Instructions to use LiquidAI/LFM3-VL-2B-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/LFM3-VL-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 LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LiquidAI/LFM3-VL-2B-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/LFM3-VL-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use LiquidAI/LFM3-VL-2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM3-VL-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": "LiquidAI/LFM3-VL-2B-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/LFM3-VL-2B-GGUF:Q4_K_M

Ollama
How to use LiquidAI/LFM3-VL-2B-GGUF with Ollama:
```
ollama run hf.co/LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use LiquidAI/LFM3-VL-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 LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use LiquidAI/LFM3-VL-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M
```

Lemonade

How to use LiquidAI/LFM3-VL-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM3-VL-2B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use LiquidAI/LFM3-VL-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 LiquidAI/LFM3-VL-2B-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 LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use LiquidAI/LFM3-VL-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 LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M

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 "LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M" \
  --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"

API Try Docs Blog

LFM3-VL-2B-GGUF

GGUF version of LFM3-VL-2B for local inference on CPUs and GPUs.

Find architecture and usage details in the original model card.

How to run LFM3-VL-2B

Recommended sampling: temperature 0.1, top_k 50, top_p 1.0, min_p 0.0, repeat penalty 1.1.

Llama.app (Use this model > Llama in the menu), for macOS and Windows: image and video input. Requires llama.cpp version v0.8.0 or newer.
LM Studio (Use this model > LM Studio in the menu), for macOS, Windows and Linux: image input. Requires LM Studio with llama.cpp engine 2.65.1 or newer.
Ollama ollama run lfm3-vl, for macOS: image input. Requires Ollama version v0.40.12 or newer.

NVFP4 GGUF

Unlike the other files in this repository, which are converted from LFM3-VL-2B, LFM3-VL-2B-NVFP4.gguf is converted from a separate repository, LiquidAI/LFM3-VL-2B-NVFP4, a checkpoint quantized to NVFP4 with NVIDIA Model Optimizer. The language model's feed-forward weights keep their NVFP4 quantization from that checkpoint, all other weights are Q8_0. The vision encoder is not quantized in that checkpoint, so the mmproj files in this repository are used unchanged. On NVIDIA Blackwell GPUs (RTX 50 series, DGX Spark) the NVFP4 layers run on FP4 tensor cores.

Supported in llama.cpp b12088 and newer (#31608). Older builds load the file but produce incorrect output.

Example usage with llama.cpp:

llama-mtmd-cli -hf LiquidAI/LFM3-VL-2B-GGUF:NVFP4 \
  --image image.jpg -p "Describe this image." \
  --temp 0.1 --top-k 50 --top-p 1.0 --min-p 0.0 --repeat-penalty 1.1

QAD Q4_0 GGUF

The Q4_0 build in this repository is the Quantization-Aware Distillation (QAD) checkpoint, LFM3-VL-2B-QAD-Q4_0.gguf. It was trained to recover the accuracy lost to Q4_0 quantization.

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GGUF

Model size

2B params

Architecture

lfm3

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for LiquidAI/LFM3-VL-2B-GGUF

Base model

LiquidAI/LFM3-1.7B-Base

Finetuned

LiquidAI/LFM3-VL-2B

Quantized

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this model