Image-Text-to-Text
Transformers
Safetensors
multilingual
qianfan_ocr
vision-language
ocr
document-intelligence
qianfan
conversational
Eval Results (legacy)
Eval Results
Instructions to use rootlocalghost/Qianfan-OCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rootlocalghost/Qianfan-OCR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="rootlocalghost/Qianfan-OCR") 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("rootlocalghost/Qianfan-OCR") model = AutoModelForMultimodalLM.from_pretrained("rootlocalghost/Qianfan-OCR") 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) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rootlocalghost/Qianfan-OCR with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rootlocalghost/Qianfan-OCR" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rootlocalghost/Qianfan-OCR", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/rootlocalghost/Qianfan-OCR
- SGLang
How to use rootlocalghost/Qianfan-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 "rootlocalghost/Qianfan-OCR" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rootlocalghost/Qianfan-OCR", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "rootlocalghost/Qianfan-OCR" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rootlocalghost/Qianfan-OCR", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use rootlocalghost/Qianfan-OCR with Docker Model Runner:
docker model run hf.co/rootlocalghost/Qianfan-OCR
| {%- if messages[0]['role'] == 'system' %} | |
| {{- '<|im_start|>system\n' }} | |
| {%- if messages[0]['content'] is string %} | |
| {{- messages[0]['content'] }} | |
| {%- else %} | |
| {%- for item in messages[0]['content'] %} | |
| {%- if item['type'] == 'text' %} | |
| {{- item['text'] }} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- endif %} | |
| {{- '<|im_end|>\n' }} | |
| {%- else %} | |
| {{- '<|im_start|>system\n你是Qianfan-VL,由百度智能云研发的多模态大语言模型。<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- set ns = namespace(found_last_user=false, last_query_index=messages|length - 1) %} | |
| {%- for message in messages[::-1] %} | |
| {%- set index = (messages|length - 1) - loop.index0 %} | |
| {%- if not ns.found_last_user and message['role'] == 'user' %} | |
| {%- set ns.found_last_user = true %} | |
| {%- set ns.last_query_index = index %} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- for message in messages %} | |
| {%- if messages[0]['role'] != 'system' or not loop.first %} | |
| {%- if message['role'] == 'user' or (message['role'] == 'system' and not loop.first) %} | |
| {%- set append_think = (enable_thinking is defined and enable_thinking and message['role'] == 'user' and loop.index0 == ns.last_query_index) %} | |
| {{- '<|im_start|>' + message['role'] + '\n' }} | |
| {%- if message['content'] is string %} | |
| {{- message['content'] }} | |
| {%- else %} | |
| {%- for item in message['content'] %} | |
| {%- if item['type'] == 'image' %} | |
| {{- '<image>\n' }} | |
| {%- elif item['type'] == 'video' %} | |
| {{- '<video>\n' }} | |
| {%- elif item['type'] == 'text' %} | |
| {{- item['text'] }} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- endif %} | |
| {%- if append_think %} | |
| {{- '<think>' }} | |
| {%- endif %} | |
| {{- '<|im_end|>\n' }} | |
| {%- elif message['role'] == 'assistant' %} | |
| {%- if message['content'] is string %} | |
| {%- set raw_content = message['content'] %} | |
| {%- else %} | |
| {%- set content_ns = namespace(raw='') %} | |
| {%- for item in message['content'] %} | |
| {%- if item['type'] == 'text' %} | |
| {%- set content_ns.raw = content_ns.raw + item['text'] %} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- set raw_content = content_ns.raw %} | |
| {%- endif %} | |
| {%- set content = raw_content %} | |
| {%- set reasoning_content = '' %} | |
| {%- if 'reasoning_content' in message and message['reasoning_content'] is not none %} | |
| {%- set reasoning_content = message['reasoning_content'] %} | |
| {%- elif '</think>' in raw_content %} | |
| {%- set content = raw_content.split('</think>')[-1].lstrip('\n') %} | |
| {%- set reasoning_content = raw_content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %} | |
| {%- endif %} | |
| {%- if loop.index0 > ns.last_query_index and reasoning_content %} | |
| {{- '<|im_start|>' + message['role'] + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }} | |
| {%- else %} | |
| {{- '<|im_start|>' + message['role'] + '\n' + content }} | |
| {%- endif %} | |
| {{- '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- if add_generation_prompt %} | |
| {{- '<|im_start|>assistant\n' }} | |
| {%- endif %} | |