Instructions to use AtomicChat/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
- llama.cpp
How to use AtomicChat/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 AtomicChat/LFM3-Audio-2B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf AtomicChat/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 AtomicChat/LFM3-Audio-2B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf AtomicChat/LFM3-Audio-2B-GGUF:Q4_K_M
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 AtomicChat/LFM3-Audio-2B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AtomicChat/LFM3-Audio-2B-GGUF:Q4_K_M
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 AtomicChat/LFM3-Audio-2B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AtomicChat/LFM3-Audio-2B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/AtomicChat/LFM3-Audio-2B-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use AtomicChat/LFM3-Audio-2B-GGUF with Ollama:
ollama run hf.co/AtomicChat/LFM3-Audio-2B-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use AtomicChat/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 AtomicChat/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": "AtomicChat/LFM3-Audio-2B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use AtomicChat/LFM3-Audio-2B-GGUF with Docker Model Runner:
docker model run hf.co/AtomicChat/LFM3-Audio-2B-GGUF:Q4_K_M
- Lemonade
How to use AtomicChat/LFM3-Audio-2B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AtomicChat/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 AtomicChat/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 AtomicChat/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 AtomicChat/LFM3-Audio-2B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use AtomicChat/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 AtomicChat/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 "AtomicChat/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"
LFM3-Audio-2B — GGUF (by AtomicChat)
Atomic-Dynamic (AD) GGUF quants of LiquidAI/LFM3-Audio-2B: a small speech model — text + audio in, text + audio out. It runs locally in llama.cpp for both speech recognition (ASR) and text-to-speech (TTS), using the LFM3 audio support merged in llama.cpp #31257.
Each repo ships the LLM ladder below plus the audio projector mmproj-LFM3-Audio-2B-F16.gguf (and BF16/Q8_0) — the projector is required for any audio in or out.
Rungs (LLM)
| Rung | GB on disk | bpw | top-1 | mean KLD |
|---|---|---|---|---|
| Q8_0 | 1.85 | 8.51 | 97.9% | 0.001 |
| AD-Q6_K | 1.59 | 7.31 | 97.2% | 0.003 |
| AD-Q5_K_M | 1.31 | 6.03 | 94.8% | 0.010 |
| AD-Q4_K_M | 1.13 | 5.20 | 91.6% | 0.028 |
| AD-IQ4_XS | 1.09 | 5.02 | 90.9% | 0.034 |
| AD-IQ3_S | 0.92 | 4.23 | 86.6% | 0.075 |
| AD-IQ3_XXS | 0.81 | 3.71 | 80.5% | 0.171 |
AD-Q4_K_M is the recommended default; AD-IQ3_XXS is the smallest. The AD rungs replace llama.cpp's stock presets (no imatrix) at every size.
top-1 = how often a rung picks the same next token as the original BF16; mean KLD = average divergence (lower is better).
Speech-to-text (ASR)
llama-mtmd-cli -hf AtomicChat/LFM3-Audio-2B-GGUF:AD-Q4_K_M \
--mmproj mmproj-LFM3-Audio-2B-F16.gguf \
--audio speech.wav -sys "Perform ASR in English." -p "<__media__>" --temp 0
Text-to-speech (TTS)
llama-tts -m LFM3-Audio-2B-AD-Q4_K_M.gguf -mm mmproj-LFM3-Audio-2B-F16.gguf \
-p "Hello world" --tts-voice male -o out.wav
--tts-lang <code> sets the language (default en); --tts-voice is female (default) or male.
Needs a llama.cpp build with LFM3-Audio support (merged in #31257; these files were produced at llama.cpp 19ff817).
Base model & license
Base model: LiquidAI/LFM3-Audio-2B. License: LFM Open License v1.0 (see LICENSE).
Quants by AtomicChat.
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Model tree for AtomicChat/LFM3-Audio-2B-GGUF
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