Instructions to use vikas117/OCR1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikas117/OCR1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="vikas117/OCR1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("vikas117/OCR1") model = AutoModelForMultimodalLM.from_pretrained("vikas117/OCR1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use vikas117/OCR1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "vikas117/OCR1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vikas117/OCR1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/vikas117/OCR1
- SGLang
How to use vikas117/OCR1 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 "vikas117/OCR1" \ --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": "vikas117/OCR1", "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 "vikas117/OCR1" \ --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": "vikas117/OCR1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use vikas117/OCR1 with Docker Model Runner:
docker model run hf.co/vikas117/OCR1
Download pytorch_model.bin from vikas117/OCR1: direct link, hf CLI and curl.
- Browser
- Download file 344 MB
-
https://huggingface.co/vikas117/OCR1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vikas117/OCR1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vikas117/OCR1/resolve/main/pytorch_model.bin
344 MB
- Xet hash:
- b684d1efe1eb61d862763e1f226341ec6563df31da01c448b2ddc4c16a18ec0e
- Size of remote file:
- 344 MB
- SHA256:
- ab1acba2f840686106b915aa65ebe04cd0f5e63689804531eed9a9c38f0be728
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