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

Use Docker

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

Llama new
LM Studio
Jan
Ollama
How to use LiquidAI/LFM3-Audio-2B-GGUF with Ollama:
```
ollama run hf.co/LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use LiquidAI/LFM3-Audio-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-Audio-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-Audio-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use LiquidAI/LFM3-Audio-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-Audio-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-Audio-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-Audio-2B-GGUF

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

LFM3-Audio-2B is a compact audio model that runs locally on laptops and edge devices. It takes text or audio as input and responds with text, speech, or both.

Find architecture and usage details in the original model card.

How to run LFM3-Audio-2B

Recommended sampling: greedy decoding (temperature 0) for text output. For speech output, audio temperature 1.0, audio top_k 128 and audio repetition penalty 1.1.

Llama.app (Use this model > Llama in the menu), for macOS and Windows: audio input. Requires llama.cpp version v0.8.0 or newer.
Ollama (ollama run lfm3-audio), for macOS: audio input. Requires Ollama version v0.40.12 or newer.

Use with llama.cpp

Supported in llama.cpp b11960 (v0.8.0) and newer, including real-time voice conversations (with tool calls) on the /v1/realtime WebSocket API of llama serve.

Add a quantization tag to pick a file. With -hf, llama.cpp also downloads the audio projector.

Start a web chat UI and an OpenAI-compatible API at http://localhost:8080:

llama serve -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --temp 0 --port 8080

To ask one question from the command line, use llama cli.

Speech recognition: use the system prompt Perform ASR in <language>., or Perform ASR. to let the model infer the language.

llama cli -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M \
  --audio speech.wav -sys "Perform ASR in English." -p "<__media__>" --temp 0

Speech synthesis: --tts-lang takes a language code (default en) and --tts-voice is female (default) or male. The audio sampling parameters of the original model card are stored in the GGUF.

llama-tts -m LFM3-Audio-2B-Q4_K_M.gguf -mm mmproj-LFM3-Audio-2B-Q8_0.gguf \
  -p "Hello world" --tts-voice male -o out.wav

More options in the llama.cpp guide.

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GGUF

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2B params

Architecture

lfm3

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