# Model Card for Diva Llama 3

<!-- Provide a quick summary of what the model is/does. [Optional] -->
This is an ablation of our Distilled Voice Assistant (DiVA) model which can handle speech and text as inputs. This ablation is trained using only token-alignment loss as described in the ablations here: https://huggingface.co/papers/2410.02678

Weights and Biases Run: https://wandb.ai/i18nlp/DiVA%20Training%20Runs/runs/4t0mvbcd?nw=nwuserheld

## Citation
This is the token-alignment only model from https://huggingface.co/papers/2410.02678
**BibTeX:**

```
@misc{DiVA,
      title={{D}istilling an {E}nd-to-{E}nd {V}oice {A}ssistant {W}ithout {I}nstruction {T}raining {D}ata}, 
      author={William Held and Ella Li and Michael Ryan and Weiyan Shi and Yanzhe Zhang and Diyi Yang},
      year={2024},
      eprint={2410.02678},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.02678}, 
}
    
```

##  Table of Contents

- [Model Card for DiVA Llama 3](#model-card-for-DiVA-Llama-3)
- [Citation](#citation)
- [Table of Contents](#table-of-contents)
- [Training Details](#training-details)
  - [Training Data](#training-data)
  - [Training Procedure](#training-procedure)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications [optional]](#technical-specifications-optional)
  - [Model Architecture and Objective](#model-architecture-and-objective)
  - [Compute Infrastructure](#compute-infrastructure)
    - [Hardware](#hardware)
    - [Software](#software)
- [Model Card Contact](#model-card-contact)

## Training Details

### Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

This model was trained on the [CommonVoice](https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1) corpus.


### Training Procedure

This model was trained for 7k gradient steps with a batch size of 512 Recordings and a linearly decaying learning rate from 5e-5 to zero, with a linear warmup of 70 steps.

### Environmental Impact

- **Hardware Type:** V4-32 TPU
- **Hours used:** 8 Hours
- **Cloud Provider:** Google Cloud.
- **Compute Region:** US Central C


### Hardware

This model was trained on at V4 TPU on Google Cloud.

### Software

This model was trained with [Levanter](https://github.com/stanford-crfm/levanter) 


## Model Card Authors [optional]

<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->

Will Held

## Model Card Contact

held@stanford.edu