Instructions to use Youssofal/Kimi-K2.6-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Youssofal/Kimi-K2.6-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Youssofal/Kimi-K2.6-GGUF",
	filename="Kimi-K2.6-BF16/Kimi-K2.6-BF16-bf16-00001-of-00046.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Youssofal/Kimi-K2.6-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Youssofal/Kimi-K2.6-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf Youssofal/Kimi-K2.6-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Youssofal/Kimi-K2.6-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf Youssofal/Kimi-K2.6-GGUF:BF16

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 Youssofal/Kimi-K2.6-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf Youssofal/Kimi-K2.6-GGUF:BF16

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 Youssofal/Kimi-K2.6-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Youssofal/Kimi-K2.6-GGUF:BF16

Use Docker

docker model run hf.co/Youssofal/Kimi-K2.6-GGUF:BF16

LM Studio
Jan

vLLM

How to use Youssofal/Kimi-K2.6-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Youssofal/Kimi-K2.6-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": "Youssofal/Kimi-K2.6-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Youssofal/Kimi-K2.6-GGUF:BF16

Ollama
How to use Youssofal/Kimi-K2.6-GGUF with Ollama:
```
ollama run hf.co/Youssofal/Kimi-K2.6-GGUF:BF16
```

Unsloth Studio new

How to use Youssofal/Kimi-K2.6-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 Youssofal/Kimi-K2.6-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 Youssofal/Kimi-K2.6-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Youssofal/Kimi-K2.6-GGUF to start chatting

Pi new

How to use Youssofal/Kimi-K2.6-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Youssofal/Kimi-K2.6-GGUF:BF16

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": "Youssofal/Kimi-K2.6-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Youssofal/Kimi-K2.6-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Youssofal/Kimi-K2.6-GGUF:BF16

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 Youssofal/Kimi-K2.6-GGUF:BF16

Run Hermes

hermes

Docker Model Runner
How to use Youssofal/Kimi-K2.6-GGUF with Docker Model Runner:
```
docker model run hf.co/Youssofal/Kimi-K2.6-GGUF:BF16
```

Lemonade

How to use Youssofal/Kimi-K2.6-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Youssofal/Kimi-K2.6-GGUF:BF16

Run and chat with the model

lemonade run user.Kimi-K2.6-GGUF-BF16

List all available models

lemonade list

🚨 UPLOADS IN PROGRESS 🚨

Some files and metadata in this repository are still being uploaded and verified.

Kimi-K2.6-GGUF

This is a GGUF release of Moonshot AI's Kimi-K2.6.

The release preserves Kimi-K2.6's native multimodal architecture and is intended as the canonical llama.cpp-compatible GGUF ladder for the original model.

Quick Benchmarks

Check	Original Kimi-K2.6	Kimi-K2.6 GGUF
Official 25-prompt refusal check	Pending	Pending
PPL / KLD reference drift	Pending	Pending

Methodology & Model Notes

Kimi-K2.6 is a large sparse-MoE vision-language model in the Kimi K2 family, exposed through the KimiK25ForConditionalGeneration wrapper with a DeepSeek V3-style text stack.

This release is built directly from moonshotai/Kimi-K2.6 and is intended to provide a clean original-model GGUF ladder without altering the base refusal behavior.

Quant Benchmarks

Quant	Official 25-prompt refusal check	Perplexity	KL divergence
Q8_0	Pending	Pending	Pending
Q6_K	Pending	Pending	Pending
Q4_K_M	Pending	Pending	Pending
Q2_K	Pending	Pending	Pending

Files

Kimi-K2.6-Q8_0/: highest-fidelity quant
Kimi-K2.6-Q6_K/: near-lossless practical quant
Kimi-K2.6-Q4_K_M/: smaller general-use quant
Kimi-K2.6-Q2_K/: lowest standard quant in this ladder
mmproj-Kimi-K2.6.gguf: matching multimodal projector file for llama.cpp vision use

Running

llama-server \
  -m <quant-file.gguf> \
  --mmproj <mmproj-file.gguf> \
  -ngl 999 -c 32768 --jinja -fa

Model Architecture

Spec	Value
Architecture Wrapper	`KimiK25ForConditionalGeneration`
Text Family	DeepSeek V3-style sparse MoE
Text Layers	61
Hidden Size	7168
Experts	384 routed, 8 active per token
Modality	Vision-language
Base Model	moonshotai/Kimi-K2.6

Disclaimer

This is the original Kimi-K2.6 model converted to GGUF. It is not an abliterated release.

Credits

Base model: moonshotai/Kimi-K2.6
GGUF runtime and quantization: llama.cpp

License

This release inherits the base Kimi-K2.6 license.

Modified MIT License.

Downloads last month: 399

GGUF

Model size

1T params

Architecture

deepseek2

Hardware compatibility

16-bit

Model tree for Youssofal/Kimi-K2.6-GGUF

Base model

moonshotai/Kimi-K2.6

Quantized

(34)

this model