Seed-OSS – ByteDance’s Open-Source Large Language Model Series

What is Seed-OSS?

Seed-OSS is a series of open-source large language models released 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 excel in general capabilities and instruction-following tasks respectively. Trained on just 12T tokens, the models demonstrate outstanding performance across multiple benchmarks. With flexible reasoning budget control and native long-text support, Seed-OSS is suitable for a wide range of applications. The models are now open-sourced, providing abundant resources and possibilities for research and development.

Key Features of Seed-OSS

• Powerful reasoning ability: Seed-OSS performs excellently in complex logical and multi-step reasoning tasks, delivering high accuracy and efficiently solving reasoning challenges.

• Long-text processing: Supports up to 512K context length with flexible reasoning budget control, making it ideal for long-text generation, summarization, and analysis.

• Intelligent agent capabilities: Excels in tool use and problem-solving tasks, effectively integrating external resources to handle complex workflows.

Technical Foundations of Seed-OSS

• Architecture design: The main version, Seed-OSS-36B, has 36 billion parameters. It adopts the Grouped Query Attention (GQA) mechanism to improve efficiency and performance, and uses SwiGLU (Swish-Gated Linear Unit) for strong training and inference performance. The model consists of 64 layers with QKV head counts of 80/8/8, head dimension of 128, and hidden size of 5120.

• Training method: Trained on only 12T tokens with efficient training techniques and data curation, delivering excellent results. Supports up to 512K context length with optimized RoPE (Rotary Position Embedding), ensuring consistency in long-text understanding. Multiple pre-trained models are available for fine-tuning to suit different applications.

• Inference optimization: Users can dynamically adjust reasoning length based on task requirements, balancing efficiency and quality. Supports 4-bit and 8-bit quantization, significantly reducing memory usage and accelerating inference. Compatible with frameworks like transformers and vLLM, offering rich configuration options.

Project Links

• GitHub Repository: https://github.com/ByteDance-Seed/seed-oss

• HuggingFace Model Collection: https://huggingface.co/collections/ByteDance-Seed/seed-oss-68a609f4201e788db05b5dcd

Application Scenarios of Seed-OSS

• Content creation & generation: Helps creators quickly produce creative text, boosting efficiency.

• Intelligent customer service & support: Serves as the core of smart customer service systems, automatically answering user questions and improving satisfaction.

• Education & learning assistance: Generates teaching materials, answers student questions, and supports more efficient teaching and learning.

• Intelligent writing & editing: Assists writers, editors, and journalists with tasks such as text refinement, grammar checking, and content expansion, improving writing quality and efficiency.

• Data analysis & report generation: Helps enterprises and researchers quickly interpret the meaning behind data and supports decision-making.