Instructions to use anaghasavit/OCR53000_y with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anaghasavit/OCR53000_y with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="anaghasavit/OCR53000_y")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("anaghasavit/OCR53000_y") model = AutoModelForMultimodalLM.from_pretrained("anaghasavit/OCR53000_y", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use anaghasavit/OCR53000_y with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "anaghasavit/OCR53000_y" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "anaghasavit/OCR53000_y", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/anaghasavit/OCR53000_y
- SGLang
How to use anaghasavit/OCR53000_y 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 "anaghasavit/OCR53000_y" \ --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": "anaghasavit/OCR53000_y", "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 "anaghasavit/OCR53000_y" \ --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": "anaghasavit/OCR53000_y", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use anaghasavit/OCR53000_y with Docker Model Runner:
docker model run hf.co/anaghasavit/OCR53000_y
- Xet hash:
- f31e5a0e209bbaaeb6fffb9f14853ed3ad04f004bd4d4c177dca346454f2cc66
- Size of remote file:
- 3.71 kB
- SHA256:
- 5350a477f5a83d6325effb165f828ab3ec17303d29a323ec6d415e6c1d33d890
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