Instructions to use LiquidAI/LFM2.5-Audio-1.5B-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 LiquidAI/LFM2.5-Audio-1.5B-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-Audio-1.5B-GGUF:F16 # Run inference directly in the terminal: llama cli -hf LiquidAI/LFM2.5-Audio-1.5B-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf LiquidAI/LFM2.5-Audio-1.5B-GGUF:F16 # Run inference directly in the terminal: llama cli -hf LiquidAI/LFM2.5-Audio-1.5B-GGUF:F16
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 LiquidAI/LFM2.5-Audio-1.5B-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf LiquidAI/LFM2.5-Audio-1.5B-GGUF:F16
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 LiquidAI/LFM2.5-Audio-1.5B-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf LiquidAI/LFM2.5-Audio-1.5B-GGUF:F16
Use Docker
docker model run hf.co/LiquidAI/LFM2.5-Audio-1.5B-GGUF:F16
- LM Studio
- Jan
- Ollama
How to use LiquidAI/LFM2.5-Audio-1.5B-GGUF with Ollama:
ollama run hf.co/LiquidAI/LFM2.5-Audio-1.5B-GGUF:F16
- Unsloth Studio
How to use LiquidAI/LFM2.5-Audio-1.5B-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for LiquidAI/LFM2.5-Audio-1.5B-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for LiquidAI/LFM2.5-Audio-1.5B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for LiquidAI/LFM2.5-Audio-1.5B-GGUF to start chatting
- Pi
How to use LiquidAI/LFM2.5-Audio-1.5B-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-Audio-1.5B-GGUF:F16
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-Audio-1.5B-GGUF:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use LiquidAI/LFM2.5-Audio-1.5B-GGUF with Docker Model Runner:
docker model run hf.co/LiquidAI/LFM2.5-Audio-1.5B-GGUF:F16
- Lemonade
How to use LiquidAI/LFM2.5-Audio-1.5B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LiquidAI/LFM2.5-Audio-1.5B-GGUF:F16
Run and chat with the model
lemonade run user.LFM2.5-Audio-1.5B-GGUF-F16
List all available models
lemonade list
- Hermes Agent
How to use LiquidAI/LFM2.5-Audio-1.5B-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-Audio-1.5B-GGUF:F16
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-Audio-1.5B-GGUF:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use LiquidAI/LFM2.5-Audio-1.5B-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-Audio-1.5B-GGUF:F16
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-Audio-1.5B-GGUF:F16" \ --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"
More Language Support
Great work on the model and GGUF support for llama.cpp.
Adding support for more languages would make this model significantly more useful for local, offline audio processing, especially on non-English systems, mobile devices, and global use cases where internet access or cloud APIs are not available.
This would greatly improve accessibility and real-world adoption.
Absolutely. We are in progress of supporting all languages supported by LFM2.5-Base also within LFM2.5-Audio. This future support will cover both multilingual input as well as output (generation).
Also we could use voice design by prompt π just like Qwen-TTS π
Future plan, never mind.
If you'd provide a small readme / notebook on fine-tuning with a specific language dataset, the community could assist.
Hello,
at:
https://www.liquid.ai/blog/lfm2-audio-an-end-to-end-audio-foundation-model
you say:
Deploy and build with LFM2-Audio
...
Emotion detection
How can we ask to the model to perform the emotion detection during ASR?
Thank you so much for the great work. Best!
Hi, emotion detection is not supported in ASR mode in this checkpoint, it would have to be specifically finetuned unlock this capability. Public finetuning support is coming though!