Instructions to use ChartFoundation/ECD_Finetuned_MLLMs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChartFoundation/ECD_Finetuned_MLLMs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ChartFoundation/ECD_Finetuned_MLLMs")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ChartFoundation/ECD_Finetuned_MLLMs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ChartFoundation/ECD_Finetuned_MLLMs with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ChartFoundation/ECD_Finetuned_MLLMs" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ChartFoundation/ECD_Finetuned_MLLMs", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ChartFoundation/ECD_Finetuned_MLLMs
- SGLang
How to use ChartFoundation/ECD_Finetuned_MLLMs 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 "ChartFoundation/ECD_Finetuned_MLLMs" \ --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": "ChartFoundation/ECD_Finetuned_MLLMs", "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 "ChartFoundation/ECD_Finetuned_MLLMs" \ --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": "ChartFoundation/ECD_Finetuned_MLLMs", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ChartFoundation/ECD_Finetuned_MLLMs with Docker Model Runner:
docker model run hf.co/ChartFoundation/ECD_Finetuned_MLLMs
Download ECD_Fintuned_phi-3-vision/preprocessor_config.json from ChartFoundation/ECD_Finetuned_MLLMs: direct link, hf CLI and curl.
- Browser
- Download file 525 Bytes
-
https://huggingface.co/ChartFoundation/ECD_Finetuned_MLLMs/resolve/main/ECD_Fintuned_phi-3-vision/preprocessor_config.json
- Command line
-
hf download hf://ChartFoundation/ECD_Finetuned_MLLMs/ECD_Fintuned_phi-3-vision/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/ChartFoundation/ECD_Finetuned_MLLMs/resolve/main/ECD_Fintuned_phi-3-vision/preprocessor_config.json
525 Bytes
| { | |
| "auto_map": { | |
| "AutoImageProcessor": "microsoft/Phi-3-vision-128k-instruct--image_processing_phi3_v.Phi3VImageProcessor", | |
| "AutoProcessor": "microsoft/Phi-3-vision-128k-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" | |
| } | |