Conversion notes

Differences from the PyTorch checkpoint:

conformer.* → audio_encoder.* (NeMo → MLX subkey aliases: feed_forward1→ff1, norm_X→X_norm, batch_norm→norm).
depthformer.* → audio_head.depthformer.blocks.*; the fused operator.qkv_proj (1536×1024) split into separate attn.{q,k,v}_proj of shape 1024/256/256.
audio_adapter.model.N → audio_adapter.layers.N.
Conv kernels reshaped for MLX channels-last: depthwise/LFM2 conv1d (O, 1, k) → (O, k, 1); pointwise (O, I, 1) → (O, I) Linear; 4D pre-encode conv (O, I, H, W) → (O, H, W, I).
Tied depth-embedding to_logits cloned from embedding (safetensors cannot store aliased tensors).
Dropped: num_batches_tracked (BatchNorm counters), audio_loss_weights, codebook_offsets.

Verified: 924/924 tensor keys and shapes match the upstream MLX layout exactly; the LFM2 backbone loads into mlx_lm.models.lfm2 and runs a forward pass.

LFM2.5-Audio-1.5B — Tool-Aware Fine-Tune (v2)

A full fine-tune of LiquidAI/LFM2.5-Audio-1.5B that handles both turns of a tool-augmented voice flow:

Turn	Trigger	Behavior
1 — acknowledge	user audio + `Tools available: …` system prompt	Short ack ("setting your alarm now.") then stop
2 — narrate	same audio + `Known facts you must use…` block injected via `set_context()`	Speaks the result naturally ("your alarm is set for 7am.")
Other classes	(any)	Refusal on missing tool, normal answer on general knowledge, chitchat reply

v2 is the successor to matbee/lfm2.5-audio-tool-aware-v1, which mastered turn 1 but regressed to always-ack on turn 2 (0/20 narration on injected context). v2 adds the tool_result_speak class to the training mix and lifts narration from 0/20 → 20/20 while improving ack accuracy from 93.3% → 100%.

How turn 2 works

The s2s dispatcher in your voice-assistant pipeline does:

python

# turn 1 — model emits "let me check the weather for you" and stops
# coordinator runs the weather tool, gets "Weather in Tokyo: 72°F, sunny."
await ctrl.audio_node.set_context("Weather in Tokyo: 72°F, sunny.")
# (no reset_history — same session continues)
# turn 2 — model says "it's 72 and sunny in tokyo"

Under the hood set_context() appends a Known facts you must use when relevant:\n{result} block to the system prompt before turn 2 generation. v2 is trained to read that block and produce a natural narration without regenerating an ack.

System-prompt format

Respond with interleaved text and audio.

Tools available:
- weather: get current weather and forecasts for a location
- alarm: set or cancel alarms
- music: play, pause, or skip music
…

If a request needs one of these tools, acknowledge briefly and stop.
If known facts are provided below, use them to answer the user directly
without acknowledging again. Otherwise answer normally.

Known facts you must use when relevant:
Weather in Tokyo: 72°F, sunny.

The Known facts block is optional — present it for turn-2 narration, omit it for turn-1 ack.

Eval results

On the matbee/lfm2-tool-aware-dataset-v2 eval split (149 parsed rows across 5 classes):

Class	v1	v2	Notes
`tool_match` (ack)	93.3%	100.0%	v1 state-query failures fixed (e.g., `"what's the thermostat set to"`)
`tool_result_speak` (narrate)	n/a (0/20 strict)	89.7%	New behavior unlocked. Failures are all `call` scenario fact-vs-query overrides
`tool_miss` (refuse)	93.1%	96.7%
`general` (answer normally)	100.0%	100.0%	No baseline regression
`chitchat` (chat)	100.0%	100.0%	(after correcting scorer false-positives on `"doing well, thanks"`)

Overall: ~97% across 5 classes, with the new turn-2 narration capability working cleanly.

Known failure modes

call scenario fact-vs-query override: "call mom" + injected fact "Calling your office" → narration says "connecting you to your office". The model trusts the fact verbatim — correct general behavior but for call the contact name shouldn't be overridden. In production the dispatcher provides query-aligned facts; this only manifests when synthesis mismatches them.
Search-adjacent generalization (carryover from v1): "recipe for miso soup" with search listed but not recipe → "searching for miso soup recipe". Arguably correct — search can find recipes.

Usage

python

from pathlib import Path
import torch
import torchaudio
from liquid_audio import LFM2AudioModel, LFM2AudioProcessor, ChatState

# Pass a Path (not str) so liquid-audio takes the local-checkpoint branch
local = Path("./lfm2.5-audio-tool-aware-v2")
processor = LFM2AudioProcessor.from_pretrained(local, device="cuda").eval()
model = LFM2AudioModel.from_pretrained(
    local, device="cuda", dtype=torch.bfloat16
).eval()

def respond(system_prompt: str, wav_path: str) -> str:
    chat = ChatState(processor)
    chat.new_turn("system"); chat.add_text(system_prompt); chat.end_turn()
    wav, sr = torchaudio.load(wav_path)
    if wav.shape[0] > 1: wav = wav.mean(0, keepdim=True)
    chat.new_turn("user"); chat.add_audio(wav, sr); chat.end_turn()
    chat.new_turn("assistant")
    pieces = []
    for token in model.generate_interleaved(
        **chat, max_new_tokens=120, audio_temperature=1.0, audio_top_k=4
    ):
        if token.numel() == 1:
            pieces.append(processor.text.decode(token))
    return "".join(pieces).strip()

# Turn 1 — ack
TOOLS = (
    "Respond with interleaved text and audio.\n\n"
    "Tools available:\n- weather: get current weather...\n\n"
    "If a request needs one of these tools, acknowledge briefly and stop. "
    "If known facts are provided below, use them to answer the user "
    "directly without acknowledging again. Otherwise answer normally."
)
print(respond(TOOLS, "user_says_whats_the_weather.wav"))
# → "let me check the weather in tokyo."

# Turn 2 — narrate (after dispatcher returned the result)
WITH_FACTS = TOOLS + "\n\nKnown facts you must use when relevant:\n" \
                     "Weather in Tokyo: 72°F, sunny."
print(respond(WITH_FACTS, "user_says_whats_the_weather.wav"))
# → "it's 72 and sunny in tokyo."

Training

Base: LiquidAI/LFM2.5-Audio-1.5B (1.45B params, bf16)
Data: 3000 train + 400 eval examples — see matbee/lfm2-tool-aware-dataset-v2. v2 mix: 28% tool_match / 29% tool_result_speak / 14% tool_miss / 18% general / 11% chitchat
Hardware: 2× RTX 4090 (DDP, CUDA_LAUNCH_BLOCKING=1, NCCL_P2P_DISABLE=1 for the no-NVLink 4090 pair)
Trainer: upstream liquid_audio.trainer.Trainer — full fine-tune, bf16 mixed precision
Hyperparams: AdamW, lr 5e-5, cosine schedule with 50-step warmup, batch_size 4 per GPU (effective 8), 700 steps (~3.5 epochs), context_length 320
Wall clock: ~21 minutes
Loss: train 2.0 → 0.25-0.55 (jagged from the 5-class mix), val 0.85 → 0.50 at step 700

The v2 val_loss (0.50) is higher than v1 (0.33) because v2 includes the harder tool_result_speak class. Per-class accuracy is the fairer comparison and shows uniform improvement.

Limitations

Same as v1, plus:

call scenario fact-vs-query alignment — see Known failure modes above.
Synthesis-side mismatches in tool_result_speak training data: synthetic facts are randomly drawn per scenario, not query-aligned. This trains the model to trust the injected fact over the user's query when they conflict. Correct in production (where dispatcher provides aligned facts), occasionally surfaces during eval.

Inherits from v1:

All training audio synthetic (Kokoro). Real human speech untested.
Assistant audio voice is am_adam (Kokoro male, American English).
20-scenario SLURP-inspired tool taxonomy. Unseen tool names untested.
English only.
Single-turn behaviour per call (the two-turn flow is composed by the coordinator across two process() calls).

License

LFM Open License v1.0, inherited from the base model. See LICENSE.

Citation

@misc{liquidai2025lfm25audio,
  title={LFM2.5-Audio: Speech-to-Speech Foundation Model},
  author={Liquid AI},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B}
}

Dataset, recipe, two-turn flow design described in matbee/lfm2-tool-aware-dataset-v2.

matbee/lfm2.5-audio-tool-aware-v2-MLX

LFM2.5-Audio Tool-Aware v2 — MLX bf16