Valley 2.0

Introduction

Valley is a cutting-edge multimodal large model designed to handle a variety of tasks involving text, images, and video data, which is developed by ByteDance. Our model not only

• Achieved the best results in the inhouse e-commerce and short-video benchmarks
• Demonstrated comparatively outstanding performance in the OpenCompass (average scores > 67) tests

when evaluated against models of the same scale.

Valley-Eagle

The foundational version of Valley is a multimodal large model aligned with Siglip and Qwen2.5, incorporating LargeMLP and ConvAdapter to construct the projector.

• In the final version, we also referenced Eagle, introducing an additional VisionEncoder that can flexibly adjust the number of tokens and is parallelized with the original visual tokens.
• This enhancement supplements the model’s performance in extreme scenarios, and we chose the Qwen2vl VisionEncoder for this purpose.

and the model structure is shown as follows:

Release

• [12/23] 🔥 Announcing Valley-Eagle-7B!

Environment Setup

pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt

Inference Demo

• Single image

from valley_eagle_chat import ValleyEagleChat
model = ValleyEagleChat(
    model_path='bytedance-research/Valley-Eagle-7B',
    padding_side = 'left',
)

url = 'http://p16-goveng-va.ibyteimg.com/tos-maliva-i-wtmo38ne4c-us/4870400481414052507~tplv-wtmo38ne4c-jpeg.jpeg'
img = urllib.request.urlopen(url=url, timeout=5).read()

request = {
    "chat_history": [
        {'role': 'system', 'content': 'You are Valley, developed by ByteDance. Your are a helpfull Assistant.'},
        {'role': 'user', 'content': 'Describe the given image.'},
    ],
    "images": [img],
}

result = model(request)
print(f"\n>>> Assistant:\n")
print(result)

• Video

from valley_eagle_chat import ValleyEagleChat
import decord
import requests
import numpy as np
from torchvision import transforms

model = ValleyEagleChat(
    model_path='bytedance-research/Valley-Eagle-7B',
    padding_side = 'left',
)

url = 'https://videos.pexels.com/video-files/29641276/12753127_1920_1080_25fps.mp4'
video_file = './video.mp4'
response = requests.get(url)
if response.status_code == 200:
    with open("video.mp4", "wb") as f:
        f.write(response.content)
else:
    print("download error!")
    exit(1)

video_reader = decord.VideoReader(video_file)
decord.bridge.set_bridge("torch")
video = video_reader.get_batch(
    np.linspace(0,  len(video_reader) - 1, 8).astype(np.int_)
).byte()
print([transforms.ToPILImage()(image.permute(2, 0, 1)).convert("RGB") for image in video])

request = {
    "chat_history": [
        {'role': 'system', 'content': 'You are Valley, developed by ByteDance. Your are a helpfull Assistant.'},
        {'role': 'user', 'content': 'Describe the given video.'},
    ],
    "images": [transforms.ToPILImage()(image.permute(2, 0, 1)).convert("RGB") for image in video],
}
result = model(request)
print(f"\n>>> Assistant:\n")
print(result)

Related Project

We list related Project

• Valley: Video Assistant with Large Language model Enhanced abilitY
• LLaVA: Large Language and Vision Assistant
• Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders
• Qwen2.5

License Agreement

All of our open-source models are licensed under the Apache-2.0 license.

Citation

Coming Soon!