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
Can you post the complete "DET_RE" regex pattern from the post-processing script?
def remove_det(raw: str) -> str:
"""
Strip <|det|>type [bbox]<|/det|> markers, group lines belonging to the
same block with \n, and separate different blocks with \n\n.
"""
blocks = []
cur = None
for line in raw.splitlines():
line = line.rstrip()
if not line:
continue
m = DET_RE.match(line)
if m:
category, content = m.group(1).strip(), m.group(2).strip()
if category == 'image':
continue
if cur is not None:
blocks.append(cur)
cur = [content] if content else []
continue
if cur is None:
cur = []
cur.append(line)
if cur is not None:
blocks.append(cur)
text = '\n\n'.join('\n'.join(b) for b in blocks).strip()
return text
Need the "DET_RE" pattern for this please.
Thanks, we have completed the post-processing script!
DET_RE = re.compile(r'<|det|>([^<\s]+)(?:\s[[^]]])?\s<|/det|>(.)', re.DOTALL)