Image-Text-to-Text
Transformers
Safetensors
monkeyocrv2
text-generation
conversational
custom_code
Eval Results
Instructions to use zenosai/MonkeyOCRv2-S-Parsing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zenosai/MonkeyOCRv2-S-Parsing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="zenosai/MonkeyOCRv2-S-Parsing", trust_remote_code=True) 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 AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("zenosai/MonkeyOCRv2-S-Parsing", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use zenosai/MonkeyOCRv2-S-Parsing with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zenosai/MonkeyOCRv2-S-Parsing" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zenosai/MonkeyOCRv2-S-Parsing", "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/zenosai/MonkeyOCRv2-S-Parsing
- SGLang
How to use zenosai/MonkeyOCRv2-S-Parsing 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 "zenosai/MonkeyOCRv2-S-Parsing" \ --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": "zenosai/MonkeyOCRv2-S-Parsing", "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 "zenosai/MonkeyOCRv2-S-Parsing" \ --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": "zenosai/MonkeyOCRv2-S-Parsing", "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 zenosai/MonkeyOCRv2-S-Parsing with Docker Model Runner:
docker model run hf.co/zenosai/MonkeyOCRv2-S-Parsing
Add library_name: transformers to metadata
#1
by nielsr HF Staff - opened
Hi! I'm opening this PR to add the library_name: transformers tag to the YAML metadata of your model card.
Since this model is compatible with Hugging Face transformers (using trust_remote_code=True / auto_map), adding this metadata will automatically enable the code snippet widget and the "Use in Transformers" button on the top right of the model repository page, making it much easier for the community to load and run your model.
zenosai changed pull request status to merged
Thank you for your contribution
zenosai deleted the
refs/pr/1 ref