Instructions to use nativ-community/LFM3-Audio-2B-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nativ-community/LFM3-Audio-2B-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download nativ-community/LFM3-Audio-2B-4bit --local-dir LFM3-Audio-2B-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
LFM3-Audio-2B · 4bit
Native MLX conversion of lq-preview/LFM3-Audio-2B at 74fafd3bf0aaba3a5927a71cdc543c38d794bb6f.
Conversion implementation: 98c05bd33cc93b64b12bb19e688a1511bbd4f82c (recorded in conversion.json).
Public inference code: Blaizzy/mlx-vlm.
Weights: affine 4-bit, group size 64. Audio and vision modules remain BF16; the bundled audio codec is copied unchanged. Activation and KV caches are not quantized.
Usage
Install the pinned public implementation:
pip install 'mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm.git@3070dcc31948f48f9d8639b606af7bc019494fac'
from mlx_vlm import load, generate
from mlx_vlm.generate import generate_audio
from mlx_vlm.prompt_utils import apply_chat_template
model, processor = load("nativ-community/LFM3-Audio-2B-4bit", trust_remote_code=False)
messages = [{"role": "system", "content": "Perform ASR in English."},
{"role": "user", "content": ""}]
prompt = apply_chat_template(processor, model.config, messages, num_audios=1)
print(generate_audio(model, processor, prompt, audio=["speech.wav"], temperature=0,
generation_mode="text").text)
messages = [{"role": "system", "content": "Perform TTS in American English using a female voice."},
{"role": "user", "content": "Hello there!"}]
prompt = apply_chat_template(processor, model.config, messages)
generate_audio(model, processor, prompt, output_audio_path="tts.wav", max_tokens=512)
Release validation
Strict native reload and short capability smoke checks on Apple M3 Ultra, MLX 0.32.4. Speech uses seed 42 and the model's default audio sampling settings. These fixtures are not a broader quality benchmark. Round-trip ASR uses the same model and is not an independent speech evaluation.
| Check | Result |
|---|---|
| finite_logits | All allowed logits finite; speech placeholders correctly suppressed |
| arithmetic | -42 (expected 42; exact match: False) |
| asr | The quick brown fox jumps over the lazy dog. (WER 0.0%) |
| tts | Finite 24000 Hz waveform, 3.52 s; finish: stop |
| tts_roundtrip | The quick brown fox jumps over the lazy dog. (WER 0.0%) |
| interleaved | Finite 24000 Hz waveform, 2.32 s; finish: stop |
Exact checks and settings are in validation.json; provenance and tensor audit are in conversion.json; file hashes are in SHA256SUMS. Generated speech examples are in samples/.
Implementation limits and terms
Speech generation supports batch one. Streaming waveform input/output and waveform-based speech history are not implemented. Video soundtrack input must be supplied separately. Omni thinking is unsupported. Lower precisions can reduce quality; each format needs task-specific evaluation before deployment.
Original checkpoint terms apply. See ORIGINAL_MODEL_CARD.md. No new weight license is assigned by this conversion.
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