Image-Text-to-Text
Transformers
Safetensors
multilingual
English
Chinese
hunyuan_vl
ocr
vision-language-model
document-parsing
text-spotting
information-extraction
text-image-translation
conversational
Instructions to use Evan-613/HunyuanOCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Evan-613/HunyuanOCR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Evan-613/HunyuanOCR") 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("Evan-613/HunyuanOCR") model = AutoModelForMultimodalLM.from_pretrained("Evan-613/HunyuanOCR") 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 Evan-613/HunyuanOCR with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Evan-613/HunyuanOCR" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Evan-613/HunyuanOCR", "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/Evan-613/HunyuanOCR
- SGLang
How to use Evan-613/HunyuanOCR 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 "Evan-613/HunyuanOCR" \ --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": "Evan-613/HunyuanOCR", "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 "Evan-613/HunyuanOCR" \ --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": "Evan-613/HunyuanOCR", "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 Evan-613/HunyuanOCR with Docker Model Runner:
docker model run hf.co/Evan-613/HunyuanOCR
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<|hy_begin▁of▁sentence|>", | |
| "clean_up_tokenization_spaces": true, | |
| "eos_token": "<|hy_Assistant|>", | |
| "image_end_token": "<|hy_place▁holder▁no▁101|>", | |
| "image_start_token": "<|hy_place▁holder▁no▁100|>", | |
| "image_token": "<|hy_place▁holder▁no▁102|>", | |
| "is_local": true, | |
| "local_files_only": false, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "model_specific_special_tokens": { | |
| "image_end_token": "<|hy_place▁holder▁no▁101|>", | |
| "image_start_token": "<|hy_place▁holder▁no▁100|>", | |
| "image_token": "<|hy_place▁holder▁no▁102|>", | |
| "video_end_token": "<|hy_place▁holder▁no▁105|>", | |
| "video_start_token": "<|hy_place▁holder▁no▁104|>", | |
| "video_token": "<|hy_place▁holder▁no▁665|>" | |
| }, | |
| "pad_token": "<|hy_▁pad▁|>", | |
| "processor_class": "HunYuanVLProcessor", | |
| "tokenizer_class": "TokenizersBackend", | |
| "video_end_token": "<|hy_place▁holder▁no▁105|>", | |
| "video_start_token": "<|hy_place▁holder▁no▁104|>", | |
| "video_token": "<|hy_place▁holder▁no▁665|>" | |
| } | |