Image-Text-to-Text
Transformers
PyTorch
TensorBoard
Arabic
vision-encoder-decoder
Generated from Trainer
trocr
Instructions to use gagan3012/TrOCR-Ar-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gagan3012/TrOCR-Ar-Small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="gagan3012/TrOCR-Ar-Small")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("gagan3012/TrOCR-Ar-Small") model = AutoModelForMultimodalLM.from_pretrained("gagan3012/TrOCR-Ar-Small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use gagan3012/TrOCR-Ar-Small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gagan3012/TrOCR-Ar-Small" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gagan3012/TrOCR-Ar-Small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/gagan3012/TrOCR-Ar-Small
- SGLang
How to use gagan3012/TrOCR-Ar-Small 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 "gagan3012/TrOCR-Ar-Small" \ --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": "gagan3012/TrOCR-Ar-Small", "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 "gagan3012/TrOCR-Ar-Small" \ --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": "gagan3012/TrOCR-Ar-Small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use gagan3012/TrOCR-Ar-Small with Docker Model Runner:
docker model run hf.co/gagan3012/TrOCR-Ar-Small
- Xet hash:
- 2b48e7babb2ab0b6acacd6f2f22560080b49461b0fc2cab317a8d44c0adea7fb
- Size of remote file:
- 3.18 kB
- SHA256:
- 1ec08d1b77201204e0f0588b3de903fd304468c7fbf2909c156a04c156873cc2
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