Instructions to use LiquidAI/LFM2.5-24B-A2B-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/LFM2.5-24B-A2B-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/LFM2.5-24B-A2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LiquidAI/LFM2.5-24B-A2B-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/LFM2.5-24B-A2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LiquidAI/LFM2.5-24B-A2B-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/LFM2.5-24B-A2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf LiquidAI/LFM2.5-24B-A2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-24B-A2B-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use LiquidAI/LFM2.5-24B-A2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2.5-24B-A2B-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/LFM2.5-24B-A2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-24B-A2B-GGUF:Q4_K_M

Ollama
How to use LiquidAI/LFM2.5-24B-A2B-GGUF with Ollama:
```
ollama run hf.co/LiquidAI/LFM2.5-24B-A2B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use LiquidAI/LFM2.5-24B-A2B-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/LFM2.5-24B-A2B-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/LFM2.5-24B-A2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use LiquidAI/LFM2.5-24B-A2B-GGUF with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2.5-24B-A2B-GGUF:Q4_K_M
```

Lemonade

How to use LiquidAI/LFM2.5-24B-A2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LiquidAI/LFM2.5-24B-A2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-24B-A2B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use LiquidAI/LFM2.5-24B-A2B-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/LFM2.5-24B-A2B-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/LFM2.5-24B-A2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use LiquidAI/LFM2.5-24B-A2B-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/LFM2.5-24B-A2B-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/LFM2.5-24B-A2B-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"

Try LFM • Docs • Discord

LFM2.5-24B-A2B-GGUF

LFM2 is a new generation of hybrid 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/LFM2.5-24B-A2B

🏃 How to run LFM2

Example usage with llama.cpp:

llama-cli -hf LiquidAI/LFM2.5-24B-A2B-GGUF \
    --temp 0.1 --top-k 50 --repeat-penalty 1.05

The sampling parameters are the ones recommended for the base model (its generation_config.json); each GGUF also carries them as general.sampling.* metadata, so llama.cpp applies them by default.

QAD Q4_0 GGUF

The Q4_0 build in this repository is the Quantization-Aware Distillation (QAD) checkpoint, LFM2.5-24B-A2B-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/LFM2.5-24B-A2B-GGUF \
    --hf-file LFM2.5-24B-A2B-QAD-Q4_0.gguf \
    --temp 0.1 --top-k 50 --repeat-penalty 1.05

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Collection

Collection of post-trained and base LFM2.5 models. • 18 items • Updated 1 day ago • 244