Instructions to use Edmon02/Kimi-K2.7-Code-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Edmon02/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="Edmon02/Kimi-K2.7-Code-GGUF",
	filename="UD-Q4_K_XL/Kimi-K2.7-Code-UD-Q4_K_XL-00001-of-00014.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Edmon02/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 Edmon02/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf Edmon02/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 Edmon02/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf Edmon02/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 Edmon02/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf Edmon02/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 Edmon02/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Edmon02/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/Edmon02/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use Edmon02/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 "Edmon02/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": "Edmon02/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/Edmon02/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Ollama
How to use Edmon02/Kimi-K2.7-Code-GGUF with Ollama:
```
ollama run hf.co/Edmon02/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use Edmon02/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 Edmon02/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 Edmon02/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 Edmon02/Kimi-K2.7-Code-GGUF to start chatting

How to use Edmon02/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 Edmon02/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

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": "Edmon02/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Edmon02/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 Edmon02/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 Edmon02/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Edmon02/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 Edmon02/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 "Edmon02/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"

Docker Model Runner
How to use Edmon02/Kimi-K2.7-Code-GGUF with Docker Model Runner:
```
docker model run hf.co/Edmon02/Kimi-K2.7-Code-GGUF:UD-Q4_K_XL
```

Lemonade

How to use Edmon02/Kimi-K2.7-Code-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Edmon02/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

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

Edmon02

docs: initial Kimi K2.7-Code GGUF model card

fb157b1 verified 25 days ago

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	---
	language:
	- en
	license: other
	license_name: modified-mit
	license_link: https://huggingface.co/moonshotai/Kimi-K2.7-Code/blob/main/LICENSE
	base_model: moonshotai/Kimi-K2.7-Code
	tags:
	- gguf
	- kimi
	- kimi-k2
	- code
	- agent
	- moe
	- multimodal
	- vision
	- llama-cpp
	- image-text-to-text
	library_name: gguf
	pipeline_tag: image-text-to-text
	inference: true
	model_name: Kimi K2.7-Code (GGUF — server-class quants)
	---

	# Kimi K2.7-Code — GGUF (coding agent MoE)

	Community GGUF mirror of [moonshotai/Kimi-K2.7-Code](https://huggingface.co/moonshotai/Kimi-K2.7-Code) for llama.cpp-compatible runtimes on server-grade hardware.

	Released June 12, 2026 by Moonshot AI. Coding-focused agent built on Kimi K2.6 with +21.8% on Kimi Code Bench v2.

	\| \| \|
	\|---\|---\|
	\| Architecture \| 1T MoE (32B active), DeepSeek2 / MLA \|
	\| Context \| 256K tokens (262144 in GGUF) \|
	\| Modalities \| Text, image, video (API-first; vision via mmproj in GGUF) \|
	\| License \| Modified MIT \|
	\| Thinking \| Forced `preserve_thinking` — reasoning retained across turns \|

	## Important: server-class model

	This is not a consumer-laptop model. Even the smallest GGUF quants are hundreds of GB. Plan for:

	- Multi-GPU or high-RAM server (512 GB+ system RAM typical for Q4-class quants)
	- Fast NVMe scratch space
	- Latest llama.cpp with DeepSeek2 / Kimi K2.5+ support

	See [docs/kimi-k27-code-analysis.md](https://github.com/Edmon02/audio_set/blob/main/docs/kimi-k27-code-analysis.md) for full analysis.

	## Why this repo exists

	- One download hub for unsloth UD quants (Q2–Q8, IQ variants) + mmproj.
	- Hub-side sync from [unsloth/Kimi-K2.7-Code-GGUF](https://huggingface.co/unsloth/Kimi-K2.7-Code-GGUF) — no re-upload from your laptop.
	- Maintainer script: `scripts/sync_kimi_k27_code_gguf_quants.py`

	## Available files

	See [`gguf-manifest.json`](gguf-manifest.json) for the live file list.

	### Essential tier (recommended start)

	\| Path \| Use \|
	\|------\|-----\|
	\| `UD-Q4_K_XL/` (14 shards) \| Recommended — maps to Kimi native int4 quality \|
	\| `mmproj-F16.gguf` \| Vision encoder weights for llama.cpp multimodal \|
	\| `config.json` \| Model metadata \|

	### Full tier

	All unsloth UD quants (`UD-IQ1_M`, `UD-IQ3_XXS`, `UD-IQ4_XS`, `UD-Q2_K_XL`, `UD-Q3_K_XL`, `UD-Q4_K_XL`, `UD-Q8_K_XL`) + mmproj BF16/F16/F32 — run `make sync-kimi-k27-gguf-full`.

	## Download

	```bash
	pip install -U huggingface_hub

	# Essential: Q4 XL + vision mmproj (hundreds of GB)
	huggingface-cli download Edmon02/Kimi-K2.7-Code-GGUF \
	config.json mmproj-F16.gguf \
	--include "UD-Q4_K_XL/*" \
	--local-dir ./models/kimi-k27-code
	```

	## Quick start (llama.cpp)

	Requires a recent llama.cpp build with Kimi K2.5 / DeepSeek2 MoE support.

	```bash
	# Text + tools (thinking mode — match Moonshot API defaults)
	llama-server -m ./models/kimi-k27-code/UD-Q4_K_XL \
	--mmproj ./models/kimi-k27-code/mmproj-F16.gguf \
	--ctx-size 32768 \
	--temp 1.0 --top-p 0.95
	```

	Moonshot recommends temperature=1.0, top_p=0.95, and thinking enabled. Instant mode is not supported.

	## Benchmark highlights (Moonshot-reported)

	\| Benchmark \| K2.6 \| K2.7-Code \| Δ vs K2.6 \|
	\|-----------\|------\|---------------\|-----------\|
	\| Kimi Code Bench v2 \| 50.9 \| 62.0 \| +21.8% \|
	\| Program Bench \| 48.3 \| 53.6 \| +11.0% \|
	\| MLS Bench Lite \| 26.7 \| 35.1 \| +31.5% \|
	\| MCP Atlas \| 69.4 \| 76.0 \| +9.5% \|
	\| MCP Mark Verified \| 72.8 \| 81.1 \| +11.4% \|

	## Deployment alternatives

	\| Path \| When \|
	\|------\|------\|
	\| Kimi API (`kimi-k2.7-code`) \| Production agents, Kimi Code CLI \|
	\| vLLM / SGLang / KTransformers \| Self-host from safetensors \|
	\| GGUF + llama.cpp \| Offline / custom infra with enough RAM \|

	API pricing (Moonshot): ~$0.95 / $4.00 per 1M tokens in/out.

	## Provenance

	\| Item \| Source \|
	\|------\|--------\|
	\| Base model \| `moonshotai/Kimi-K2.7-Code` \|
	\| GGUF quants \| Mirrored from `unsloth/Kimi-K2.7-Code-GGUF` \|
	\| Maintainer \| [Edmon02/audio_set](https://github.com/Edmon02/audio_set) \|

	## Limitations

	- Sharded GGUF folders — download entire quant prefix, not individual shards only.
	- Video input in GGUF may lag official API support.
	- Vendor-run benchmarks; validate on your coding/agent workloads.
	- GGUF community quants — compare against native int4 safetensors when possible.

	## Citation

	```bibtex
	@misc{kimi_k27_code_2026,
	title={Kimi K2.7-Code},
	author={Moonshot AI},
	year={2026},
	url={https://huggingface.co/moonshotai/Kimi-K2.7-Code}
	}
	```