Instructions to use ReFocus/Trained_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ReFocus/Trained_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ReFocus/Trained_Model")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ReFocus/Trained_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ReFocus/Trained_Model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ReFocus/Trained_Model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ReFocus/Trained_Model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ReFocus/Trained_Model
- SGLang
How to use ReFocus/Trained_Model 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 "ReFocus/Trained_Model" \ --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": "ReFocus/Trained_Model", "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 "ReFocus/Trained_Model" \ --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": "ReFocus/Trained_Model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ReFocus/Trained_Model with Docker Model Runner:
docker model run hf.co/ReFocus/Trained_Model
Download lr1e-06_ep2_bb1_edit0_aug0/preprocessor_config.json from ReFocus/Trained_Model: direct link, hf CLI and curl.
- Browser
- Download file 513 Bytes
-
https://huggingface.co/ReFocus/Trained_Model/resolve/main/lr1e-06_ep2_bb1_edit0_aug0/preprocessor_config.json
- Command line
-
hf download hf://ReFocus/Trained_Model/lr1e-06_ep2_bb1_edit0_aug0/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/ReFocus/Trained_Model/resolve/main/lr1e-06_ep2_bb1_edit0_aug0/preprocessor_config.json
513 Bytes
| { | |
| "auto_map": { | |
| "AutoImageProcessor": "microsoft/Phi-3.5-vision-instruct--processing_phi3_v.Phi3VImageProcessor", | |
| "AutoProcessor": "microsoft/Phi-3.5-vision-instruct--processing_phi3_v.Phi3VProcessor" | |
| }, | |
| "do_convert_rgb": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "Phi3VImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "num_crops": 16, | |
| "num_img_tokens": 144, | |
| "processor_class": "Phi3VProcessor" | |
| } | |