FlashVL/FlashVL-2B-Static
Flash-VL-2B-Static
[\[📜 Flash-VL Tech Report\]](https://www.arxiv.org/abs/2505.09498)
Introduction
We are excited to introduce Flash-VL, 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.dev0How to use it?
import torch
from PIL import Image
import requests
from io import BytesIO
from transformers import AutoModel, AutoTokenizer, AutoProcessor
model_path = "FlashVL/FlashVL-2B-Static"
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 = AutoProcessor.from_pretrained(model_path).image_processor
# single-image single-round conversation (单图单轮对话)
image_url ="https://s3plus.meituan.net/automl-datasets/mlm/0516.png"
response = requests.get(image_url)
image_data = BytesIO(response.content)
pil_image = Image.open(image_data).convert('RGB')
messages = [{'role': 'user', 'content': "生成图中菜品的菜谱"}] # answer: EXTRA
answer = model.chat(pil_image, messages, do_sample=False, max_new_tokens=256)
print(answer)
# single-image multi-round conversation (单图多轮对话)
messages = [
{'role': 'user', 'content': '这是什么'},
{"role": "assistant", "content": '这是一道看起来像是银耳莲子汤的甜品。\
银耳是一种常见的食材，通常用于制作甜品和汤品，具有软糯的口感和清润的口感。莲 \
子是莲子的干燥部分，常用于中医和食疗中，具有补脾止泻的功效。图片中还可以看到 \
一些枸杞和核桃，枸杞富含维生素和抗氧化物质，核桃则提供丰富的蛋白质和健康脂肪。 \
整体来看，这道甜品不仅美味，还具有一定的营养价值。'},
{'role': 'user', 'content': '对图中菜品卡路里分析'}
]
answer = model.chat(pil_image, messages, do_sample=False, max_new_tokens=256)
print(answer)
# pure-text single-round conversation (纯文本对话）
messages = [{'role': 'user', 'content': "who are you"}]
answer = model.chat(None, messages, do_sample=False, max_new_tokens=256)
print(answer)Evaluation
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},
}