FlashVL-2B-Static-GRPO

[📜 FlashVL]

Introduction

We are excited to introduce FlashVL, a novel approach to optimizing Vision-Language Models (VLMs) for real-time applications, targeting ultra-low latency and high throughput without sacrificing accuracy. Leveraging advanced architectural enhancements and efficient computational strategies, Flash-VL 2B is designed to maximize throughput by reducing processing time while maintaining competitive performance across multiple vision-language benchmarks. Our approach includes tailored architectural choices, token compression mechanisms, data curation, training schemes, and a novel image processing technique called implicit semantic stitching that effectively balances computational load and model performance. Through extensive evaluations on 11 standard VLM benchmarks, we demonstrate that Flash-VL 2B achieves state-of-the-art results in both speed and accuracy, making it a promising solution for deployment in resource-constrained environments and large-scale real-time applications.

Environment Setup

pip install torch==2.1.2
pip install transformers==4.50.0.dev0

How to use it?

import torch
from PIL import Image
import requests
from io import BytesIO
from transformers import AutoModel, AutoTokenizer, SiglipProcessor

model_path = "Flash-VL/FlashVL-2B-Static-GRPO"
model = AutoModel.from_pretrained(model_path, torch_dtype=torch.bfloat16,trust_remote_code=True,device_map='cuda')
model.tokenizer = AutoTokenizer.from_pretrained(model_path,device_map='cuda')
model.im_trans = SiglipProcessor.from_pretrained(model_path).image_processor

image_url ="https://s3plus.meituan.net/automl-datasets/mlm/3FF4.png"
response = requests.get(image_url)
image_data = BytesIO(response.content)
pil_image = Image.open(image_data).convert('RGB')

messages = [{'role': 'user', 'content': "说说图中第一行第二列是什么蔬菜，买一斤多少钱"}] 
answer = model.chat(pil_image, messages, do_sample=False, max_new_tokens=256)
print(answer)
# 图片中第一行第二列的蔬菜是**荷兰豆**，买一斤的价格是**￥16.8**。

Evaluation

Method/model	Average	DynaMath	MathVision	MathVerse	MMMU Pro	WeMath
Flash-VL-2Bs	23.80	23.19	26.72	16.84	16.24	36.03
InternVL3-2B	27.03	32.55	26.49	17	22.56	36.55
+ SFT	26.08 (+2.28)	28.28	31.06	16.97	15.95	38.16
+ RL	27.23 (+3.43)	26.94	27.94	17.73	16.99	46.55
FlashVL-2B-Static-GRPO	29.05 (+5.25)	30.61	32.48	18.45	16.53	47.18

Note: FlashVL-2B-Static-GRPO applies both SFT and RL.

We use VLMEvalKit to evaluate FlashVL-2B-Static.

Citation

If you find this project useful in your research, please consider citing:

@misc{zhang2025flashvl2boptimizingvisionlanguage,
      title={Flash-VL 2B: Optimizing Vision-Language Model Performance for Ultra-Low Latency and High Throughput}, 
      author={Bo Zhang and Shuo Li and Runhe Tian and Yang Yang and Jixin Tang and Jinhao Zhou and Lin Ma},
      year={2025},
      eprint={2505.09498},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.09498}, 
}