Kimi-K2-Instruct Model

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.

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.

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

1. 1

   Access page: Visit the Hugging Face model page

2. 2

   Load model: Download and integrate Kimi-K2-Instruct

3. 3

   Configure environment: Set up compatible inference engines

4. 4

   Integrate: Use API for deployment

5. 5

   Fine-tune: Customize model for specific tasks

Kimi-K2-Instruct Model's Use Cases

• Advanced Coding
• Multilingual Support
• Tool Integration
• AI Research
• Chat Applications

FAQ from Kimi-K2-Instruct Model

• What is Kimi-K2-Instruct?

  Kimi-K2-Instruct is a mixture-of-experts language model designed for advanced AI tasks like reasoning and coding.

• How many parameters does Kimi-K2-Instruct have?

  It features 32 billion activated parameters and a total of 1 trillion parameters.

• What optimizer is used in Kimi-K2-Instruct?

  The model uses the Muon optimizer for training stability and efficiency.

• What are the key features of Kimi-K2-Instruct?

  Key features include a large vocabulary size, high context length, and advanced tool-calling capabilities.

• Is Kimi-K2-Instruct open source?

  Yes, it is released under the Modified MIT License.

• What benchmarks does Kimi-K2-Instruct excel in?

  It performs well in coding and multilingual agentic coding tasks, among others.

• What inference engines are compatible with Kimi-K2-Instruct?

  Compatible engines include vLLM, SGLang, KTransformers, and TensorRT-LLM.

• How can I deploy Kimi-K2-Instruct?

  Deployment is possible via an API available on the moonshot.ai platform.