What is Seed-OSS
Seed-OSS is a series of large language models open-sourced by ByteDance's Seed team, focusing on long-text processing, reasoning, and intelligent agent capabilities. The models include multiple versions, such as Seed-OSS-36B-Base and Seed-OSS-36B-Instruct, which perform excellently in general capabilities and instruction-following tasks, respectively. Trained with only 12T tokens, they perform excellently across multiple benchmarks. The models offer flexible thinking budget control and native long-text support, suitable for a variety of application scenarios. The Seed-OSS models are now open-sourced, providing rich resources and possibilities for research and development.
Main Features of Seed-OSS
- Powerful Reasoning Capabilities: Seed-OSS performs excellently in complex logical reasoning and multi-step reasoning tasks, with high accuracy, and can efficiently solve reasoning challenges.
- Long-Text Processing: The models support long-text contexts of up to 512K, with flexible thinking budget control, suitable for tasks such as long-text generation, summarization, and analysis.
- Intelligent Agent Capabilities: They perform excellently in intelligent agent tasks such as tool calling and problem solving, and can effectively combine external resources to complete complex tasks.
Technical Principles of Seed-OSS
- Architecture Design: Seed-OSS-36B is the main version, with 36 billion parameters. Based on the Grouped Query Attention (GQA) mechanism, it improves the model's efficiency and performance. It uses SwiGLU (Swish-Gated Linear Unit), performing excellently in training and inference. The model has 64 layers, with QKV head counts of 80/8/8, a head size of 128, and a hidden layer size of 5120.
- Training Method: Trained with only 12T tokens, it achieves excellent performance based on efficient training methods and data selection. The model supports long-text contexts of up to 512K, and through an optimized RoPE (Rotary Position Embedding) mechanism, ensures context consistency for long texts. The model provides multiple pre-trained models, and users can fine-tune them according to specific tasks to adapt to different application scenarios.
- Inference Optimization: Users 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 the model's 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 Repository：https://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 Generation: Helps content creators quickly produce creative text and improves creation efficiency.
- Intelligent Customer Service and Customer Support: As the core of intelligent customer service systems, it automatically answers user questions and improves customer satisfaction.
- Education and Learning Assistance: Generates teaching materials, answers students' questions, and helps teachers and students conduct teaching and learning more efficiently.
- Intelligent Writing and Editing: Provides writing assistance for writers, editors, and journalists, including text polishing, grammar checking, content expansion, etc., improving writing quality and efficiency.
- Data Analysis and Report Generation: Helps enterprises and researchers quickly understand the meaning behind data and assists in decision-making.