Instructions to use gghfexp/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 gghfexp/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 gghfexp/Kimi-K2.7-Code-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf gghfexp/Kimi-K2.7-Code-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf gghfexp/Kimi-K2.7-Code-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf gghfexp/Kimi-K2.7-Code-GGUF:Q2_K

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 gghfexp/Kimi-K2.7-Code-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf gghfexp/Kimi-K2.7-Code-GGUF:Q2_K

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 gghfexp/Kimi-K2.7-Code-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gghfexp/Kimi-K2.7-Code-GGUF:Q2_K

Use Docker

docker model run hf.co/gghfexp/Kimi-K2.7-Code-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use gghfexp/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 "gghfexp/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": "gghfexp/Kimi-K2.7-Code-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gghfexp/Kimi-K2.7-Code-GGUF:Q2_K

Ollama
How to use gghfexp/Kimi-K2.7-Code-GGUF with Ollama:
```
ollama run hf.co/gghfexp/Kimi-K2.7-Code-GGUF:Q2_K
```

Unsloth Studio

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

How to use gghfexp/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 gghfexp/Kimi-K2.7-Code-GGUF:Q2_K

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": "gghfexp/Kimi-K2.7-Code-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use gghfexp/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 gghfexp/Kimi-K2.7-Code-GGUF:Q2_K

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 gghfexp/Kimi-K2.7-Code-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use gghfexp/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 gghfexp/Kimi-K2.7-Code-GGUF:Q2_K

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 "gghfexp/Kimi-K2.7-Code-GGUF:Q2_K" \
  --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 gghfexp/Kimi-K2.7-Code-GGUF with Docker Model Runner:
```
docker model run hf.co/gghfexp/Kimi-K2.7-Code-GGUF:Q2_K
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gghfexp/Kimi-K2.7-Code-GGUF:Q2_K

Run and chat with the model

lemonade run user.Kimi-K2.7-Code-GGUF-Q2_K

List all available models

lemonade list

Kimi-K2.7-Code-GGUF / README.md

gghfez

Update README.md

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	---
	quantized_by: gghfez
	pipeline_tag: text-generation
	base_model:
	- moonshotai/Kimi-K2.7-Code
	license: other
	license_name: modified-mit
	license_link: https://huggingface.co/moonshotai/Kimi-K2.7/blob/main/LICENSE
	base_model_relation: quantized
	tags:
	- mla
	- imatrix
	- conversational
	- ik_llama.cpp
	---



	## imatrix Quantization of moonshotai/Kimi-K2.7

	ik_llama.cpp quants of moonshotai/Kimi-K2.7 using Unsloth's imatrix and Ubergarm's quant recipes*.
	*embedding and output tensors left at q8_0

	The other quants in this collection REQUIRE [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp/) fork to support the ik's latest SOTA quants and optimizations! Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!
	NOTE `ik_llama.cpp` can also run your existing GGUFs from AesSedai, unsloth, bartowski, mradermacher, etc

	Some of ik's new quants are supported with [Nexesenex/croco.cpp](https://github.com/Nexesenex/croco.cpp) fork of KoboldCPP with Windows builds for CUDA 12.9. Also check for [Windows builds by Thireus here.](https://github.com/Thireus/ik_llama.cpp/releases) which have been CUDA 12.8.

	These quants provide best in class perplexity for the given memory footprint.

	The IQ2_KT is the most accurate 2-bit Kimi-K2.7-Code quant I've found on huggingface but it's slower to run.

	### Available quants

	IQ2_KT - 264.5 GiB

	Final estimate: PPL over 568 chunks for n_ctx=512 = 2.8960 +/- 0.01474 (+44.14% vs baseline)

	IQ2_KS - 270.9 GiB

	Final estimate: PPL over 568 chunks for n_ctx=512 = 2.9740 +/- 0.01518 (+48.02% vs baseline)

	IQ2_KL - 329.7 GiB

	Final estimate: PPL over 568 chunks for n_ctx=512 = 2.4417 +/- 0.01166 (+21.52% vs baseline)

	IQ3_KT - 381.8 GiB

	PPL Untested / don't have the hardware. Responds coherently to a few prompts.

	## References
	* [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp)
	* [Great mainline MoE optimizd quants AesSedai/Kimi-K2.7-GGUF](https://huggingface.co/AesSedai/Kimi-K2.7-GGUF)

	## ACK

	* Original Imatrix from [Unsloth/Kimi-K2.7-Code-GGUF](https://huggingface.co/unsloth/Kimi-K2.7-Code-GGUF/blob/main/imatrix_unsloth.gguf_file) converted via https://gghfez-ik-llama-imatrix-converter.hf.space/
	* Quant Recipes and parts of the README.md based off [ubergarm/Kimi-K2.6-GGUF](https://huggingface.co/ubergarm/Kimi-K2.6-GGUF)