Instructions to use LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF",
	filename="LFM2.5-Audio-1.5B-JP-F16.gguf",
)

llm.create_chat_completion(
	messages = "\"sample1.flac\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf LiquidAI/LFM2.5-Audio-1.5B-JP-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-JP-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf LiquidAI/LFM2.5-Audio-1.5B-JP-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-JP-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF:F16

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF:F16

LM Studio
Jan
Ollama
How to use LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF with Ollama:
```
ollama run hf.co/LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF:F16
```

Unsloth Studio

How to use LiquidAI/LFM2.5-Audio-1.5B-JP-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-JP-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-JP-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-JP-GGUF to start chatting

How to use LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/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-JP-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf LiquidAI/LFM2.5-Audio-1.5B-JP-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-JP-GGUF:F16

Run Hermes

hermes

Docker Model Runner
How to use LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF:F16
```

Lemonade

How to use LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF:F16

Run and chat with the model

lemonade run user.LFM2.5-Audio-1.5B-JP-GGUF-F16

List all available models

lemonade list

Try LFM • Docs • LEAP • Discord

LFM2.5-Audio-1.5B-JP

This repository contains GGUF quantizations of LiquidAI/LFM2.5-Audio-1.5B-JP for use with llama.cpp.

Available files

File	Quantization	Size
`LFM2.5-Audio-1.5B-JP-F32.gguf`	F32 (language model)	4.4 GB
`LFM2.5-Audio-1.5B-JP-F16.gguf`	F16 (language model)	2.2 GB
`LFM2.5-Audio-1.5B-JP-Q8_0.gguf`	Q8_0 (language model)	1.2 GB
`LFM2.5-Audio-1.5B-JP-Q4_0.gguf`	Q4_0 (language model)	664 MB
`mmproj-LFM2.5-Audio-1.5B-JP-F32.gguf`	F32 (audio encoder / multimodal projector)	695 MB
`mmproj-LFM2.5-Audio-1.5B-JP-F16.gguf`	F16 (audio encoder / multimodal projector)	413 MB
`mmproj-LFM2.5-Audio-1.5B-JP-Q8_0.gguf`	Q8_0 (audio encoder / multimodal projector)	280 MB
`mmproj-LFM2.5-Audio-1.5B-JP-Q4_0.gguf`	Q4_0 (audio encoder / multimodal projector)	210 MB
`vocoder-LFM2.5-Audio-1.5B-JP-F32.gguf`	F32 (vocoder / audio detokenizer)	739 MB
`vocoder-LFM2.5-Audio-1.5B-JP-F16.gguf`	F16 (vocoder / audio detokenizer)	370 MB
`vocoder-LFM2.5-Audio-1.5B-JP-Q8_0.gguf`	Q8_0 (vocoder / audio detokenizer)	197 MB
`vocoder-LFM2.5-Audio-1.5B-JP-Q4_0.gguf`	Q4_0 (vocoder / audio detokenizer)	104 MB
`tokenizer-LFM2.5-Audio-1.5B-JP-F32.gguf`	F32 (audio tokenizer)	268 MB
`tokenizer-LFM2.5-Audio-1.5B-JP-F16.gguf`	F16 (audio tokenizer)	134 MB
`tokenizer-LFM2.5-Audio-1.5B-JP-Q8_0.gguf`	Q8_0 (audio tokenizer)	72 MB
`tokenizer-LFM2.5-Audio-1.5B-JP-Q4_0.gguf`	Q4_0 (audio tokenizer)	46 MB

Runners

runners folder contains pre-built binaries for various architectures:

llama-liquid-audio-cli
llama-liquid-audio-server

🏃 How to run LFM2.5-Audio-JP

CLI

Set env variables.

export CKPT=/path/to/LFM2.5-Audio-1.5B-JP-GGUF
export INPUT_WAV=/path/to/input.wav
export OUTPUT_WAV=/path/to/output.wav

ASR (audio -> text)

./llama-liquid-audio-cli -m $CKPT/LFM2.5-Audio-1.5B-Q4_0.gguf -mm $CKPT/mmproj-LFM2.5-Audio-1.5B-Q4_0.gguf -mv $CKPT/vocoder-LFM2.5-Audio-1.5B-Q4_0.gguf --tts-speaker-file $CKPT/tokenizer-LFM2.5-Audio-1.5B-Q4_0.gguf -sys "Perform ASR in japanese." --audio $INPUT_WAV

TTS (text -> audio)

./llama-liquid-audio-cli -m $CKPT/LFM2.5-Audio-1.5B-Q4_0.gguf -mm $CKPT/mmproj-LFM2.5-Audio-1.5B-Q4_0.gguf -mv $CKPT/vocoder-LFM2.5-Audio-1.5B-Q4_0.gguf --tts-speaker-file $CKPT/tokenizer-LFM2.5-Audio-1.5B-Q4_0.gguf -sys "Perform TTS in japanese." -p "こんにちは、お元気ですか？" --output $OUTPUT_WAV

Interleaved (audio/text -> audio + text)

./llama-liquid-audio-cli -m $CKPT/LFM2.5-Audio-1.5B-Q4_0.gguf -mm $CKPT/mmproj-LFM2.5-Audio-1.5B-Q4_0.gguf -mv $CKPT/vocoder-LFM2.5-Audio-1.5B-Q4_0.gguf --tts-speaker-file $CKPT/tokenizer-LFM2.5-Audio-1.5B-Q4_0.gguf -sys "Respond with interleaved text and audio." --audio $INPUT_WAV --output $OUTPUT_WAV

Server

Start server

export CKPT=/path/to/LFM2.5-Audio-1.5B-JP-GGUF
./llama-liquid-audio-server -m $CKPT/LFM2.5-Audio-1.5B-Q4_0.gguf -mm $CKPT/mmproj-LFM2.5-Audio-1.5B-Q4_0.gguf -mv $CKPT/vocoder-LFM2.5-Audio-1.5B-Q4_0.gguf --tts-speaker-file $CKPT/tokenizer-LFM2.5-Audio-1.5B-Q4_0.gguf

Use liquid_audio_chat.py script to communicate with the server.

uv run liquid_audio_chat.py

Source Code for Runners

Runners are built from https://github.com/ggml-org/llama.cpp/pull/18641.

📬 Contact

Got questions or want to connect? Join our Discord community
If you are interested in custom solutions with edge deployment, please contact our sales team.

License

The code in this repository and associated weights are licensed under the LFM Open License v1.0.

The code for the audio encoder is based on Nvidia NeMo, licensed under Apache 2.0, and the canary-180m-flash checkpoint, licensed under CC-BY 4.0. To simplify dependency resolution, we also ship the Python code of Kyutai Mimi, licensed under the MIT License. We also redistribute weights for Kyutai Mimi, licensed under CC-BY-4.0.

Citation

@article{liquidai2025lfm2,
 title={LFM2 Technical Report},
 author={Liquid AI},
 journal={arXiv preprint arXiv:2511.23404},
 year={2025}
}

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GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

4-bit

8-bit

16-bit

32-bit

Model tree for LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF

Base model

LiquidAI/LFM2-1.2B

Finetuned

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Finetuned

LiquidAI/LFM2.5-Audio-1.5B-JP

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Paper for LiquidAI/LFM2.5-Audio-1.5B-JP-GGUF

LFM2 Technical Report

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