Instructions to use LiquidAI/LFM2-VL-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2-VL-3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LiquidAI/LFM2-VL-3B-GGUF",
	filename="LFM2-VL-3B-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use LiquidAI/LFM2-VL-3B-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-VL-3B-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf LiquidAI/LFM2-VL-3B-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LiquidAI/LFM2-VL-3B-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf LiquidAI/LFM2-VL-3B-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 LiquidAI/LFM2-VL-3B-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf LiquidAI/LFM2-VL-3B-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 LiquidAI/LFM2-VL-3B-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LiquidAI/LFM2-VL-3B-GGUF:F16

Use Docker

docker model run hf.co/LiquidAI/LFM2-VL-3B-GGUF:F16

LM Studio
Jan

vLLM

How to use LiquidAI/LFM2-VL-3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2-VL-3B-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-VL-3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2-VL-3B-GGUF:F16

Ollama
How to use LiquidAI/LFM2-VL-3B-GGUF with Ollama:
```
ollama run hf.co/LiquidAI/LFM2-VL-3B-GGUF:F16
```

Unsloth Studio new

How to use LiquidAI/LFM2-VL-3B-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-VL-3B-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-VL-3B-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-VL-3B-GGUF to start chatting

Docker Model Runner
How to use LiquidAI/LFM2-VL-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2-VL-3B-GGUF:F16
```

Lemonade

How to use LiquidAI/LFM2-VL-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LiquidAI/LFM2-VL-3B-GGUF:F16

Run and chat with the model

lemonade run user.LFM2-VL-3B-GGUF-F16

List all available models

lemonade list

Try LFM • Docs • LEAP • Discord

LFM2-VL-3B-GGUF

LFM2-VL is a new generation of vision models developed by 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-VL-3B

🏃 How to run LFM2-VL

Example usage with llama.cpp:

full precision (F16/F16):

llama-mtmd-cli -hf LiquidAI/LFM2-VL-3B-GGUF:F16

fastest inference (Q4_0/Q8_0):

llama-mtmd-cli -hf LiquidAI/LFM2-VL-3B-GGUF:Q4_0

📬 Contact

Got questions or want to connect? Join our Discord community
If you are interested in custom solutions with edge deployment, please contact our sales team.

Downloads last month: 15,337

GGUF

Model size

3B params

Architecture

lfm2

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for LiquidAI/LFM2-VL-3B-GGUF

Base model

LiquidAI/LFM2-VL-3B

Quantized

(5)

this model

Collection including LiquidAI/LFM2-VL-3B-GGUF

👁️ LFM2-VL

Collection

LFM2-VL is our first series of vision-language models, designed for on-device deployment. • 10 items • Updated Apr 8 • 66