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

Libraries

How to use unsloth/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="unsloth/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("unsloth/Kimi-K2.7-Code-GGUF", trust_remote_code=True)
model = AutoModel.from_pretrained("unsloth/Kimi-K2.7-Code-GGUF", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use unsloth/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 unsloth/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/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 unsloth/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/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 unsloth/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/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 unsloth/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Use Docker

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

LM Studio
Jan

vLLM

How to use unsloth/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 "unsloth/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": "unsloth/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/unsloth/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

SGLang

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

How to use unsloth/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 unsloth/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Configure the model in Pi

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

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use unsloth/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 unsloth/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 "unsloth/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"

danielhanchen commited on Jun 15

Commit

c6a6bc5

verified ·

1 Parent(s): 74a2085

Update README.md

Browse files

Files changed (1) hide show

README.md +26 -23

README.md CHANGED Viewed

@@ -1,6 +1,7 @@
 ---
 tags:
 - compressed-tensors
 license: other
 license_name: modified-mit
 library_name: transformers
@@ -8,11 +9,32 @@ pipeline_tag: image-text-to-text
 base_model:
 - moonshotai/Kimi-K2.7-Code
 ---
-<div align="center">
-  <picture>
-      <img src="figures/kimi-logo.png" width="30%" alt="Kimi K2.7 Code">
-  </picture>
 </div>
 <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>
@@ -29,9 +51,6 @@ base_model:
   <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.
@@ -148,22 +167,6 @@ Kimi K2.7 Code is a coding-focused agentic model built upon Kimi K2.6. With subs
 ## 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).
-## 5. Deployment
-> [!Note]
-> You can access Kimi-K2.7-Code's API on https://platform.moonshot.ai and we provide OpenAI/Anthropic-compatible API for you.
-Currently, Kimi-K2.7-Code is recommended to run on the following inference engines:
-* vLLM
-* SGLang
-* KTransformers
-Kimi-K2.7-Code has the same architecture as Kimi-K2.5/Kimi-K2.6, and the deployment method can be directly reused.
-The version requirement for `transformers` is `>=4.57.1, <5.0.0`.
-Deployment examples can be found in the [Model Deployment Guide](docs/deploy_guidance.md).
 ---
 ## 6. Model Usage

 ---
 tags:
 - compressed-tensors
+- kimi_k25
 license: other
 license_name: modified-mit
 library_name: transformers
 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://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.
 ## 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