ModelCloud / Kimi-K2-Thinking-BF16

BF16 model dequantized by @Qubitum at ModelCloud using GPT-QModel .

SPDX-License-Identifier: NonCommercial-ModelCloud
Copyright (c) 2024–2025 ModelCloud.ai
Released for non-profit use only.
Commercial use requires permission from [@qubitium](https://x.com/qubitium).

📰 Tech Blog

1. Model Introduction

Kimi K2 Thinking is the latest, most capable version of open-source thinking model. Starting with Kimi K2, we built it as a thinking agent that reasons step-by-step while dynamically invoking tools. It sets a new state-of-the-art on Humanity's Last Exam (HLE), BrowseComp, and other benchmarks by dramatically scaling multi-step reasoning depth and maintaining stable tool-use across 200–300 sequential calls. At the same time, K2 Thinking is a native INT4 quantization model with 256k context window, achieving lossless reductions in inference latency and GPU memory usage.

Key Features

• Deep Thinking & Tool Orchestration : End-to-end trained to interleave chain-of-thought reasoning with function calls, enabling autonomous research, coding, and writing workflows that last hundreds of steps without drift.
• Native INT4 Quantization : Quantization-Aware Training (QAT) is employed in post-training stage to achieve lossless 2x speed-up in low-latency mode.
• Stable Long-Horizon Agency : Maintains coherent goal-directed behavior across up to 200–300 consecutive tool invocations, surpassing prior models that degrade after 30–50 steps.

2. Model Summary

3. Evaluation Results

Reasoning Tasks

General Tasks

Agentic Search Tasks

Coding Tasks

4. Native INT4 Quantization

Low-bit quantization is an effective way to reduce inference latency and GPU memory usage on large-scale inference servers. However, thinking models use excessive decoding lengths, and thus quantization often results in substantial performance drops.

To overcome this challenge, we adopt Quantization-Aware Training (QAT) during the post-training phase, applying INT4 weight-only quantization to the MoE components. It allows K2 Thinking to support native INT4 inference with a roughly 2x generation speed improvement while achieving state-of-the-art performance. All benchmark results are reported under INT4 precision.

The checkpoints are saved in compressed-tensors format, supported by most of mainstream inference engine. If you need the checkpoints in higher precision such as FP8 or BF16, you can refer to official repo of compressed-tensors to unpack the int4 weights and convert to any higher precision.

5. Deployment

You can access K2 Thinking's API on https://platform.moonshot.ai , we provide OpenAI/Anthropic-compatible API for you.

Currently, Kimi-K2-Thinking is recommended to run on the following inference engines:

• vLLM
• SGLang
• KTransformers

Deployment examples can be found in the Model Deployment Guide .

6. Model Usage

Chat Completion

Once the local inference service is up, you can interact with it through the chat endpoint:

def simple_chat(client: openai.OpenAI, model_name: str):
    messages = [
        {"role": "system", "content": "You are Kimi, an AI assistant created by Moonshot AI."},
        {"role": "user", "content": [{"type": "text", "text": "which one is bigger, 9.11 or 9.9? think carefully."}]},
    ]
    response = client.chat.completions.create(
        model=model_name,
        messages=messages,
        stream=False,
        temperature=1.0,
        max_tokens=4096
    )
    print(f"k2 answer: {response.choices[0].message.content}")
    print("=====below is reasoning content======")
    print(f"reasoning content: {response.choices[0].message.reasoning_content}")

The recommended temperature for Kimi-K2-Thinking is temperature = 1.0 . If no special instructions are required, the system prompt above is a good default.

Tool Calling

Kimi-K2-Thinking has the same tool calling settings as Kimi-K2-Instruct.

To enable them, you need to pass the list of available tools in each request, then the model will autonomously decide when and how to invoke them.

The following example demonstrates calling a weather tool end-to-end:

# Your tool implementation
def get_weather(city: str) -> dict:
    return {"weather": "Sunny"}
# Tool schema definition
tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Retrieve current weather information. Call this when the user asks about the weather.",
        "parameters": {
            "type": "object",
            "required": ["city"],
            "properties": {
                "city": {
                    "type": "string",
                    "description": "Name of the city"
                }
            }
        }
    }
}]
# Map tool names to their implementations
tool_map = {
    "get_weather": get_weather
}
def tool_call_with_client(client: OpenAI, model_name: str):
    messages = [
        {"role": "system", "content": "You are Kimi, an AI assistant created by Moonshot AI."},
        {"role": "user", "content": "What's the weather like in Beijing today? Use the tool to check."}
    ]
    finish_reason = None
    while finish_reason is None or finish_reason == "tool_calls":
        completion = client.chat.completions.create(
            model=model_name,
            messages=messages,
            temperature=1.0,
            tools=tools,          # tool list defined above
            tool_choice="auto"
        )
        choice = completion.choices[0]
        finish_reason = choice.finish_reason
        if finish_reason == "tool_calls":
            messages.append(choice.message)
            for tool_call in choice.message.tool_calls:
                tool_call_name = tool_call.function.name
                tool_call_arguments = json.loads(tool_call.function.arguments)
                tool_function = tool_map[tool_call_name]
                tool_result = tool_function(**tool_call_arguments)
                print("tool_result:", tool_result)
                messages.append({
                    "role": "tool",
                    "tool_call_id": tool_call.id,
                    "name": tool_call_name,
                    "content": json.dumps(tool_result)
                })
    print("-" * 100)
    print(choice.message.content)

The tool_call_with_client function implements the pipeline from user query to tool execution. This pipeline requires the inference engine to support Kimi-K2’s native tool-parsing logic. For more information, see the Tool Calling Guide .

7. License

Both the code repository and the model weights are released under the Modified MIT License .

8. Third Party Notices

See THIRD PARTY NOTICES

9. Contact Us

If you have any questions, please reach out at [email protected] .