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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use LiquidAI/LFM2-350M-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 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 serve -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 Desktop
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

Atomic Chat

LFM2-350M-GGUF / README.md

mlabonne

Update README.md

4ca024a verified about 1 year ago

preview code

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	---
	license: other
	license_name: lfm1.0
	license_link: LICENSE
	language:
	- en
	- ar
	- zh
	- fr
	- de
	- ja
	- ko
	- es
	pipeline_tag: text-generation
	tags:
	- liquid
	- lfm2
	- edge
	- llama.cpp
	- gguf
	base_model:
	- LiquidAI/LFM2-350M
	---

	<center>
	<div style="text-align: center;">
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	# 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
	```