Seed-Thinking-v1.5 is a Mixture-of-Experts (MoE) large language model designed to enhance reasoning capabilities across STEM and coding domains. It aims to improve performance by incorporating a "think before responding" mechanism, making it suitable for researchers and developers seeking advanced reasoning models.

How It Works

Seed-Thinking-v1.5 employs a Mixture-of-Experts (MoE) architecture, featuring 20 billion activated parameters and 200 billion total parameters. This approach allows for efficient scaling and specialization of model components, contributing to its strong performance on complex reasoning tasks. The model's core innovation lies in its reinforcement learning-driven reasoning process, enabling it to "think" through problems before generating a response.

Quick Start & Requirements

• Installation: Not explicitly detailed in the README.
• Prerequisites: Requires significant computational resources due to its large parameter count (200B total, 20B activated). Specific hardware (e.g., GPUs with substantial VRAM) and software dependencies (e.g., PyTorch, CUDA) are implied but not listed.
• Resources: Setup and inference will likely demand high-end GPU hardware and considerable memory.
• Documentation: A technical report is referenced for full details.

Highlighted Details

• Achieves 86.7% on AIME 2024 and 55.0% on Codeforces (pass@8).
• Outperforms DeepSeek R1 by 8% in win rate on non-reasoning tasks.
• Demonstrates strong performance on benchmarks like GPQA (77.3%) and MMLU-PRO (87.0%).
• Introduces two internal benchmarks, BeyondAIME and Codeforces, for generalized reasoning assessment.

Maintenance & Community

• Developed by ByteDance.
• No specific community channels (Discord, Slack) or roadmap links are provided in the README.

Licensing & Compatibility

• The license is not specified in the provided README text.

Limitations & Caveats

• The README indicates that internal sandbox results may differ from reported benchmarks due to testing environment inconsistencies.
• Specific setup instructions, dependencies, and licensing information are not readily available, potentially hindering adoption.