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# Model Card for Diva Llama 3
<!-- Provide a quick summary of what the model is/does. [Optional] -->
This is an end-to-end Voice Assistant Model which can handle speech and text as inputs. It is trained using distillation loss. More details will be in a paper [COMING SOON]!
See also [value-nlp.github.io/DiVA-Demo](value-nlp.github.io/DiVA-Demo).
## Citation
No Publication As of Yet, But If You Use Please Cite the Below
**BibTeX:**
```
@InProceedings{hewitt2023backpack,
author = "Held, Will and Zhang, Yanzhe and Ryan, Michael and Shi, Weiyan and Li, Ella and Yang, Diyi",
title = "Distilling an End-to-End Voice Assistant from Speech Recognition Data",
year = "2024",
publisher = "HuggingFace",
}
```
## 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
[email protected]
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