Image-Text-to-Text
Transformers
Safetensors
multilingual
unlimited-ocr
feature-extraction
baidu
vision-language
ocr
custom_code
Eval Results
Instructions to use baidu/Unlimited-OCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baidu/Unlimited-OCR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="baidu/Unlimited-OCR", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("baidu/Unlimited-OCR", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use baidu/Unlimited-OCR with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "baidu/Unlimited-OCR" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "baidu/Unlimited-OCR", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/baidu/Unlimited-OCR
- SGLang
How to use baidu/Unlimited-OCR with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "baidu/Unlimited-OCR" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "baidu/Unlimited-OCR", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "baidu/Unlimited-OCR" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "baidu/Unlimited-OCR", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use baidu/Unlimited-OCR with Docker Model Runner:
docker model run hf.co/baidu/Unlimited-OCR
Update README.md
Browse files
README.md
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@@ -280,6 +280,37 @@ generate("Multi page parsing.", ["page1.png", "page2.png"], image_mode="base", n
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generate("Multi page parsing.", pdf_to_images("your_doc.pdf", dpi=300), image_mode="base", ngram_window=1024)
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```
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## Visualization
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generate("Multi page parsing.", pdf_to_images("your_doc.pdf", dpi=300), image_mode="base", ngram_window=1024)
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```
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For OmniDocBench evaluation, you need to perform the following post-processing.
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```python
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def remove_det(raw: str) -> str:
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"""
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Strip <|det|>type [bbox]<|/det|> markers, group lines belonging to the
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same block with \\n, and separate different blocks with \\n\\n.
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"""
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blocks = []
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cur = None
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for line in raw.splitlines():
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line = line.rstrip()
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if not line:
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continue
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m = DET_RE.match(line)
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if m:
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category, content = m.group(1).strip(), m.group(2).strip()
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if category == 'image':
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continue
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if cur is not None:
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blocks.append(cur)
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cur = [content] if content else []
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continue
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if cur is None:
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cur = []
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cur.append(line)
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if cur is not None:
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blocks.append(cur)
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text = '\n\n'.join('\n'.join(b) for b in blocks).strip()
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return text
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```
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## Visualization
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