README.md

---
datasets:
- homebrewltd/instruction-speech-whispervq-v2
language:
- en
license: apache-2.0
tags:
- sound language model
- audio-text-to-text
---

## Model Details

We have developed and released the family [Ichigo-llama3s](https://huggingface.co/collections/homebrew-research/llama3-s-669df2139f0576abc6eb7405). This family is natively understanding audio and text input.

This model is a supervised fine-tuned (SFT) version of homebrewltd/Ichigo-llama3.1-s-base-v0.3, trained on over 1 billion tokens from the [Instruction Speech WhisperVQ v4](https://huggingface.co/datasets/homebrewltd/mixed-instruction-speech-whispervq-v4) dataset which built upon [Instruction Speech WhisperVQ v3](https://huggingface.co/datasets/homebrewltd/mixed-instruction-speech-whispervq-v3-full), adding multi-turn speech conversations and noise rejection capabilities for enhanced performance.  As a result, the model demonstrates improved robustness against noisy environmental inputs and enhanced multi-turn conversation capabilities, making it more reliable in real-world applications.

**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.

## How to Get Started with the Model

Try this model using [Google Colab Notebook](https://colab.research.google.com/drive/18IiwN0AzBZaox5o0iidXqWD1xKq11XbZ?usp=sharing).

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)
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, 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.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/DmZOYY_-NQtNS610HXR8L.png)

**[MMLU](https://huggingface.co/datasets/cais/mmlu)**:

| Model | MMLU Score |
| --- | --- |
| llama3.1-instruct-8b | 69.40 |
| ichigo-llama3.1-s-v0.4| **64.66** |
| 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 |
| llama3.5-instruct-v0.2 | 50.27 |

**[AudioBench](https://arxiv.org/abs/2406.16020) Eval**:

| Model Bench | [Open-hermes Instruction Audio](https://huggingface.co/datasets/AudioLLMs/openhermes_instruction_test) (GPT-4-O judge 0:5) | [Alpaca Instruction Audio](https://huggingface.co/datasets/AudioLLMs/alpaca_audio_test) (GPT-4-O judge 0:5) |
| --- | --- | --- |
| [Llama3.1-s-v2](https://huggingface.co/homebrewltd/llama3-s-instruct-v0.2) | 3.45 | 3.53 |
| [Ichigo-llama3.1-s v0.4](homebrewltd/Ichigo-llama3.1-s-instruct-v0.4) | **3.5** | **3.52** |
| [Ichigo-llama3.1-s v0.3-phase2 -cp7000](https://huggingface.co/homebrewltd/Ichigo-llama3.1-s-instruct-v0.3-phase-2) | 3.42 | 3.62 |
| [Ichigo-llama3.1-s v0.3-phase2-cplast](https://huggingface.co/jan-hq/llama3-s-instruct-v0.3-checkpoint-last) | 3.31 | 3.6 |
| [Ichigo-llama3.1-s v0.3-phase3](https://huggingface.co/homebrewltd/Ichigo-llama3.1-s-instruct-v0.3-phase-3) | 3.64 | 3.68 |
| [Qwen2-audio-7B](https://huggingface.co/Qwen/Qwen2-Audio-7B) | 2.63 | 2.24 |

### Hardware

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

**GPU Usage**:
  - **Continual Training**: 12 hours.

### Training Arguments

We utilize [torchtune](https://github.com/pytorch/torchtune) library for the latest FSDP2 training code implementation. 

| Parameter                  | Instruction Fine-tuning      | 
|----------------------------|-------------------------|
| **Epoch**                  | 1                       | 
| **Global batch size**      | 256                     | 
| **Learning Rate**          | 7e-5                  | 
| **Learning Scheduler**     | Cosine with warmup      | 
| **Optimizer**              | Adam torch fused        | 
| **Warmup Ratio**           | 0.01                    | 
| **Weight Decay**           | 0.005                   |
| **Max Sequence Length**    | 4096                     |


## Examples

1. Good example:

<details>
<summary>Click to toggle Example 1</summary>

```

```
</details>

<details>
<summary>Click to toggle Example 2</summary>

```

```
</details>


2. Misunderstanding example:

<details>
<summary>Click to toggle Example 3</summary>
  
```

```
</details>

3. Off-tracked example:

<details>
<summary>Click to toggle Example 4</summary>

```

```
</details>


## 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](https://github.com/collabora/WhisperSpeech)**

- **[Meta-Llama-3.1-8B-Instruct ](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)**