---
language:
- en
pipeline_tag: audio-text-to-text
tags:
- audio
- speech
- voice-assistant
- voicebench
- gemma
---


![LFG-3-hero](https://cdn-uploads.huggingface.co/production/uploads/61b37e66986f43ddf4956d21/EeQbXGjXA4z82M6VlegVf.png)

LFG-3 is an audio-language model that fuses the best-in-class conversational intelligence of Gemma 4 31B with a Parakeet audio encoder through a trained projection layer. Speech goes in, text comes out.

The model is the third iteration in a personal learning journey to answer the question: "Can I stand on the shoulders of giants and use limited compute resources to build a standout model that can understand what and how you say things, not just speech to text?"

The answer is Yes. LFG-3 was trained entirely on a single H100 from my house on nights and weekends. It ranks #1 on Voice Bench at the time of this release.


LFG-3 serves as an open exploration of just how adaptable the Gemma architecture can be for independent developers building multimodal applications. 




## Voice Bench Results
| Subset |Metric | 	Score	| 
| --- | --- | ---: |
| AlpacaEval | (1-5, GPT) |	4.73	|
| CommonEval | (1-5, GPT) |	4.40	|
| WildVoice | (1-5, GPT)	| 4.45	|
| SD-QA | (% GPT majority) | 78.12	|
| MMSU | (% accuracy)	| 85.52	 |
| OpenBookQA | (% accuracy)	| 94.73	|
| BBH | (% accuracy)	| 92.20	|
| IFEval  |(% strict-loose avg)	| 88.54	|
|AdvBench | (% refusal rate)	| 98.27	|
| Overall | 	| 89.88 |


![Screenshot 2026-08-12 at 8.24.27 AM](https://cdn-uploads.huggingface.co/production/uploads/61b37e66986f43ddf4956d21/sY5XB1cxMVufI8YMn6m8S.png)

## Usage

```python
import soundfile as sf
from transformers import AutoModelForMultimodalLM, AutoProcessor

MODEL = "glenn2/LFG-3"
processor = AutoProcessor.from_pretrained(MODEL, trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained(
    MODEL, trust_remote_code=True, dtype="bfloat16", device_map="cuda"
)
audio, sr = sf.read("question.wav")  # 16 kHz mono


messages = [
    {"role": "system", "content": [{"type": "text", "text": "You are a helpful voice assistant. The user is speaking to you, and your reply will be read aloud."}]},
    {"role": "user", "content": [{"type": "audio", "audio": audio}]}, # Audio should be 16 kHz mono. 
]
# Process input
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    add_generation_prompt=True,
    enable_thinking=True
).to(model.device)
input_len = inputs["input_ids"].shape[-1]

# Generate output
outputs = model.generate(**inputs, max_new_tokens=4096)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)

# Parse output
print(processor.parse_response(response)["content"])  
```


## Intended use

- Designed for **English** spoken questions/instructions → text answers.
- Inherits knowledge from the Gemma 4 31B IT model.