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")# 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
| # ollama modelfile auto-generated by llamafactory | |
| FROM . | |
| TEMPLATE """{{ if .System }}{{ .System }}{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}USER: {{ .Content }} ASSISTANT:{{ else if eq .Role "assistant" }}{{ .Content }}<|eot_id|>{{ end }}{{ end }}""" | |
| SYSTEM """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.""" | |
| PARAMETER stop "<|eot_id|>" | |
| PARAMETER num_ctx 4096 | |