Intended Use

Intended Use Cases This family is primarily intended for research applications. This version aims to further improve the LLM on sound understanding capabilities.

Out-of-scope The use of llama3-s in any manner that violates applicable laws or regulations is strictly prohibited.

How to Get Started with the Model

Try this model using Google Colab Notebook.

First, we need to convert the audio file to sound tokens

python

device = "cuda" if torch.cuda.is_available() else "cpu"
if not os.path.exists("whisper-vq-stoks-medium-en+pl-fixed.model"):
    hf_hub_download(
        repo_id="jan-hq/WhisperVQ",
        filename="whisper-vq-stoks-medium-en+pl-fixed.model",
        local_dir=".",
    )
vq_model = RQBottleneckTransformer.load_model(
        "whisper-vq-stoks-medium-en+pl-fixed.model"
    ).to(device)
def audio_to_sound_tokens(audio_path, target_bandwidth=1.5, device=device):
    vq_model.ensure_whisper(device)

    wav, sr = torchaudio.load(audio_path)
    if sr != 16000:
        wav = torchaudio.functional.resample(wav, sr, 16000)
    with torch.no_grad():
        codes = vq_model.encode_audio(wav.to(device))
        codes = codes[0].cpu().tolist()

    result = ''.join(f'<|sound_{num:04d}|>' for num in codes)
    return f'<|sound_start|>{result}<|sound_end|>'

def audio_to_sound_tokens_transcript(audio_path, target_bandwidth=1.5, device=device):
    vq_model.ensure_whisper(device)

    wav, sr = torchaudio.load(audio_path)
    if sr != 16000:
        wav = torchaudio.functional.resample(wav, sr, 16000)
    with torch.no_grad():
        codes = vq_model.encode_audio(wav.to(device))
        codes = codes[0].cpu().tolist()

    result = ''.join(f'<|sound_{num:04d}|>' for num in codes)
    return f'<|reserved_special_token_69|><|sound_start|>{result}<|sound_end|>'

Then, we can inference the model the same as any other LLM.

python

def setup_pipeline(model_path, use_4bit=False, use_8bit=False):
    tokenizer = AutoTokenizer.from_pretrained(model_path)

    model_kwargs = {"device_map": "auto"}

    if use_4bit:
        model_kwargs["quantization_config"] = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_compute_dtype=torch.bfloat16,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type="nf4",
        )
    elif use_8bit:
        model_kwargs["quantization_config"] = BitsAndBytesConfig(
            load_in_8bit=True,
            bnb_8bit_compute_dtype=torch.bfloat16,
            bnb_8bit_use_double_quant=True,
        )
    else:
        model_kwargs["torch_dtype"] = torch.bfloat16

    model = AutoModelForCausalLM.from_pretrained(model_path, **model_kwargs)

    return pipeline("text-generation", model=model, tokenizer=tokenizer)

def generate_text(pipe, messages, max_new_tokens=64, temperature=0.0, do_sample=False):
    generation_args = {
        "max_new_tokens": max_new_tokens,
        "return_full_text": False,
        "temperature": temperature,
        "do_sample": do_sample,
    }

    output = pipe(messages, **generation_args)
    return output[0]['generated_text']

# Usage
llm_path = "homebrewltd/llama3.1-s-instruct-v0.2"
pipe = setup_pipeline(llm_path, use_8bit=True)

Training process

Training Metrics Image: Below is a snapshot of the training loss curve visualized.

Hardware

GPU Configuration: Cluster of 8x NVIDIA H100-SXM-80GB. GPU Usage:

Continual Training: 6 hours.

Training Arguments

We utilize torchtune library for the latest FSDP2 training code implementation.

Parameter	Continual Training
Epoch	1
Global batch size	128
Learning Rate	0.5e-4
Learning Scheduler	Cosine with warmup
Optimizer	Adam torch fused
Warmup Ratio	0.01
Weight Decay	0.005
Max Sequence Length	512

Examples

Good example:

Click to toggle Example 1

Click to toggle Example 2

Misunderstanding example:

Click to toggle Example 3

Off-tracked example:

Click to toggle Example 4

Citation Information

BibTeX:

@article{Llama3-S: Sound Instruction Language Model 2024,
  title={Llama3-S},
  author={Homebrew Research},
  year=2024,
  month=August},
  url={https://huggingface.co/homebrewltd/llama3.1-s-2024-08-20}

Acknowledgement

WhisperSpeech
Meta-Llama-3.1-8B-Instruct