Instructions to use kurakurai/Luth-LFM2-1.2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kurakurai/Luth-LFM2-1.2B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="kurakurai/Luth-LFM2-1.2B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kurakurai/Luth-LFM2-1.2B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kurakurai/Luth-LFM2-1.2B-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 kurakurai/Luth-LFM2-1.2B-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf kurakurai/Luth-LFM2-1.2B-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kurakurai/Luth-LFM2-1.2B-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf kurakurai/Luth-LFM2-1.2B-GGUF:F16

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 kurakurai/Luth-LFM2-1.2B-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf kurakurai/Luth-LFM2-1.2B-GGUF:F16

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 kurakurai/Luth-LFM2-1.2B-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kurakurai/Luth-LFM2-1.2B-GGUF:F16

Use Docker

docker model run hf.co/kurakurai/Luth-LFM2-1.2B-GGUF:F16

LM Studio
Jan

vLLM

How to use kurakurai/Luth-LFM2-1.2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kurakurai/Luth-LFM2-1.2B-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": "kurakurai/Luth-LFM2-1.2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kurakurai/Luth-LFM2-1.2B-GGUF:F16

SGLang

How to use kurakurai/Luth-LFM2-1.2B-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 "kurakurai/Luth-LFM2-1.2B-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": "kurakurai/Luth-LFM2-1.2B-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 "kurakurai/Luth-LFM2-1.2B-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": "kurakurai/Luth-LFM2-1.2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use kurakurai/Luth-LFM2-1.2B-GGUF with Ollama:
```
ollama run hf.co/kurakurai/Luth-LFM2-1.2B-GGUF:F16
```

Unsloth Studio

How to use kurakurai/Luth-LFM2-1.2B-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 kurakurai/Luth-LFM2-1.2B-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 kurakurai/Luth-LFM2-1.2B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kurakurai/Luth-LFM2-1.2B-GGUF to start chatting

How to use kurakurai/Luth-LFM2-1.2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kurakurai/Luth-LFM2-1.2B-GGUF:F16

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": "kurakurai/Luth-LFM2-1.2B-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use kurakurai/Luth-LFM2-1.2B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kurakurai/Luth-LFM2-1.2B-GGUF:F16

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 "kurakurai/Luth-LFM2-1.2B-GGUF:F16" \
  --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 kurakurai/Luth-LFM2-1.2B-GGUF with Docker Model Runner:
```
docker model run hf.co/kurakurai/Luth-LFM2-1.2B-GGUF:F16
```

Lemonade

How to use kurakurai/Luth-LFM2-1.2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kurakurai/Luth-LFM2-1.2B-GGUF:F16

Run and chat with the model

lemonade run user.Luth-LFM2-1.2B-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use kurakurai/Luth-LFM2-1.2B-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 kurakurai/Luth-LFM2-1.2B-GGUF:F16

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 kurakurai/Luth-LFM2-1.2B-GGUF:F16

Run Hermes

hermes

Atomic Chat

Luth-LFM2-1.2B-GGUF

Luth-LFM2 are French fine-tuned versions of LFM2 models by Kurakura AI, trained on the Luth-SFT dataset. The models have improved their French capabilities in instruction following, math, and general knowledge. Additionally, their English capabilities have remained stable.

Find more details in the original model card: https://huggingface.co/kurakurai/Luth-LFM2-1.2B

🏃 How to run Luth-LFM2

Example usage with llama.cpp:

llama-cli -hf kurakurai/Luth-LFM2-1.2B-GGUF

Downloads last month: 123

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

8-bit

16-bit

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