QuantFactory/Ichigo-llama3.1-s-instruct-v0.4-GGUF

This is quantized version of homebrewltd/Ichigo-llama3.1-s-instruct-v0.4 created using llama.cpp

Original Model Card

Model Details

We have developed and released the family Ichigo-llama3s. 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 dataset which built upon Instruction Speech WhisperVQ v3, 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.

First, we need to 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, we can inference the model the same as any other LLM.

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.

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 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 v0.4	3.5	3.52
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
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 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

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

QuantFactory
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Ichigo-llama3.1-s-instruct-v0.4-GGUF