Instructions to use ChartFoundation/ECD_Finetuned_MLLMs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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", dtype="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
```

Improve model card: add correct pipeline tag, library_name, and relevant links

by nielsr HF Staff - opened Aug 11, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-4

nielsr

Aug 11, 2025

This PR improves the model card for the model presented in Effective Training Data Synthesis for Improving MLLM Chart Understanding.

Specifically, it:

Corrects the pipeline_tag to image-text-to-text for better discoverability on the Hugging Face Hub.
Adds library_name: transformers as the model is compatible with the 🤩 Transformers library, making it easier for users to identify how to use it.
Updates the introductory sentence to include a direct link to the Hugging Face paper page and the GitHub repository for easier access to the code.

Please review and merge this PR if everything looks good.

Improve model card: add correct pipeline tag, library_name, and relevant linksff581dde

Ruler138 changed pull request status to merged Aug 14, 2025

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