Instructions to use batiai/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 batiai/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 batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS

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 batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./llama-cli -hf batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS

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 batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS

Use Docker

docker model run hf.co/batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS

LM Studio
Jan

vLLM

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

Use Docker

docker model run hf.co/batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS

Ollama
How to use batiai/Kimi-K2.7-Code-GGUF with Ollama:
```
ollama run hf.co/batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS
```

Unsloth Studio

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

How to use batiai/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 batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS

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": "batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use batiai/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 batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS

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 batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use batiai/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 batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS

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 "batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS" \
  --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 batiai/Kimi-K2.7-Code-GGUF with Docker Model Runner:
```
docker model run hf.co/batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull batiai/Kimi-K2.7-Code-GGUF:IQ3_XXS

Run and chat with the model

lemonade run user.Kimi-K2.7-Code-GGUF-IQ3_XXS

List all available models

lemonade list

Kimi-K2.7-Code GGUF — Quantized by BatiAI

The coding upgrade to Kimi K2.6 — +21.8% on Kimi Code Bench v2, running on a 512GB Mac Studio. IQ3_XXS / IQ4_XS GGUF of moonshotai/Kimi-K2.7-Code (1T total / 32.6B active MoE, DeepSeek-V3-family architecture). Quantized directly from official Moonshot weights — code+multilingual imatrix, BatiAI-signed.

📦 Quantizations

Quant	Size	Shards	Target
IQ3_XXS	394 GB (GiB: 367)	10	M3 Ultra 512GB Mac Studio
IQ4_XS	546 GB (GiB: 509)	13	512GB+ / multi-node / server

Both built from official weights via a Q8_0 intermediate, quantized with a code + EN + KO + ZH imatrix (included: Kimi-K2.7-Code-imatrix.dat). Text-only (the vision tower of the K2.5-family checkpoint is not included; same as other K2 GGUFs).

✅ Verified (this build, IQ3_XXS) — captured greedy runs:

Math: 127+58 → 185 (clean reasoning trace)
Korean: 서울 소개 + 김치·비빔밥·불고기 각 한 문장 — fluent, zero token-mixing or loops
Tool-call: {"tool":"get_weather","args":{"city":"부산"}} — exact JSON

🚀 Usage (llama.cpp — mainline, no fork needed)

hf download batiai/Kimi-K2.7-Code-GGUF "Kimi-K2.7-Code-IQ3_XXS-*.gguf" --local-dir ./k27

# llama.cpp auto-loads all shards from the first one
./llama-cli -m ./k27/Kimi-K2.7-Code-IQ3_XXS-00001-of-00010.gguf -ngl 99 -c 16384 \
  -p "Refactor this function and add tests."

Recommended sampling (Moonshot): --temp 1.0 --top-p 0.95 (thinking mode). Architecture is deepseek2 — supported by mainline llama.cpp out of the box. Ollama tags (batiai/kimi-k2.7-code) follow shortly.

📜 License

Modified MIT (Moonshot) — commercial use permitted; products exceeding 100M MAU / $20M monthly revenue must display "Kimi K2.7" attribution. Full text at the base model repo. Quantized weights redistributed under the same terms.

✨ What BatiAI did

Direct from official Moonshot weights (never a re-quant of third-party GGUFs)
Q8_0 intermediate + diverse imatrix (code/EN/KO/ZH) for balanced fidelity
Verified: load ✅ · math ✅ · Korean ✅ · tool-call JSON ✅ — BatiAI metadata-signed

— BatiAI · on-device frontier AI · https://flow.bati.ai

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GGUF

Model size

1T params

Architecture

deepseek2

Hardware compatibility

3-bit

4-bit

Model tree for batiai/Kimi-K2.7-Code-GGUF

Base model

moonshotai/Kimi-K2.7-Code

Quantized

(29)

this model