.6B-seq-cls | tomaarsen | {
"models": [
{
"_id": "680f0da2f99a20ddfc9a9f26",
"id": "Qwen/Qwen3-0.6B-Base"
}
],
"relation": "finetune"
} | 69,536 | 431,190 | False | 2025-06-06T16:36:00Z | 2025-07-10T10:53:12Z | sentence-transformers | 29 | 0 | null | text-ranking | {"parameters": {"F32": 595777536}, "total": 595777536} | [
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Код инференса:
```py
from transformers import pipeline
from transformers import AutoTokenizer
pt = "RUPunct/RUPunct_big"
tk = AutoTokenizer.from_pretrained(pt, strip_accents=False, add_prefix_space=True)
classifier = pipeline("... | null | [
"mit"
] | null | [
"ru"
] | null | null | null | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
687a0b9aab88e029552ea09a | e034328a2d067fa5 | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 237 | OpenMed/OpenMed-NER-DiseaseDetect-SuperClinical-184M | OpenMed | null | 91,102 | 530,278 | False | 2025-07-18T08:53:46Z | 2025-08-05T09:44:55Z | transformers | 0 | 0 | null | token-classification | {"parameters": {"BF16": 183833859}, "total": 183833859} | [
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"special_tokens_map.json",
"spm.model",
"test_results.json",
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] | 1d72b9a9bf3e755fef613337a7d6b17e1b7ef48f | [
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"pathology",
"biocuration",
"disease",
"en",
"arxiv:2508.01630",
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"apache-2.0"
] | null | [
"en"
] | 183,833,859 | null | null | [
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654200ee807b8183e9cb04fb | 28d4a123ad8bfad0 | 5bf01c277bad844f | 65,000 | BPE | 64,756 | true | true | true | true | 2 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 717 | cyberagent/calm2-7b-chat | cyberagent | null | 1,021 | 206,672 | False | 2023-11-01T07:40:30Z | 2024-02-17T04:30:14Z | transformers | 77 | 0 | null | text-generation | {"parameters": {"BF16": 7008948224}, "total": 7008948224} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
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"pytorch_model-00001-of-00002.bin",
"pytorch_model-00002-of-00002.bin",
"pytorch_model.bin.index.json",
"special_tokens_... | 3e110c7624ff7a940badebfd5f9a91569f98cc56 | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"japanese",
"causal-lm",
"ja",
"en",
"arxiv:2302.13971",
"license:apache-2.0",
"text-generation-inference",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|padding|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ja", "en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["japanese", "causal-lm"], "inference": false} | # CyberAgentLM2-7B-Chat (CALM2-7B-Chat)
## Model Description
CyberAgentLM2-Chat is a fine-tuned model of [CyberAgentLM2](https://huggingface.co/cyberagent/calm2-7b) for dialogue use cases.
## Requirements
- transformers >= 4.34.1
- accelerate
## Usage
```python
import transformers
from transformers import AutoMode... | null | [
"apache-2.0"
] | null | [
"ja",
"en"
] | 7,008,948,224 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
643cda88f925266fb59fc593 | 8a8c94e1a59f5186 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 11 | MBZUAI/LaMini-T5-738M | MBZUAI | null | 585 | 200,614 | False | 2023-04-17T05:35:04Z | 2023-04-28T12:07:40Z | transformers | 49 | 0 | [{"name": "flan-t5-small-distil-v2", "results": []}] | text-generation | null | [
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] | 5fc690b5cf88f188fd7b8fd19aee6c43af183d8b | [
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should probably proofread and complete it, then remove this comment. -->
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt=... | null | [
"cc-by-nc-4.0"
] | null | [
"en"
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"text"
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6877f5a826f64d5d89fbd078 | d13dc58d71bbfa73 | 291a9305acaae14c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 413 | OpenMed/OpenMed-NER-ProteinDetect-PubMed-109M | OpenMed | null | 91,064 | 529,595 | False | 2025-07-16T18:55:36Z | 2025-08-05T09:34:21Z | transformers | 0 | 0 | null | token-classification | {"parameters": {"BF16": 108900107}, "total": 108900107} | [
".gitattributes",
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"special_tokens_map.json",
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] | 639e18bf11c277fb16a2970a27b37180e6eeb5fa | [
"transformers",
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"molecular-biology",
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"systems-biology",
"protein",
"protein_complex",
"protein_enum",
"protein_familiy_or_group",
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"en"... | null | {"architectures": ["BertForTokenClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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662afb0430911c7590cb5915 | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | MoaData/Myrrh_solar_10.7b_3.0 | MoaData | null | 10,938 | 251,024 | False | 2024-04-26T00:53:24Z | 2024-04-26T01:02:39Z | transformers | 3 | 0 | null | text-generation | {"parameters": {"F16": 10731524096}, "total": 10731524096} | [
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"ko",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "</s>", "unk_token": "<unk>", "use_default_system_prompt": true}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ko"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ## Model Details
**Model Developers** : Taeeon Park, Gihong Lee
**dataset** : dpo medical dataset (AI-hub dataset 활용 자체 제작)
**Training Method Method** : DPO.
**Company** : MoAData
## Usage
```
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "MoaData/Myrrh_solar_10.7b_3.0"
model = ... | null | [
"apache-2.0"
] | null | [
"ko"
] | 10,731,524,096 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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] | [
"Metaspace"
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"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"named-entity-recognition",
"biomedical-nlp",
"species-recognition",
"taxonomy",
"organism-identification",
"biology",
"species",
"en",
"arxiv:2508.01630",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["XLMRobertaForTokenClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["token-classification", "named-entity-recognition", "biomedical-nlp", "transform... | # 🧬 [OpenMed-NER-SpeciesDetect-BigMed-560M](https://huggingface.co/OpenMed/OpenMed-NER-SpeciesDetect-BigMed-560M)
**Specialized model for Species Entity Recognition - Species and organism names**
[](https://opensource.org/licenses/Apache-2.0)
[![P... | null | [
"apache-2.0"
] | null | [
"en"
] | 558,843,907 | null | null | [
"XLMRobertaForTokenClassification",
"AutoModelForTokenClassification",
"xlm-roberta"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17b6ee | 6cba9ab7798b77f3 | f560a7827840effb | 250,300 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Replace",
"Replace",
"Replace",
"Precompiled"
] | [
"Metaspace"
] | [
"Metaspace"
] | 718 | google/rembert | google | null | 126,362 | 610,534 | False | 2022-03-02T23:29:05Z | 2022-05-27T15:05:23Z | transformers | 22 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.model",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json"
] | 65da5133da36e29dfca67d4f0dd9f7f9db21b563 | [
"transformers",
"pytorch",
"tf",
"rembert",
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"bs",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha"... | null | {"model_type": "rembert", "tokenizer_config": {"bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": ["wikipedia"], "eval_results": null, "language": ["multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "bs", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "hr", "ht", "... | # RemBERT (for classification)
Pretrained RemBERT model on 110 languages using a masked language modeling (MLM) objective. It was introduced in the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/abs/2010.12821). A direct export of the model checkpoint was first made available i... | null | [
"apache-2.0"
] | [
"wikipedia"
] | [
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"bs",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"haw",
"hi",
"hmn",
"hr",
"ht",
"hu",
... | null | null | null | [
"rembert",
"AutoModel"
] | [
null
] | null | null | null |
67dd2cbb7f3bf8de27aefb88 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | AIDC-AI/Ovis2-34B-GPTQ-Int4 | AIDC-AI | {
"models": [
{
"_id": "67a9c2b8d63b2cd66df759bb",
"id": "AIDC-AI/Ovis2-34B"
}
],
"relation": "quantized"
} | 56 | 200,591 | False | 2025-03-21T09:09:15Z | 2025-03-28T03:53:34Z | transformers | 8 | 0 | null | image-text-to-text | {"parameters": {"I32": 31205621760, "BF16": 3666832374, "F16": 458752}, "total": 34872912886} | [
".gitattributes",
"LICENSE",
"NOTICE",
"README.md",
"added_tokens.json",
"config.json",
"configuration_aimv2.py",
"configuration_ovis.py",
"generation_config.json",
"merges.txt",
"model-00001-of-00005.safetensors",
"model-00002-of-00005.safetensors",
"model-00003-of-00005.safetensors",
"mo... | d16bdfed736b2ff09b452723d5f3a0b3c7254f7a | [
"transformers",
"safetensors",
"ovis",
"text-generation",
"MLLM",
"image-text-to-text",
"conversational",
"custom_code",
"en",
"zh",
"dataset:AIDC-AI/Ovis-dataset",
"arxiv:2405.20797",
"base_model:AIDC-AI/Ovis2-34B",
"base_model:quantized:AIDC-AI/Ovis2-34B",
"license:apache-2.0",
"4-bi... | null | {"architectures": ["Ovis"], "auto_map": {"AutoConfig": "configuration_ovis.OvisConfig", "AutoModelForCausalLM": "modeling_ovis.Ovis"}, "model_type": "ovis", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_ovis.Ovis",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": ["AIDC-AI/Ovis2-34B"], "datasets": ["AIDC-AI/Ovis-dataset"], "eval_results": null, "language": ["en", "zh"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["MLLM"]} | # Ovis2-34B-GPTQ-Int4
<div align="center">
<img src=https://cdn-uploads.huggingface.co/production/uploads/637aebed7ce76c3b834cea37/3IK823BZ8w-mz_QfeYkDn.png width="30%"/>
</div>
## Introduction
[GitHub](https://github.com/AIDC-AI/Ovis) | [Paper](https://arxiv.org/abs/2405.20797)
We are pleased to announce the rele... | null | [
"apache-2.0"
] | [
"AIDC-AI/Ovis-dataset"
] | [
"en",
"zh"
] | 34,872,912,886 | null | null | [
"ovis",
"AutoModelForCausalLM",
"modeling_ovis.Ovis",
"Ovis"
] | [
"image-text-to-text",
"text-generation"
] | [
"text",
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
680f7626a95a28ecf6f0fcf7 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Qwen/Qwen3-14B-FP8 | Qwen | {
"models": [
{
"_id": "680da7b53a9818192728e751",
"id": "Qwen/Qwen3-14B"
}
],
"relation": "quantized"
} | 237,124 | 543,184 | False | 2025-04-28T12:35:50Z | 2025-07-26T03:46:09Z | transformers | 47 | 0 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"tokenizer.json",
"... | 9a283b4a5efbc09ce247e0ae5b02b744739e525a | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2309.00071",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-14B",
"base_model:quantized:Qwen/Qwen3-14B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"fp8",
"region:us"
] | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "quantization_config": {"quant_method": "fp8"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%-... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Qwen/Qwen3-14B"], "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-14B-FP8/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation... | null | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-14B-FP8/blob/main/LICENSE"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"Qwen3ForCausalLM",
"qwen3"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64edb42764c18067293d8f7e | 325a424394535720 | b927f436c0f7aef5 | 128,000 | WordPiece | null | true | true | true | false | 6 | Sequence | Split | null | [
"NFD",
"Lowercase",
"StripAccents",
"Replace",
"Replace"
] | [
"Split"
] | null | 613 | dicta-il/dictabert-seg | dicta-il | null | 678 | 198,990 | False | 2023-08-29T09:02:31Z | 2024-12-16T19:00:07Z | transformers | 0 | 0 | null | feature-extraction | {"parameters": {"F32": 184948336}, "total": 184948336} | [
".gitattributes",
"BertForPrefixMarking.py",
"LICENSE",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 6eb0bfefafd7f1addba10d922c2d637fb7fd4167 | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"custom_code",
"he",
"arxiv:2308.16687",
"license:cc-by-4.0",
"text-embeddings-inference",
"region:us"
] | null | {"architectures": ["BertForPrefixMarking"], "auto_map": {"AutoModel": "BertForPrefixMarking.BertForPrefixMarking"}, "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": "BertForPrefixMarking.BertForPrefixMarking",
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["he"], "library_name": null, "license": "cc-by-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "inference": false} | # DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew
State-of-the-art language model for Hebrew, released [here](https://arxiv.org/abs/2308.16687).
This is the fine-tuned model for the prefix segmentation task.
For the bert-base models for other tasks, see [here](https://huggingface.co/collections/dicta-il... | null | [
"cc-by-4.0"
] | null | [
"he"
] | 184,948,336 | null | null | [
"BertForPrefixMarking.BertForPrefixMarking",
"AutoModel",
"bert",
"BertForPrefixMarking"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
657a7f4bd753af70cda4d720 | bac9563752f6b149 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 719 | rungalileo/xlm-roberta-base-misogyny-sexism-tweets-quantized-traced | rungalileo | null | 14 | 197,559 | False | 2023-12-14T04:06:35Z | 2023-12-14T04:08:21Z | transformers | 0 | 0 | null | text-classification | null | [
".gitattributes",
"config.json",
"model.pth",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 7c5b47545e10fdaacdfa85991847163026952a31 | [
"transformers",
"xlm-roberta",
"text-classification",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["XLMRobertaForSequenceClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | null | null | null | [
"AutoModelForSequenceClassification",
"XLMRobertaForSequenceClassification",
"xlm-roberta"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
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