InclusionAI 发布开源 OCR 模型 ArmorOCR
InclusionAI 在 Hugging Face 上发布了开源模型 ArmorOCR,采用 Apache-2.0 许可证。
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InclusionAI inclusionAI/ArmorOCR inclusionAI/ArmorOCR https://huggingface.co/inclusionAI/ArmorOCR
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inclusionAI/ArmorOCR · Hugging Face Hugging Face (https://huggingface.co/) Models (https://huggingface.co/models) Datasets (https://huggingface.co/datasets) Spaces (https://huggingface.co/spaces) Buckets new (https://huggingface.co/storage) Docs (https://huggingface.co/docs) Enterprise (https://huggingface.co/enterprise) Pricing (https://huggingface.co/pricing) Website Tasks (https://huggingface.co/tasks) HuggingChat (https://huggingface.co/chat) Collections (https://huggingface.co/collections) Languages (https://huggingface.co/languages) Organizations (https://huggingface.co/organizations) Community Blog (https://huggingface.co/blog) Posts (https://huggingface.co/posts) Daily Papers (https://huggingface.co/papers) Hardware (https://huggingface.co/hardware) Learn (https://huggingface.co/learn) Discord (https://huggingface.co/join/discord) Forum (https://discuss.huggingface.co/) GitHub (https://github.com/huggingface) Solutions Team & Enterprise (https://huggingface.co/enterprise) Hugging Face PRO (https://huggingface.co/pro) Enterprise Support (https://huggingface.co/support) Inference Providers (https://huggingface.co/inference/models) Inference Endpoints (https://huggingface.co/inference-endpoints) Storage Buckets (https://huggingface.co/storage) Log In (https://huggingface.co/login) Sign Up (https://huggingface.co/join) (https://huggingface.co/inclusionAI) inclusionAI (https://huggingface.co/inclusionAI) / ArmorOCR (https://huggingface.co/inclusionAI/ArmorOCR) like 11 Follow inclusionAI 2.75k Image-Text-to-Text (https://huggingface.co/models?pipeline_tag=image-text-to-text) Transformers (https://huggingface.co/models?library=transformers) Safetensors (https://huggingface.co/models?library=safetensors) English (https://huggingface.co/models?language=en) Chinese (https://huggingface.co/models?language=zh) qwen3_vl (https://huggingface.co/models?other=qwen3_vl) ocr (https://huggingface.co/models?other=ocr) multimodal (https://huggingface.co/models?other=multimodal) vision-language (https://huggingface.co/models?other=vision-language) adversarial ocr (https://huggingface.co/models?other=adversarial+ocr) grounded ocr (https://huggingface.co/models?other=grounded+ocr) qwen3-vl (https://huggingface.co/models?other=qwen3-vl) conversational (https://huggingface.co/models?other=conversational) arxiv: 2608.20122 License: apache-2.0 Model card (https://huggingface.co/inclusionAI/ArmorOCR) Files Files and versions xet (https://huggingface.co/inclusionAI/ArmorOCR/tree/main) Community (https://huggingface.co/inclusionAI/ArmorOCR/discussions) Deploy Copy to bucket new Use this model Instructions to use inclusionAI/ArmorOCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started. Libraries Transformers (https://huggingface.co/inclusionAI/ArmorOCR?library=transformers) How to use inclusionAI/ArmorOCR with Transformers: # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="inclusionAI/ArmorOCR") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages) # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("inclusionAI/ArmorOCR") model = AutoModelForMultimodalLM.from_pretrained("inclusionAI/ArmorOCR", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) o
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