Instructions to use Jamil4321/PurchaseOrder_v6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jamil4321/PurchaseOrder_v6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Jamil4321/PurchaseOrder_v6")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("Jamil4321/PurchaseOrder_v6") model = AutoModelForImageTextToText.from_pretrained("Jamil4321/PurchaseOrder_v6") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Jamil4321/PurchaseOrder_v6 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Jamil4321/PurchaseOrder_v6" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jamil4321/PurchaseOrder_v6", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Jamil4321/PurchaseOrder_v6
- SGLang
How to use Jamil4321/PurchaseOrder_v6 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 "Jamil4321/PurchaseOrder_v6" \ --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": "Jamil4321/PurchaseOrder_v6", "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 "Jamil4321/PurchaseOrder_v6" \ --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": "Jamil4321/PurchaseOrder_v6", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Jamil4321/PurchaseOrder_v6 with Docker Model Runner:
docker model run hf.co/Jamil4321/PurchaseOrder_v6
- Xet hash:
- e0a12b45eb41d57892d469f6ba3b02ee49b345ebd42e89c71e17696e5d8afe77
- Size of remote file:
- 809 MB
- SHA256:
- 5304aafe57d6285232e40e283ea56c9a5bc68b0803d9c90020d30d5db4bbdc85
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.