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 Settings

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

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

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

Atomic Chat new
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

ymcki commited on Jan 27

Commit

d2293a3

verified ·

1 Parent(s): 8e5d6ec

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +20 -6

README.md CHANGED Viewed

@@ -20,6 +20,15 @@ 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
 ./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
 ```
@@ -33,19 +42,24 @@ Seems like MLA KV cache can only be run at F16 probably due to itself being a ki
 | Quant Type | imatrix | File Size | Delta Perplexity | KL Divergence | Description |
 | ---------- | ------- | ----------| ---------------- | ------------- | ----------- |
-| Q4_K_M | c4_en_ja_imatrix.txt | 29.70GB | 7.147482 ± 0.047851 | 0.081894 ± 0.001521 | Good |
 | Q4_K_M | None | 29.70GB | 7.172188 ± 0.048107 | 0.083700 ± 0.00152 | Good. Slightly worse than imatrix |
-| MXFP4_MOE | c4_en_ja_imatrix.txt | 27.21GB | 7.179840 ± 0.047966 | 0.088789 ± 0.001544 | Good |
-| MXFP4_MOE | None | 27.21GB | 7.179840 ± 0.047966 | 0.088789 ± 0.001544 | Good. Same as the imatrix version |
 | 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. |
 | 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. |
 | 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](https://www.reddit.com/r/LocalLLaMA/comments/1oav3r1/quantized_some_moe_models_with_mxfp4/), its perplexity
-is about the same as IQ4_XS but about 6% bigger file size. Since it doesn't support imatrix,
-it probably only makes sense if you are using 50x0 cards that has FP4 support but even that is a question mark.

 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
 ```
 | 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](https://www.reddit.com/r/LocalLLaMA/comments/1oav3r1/quantized_some_moe_models_with_mxfp4/), 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.