---
datasets:
- homebrewltd/instruction-speech-whispervq-v2
language:
- en
license: apache-2.0
tags:
- sound language model
---
# QuantFactory/llama3.1-s-instruct-v0.2-GGUF
This is quantized version of [homebrewltd/llama3.1-s-instruct-v0.2](https://huggingface.co./homebrewltd/llama3.1-s-instruct-v0.2) created using llama.cpp
# Original Model Card
## Model Details
We have developed and released the family [llama3s](https://huggingface.co./collections/homebrew-research/llama3-s-669df2139f0576abc6eb7405). This family is natively understanding audio and text input.
We expand the Semantic tokens experiment with WhisperVQ as a tokenizer for audio files from [homebrewltd/llama3.1-s-base-v0.2](https://huggingface.co./homebrewltd/llama3.1-s-base-v0.2) with nearly 1B tokens from [Instruction Speech WhisperVQ v2](https://huggingface.co./datasets/homebrewltd/instruction-speech-whispervq-v2) dataset.
**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)
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](https://github.com/pytorch/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
1. Good example:
Click to toggle Example 1
```
```
Click to toggle Example 2
```
```
2. Misunderstanding example:
Click to toggle Example 3
```
```
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](https://github.com/collabora/WhisperSpeech)**
- **[Meta-Llama-3.1-8B-Instruct ](https://huggingface.co./meta-llama/Meta-Llama-3.1-8B-Instruct)**