

Valley 2.0
Overview :
Valley is a multimodal large model (MLLM) developed by ByteDance, designed to handle a variety of tasks involving text, image, and video data. The model has achieved the best results in internal e-commerce and short video benchmarks, significantly outperforming other open-source models, and has demonstrated outstanding performance on the OpenCompass multimodal model evaluation leaderboard, with an average score of 67.40, ranking among the top two known open-source MLLMs (<10B).
Target Users :
The target audience for Valley includes researchers, developers, and enterprises that need to process multimodal data. It is suitable for them as it provides powerful tools to understand and analyze text, image, and video data, helping them achieve more efficient data processing and analysis in their respective fields.
Use Cases
1. E-commerce platforms use Valley to analyze user reviews and product images to improve product recommendation systems.
2. Short video platforms utilize Valley for content moderation, automatically identifying and filtering inappropriate content.
3. Educational platforms use Valley to analyze instructional videos, automatically generating course summaries and key points.
Features
- Process text, image, and video data: Valley can understand and handle various types of data, offering more comprehensive services.
- Best results in internal e-commerce and short video benchmarks: It performs exceptionally well in internal tests, exceeding other models.
- Top ranking on the OpenCompass leaderboard: It ranks high in multimodal model evaluations, showcasing its robust performance.
- Supports multiple tasks: Valley can handle various tasks, including but not limited to text comprehension, image recognition, and video analysis.
- Open-source model: The source code for Valley is available on GitHub, facilitating community contributions and further development.
- Collaboration with Hugging Face: The Valley model is offered on the Hugging Face platform for convenient access by researchers and developers.
- Academic paper support: Valley's research paper is published on arXiv, providing support for technical details and theoretical foundations.
How to Use
1. Visit Valley's GitHub page and download the model code.
2. Read Valley's academic paper to understand the model's operation and technical details.
3. Find the Valley model on the Hugging Face platform and follow the guidelines for model training or inference.
4. Customize and optimize the Valley model according to specific needs.
5. Integrate the Valley model into your project to start processing text, image, and video data.
6. Participate in Valley's community discussions to exchange experiences and best practices with other developers.
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