Instructions to use LiquidAI/LFM2-350M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2-350M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LiquidAI/LFM2-350M-GGUF",
	filename="LFM2-350M-F16.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 LiquidAI/LFM2-350M-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LiquidAI/LFM2-350M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LiquidAI/LFM2-350M-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 LiquidAI/LFM2-350M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LiquidAI/LFM2-350M-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 LiquidAI/LFM2-350M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LiquidAI/LFM2-350M-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 LiquidAI/LFM2-350M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LiquidAI/LFM2-350M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/LiquidAI/LFM2-350M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use LiquidAI/LFM2-350M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2-350M-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": "LiquidAI/LFM2-350M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2-350M-GGUF:Q4_K_M

Ollama
How to use LiquidAI/LFM2-350M-GGUF with Ollama:
```
ollama run hf.co/LiquidAI/LFM2-350M-GGUF:Q4_K_M
```

Unsloth Studio new

How to use LiquidAI/LFM2-350M-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 LiquidAI/LFM2-350M-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 LiquidAI/LFM2-350M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LiquidAI/LFM2-350M-GGUF to start chatting

Docker Model Runner
How to use LiquidAI/LFM2-350M-GGUF with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2-350M-GGUF:Q4_K_M
```

Lemonade

How to use LiquidAI/LFM2-350M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LiquidAI/LFM2-350M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2-350M-GGUF-Q4_K_M

List all available models

lemonade list

mlabonne commited on Sep 4, 2025

Commit

31cd51d

verified ·

1 Parent(s): d173cf7

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -60,14 +60,14 @@ base_model:
 # LFM2-350M-GGUF
-LFM2 is a new generation of hybrid models developed by [Liquid AI](https://www.liquid.ai/), specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency.
-Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2-350M
 ## 🏃 How to run LFM2
 Example usage with [llama.cpp](https://github.com/ggml-org/llama.cpp):
 ```
-llama-cli -hf LiquidAI/LFM2-350M-GGUF
 ```

 # LFM2-350M-GGUF
+Based on the [LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M) model, this checkpoint has been fine-tuned for near real-time **bi-directional Japanese/English translation** of short-to-medium inputs.
+Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2-350M-ENJP-MT
 ## 🏃 How to run LFM2
 Example usage with [llama.cpp](https://github.com/ggml-org/llama.cpp):
 ```
+llama-cli -hf LiquidAI/LFM2-350M-ENJP-MT-GGUF
 ```