Seed-OSS - ByteDance's open-source large language model series

What is Seed-OSS?

Seed-OSS is a series of large-scale language models open-sourced by ByteDance's Seed team, focusing on long text processing, reasoning, and intelligent agent capabilities. The model includes multiple versions, such as Seed-OSS-36B-Base and Seed-OSS-36B-Instruct, which excel in general capabilities and instruction following tasks, respectively. Trained with only 12T tokens, it demonstrates superior performance in multiple benchmark tests. The model offers flexible thinking budget control and native long text support, making it suitable for various application scenarios. The Seed-OSS model is now open-source, providing abundant resources and possibilities for research and development.

Main functions of Seed-OSS

• Strong reasoning abilitySeed-OSS performs exceptionally well in complex logical reasoning and multi-step reasoning tasks, achieving high accuracy and efficiently solving reasoning problems.
• Long text processingThe model supports long text contexts of up to 512K characters and has flexible thinking budget control, making it suitable for tasks such as long text generation, summarization, and analysis.
• Intelligent agent capabilitiesIt performs exceptionally well in intelligent agent tasks such as tool invocation and problem solving, and can effectively combine external resources to complete complex tasks.

The technical principle of Seed-OSS

• Architecture DesignSeed-OSS-36B is the major version with 36 billion parameters. It utilizes Grouped Query Attention (GQA) to improve model efficiency and performance. It employs SwiGLU (Swish-Gated Linear Unit), demonstrating excellent performance in both training and inference. The model has 64 layers, with 80/8/8 QKV heads, a head size of 128, and a hidden layer size of 5120.
• Training methodsTraining with only 12T tokens, the model achieves excellent performance through efficient training methods and data selection. It supports long text contexts of up to 512K characters, ensuring contextual consistency through an optimized RoPE (Rotary Position Embedding) mechanism. The model provides multiple pre-trained models, allowing users to fine-tune them for specific tasks and adapt them to different application scenarios.
• Inference optimizationUsers can dynamically adjust the inference length according to task requirements, balancing inference efficiency and result quality. The model supports 4-bit and 8-bit quantization, significantly reducing model memory usage and improving inference speed. It supports inference through frameworks such as transformers and vLLM, providing rich configuration options.

Seed-OSS project address

• GitHub repositoryhttps://github.com/ByteDance-Seed/seed-oss
• HuggingFace model library: https://huggingface.co/collections/ByteDance-Seed/seed-oss-68a609f4201e788db05b5dcd

Application scenarios of Seed-OSS

• Content creation and generationIt helps content creators quickly produce creative text and improve their creative efficiency.
• Intelligent Customer Service and Customer SupportAs the core of an intelligent customer service system, it automatically answers user questions and improves customer satisfaction.
• Education and learning supportIt generates teaching materials, answers student questions, and helps teachers and students teach and learn more efficiently.
• Intelligent writing and editingIt provides writing assistance to writers, editors, and journalists, including text polishing, grammar checking, and content expansion, to improve writing quality and efficiency.
• Data Analysis and Report GenerationIt helps businesses and researchers quickly understand the meaning behind data and assists in decision-making.