Instructions to use MohamedExperio/ICDAR2019_ILYAS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MohamedExperio/ICDAR2019_ILYAS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="MohamedExperio/ICDAR2019_ILYAS")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("MohamedExperio/ICDAR2019_ILYAS") model = AutoModelForMultimodalLM.from_pretrained("MohamedExperio/ICDAR2019_ILYAS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MohamedExperio/ICDAR2019_ILYAS with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MohamedExperio/ICDAR2019_ILYAS" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MohamedExperio/ICDAR2019_ILYAS", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MohamedExperio/ICDAR2019_ILYAS
- SGLang
How to use MohamedExperio/ICDAR2019_ILYAS 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 "MohamedExperio/ICDAR2019_ILYAS" \ --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": "MohamedExperio/ICDAR2019_ILYAS", "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 "MohamedExperio/ICDAR2019_ILYAS" \ --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": "MohamedExperio/ICDAR2019_ILYAS", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MohamedExperio/ICDAR2019_ILYAS with Docker Model Runner:
docker model run hf.co/MohamedExperio/ICDAR2019_ILYAS
Download pytorch_model.bin from MohamedExperio/ICDAR2019_ILYAS: direct link, hf CLI and curl.
- Browser
- Download file 809 MB
-
https://huggingface.co/MohamedExperio/ICDAR2019_ILYAS/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MohamedExperio/ICDAR2019_ILYAS/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MohamedExperio/ICDAR2019_ILYAS/resolve/main/pytorch_model.bin
809 MB
- Xet hash:
- a11f3ec60b3c576b4c5f2a01cf095645baed966b086ae56b37458f4afec5acf4
- Size of remote file:
- 809 MB
- SHA256:
- 5f3b7d24570045aaa432e01a3de810b27ebbcec0eaf0e0f18c7ed96826de255e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.