Bespoke-MiniChart-7B
This is an open‑source chart‑understanding Vision‑Language Model (VLM) developed at Bespoke Labs and maintained by Liyan Tang and Bespoke Labs. It sets a new state‑of‑the‑art in chart question‑answering (Chart‑QA) for 7 billion‑parameter models, outperforming much larger closed models such as Gemini‑1.5‑Pro and Claude‑3.5 on seven public benchmarks.
- Blog Post: https://www.bespokelabs.ai/blog/bespoke-minichart-7b
- Playground: https://playground.bespokelabs.ai/minichart
Example Outputs
The examples below showcase how Bespoke-MiniChart-7B can perform both visual perception and textual reasoning.
Model Performance
Bespoke-MiniChart-7B achieves state-of-the-art performance on chart understanding among models with similar sizes. In addition to that, the model can even surpass closed-models such as Gemini-1.5-Pro and Claude-3.5.
We also compare the performance of our model finetuned using SFT+DPO vs SFT only.
In the table below, M1 and M2 are finetuned models with 270K and 1M SFT examples respsectively, and Bespoke-MiniChart-7B is the model finetuned using SFT+DPO.
Model Use:
The model is available on the playground here: https://playground.bespokelabs.ai/minichart
You can also run the model with the following snippet:
import requests
from PIL import Image
from io import BytesIO
import base64
import matplotlib.pyplot as plt
from vllm import LLM, SamplingParams
QA_PROMPT = """Please answer the question using the chart image.
Question: [QUESTION]
Please first generate your reasoning process and then provide the user with the answer. Use the following format:
<think>
... your thinking process here ...
</think>
<answer>
... your final answer (entity(s) or number) ...
</answer>"""
def get_image_from_url(image_url):
try:
response = requests.get(image_url, stream=True)
response.raise_for_status()
return Image.open(BytesIO(response.content))
except Exception as e:
print(f"Error with image: {e}")
return None
def get_answer(image_url, question, display=True):
image = get_image_from_url(image_url)
if display:
plt.figure(figsize=(10, 8))
plt.imshow(image)
plt.axis('off')
plt.show()
if not image:
return "Error downloading image"
buffered = BytesIO()
image.save(buffered, format=image.format or 'JPEG')
encoded_image = base64.b64encode(buffered.getvalue()).decode('utf-8')
messages = [{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encoded_image}"}},
{"type": "text", "text": QA_PROMPT.replace("[QUESTION]", question)}
]
}]
response = llm.chat([messages], sampling_params=SamplingParams(temperature=0, max_tokens=500))
return response[0].outputs[0].text
# Initialize the LLM
llm = LLM(
model="bespokelabs/Bespoke-MiniChart-7B",
tokenizer_mode="auto",
max_model_len=15000,
tensor_parallel_size=1,
gpu_memory_utilization=0.9,
mm_processor_kwargs={"max_pixels": 1600*28*28},
seed=2025,
trust_remote_code=True,
)
# Running inference
image_url = "https://github.com/bespokelabsai/minichart-playground-examples/blob/main/images/ilyc9wk4jf8b1.png?raw=true"
question = "How many global regions maintained their startup funding losses below 30% in 2022?"
print("\n\n=================Model Output:===============\n\n", get_answer(image_url, question))
Licence
This work is licensed under CC BY-NC 4.0. For commercial licensing, please contact company@bespokelabs.ai.
Citation
@misc{bespoke_minichart_7b,
title = {Bespoke-MiniChart-7B: pushing the frontiers of open VLMs for chart understanding},
author = {Liyan Tang and Shreyas Pimpalgaonkar and Kartik Sharma and Alexandros G. Dimakis and Mahesh Sathiamoorthy and Greg Durrett},
howpublished = {blog post},
year = {2025},
url={https://huggingface.co/bespokelabs/Bespoke-MiniChart-7B},
}
Acknowledgements
Bespoke Labs team:
- Liyan Tang
- Shreyas Pimpalgaonkar
- Kartik Sharma
- Alex Dimakis
- Mahesh Sathiamoorthy
- Greg Durrett
Model perfected at Bespoke Labs — where careful curation meets cutting‑edge modeling.