Mini Ichigo Llama3.2 3B S Instruct

🚀 Ichigo-llama3s Family Model

The Ichigo-llama3s family is a model that natively understands audio and text input, expanding the semantic tokens experiment with WhisperVQ for audio files.

📚 Documentation

✨ Features

• The Ichigo-llama3s family natively understands both audio and text input.
• It expands the Semantic tokens experiment with WhisperVQ as a tokenizer for audio files using nearly 1B tokens from the Instruction Speech WhisperVQ v3 dataset.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

First, convert the audio file to sound tokens:

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)
vq_model.ensure_whisper(device)
def audio_to_sound_tokens(audio_path, target_bandwidth=1.5, device=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|>'

Then, inference the model:

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)

🔧 Technical Details

Model Details

• Model developers: Homebrew Research.
• Input: Text and sound.
• Output: Text.
• Model Architecture: Llama-3.
• Language(s): English.

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.

Training process

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

• MMLU: | Model | MMLU Score | | --- | --- | | llama3.1-instruct-8b | 69.40 | | ichigo-llama3.1-s-v0.3: phase 3 | 63.79 | | ichigo-llama3.1-s-v0.3: phase 2 | 63.08 | | ichigo-llama3.1-s-base-v0.3 | 42.11 | | mini-ichigo-llama3.2-3B-s-instruct | 58.60 | | mini-ichigo-llama3.2-3B-s-base | 59.61 | | llama3.1-s-instruct-v0.2 | 50.27 |

• AudioBench Eval: | Model Bench | Open-hermes Instruction Audio (GPT-4-O judge 0:5) | Alpaca Instruction Audio (GPT-4-O judge 0:5) | | --- | --- | --- | | Llama3.1-s-v2 | 3.45 | 3.53 | | Ichigo-llama3.1-s v0.3-phase2 -cp7000 | 3.42 | 3.62 | | Ichigo-llama3.1-s v0.3-phase2-cplast | 3.31 | 3.6 | | Ichigo-llama3.1-s v0.3-phase3 | 3.64 | 3.68 | | mini-Ichigo-llama3.2-3B-s-instruct | 2.58 | 2.07 | | Qwen2-audio-7B | 2.63 | 2.24 |

Hardware

• GPU Configuration: Cluster of 10x NVIDIA A6000-48GB.
• GPU Usage:
  • Fine-tuning: 12 hours.

Training Arguments

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

📄 License

The license for this project is apache-2.0.

📚 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