Instructions to use Chen2921/Kimi-K2.7-Code-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Chen2921/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 Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S
# Run inference directly in the terminal:
llama cli -hf Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S
# Run inference directly in the terminal:
llama cli -hf Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S

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 Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S
# Run inference directly in the terminal:
./llama-cli -hf Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S

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 Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S

Use Docker

docker model run hf.co/Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S

LM Studio
Jan
Ollama
How to use Chen2921/Kimi-K2.7-Code-GGUF with Ollama:
```
ollama run hf.co/Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S
```

Unsloth Studio

How to use Chen2921/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 Chen2921/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 Chen2921/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 Chen2921/Kimi-K2.7-Code-GGUF to start chatting

How to use Chen2921/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 Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S

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": "Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Chen2921/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 Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S

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 Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Chen2921/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 Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S

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 "Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S" \
  --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"

Docker Model Runner
How to use Chen2921/Kimi-K2.7-Code-GGUF with Docker Model Runner:
```
docker model run hf.co/Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S
```

Lemonade

How to use Chen2921/Kimi-K2.7-Code-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Chen2921/Kimi-K2.7-Code-GGUF:IQ2_S

Run and chat with the model

lemonade run user.Kimi-K2.7-Code-GGUF-IQ2_S

List all available models

lemonade list

Updates

06/15/2026: I've updated the Q3_K_L quant, it wasn't imatrixed (oops) and the new upload is. The PPL and KLD are slightly improved. Working on smaller quants still.

Model

This is a text-and-image-only GGUF quantization of moonshotai/Kimi-K2.7-Code. This means that video input is not present in this GGUF, and will not be available until support is added upstream in llama.cpp. MMPROJ files for image vision input have been provided.

This Q4_X quant is the "full quality" equivalent since the conditional experts are natively INT4 quantized directly from the original model, and the rest of the model is Q8_0.

Quant	Size	Mixture	PPL	1-(Mean PPL(Q)/PPL(base))	KLD
Q4_X	543.62 GiB (4.55 BPW)	Q8_0 / Q4_0 / Q4_0 / Q4_0	2.009155 ± 0.008931	+0%	0
Q3_K_L	459.94 GiB (3.85 BPW)	Q8_0 / Q3_K / Q3_K / Q4_0	2.085207 ± 0.009398	+3.9167%	0.063007 ± 0.000544
IQ3_S	377.54 GiB (3.16 BPW)	Q8_0 / varies	2.310321 ± 0.010823	+15.1353%	0.178673 ± 0.001326
IQ2_S	311.80 GiB (2.61 BPW)	Q8_0 / varies	2.690751 ± 0.013248	+34.0941%	0.343187 ± 0.002100
IQ2_XXS	262.78 GiB (2.20 BPW)	Q8_0 / varies	3.420305 ± 0.018189	+70.4515%	0.587342 ± 0.002980