Instructions to use ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF",
	filename="Kimi-Linear-48B-A3B-Instruct-jp-imatrix.IQ3_M.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 ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ymcki/Kimi-Linear-48B-A3B-Instruct-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 ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ymcki/Kimi-Linear-48B-A3B-Instruct-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 ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ymcki/Kimi-Linear-48B-A3B-Instruct-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": "ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF:Q4_K_M

SGLang

How to use ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF with Ollama:
```
ollama run hf.co/ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use ymcki/Kimi-Linear-48B-A3B-Instruct-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 ymcki/Kimi-Linear-48B-A3B-Instruct-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 ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF to start chatting

Pi new

How to use ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ymcki/Kimi-Linear-48B-A3B-Instruct-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": "ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ymcki/Kimi-Linear-48B-A3B-Instruct-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 ymcki/Kimi-Linear-48B-A3B-Instruct-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 ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Kimi-Linear-48B-A3B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

This is a repo for experimental GGUFs for the backend agnostic implementation of the Kimi-Linear model support that requires a llama.cpp from this repo. You can git clone it and compile locally.

git clone https://github.com/ymcki/llama.cpp --branch Kimi-Linear
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release -j 6

If you have enough VRAM, you can run it purely on your graphics card:

./build/bin/llama-cli -m ~/Kimi-Linear-48B-A3B-Instruct-GGUF/Kimi-Linear-48B-A3B-Instruct.Q4_K_M.gguf -c 8192 -ngl 100 --mmap

Otherwise, you can only load the shared experts and KV cache to your graphics card and the rest to CPU and RAM.

./build/bin/llama-cli -m ~/Kimi-Linear-48B-A3B-Instruct-GGUF/Kimi-Linear-48B-A3B-Instruct.Q4_K_M.gguf -c 8192 -cmoe -ngl 100 --mmap

I am going to only make ggufs without imatrix and ggufs with an imatrix based on c4_en_ja_imatrix.txt for better Japanese performance as bartowski and unsloth will make ggufs with English imatrix anyway.

Base perplexity for f16 gguf is 7.291970 ± 0.048577.

Seems like MLA KV cache can only be run at F16 probably due to itself being a kind of compression. You can use this table to see how much context you can run with a single 24GB card.

Quant Type	imatrix	File Size	Delta Perplexity	KL Divergence	Description
Q5_K_M	c4_en_ja_imatrix.txt	34.87GB	7.115874 ± 0.047587	0.074066 ± 0.001537	Good
Q5_K_M	None	34.87GB	7.133672 ± 0.047741	0.074684 ± 0.001535	Good. Slightly worse than imatrix
Q4_K_M	c4_en_ja_imatrix.txt	29.70GB	7.147482 ± 0.047851	0.081894 ± 0.001521	Good. Can run 128k context on a single 32GB card.
Q4_K_M	None	29.70GB	7.172188 ± 0.048107	0.083700 ± 0.00152	Good. Slightly worse than imatrix
MXFP4_MOE	None	27.21GB	7.179840 ± 0.047966	0.088789 ± 0.001544	Good. Can run 240k context on a single 32GB card.
MXFP4_MOE	c4_en_ja_imatrix.txt	27.21GB	7.179840 ± 0.047966	0.088789 ± 0.001544	Good. Same as the no imatrix version.
IQ4_XS	c4_en_ja_imatrix.txt	26.27GB	7.208724 ± 0.048490	0.088246 ± 0.001528	Good. Can run 304k context on a single 32GB card.
IQ4_NL	c4_en_ja_imatrix.txt	27.79GB	7.209342 ± 0.048412	0.087678 ± 0.001532	Doesn't make sense compare to MXFP4_MOE
IQ3_M	c4_en_ja_imatrix.txt	21.55GB	7.368516 ± 0.048425	0.113435 ± 0.001457	Quite Good. Can run 96k context on a single 24GB card.
IQ3_S	c4_en_ja_imatrix.txt	21.33GB	7.448991 ± 0.049167	0.119987 ± 0.001466	Quite Good. Can run 112k context on a single 24GB card.
IQ3_XS	c4_en_ja_imatrix.txt	20.17GB	7.534649 ± 0.049461	0.129645 ± 0.001448	Quite Good. Can run 176k context on a single 24GB card.
Q3_K_S	c4_en_ja_imatrix.txt	21.33GB	7.557247 ± 0.051236	0.131708 ± 0.001521	Quite Good. Can run 112k context on a single 24GB card.
Q3_K_S	None	21.33GB	7.632887 ± 0.051792	0.146355 ± 0.001534	Quite Good but worse than imatrix. Good for CPU use.
IQ3_XXS	c4_en_ja_imatrix.txt	18.99GB	7.780732 ± 0.052592	0.164925 ± 0.001537	Not so good but can run 240k context on a single 24GB card.
IQ2_M	c4_en_ja_imatrix.txt	16.13GB	8.207663 ± 0.054957	0.224437 ± 0.001536	Slightly batter than Q2_K but you can run 400k context on a single 24GB card.
Q2_K	c4_en_ja_imatrix.txt	18.03GB	8.295144 ± 0.057566	0.221437 ± 0.001617	So-so but you can run 288k context on a single 24GB card. Good for performance evaluation.
Q2_K	None	18.03GB	8.648201 ± 0.059234	0.267082 ± 0.001659	Worse than imatrix

As expected, imatrix has no effect on MXFP4_MOE. From this reddit thread, its perplexity is about the same as IQ4_XS but about 6% bigger file size. Here, its perplexity is better than IQ4_XS. This makes it a viable option.

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GGUF

Model size

49B params

Architecture

kimi-linear

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

Model tree for ymcki/Kimi-Linear-48B-A3B-Instruct-GGUF

Base model

moonshotai/Kimi-Linear-48B-A3B-Instruct

Quantized

(21)

this model