Instructions to use eadx/Kimi-K2.7-Code-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eadx/Kimi-K2.7-Code-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="eadx/Kimi-K2.7-Code-GGUF", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModel

processor = AutoProcessor.from_pretrained("eadx/Kimi-K2.7-Code-GGUF", trust_remote_code=True)
model = AutoModel.from_pretrained("eadx/Kimi-K2.7-Code-GGUF", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use eadx/Kimi-K2.7-Code-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use eadx/Kimi-K2.7-Code-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eadx/Kimi-K2.7-Code-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eadx/Kimi-K2.7-Code-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

SGLang

How to use eadx/Kimi-K2.7-Code-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "eadx/Kimi-K2.7-Code-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eadx/Kimi-K2.7-Code-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "eadx/Kimi-K2.7-Code-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eadx/Kimi-K2.7-Code-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use eadx/Kimi-K2.7-Code-GGUF with Ollama:
```
ollama run hf.co/eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use eadx/Kimi-K2.7-Code-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for eadx/Kimi-K2.7-Code-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for eadx/Kimi-K2.7-Code-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for eadx/Kimi-K2.7-Code-GGUF to start chatting

How to use eadx/Kimi-K2.7-Code-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use eadx/Kimi-K2.7-Code-GGUF with Docker Model Runner:
```
docker model run hf.co/eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
```

Lemonade

How to use eadx/Kimi-K2.7-Code-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.Kimi-K2.7-Code-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use eadx/Kimi-K2.7-Code-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use eadx/Kimi-K2.7-Code-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "eadx/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

eadx

danielhanchen commited on Jun 15

Commit

d2c29db

0 Parent(s):

Duplicate from unsloth/Kimi-K2.7-Code-GGUF

Browse files

Co-authored-by: Daniel (Unsloth) <danielhanchen@users.noreply.huggingface.co>

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README.md ADDED Viewed

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+---
+tags:
+- compressed-tensors
+- kimi_k25
+- unsloth
+license: other
+license_name: modified-mit
+library_name: transformers
+pipeline_tag: image-text-to-text
+base_model:
+- moonshotai/Kimi-K2.7-Code
+---
+# Read our How to [Run Kimi K2.7 Code Guide!](https://unsloth.ai/docs/models/kimi-k2.7-code)
+<div>
+  <p style="margin: 0 0 0px 0; margin-top: 0px;">
+    <em>See <a href="https://unsloth.ai/docs/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0 GGUFs</a> for our quantization benchmarks.</em>
+  </p>
+  <div style="display: flex; gap: 5px; align-items: center; margin-bottom: 0px;">
+    <a href="https://github.com/unslothai/unsloth/">
+      <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
+    </a>
+    <a href="https://discord.gg/unsloth">
+      <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
+    </a>
+    <a href="https://unsloth.ai/docs/models/kimi-k2.7-code">
+      <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
+    </a>
+  </div>
+    <ul style="margin: 0;">
+    <li>To run Kimi K2.7 Code in full precision lossless, run Q8 (UD-Q8_K_XL), which is 595GB and only 10GB bigger than Q4 (UD-Q4_K_XL).</li>
+    <li>See our <a href="https://unsloth.ai/docs/models/kimi-k2.7-code">Kimi K2.7 Code guide</a> for quantization analysis and instructions.</li>
+  </ul>
+</div>
+<br>
+---
+# Kimi-K2.7-Code
+<hr>
+<div align="center" style="line-height:1">
+  <a href="https://www.kimi.com/code" target="_blank"><img alt="Chat" src="https://img.shields.io/badge/🤖-Kimi--Code-ff6b6b?color=1783ff&logoColor=white"/></a>
+  <a href="https://www.moonshot.ai" target="_blank"><img alt="Homepage" src="https://img.shields.io/badge/Homepage-Moonshot%20AI-white?logo=Kimi&logoColor=white"/></a>
+</div>
+<div align="center" style="line-height: 1;">
+  <a href="https://huggingface.co/moonshotai" target="_blank"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Moonshot%20AI-ffc107?color=ffc107&logoColor=white"/></a>
+  <a href="https://twitter.com/kimi_moonshot" target="_blank"><img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-Kimi.ai-white?logo=x&logoColor=white"/></a>
+  <a href="https://discord.gg/TYU2fdJykW" target="_blank"><img alt="Discord" src="https://img.shields.io/badge/Discord-Kimi.ai-white?logo=discord&logoColor=white"/></a>
+  <a href="https://modelscope.cn/organization/moonshotai" target="_blank"><img alt="ModelScope" src="https://img.shields.io/badge/ModelScope-Moonshot%20AI-white?labelColor=rgb(99%2C%2074%2C%20255)"/></a>
+</div>
+<div align="center" style="line-height: 1;">
+  <a href="https://huggingface.co/moonshotai/Kimi-K2.7-Code/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Modified_MIT-f5de53?&color=f5de53"/></a>
+</div>
+## 1. Model Introduction
+Kimi K2.7 Code is a coding-focused agentic model built upon Kimi K2.6. With substantial improvements on real-world long-horizon coding tasks, it strengthens end-to-end task completion across complex software engineering workflows while improving token efficiency, reducing thinking-token usage by approximately 30% compared with Kimi K2.6.
+## 2. Model Summary
+<div align="center">
+| | |
+|:---:|:---:|
+| **Architecture** | Mixture-of-Experts (MoE) |
+| **Total Parameters** | 1T |
+| **Activated Parameters** | 32B |
+| **Number of Layers** (Dense layer included) | 61 |
+| **Number of Dense Layers** | 1 |
+| **Attention Hidden Dimension** | 7168 |
+| **MoE Hidden Dimension** (per Expert) | 2048 |
+| **Number of Attention Heads** | 64 |
+| **Number of Experts** | 384 |
+| **Selected Experts per Token** | 8 |
+| **Number of Shared Experts** | 1 |
+| **Vocabulary Size** | 160K |
+| **Context Length** | 256K |
+| **Attention Mechanism** | MLA |
+| **Activation Function** | SwiGLU |
+| **Vision Encoder** | MoonViT |
+| **Parameters of Vision Encoder** | 400M |
+</div>
+## 3. Evaluation Results
+<div align="center">
+<table>
+<thead>
+<tr>
+<th align="center">Benchmark</th>
+<th align="center">Kimi K2.6</th>
+<th align="center">Kimi K2.7 Code</th>
+<th align="center">GPT-5.5</th>
+<th align="center">Claude Opus 4.8</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center" colspan=5><strong>Coding</strong></td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">Kimi Code Bench v2</td>
+<td align="center" style="vertical-align: middle">50.9</td>
+<td align="center" style="vertical-align: middle">62.0</td>
+<td align="center" style="vertical-align: middle">69.0</td>
+<td align="center" style="vertical-align: middle">67.4</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">Program Bench</td>
+<td align="center" style="vertical-align: middle">48.3</td>
+<td align="center" style="vertical-align: middle">53.6</td>
+<td align="center" style="vertical-align: middle">69.1</td>
+<td align="center" style="vertical-align: middle">63.8</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">MLS Bench Lite</td>
+<td align="center" style="vertical-align: middle">26.7</td>
+<td align="center" style="vertical-align: middle">35.1</td>
+<td align="center" style="vertical-align: middle">35.5</td>
+<td align="center" style="vertical-align: middle">42.8</td>
+</tr>
+<tr>
+<td align="center" colspan=5><strong>Agentic</strong></td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">Kimi Claw 24/7 Bench</td>
+<td align="center" style="vertical-align: middle">42.9</td>
+<td align="center" style="vertical-align: middle">46.9</td>
+<td align="center" style="vertical-align: middle">52.8</td>
+<td align="center" style="vertical-align: middle">50.4</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">MCP Atlas</td>
+<td align="center" style="vertical-align: middle">69.4</td>
+<td align="center" style="vertical-align: middle">76.0</td>
+<td align="center" style="vertical-align: middle">79.4</td>
+<td align="center" style="vertical-align: middle">81.3</td>
+</tr>
+<tr>
+<td align="center" style="vertical-align: middle">MCP Mark Verified</td>
+<td align="center" style="vertical-align: middle">72.8</td>
+<td align="center" style="vertical-align: middle">81.1</td>
+<td align="center" style="vertical-align: middle">92.9</td>
+<td align="center" style="vertical-align: middle">76.4</td>
+</tr>
+</tbody>
+</table>
+</div>
+<details>
+<summary><b>Footnotes</b></summary>
+1. **General Testing Details**
+   - Unless stated otherwise, Kimi K2.7 Code and K2.6 were tested with thinking mode enabled via Kimi Code CLI at temperature = 1.0, top-p = 0.95, and a 262,144-token context length; GPT-5.5 ran in Codex with xhigh mode, and Opus 4.8 in Claude Code with xhigh mode. Aside from these differences, all benchmarks were evaluated under the same conditions.
+2. **Coding Benchmarks**
+   - Kimi Code Bench V2 is our in-house benchmark designed to evaluate coding agents on realistic tasks. It has diversed software engineering tasks across 10+ mainstream programming languages and a full production tech stack covering tasks from internal engineering use cases, production incidents, and real-world open-source projects, with emphasis on backend services, infrastructure, performance engineering, systems programming, security, frontend development, and ML/data engineering.
+   - [Program Bench](https://programbench.com/) evaluates code-generation agents by asking them to recreate a program’s behavior from only a compiled binary and its documentation. It spans 200 tasks, from small CLI tools to large systems like FFmpeg and SQLite. Submissions are judged against over 248,000 fuzz-generated behavioral tests. In each task, the agent is given an executable and its documentation, but no source code, decompilation, or internet access. It must choose its own implementation language, build the full program from scratch, and pass a behavioral test suite comparing its output against the original binary.
+   - [MLS-Bench](https://mls-bench.com) evaluates whether AI systems can invent generalizable and scalable ML methods. MLS-Bench-Lite is the official 30-task subset of MLS-Bench, covering LLM pretraining and post-training, robotics, world models, computer vision, reinforcement learning, optimization, ML systems, AI for Science, and more. Agents are given 5 hours to explore before submitting their solutions. Opus 4.8 is evaluated with the max effort setting in Claude Code.
+3. **Agentic Benchmarks**
+   - Kimi Claw 24/7 Bench is our in-house benchmark for evaluating long-horizon agentic performance in persistent, multi-day coworking tasks. It spans 17 professional scenarios across 610 evaluation points, covering domains such as software engineering, ML research, recruiting, trading, marketing. All tasks are executed through the OpenClaw harness. The final score is the average pass rate across all evaluation points, and is averaged over 3 runs.
+   - [MCP-Atlas](https://labs.scale.com/leaderboard/mcp_atlas) evaluates LLM performance on realistic tool-use tasks through the scalable MCPs. We followed the official MCP-Atlas evaluation configuration with a 100 tool-call budget, and with 32k max tokens per step. The final result is averaged over 3 runs.
+   - MCPMark-Verified is a human-verified edition of [MCPMark](https://mcpmark.ai/), a benchmark for evaluating MCP tool use across five real server environments — Notion, GitHub, Filesystem, Postgres, and Playwright. Each task has been re-checked by our team and the benchmark offical and will be open-sourced soon. We followed the official MCPMark evaluation configuration with a 100-step tool-call budget and 32k max tokens per step. The final result is averaged over 3 runs.
+</details>
+## 4. Native INT4 Quantization
+Kimi-K2.7-Code adopts the same native int4 quantization method as [Kimi-K2-Thinking](https://huggingface.co/moonshotai/Kimi-K2-Thinking#4-native-int4-quantization).
+---
+## 6. Model Usage
+The usage demos below demonstrate how to call our official API. Note that Kimi-K2.7-Code forces thinking and preserve_thinking as True.
+For third-party APIs deployed with vLLM or SGLang, please note that:
+> [!Note]
+> - Chat with video content is an experimental feature and is only supported in our official API for now.
+>
+> - The recommended `temperature` will be `1.0` for Thinking mode.
+>
+> - The recommended `top_p` is `0.95`.
+>
+> - Instant mode is not supported.
+### Chat Completion
+This is a simple chat completion script which shows how to call K2.7-Code API in Thinking mode.
+```python
+import openai
+import base64
+import requests
+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, max_tokens=4096
+    )
+    print('====== Below is reasoning content in Thinking Mode ======')
+    print(f'reasoning content: {response.choices[0].message.reasoning}')
+    print('====== Below is response in Thinking Mode ======')
+    print(f'response: {response.choices[0].message.content}')
+```
+### Chat Completion with visual content
+K2.7-Code supports Image and Video input.
+The following example demonstrates how to call K2.7-Code API with image input:
+```python
+import openai
+import base64
+import requests
+def chat_with_image(client: openai.OpenAI, model_name: str):
+    url = 'https://huggingface.co/moonshotai/Kimi-K2.7-Code/resolve/main/figures/kimi-logo.png'
+    image_base64 = base64.b64encode(requests.get(url).content).decode()
+    messages = [
+        {
+            'role': 'user',
+            'content': [
+                {'type': 'text', 'text': 'Describe this image in detail.'},
+                {
+                    'type': 'image_url',
+                    'image_url': {'url': f'data:image/png;base64,{image_base64}'},
+                },
+            ],
+        }
+    ]
+    response = client.chat.completions.create(
+        model=model_name, messages=messages, stream=False, max_tokens=8192
+    )
+    print('====== Below is reasoning content in Thinking Mode ======')
+    print(f'reasoning content: {response.choices[0].message.reasoning}')
+    print('====== Below is response in Thinking Mode ======')
+    print(f'response: {response.choices[0].message.content}')
+```
+The following example demonstrates how to call K2.7-Code API with video input:
+```python
+import openai
+import base64
+import requests
+def chat_with_video(client: openai.OpenAI, model_name:str):
+    url = 'https://huggingface.co/moonshotai/Kimi-K2.7-Code/resolve/main/figures/demo_video.mp4'
+    video_base64 = base64.b64encode(requests.get(url).content).decode()
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text","text": "Describe the video in detail."},
+                {
+                    "type": "video_url",
+                    "video_url": {"url": f"data:video/mp4;base64,{video_base64}"},
+                },
+            ],
+        }
+    ]
+    response = client.chat.completions.create(model=model_name, messages=messages)
+    print('====== Below is reasoning content in Thinking Mode ======')
+    print(f'reasoning content: {response.choices[0].message.reasoning}')
+    print('====== Below is response in Thinking Mode ======')
+    print(f'response: {response.choices[0].message.content}')
+```
+### Preserve Thinking
+Kimi K2.7 Code forces `preserve_thinking` mode, which retains full reasoning content across multi-turn interactions and enhances performance in coding agent scenarios.
+This feature is enabled by default and can't be disabled. The following example demonstrates how to call K2.7-Code API in `preserve_thinking` mode:
+```python
+def chat_with_preserve_thinking(client: openai.OpenAI, model_name: str):
+    messages = [
+        {
+            "role": "user",
+            "content": "Tell me three random numbers."
+        },
+        {
+            "role": "assistant",
+            "reasoning_content": "I'll start by listing five numbers: 473, 921, 235, 215, 222, and I'll tell you the first three.",
+            # Some API (e.g. vLLM) may not support reasoning_content, you can try reasoning instead
+            "content": "473, 921, 235"
+        },
+        {
+            "role": "user",
+            "content": "What are the other two numbers you have in mind?"
+        }
+    ]
+    response = client.chat.completions.create(
+        model=model_name,
+        messages=messages,
+        stream=False,
+        max_tokens=4096,
+    )
+    # the assistant should mention 215 and 222 that appear in the prior reasoning content
+    print(f"response: {response.choices[0].message.reasoning}")
+    return response.choices[0].message.content
+```
+### Interleaved Thinking and Multi-Step Tool Call
+K2.7-Code shares the same design of Interleaved Thinking and Multi-Step Tool Call as K2 Thinking. For usage example, please refer to the [K2 Thinking documentation](https://platform.moonshot.ai/docs/guide/use-kimi-k2-thinking-model#complete-example).
+### Coding Agent Framework
+Kimi K2.7-Code works best with Kimi Code CLI as its agent framework — give it a try at https://www.kimi.com/code.
+---
+## 7. License
+Both the code repository and the model weights are released under the [Modified MIT License](LICENSE).
+---
+## 8. Third Party Notices
+See [THIRD PARTY NOTICES](THIRD_PARTY_NOTICES.md)
+---
+## 9. Contact Us
+If you have any questions, please reach out at [support@moonshot.ai](mailto:support@moonshot.ai).

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