Instructions to use YadavJI69/Optical-Character-Recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use YadavJI69/Optical-Character-Recognition with PEFT:
Task type is invalid.
- Transformers
How to use YadavJI69/Optical-Character-Recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="YadavJI69/Optical-Character-Recognition")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("YadavJI69/Optical-Character-Recognition") model = AutoModelForMultimodalLM.from_pretrained("YadavJI69/Optical-Character-Recognition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use YadavJI69/Optical-Character-Recognition with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "YadavJI69/Optical-Character-Recognition" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "YadavJI69/Optical-Character-Recognition", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/YadavJI69/Optical-Character-Recognition
- SGLang
How to use YadavJI69/Optical-Character-Recognition 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 "YadavJI69/Optical-Character-Recognition" \ --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": "YadavJI69/Optical-Character-Recognition", "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 "YadavJI69/Optical-Character-Recognition" \ --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": "YadavJI69/Optical-Character-Recognition", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use YadavJI69/Optical-Character-Recognition with Docker Model Runner:
docker model run hf.co/YadavJI69/Optical-Character-Recognition
Commit History
-
Upload folder using huggingface_hub
Verified signature3c1e239 -
Upload folder using huggingface_hub
Verified signature2177f6c
-
Upload folder using huggingface_hub
Verified signature5bf7fb5
-
Delete adapter_model.safetensors with huggingface_hub
Verified signatureda2b4bb -
Delete adapter_config.json with huggingface_hub
Verified signaturee18e20e -
Upload folder using huggingface_hub
Verified signature71c2e16
-
Upload processor
Verified signature5938b6e -
Upload folder using huggingface_hub
Verified signatured8cfa7d -
Upload processor
Verified signature06e4565 -
Upload folder using huggingface_hub
Verified signature9c629f5 -
Upload processor
Verified signature26cd2b9 -
initial commit
Verified signaturee129908