Description
Kimi-K2-Instruct is a sophisticated mixture-of-experts (MoE) language model developed by moonshotai, featuring 32 billion activated parameters and a total of 1 trillion parameters. This model is optimized for high-level AI tasks, including reasoning, coding, and autonomous problem-solving. It is trained using the Muon optimizer, which ensures stability and efficiency during large-scale training on 15.5 trillion tokens. The model is particularly suited for general-purpose chat and agentic experiences, making it a versatile tool for developers and researchers.
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The architecture of Kimi-K2-Instruct includes 61 layers, with a dense layer and an attention hidden dimension of 7168. It employs 64 attention heads and 384 experts, with 8 selected experts per token. The vocabulary size is 160K, and it supports a context length of 128K. The model uses the SwiGLU activation function and the MLA attention mechanism.
Kimi-K2-Instruct has demonstrated impressive performance across various benchmarks. For instance, it achieved a Pass@1 score of 53.7 on the LiveCodeBench v6 and 27.1 on the OJBench. It also excels in multilingual agentic coding tasks, with a score of 47.3 on the SWE-bench Multilingual benchmark. These results highlight its capability in handling complex coding and reasoning tasks.
Deployment of Kimi-K2-Instruct is facilitated through an API available on the moonshot.ai platform, compatible with OpenAI and Anthropic standards. The model supports various inference engines, including vLLM, SGLang, KTransformers, and TensorRT-LLM. The model weights are released under the Modified MIT License, ensuring accessibility for further development and customization.
Overall, Kimi-K2-Instruct is a powerful AI model designed for advanced applications, offering flexibility and high performance for a wide range of AI-driven tasks.
- CategoryAI Models & LLMs
- DeploymentCloud
- PlatformsWeb
Kimi-K2-Instruct Model Highlights
32 billion activated parameters
1 trillion total parameters
Muon optimizer for stability
Mixture-of-Experts architecture
160K vocabulary size
128K context length
SwiGLU activation function
MLA attention mechanism
Supports tool-calling capabilities
OpenAI/Anthropic-compatible API
Modified MIT License
Robust performance in coding tasks
Multilingual agentic coding support
Supports vLLM, SGLang, KTransformers, TensorRT-LLM
Standalone chat template
Getting Started with Kimi-K2-Instruct Model
Access page: Visit the Hugging Face model page
Load model: Download and integrate Kimi-K2-Instruct
Configure environment: Set up compatible inference engines
Integrate: Use API for deployment
Fine-tune: Customize model for specific tasks
Kimi-K2-Instruct Model's Use Cases
- Advanced Coding
- Multilingual Support
- Tool Integration
- AI Research
- Chat Applications
