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