Instructions to use freakyskittle/kimi-k2.7-code-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use freakyskittle/kimi-k2.7-code-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="freakyskittle/kimi-k2.7-code-GGUF",
	filename="Q4_K_M/kimi-k2.7-code-Q4_K_M-00001-of-00061.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use freakyskittle/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 freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M

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 freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M

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 freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M

Use Docker

docker model run hf.co/freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use freakyskittle/kimi-k2.7-code-GGUF with Ollama:
```
ollama run hf.co/freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M
```

Unsloth Studio

How to use freakyskittle/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 freakyskittle/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 freakyskittle/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 freakyskittle/kimi-k2.7-code-GGUF to start chatting

How to use freakyskittle/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 freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M

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": "freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use freakyskittle/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 freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M

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 freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use freakyskittle/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 freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M

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 "freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M" \
  --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 freakyskittle/kimi-k2.7-code-GGUF with Docker Model Runner:
```
docker model run hf.co/freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M
```

Lemonade

How to use freakyskittle/kimi-k2.7-code-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull freakyskittle/kimi-k2.7-code-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.kimi-k2.7-code-GGUF-Q4_K_M

List all available models

lemonade list

Kimi K2.7 Code GGUF

GGUF conversions of moonshotai/Kimi-K2.7-Code for llama.cpp-compatible runtimes.

Available files

Variant	Files	Approx local size	Status	Notes
Q8_0	`Q8_0/kimi-k2.7-code-Q8_0-00001-of-00061.gguf` ... `00061-of-00061.gguf`	~1017 GiB	Uploaded	8-bit GGUF, split into 61 shards.
Q4_K_M	`Q4_K_M/kimi-k2.7-code-Q4_K_M-00001-of-00061.gguf` ... `00061-of-00061.gguf`	~578 GiB	Uploaded	Standard high-quality 4-bit GGUF, split because the single file exceeds Hugging Face’s 500GB per-file limit.
TQ2_0	`TQ2_0/kimi-k2.7-code-TQ2_0.gguf`	~249 GiB	Uploaded	2-bit-class ternary quantization, single file.
TQ1_0	`TQ1_0/kimi-k2.7-code-TQ1_0.gguf`	~204 GiB	Uploaded	1-bit-class ternary quantization, single file.

Q6_K is not currently uploaded in this repository.

Loading split GGUF files

For split GGUF variants, download all shards for the variant into the same directory and point llama.cpp at shard 00001. llama.cpp will discover the remaining shards automatically.

Examples:

# BF16
llama-cli -m BF16/kimi-k2.7-code-BF16-00001-of-00061.gguf -p "Write a Python function for quicksort."

# Q8_0
llama-cli -m Q8_0/kimi-k2.7-code-Q8_0-00001-of-00061.gguf -p "Write a Rust HTTP server."

# Q4_K_M
llama-cli -m Q4_K_M/kimi-k2.7-code-Q4_K_M-00001-of-00061.gguf -p "Explain async/await."

Single-file variants can be loaded directly:

llama-cli -m TQ2_0/kimi-k2.7-code-TQ2_0.gguf -p "Hello"
llama-cli -m TQ1_0/kimi-k2.7-code-TQ1_0.gguf -p "Hello"

Quantization notes

BF16 was converted from the original SafeTensors using llama.cpp convert_hf_to_gguf.py with BF16 output.
Q8_0 and Q4_K_M were quantized from the BF16 GGUF source and uploaded as split GGUF shards.
TQ1_0 and TQ2_0 are llama.cpp ternary low-bit formats.
IQ1_S was not produced because llama.cpp requires an importance matrix for that quantization.
Very large variants are split to stay under Hugging Face’s individual file-size limit.

License

See LICENSE. This model uses Moonshot AI’s Modified MIT License for Kimi K2.7 Code.

Attribution

Base model by Moonshot AI: moonshotai/Kimi-K2.7-Code.

Downloads last month: 5,757

GGUF

Model size

1T params

Architecture

deepseek2

Hardware compatibility

1-bit

2-bit

4-bit

8-bit

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