Instructions to use IDEA-FinAI/chartmoe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IDEA-FinAI/chartmoe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="IDEA-FinAI/chartmoe", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("IDEA-FinAI/chartmoe", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IDEA-FinAI/chartmoe with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IDEA-FinAI/chartmoe" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IDEA-FinAI/chartmoe", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IDEA-FinAI/chartmoe
- SGLang
How to use IDEA-FinAI/chartmoe 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 "IDEA-FinAI/chartmoe" \ --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": "IDEA-FinAI/chartmoe", "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 "IDEA-FinAI/chartmoe" \ --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": "IDEA-FinAI/chartmoe", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IDEA-FinAI/chartmoe with Docker Model Runner:
docker model run hf.co/IDEA-FinAI/chartmoe
metadata
license: apache-2.0
pipeline_tag: image-text-to-text
library_name: transformers
paper: https://arxiv.org/abs/2409.03277
ChartMoE
ICLR2025 Oral
ChartMoE is a multimodal large language model with Mixture-of-Expert connector, based on InternLM-XComposer2 for advanced chart 1)understanding, 2)replot, 3)editing, 4)highlighting and 5)transformation.
Import from Transformers
To load the ChartMoE model using Transformers, use the following code:
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
ckpt_path = "IDEA-FinAI/chartmoe"
tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(ckpt_path, trust_remote_code=True).half().cuda().eval()
Quickstart & Gradio Demo
We provide a simple example and a gradio webui demo to show how to use ChartMoE. Please refer to https://github.com/IDEA-FinAI/ChartMoE.
Open Source License
The code is licensed under Apache-2.0.
