> ## Documentation Index
> Fetch the complete documentation index at: https://docs.liquid.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# LFM2-Audio-1.5B

> 1.5B audio model (deprecated - use LFM2.5-Audio-1.5B instead)

<a href="/lfm/models/audio-models" className="back-button">← Back to Audio Models</a>

<Warning>
  This model is deprecated. Use [LFM2.5-Audio-1.5B](/lfm/models/lfm25-audio-1.5b) for improved ASR, TTS, and CPU-friendly inference.
</Warning>

LFM2-Audio-1.5B was the original fully interleaved audio/text model. It has been superseded by LFM2.5-Audio-1.5B, which features a custom LFM-based audio detokenizer and improved performance.

<div style={{display: 'flex', gap: '0.5rem', margin: '0.5rem 0 1.5rem 0'}}>
  <a href="https://huggingface.co/LiquidAI/LFM2-Audio-1.5B" style={{padding: '0.35rem 0.7rem', borderRadius: '4px', fontSize: '0.85rem', fontWeight: 600, textDecoration: 'none', backgroundColor: '#fbbf24'}}><span style={{color: '#000'}}>HF</span></a>
  <a href="https://huggingface.co/LiquidAI/LFM2-Audio-1.5B-GGUF" style={{padding: '0.35rem 0.7rem', borderRadius: '4px', fontSize: '0.85rem', fontWeight: 600, textDecoration: 'none', backgroundColor: '#60a5fa'}}><span style={{color: '#000'}}>GGUF</span></a>
</div>

## Specifications

| Property           | Value                               |
| ------------------ | ----------------------------------- |
| Parameters         | 1.5B (1.2B LM + 115M audio encoder) |
| Context Length     | 32K tokens                          |
| Audio Output       | 24kHz (Mimi codec)                  |
| Supported Language | English                             |

## Quick Start

<Tabs>
  <Tab title="liquid-audio">
    **Install:**

    ```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
    pip install liquid-audio
    pip install "liquid-audio[demo]"  # optional, for demo dependencies
    pip install flash-attn --no-build-isolation  # optional, for flash attention 2
    ```

    **Gradio Demo:**

    ```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
    liquid-audio-demo
    # Starts webserver on http://localhost:7860/
    ```

    **Multi-Turn Chat:**

    ```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
    import torch
    import torchaudio
    from liquid_audio import LFM2AudioModel, LFM2AudioProcessor, ChatState

    # Load models
    HF_REPO = "LiquidAI/LFM2-Audio-1.5B"
    processor = LFM2AudioProcessor.from_pretrained(HF_REPO).eval()
    model = LFM2AudioModel.from_pretrained(HF_REPO).eval()

    # Set up chat
    chat = ChatState(processor)
    chat.new_turn("system")
    chat.add_text("Respond with interleaved text and audio.")
    chat.end_turn()

    chat.new_turn("user")
    wav, sampling_rate = torchaudio.load("question.wav")
    chat.add_audio(wav, sampling_rate)
    chat.end_turn()

    chat.new_turn("assistant")

    # Generate text and audio tokens
    text_out, audio_out = [], []
    for t in model.generate_interleaved(**chat, max_new_tokens=512, audio_temperature=1.0, audio_top_k=4):
        if t.numel() == 1:
            print(processor.text.decode(t), end="", flush=True)
            text_out.append(t)
        else:
            audio_out.append(t)

    # Detokenize audio and save (Mimi returns audio at 24kHz)
    mimi_codes = torch.stack(audio_out[:-1], 1).unsqueeze(0)
    with torch.no_grad():
        waveform = processor.mimi.decode(mimi_codes)[0]
    torchaudio.save("answer.wav", waveform.cpu(), 24_000)
    ```
  </Tab>

  <Tab title="llama.cpp">
    **Setup:**

    ```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
    export CKPT=/path/to/LFM2-Audio-1.5B-GGUF
    export INPUT_WAV=/path/to/input.wav
    export OUTPUT_WAV=/path/to/output.wav
    ```

    **ASR (Audio to Text):**

    ```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
    ./llama-lfm2-audio -m $CKPT/LFM2-Audio-1.5B-Q8_0.gguf \
      --mmproj $CKPT/mmproj-audioencoder-LFM2-Audio-1.5B-Q8_0.gguf \
      -mv $CKPT/audiodecoder-LFM2-Audio-1.5B-Q8_0.gguf \
      -sys "Perform ASR." --audio $INPUT_WAV
    ```

    **TTS (Text to Audio):**

    ```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
    ./llama-lfm2-audio -m $CKPT/LFM2-Audio-1.5B-Q8_0.gguf \
      --mmproj $CKPT/mmproj-audioencoder-LFM2-Audio-1.5B-Q8_0.gguf \
      -mv $CKPT/audiodecoder-LFM2-Audio-1.5B-Q8_0.gguf \
      -sys "Perform TTS." \
      -p "What is this obsession people have with books?" \
      --output $OUTPUT_WAV
    ```

    **Interleaved Mode:**

    ```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
    ./llama-lfm2-audio -m $CKPT/LFM2-Audio-1.5B-Q8_0.gguf \
      --mmproj $CKPT/mmproj-audioencoder-LFM2-Audio-1.5B-Q8_0.gguf \
      -mv $CKPT/audiodecoder-LFM2-Audio-1.5B-Q8_0.gguf \
      -sys "Respond with interleaved text and audio." \
      --audio $INPUT_WAV --output $OUTPUT_WAV
    ```

    <Info>
      Runners are available for macos-arm64, ubuntu-arm64, ubuntu-x64, and android-arm64.
    </Info>
  </Tab>
</Tabs>