Hugging Face's logo

BytedanceDouyinContent
/
SAIL-VL-8B

Safetensors
English
Chinese
Model card Files Community

Introduction

lidar_map

SAIL-VL is a state-of-the-art vision-language model (VLM) developed by the Bytedance Douyin Content Team. The goal of SAIL-VL is to develope a high-performance vision language model that facilitates deployment on mobile devices and ensures accessibility and affordability for a broad audience. Through careful tuning of data and training recipes, SAIL-VL demonstrates that even a small VLM can benefit significantly from data scaling. Our model outperforms Qwen2-VL, InternVL2.5-MPO and even recent SoTA models of comparable sizes.

In a word, SAIL-VL is a foundational VLM for vision-language applications. Welcome to explore its capabilities and feel free to contact us for any questions or opportunities.

News🚀🚀🚀

Model Card

Model Architecture:

Architecture ViT LLM Adapter Token Merge Resolution
🤗SAIL-VL-2B 🤗InternViT-300M 🤗Qwen2.5-1.5B 2-layer MLP 2x2 448x448xN
🤗SAIL-VL-8B 🤗InternViT-300M 🤗Qwen2.5-7B 2-layer MLP 2x2 448x448xN

Training Recipes Overview:

Sail-VL benefits from high-quality data and carefully curated training recipes. We find the data quality, quantity and the design of curriculum training pipeline are crucial for model performance. With the proper design and data, the model's capacity scales effectively with data expansion at all stages, leading to enhanced performance.

Evaluation

SAIL-VL is competitive compared with Qwen2-VL, DeepSeekVL-2 and recently released InternVL2.5-MPO, please see the following table for details.

Detail Evaluations:

Benchmark SAIL-VL-8B Qwen2-VL-8B InternVL2.5-MPO-8B DeepSeekVL-2-Small
Overall Performance 74.5 73.0 74.3 72.7
General VQA 68.3 68.5 71.2 66.8
OCR VQA 79.8 79.6 76.3 79.0
Math&Knowledge 83.3 71.0 83.2 79.0
Hallucination 68.7 67.5 69.7 65.3
General VQA
MMStar 64.2 58.3 65.3 57.7
MMBench_DEV 79.5 79.5 83.3 78.1
MMMU_VAL 48.2 50.9 52.8 47.6
MME 2244 2321 2321 2149
SEEDBench_IMG 75.5 75.3 76.9 76.8
RealWorldQA 71.9 69.7 70.2 70.2
MMVET 58.3 62.6 66.8 60.3
OCR VQA
AI2D_TEST 83.7 82.9 84.1 82.0
DocVQA_Val 92.2 93.7 92.1 92.3
InfoVQA_Val 75.2 75.9 76.2 72.5
ChartQA_Test 84.6 81.6 77.6 84.6
TextVQA_Val 77.7 83.8 79.2 83.3
OCRVQA_Test 61.4 56.2 36.7 54.5
OCRBench 835 833 880 834
Math&Knowledge
MathVistaMini 68.4 57.3 68.5 61.8
ScienceQA_Val 98.2 84.6 97.9 96.2
Hallucination
HallucinationBench 52.2 48.5 50.3 41.2
POPE 85.2 86.5 89.1 89.4

Demo Cases

We visualize some examples to show the capabilities of our model. Our model is able to give detail and complex answer for a variety of questions.

Image Question Answer
Please describe the image shortly. The image prominently features the Golden Gate Bridge, an iconic suspension bridge in San Francisco, California. It spans across a body of water, presumably the San Francisco Bay. The photograph captures the bridge in a clear day with minimal cloud cover. Visible in the image's lower part is the adjacent cityscape and landscape, including parts of a building on the left side which looks like a low-lying brick structure, likely part of nearby infrastructure or a historical site in San Francisco.
Why is this meme funny? This meme is funny because it cleverly plays on the contrast between the human's focused strategy and the dog's random play. The image juxtaposes a child intensely trying to predict the dog's next move with the dog itself, which shows a checkers board and the caption "Me playing it on random." It humorously suggests that the dog, despite its seemingly haphazard approach, is the true master of the game. This creates a comical situation where a human child is perplexed by the dog's unpredictable moves, while the dog seems completely at ease with its own casual, random play style.
Do you recognize this place ? Yes, I recognize the place. This is the Temple of Heaven, located in Beijing, China. It's a famous historical site and architectural gem known for its distinctive appearance and cultural significance. The Temple of Heaven is renowned for its circular shape and beautiful architecture, combining elements of East Asian design with unique features that set it apart from other Chinese temple structures. It was originally built in the early 15th century during the Ming Dynasty and has since become an iconic representation of Chinese architecture and imperial history.
Help me write a travel plan for this place. Travel Plan for Temple of Heaven:\n 1. Morning: Depart early to avoid crowds.\n 2. Arrive at Temple of Heaven, explore the main structure, learn about its significance.\n 3. Take a guided tour of the surrounding gardens and historical sites.\n 4. Enjoy an authentic lunch in the vicinity. \n 5. Continue exploration, visit the Prayer Hall. \n 6. Visit during sunset for a serene experience. \n 7. End with evening meals showcasing local cuisine. \n 8. Take time to reflect on the experience as you leave.
Introduce me this painting. This iconic artwork is "Starry Night," painted by Vincent van Gogh in 1889. The canvas captures a swirling night sky filled with vibrant blue and yellow hues, giving it a mesmerizing, dream-like quality. Van Gogh's bold, expressive brushstrokes bring the night scene to life, creating a sense of movement and energy that captivates viewers. Below the ethereal sky, a small village and church anchor the composition, adding a touch of human presence to the vast, celestial expanse. This masterpiece exemplifies Van Gogh's unique style, showcasing his deep emotional connection to nature and the universe.

How to Use

The basic usage and dynamic crop strategy of SAIL-VL follows InternVL2, you can easily switch Intern-VL series of models to our model. Here is a simple example of using our model:

Requirements: 

pip3 install einops transformers timm

Code: 

import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer

IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)

def build_transform(input_size):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose([
        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
        T.ToTensor(),
        T.Normalize(mean=MEAN, std=STD)
    ])
    return transform

def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
    best_ratio_diff = float('inf')
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio

def dynamic_preprocess(image, min_num=1, max_num=10, image_size=448, use_thumbnail=False):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image aspect ratio
    target_ratios = set(
        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
        i * j <= max_num and i * j >= min_num)
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, target_ratios, orig_width, orig_height, image_size)

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images

def load_image(image_file, input_size=448, max_num=10):
    image = Image.open(image_file).convert('RGB')
    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values

path = "BytedanceDouyinContent/SAIL-VL-8B"
model = AutoModel.from_pretrained(
    path,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)

# set the max number of tiles in `max_num`
pixel_values = load_image('./test.png', max_num=10).to(torch.bfloat16).cuda()
generation_config = dict(max_new_tokens=1024, do_sample=True)

# pure-text conversation
question = 'Hello, who are you?'
response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True)
print(f'User: {question}         Assistant: {response}')

question = 'Can you tell me a story?'
response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True)
print(f'User: {question}         Assistant: {response}')

# single-image single-round conversation
question = '<image>         Please describe the image shortly.'
response = model.chat(tokenizer, pixel_values, question, generation_config)
print(f'User: {question}         Assistant: {response}')

# single-image multi-round conversation
question = '<image>         Please describe the image in detail.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
print(f'User: {question}         Assistant: {response}')

question = 'Please write a poem according to the image.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
print(f'User: {question}         Assistant: {response}')

Acknowledge

Our model is built upon numerous outstanding open-source projects, and we are grateful for their contributions. We extend special thanks to the InternVL team and Qwen team for their great base models, and to the BAAI team (Infinity-MM) for their generous release of data.

Citation 

@article{dong2025scalable,
  title={Scalable vision language model training via high quality data curation},
  author={Dong, Hongyuan and Kang, Zijian and Yin, Weijie and Liang, Xiao and Feng, Chao and Ran, Jiao},
  journal={arXiv preprint arXiv:2501.05952},
  year={2025}
}

Contributions

This work is conducted by Bytedance Douyin Content Team, authored by:

{Hongyuan Dong, Zijian Kang, Weijie Yin}, Xiao Liang, Chao Feng, Jiao Ran

{*} Equal Contributions.

We also appreciate the support from the model evaluation team:

Zirui Guo, Yan Qiu, Yaling Mou, Ming Jiang

And from AI platform team:

Huiyu Yu, Lin Dong, Yong Zhang

License

This project is licensed under Apache License 2.0.

Contact

If you have any question, please feel free to contact us: [email protected]

Downloads last month
7
Safetensors
Model size
7.95B params
Tensor type
BF16
·
Inference Providers NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API: The model has no library tag.

Model tree for BytedanceDouyinContent/SAIL-VL-8B

Base model

Qwen/Qwen2.5-7B
Finetuned
Qwen/Qwen2.5-7B-Instruct
Finetuned
(494)
this model