import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the LoRA configuration
config = PeftConfig.from_pretrained("cdreetz/audio-llama")

# Load the base model
model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Load the LoRA adapter
model = PeftModel.from_pretrained(model, "cdreetz/audio-llama")

# Run inference
prompt = "Transcribe this audio:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training

Base Model: meta-llama/Llama-3.2-3B-Instruct
Audio Model: openai/whisper-large-v3-turbo
LoRA Rank: 32
Task: Audio transcription from LibriSpeech dataset
Training Framework: PEFT (Parameter-Efficient Fine-Tuning)

This model was fine-tuned using LoRA on audio transcription tasks. It starts with a Llama 3 base model and uses Whisper-processed audio features for audio understanding.

Limitations

This model requires special code for audio processing with Whisper before passing to the Llama model. See the Audio-LLaMA repository for full usage instructions.