glenn2/LFG-3

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?"

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

Usage

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.