Create README.md

README.md

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+---
+datasets:
+- homebrewltd/instruction-speech-whispervq-v2
+language:
+- en
+license: apache-2.0
+tags:
+- sound language model
+---
+
+## 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 continue to supervised finetune our last checkpoint using WhisperVQ as a tokenizer for audio files [homebrewltd/...](...) with 2B 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
+
+First, we need to convert the audio file to sound tokens
+
+```python
+
+```
+
+Then, we can inference the model the same as any other LLM.
+
+```python
+
+```
+
+## Training process
+**Training Metrics Image**: Below is a snapshot of the training loss curve visualized.
+
+![training_loss](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/Mo_FGQvhkcHl3y1REf76f.png)
+
+### 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**    | 1024                    |
+
+
+## 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-2024-08-15}
+```
+
+## Acknowledgement
+
+- **[WhisperSpeech](https://github.com/collabora/WhisperSpeech)**
+
+- **[Meta-Llama-3.1-8B-Instruct ](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)**