Instructions to use LiquidAI/LFM3-1.7B-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/LFM3-1.7B-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/LFM3-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LiquidAI/LFM3-1.7B-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/LFM3-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LiquidAI/LFM3-1.7B-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/LFM3-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/LiquidAI/LFM3-1.7B-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use LiquidAI/LFM3-1.7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM3-1.7B-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/LFM3-1.7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM3-1.7B-GGUF:Q4_K_M

Ollama
How to use LiquidAI/LFM3-1.7B-GGUF with Ollama:
```
ollama run hf.co/LiquidAI/LFM3-1.7B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use LiquidAI/LFM3-1.7B-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/LFM3-1.7B-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/LFM3-1.7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use LiquidAI/LFM3-1.7B-GGUF with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM3-1.7B-GGUF:Q4_K_M
```

Lemonade

How to use LiquidAI/LFM3-1.7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LiquidAI/LFM3-1.7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM3-1.7B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use LiquidAI/LFM3-1.7B-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/LFM3-1.7B-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/LFM3-1.7B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use LiquidAI/LFM3-1.7B-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/LFM3-1.7B-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/LFM3-1.7B-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

LFM3-1.7B-GGUF

GGUF version of LFM3-1.7B for local inference on CPUs and GPUs.

Find architecture and usage details in the original model card.

How to run LFM3-1.7B

Recommended sampling: temperature 0.1, top_k 50, repeat penalty 1.1. For math and science: temperature 1.0, top_p 0.95, top_k 20.

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

Use with llama.cpp

Supported in llama.cpp b11960 (v0.8.0) and newer.

Add a quantization tag to pick a file.

llama-cli -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M \
  --temp 0.1 --top-k 50 --repeat-penalty 1.1

For math and science, use --temp 1.0 --top-p 0.95 --top-k 20 instead. More options in the llama.cpp guide.

QAD Q4_0 GGUF

The Q4_0 build in this repository is the Quantization-Aware Distillation (QAD) checkpoint, LFM3-1.7B-QAD-Q4_0.gguf. It was trained to recover the accuracy lost to Q4_0 quantization.

Example usage with llama.cpp:

llama-cli -hf LiquidAI/LFM3-1.7B-GGUF \
  --hf-file LFM3-1.7B-QAD-Q4_0.gguf \
  --temp 0.1 --top-k 50 --repeat-penalty 1.1

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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/LFM3-1.7B-GGUF

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LFM3-1.7B-GGUF

LFM3-1.7B-GGUF

How to run LFM3-1.7B

Use with llama.cpp

QAD Q4_0 GGUF

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