|
|
|
--- |
|
|
|
datasets: |
|
- homebrewltd/instruction-speech-whispervq-v2 |
|
language: |
|
- en |
|
license: apache-2.0 |
|
tags: |
|
- sound language model |
|
|
|
--- |
|
|
|
[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) |
|
|
|
|
|
# QuantFactory/Ichigo-llama3.1-s-instruct-v0.4-GGUF |
|
This is quantized version of [homebrewltd/Ichigo-llama3.1-s-instruct-v0.4](https://huggingface.co/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](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)** |
|
|