Instructions to use sivan22/testing-trOCR-hebrew-handwritten with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sivan22/testing-trOCR-hebrew-handwritten with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="sivan22/testing-trOCR-hebrew-handwritten")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("sivan22/testing-trOCR-hebrew-handwritten") model = AutoModelForMultimodalLM.from_pretrained("sivan22/testing-trOCR-hebrew-handwritten", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sivan22/testing-trOCR-hebrew-handwritten with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sivan22/testing-trOCR-hebrew-handwritten" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sivan22/testing-trOCR-hebrew-handwritten", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sivan22/testing-trOCR-hebrew-handwritten
- SGLang
How to use sivan22/testing-trOCR-hebrew-handwritten 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 "sivan22/testing-trOCR-hebrew-handwritten" \ --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": "sivan22/testing-trOCR-hebrew-handwritten", "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 "sivan22/testing-trOCR-hebrew-handwritten" \ --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": "sivan22/testing-trOCR-hebrew-handwritten", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sivan22/testing-trOCR-hebrew-handwritten with Docker Model Runner:
docker model run hf.co/sivan22/testing-trOCR-hebrew-handwritten
Download pytorch_model.bin from sivan22/testing-trOCR-hebrew-handwritten: direct link, hf CLI and curl.
- Browser
- Download file 965 MB
-
https://huggingface.co/sivan22/testing-trOCR-hebrew-handwritten/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sivan22/testing-trOCR-hebrew-handwritten/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sivan22/testing-trOCR-hebrew-handwritten/resolve/main/pytorch_model.bin
965 MB
- Xet hash:
- 89e42e571eca8e1d4f9302f61f0ee08e7e6a94055e1ab36a0e3eed634b6ec646
- Size of remote file:
- 965 MB
- SHA256:
- 4bc9b38745a16b4f2b5333edc8e6576c0712e325603b82c71fbf54831c9e2606
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