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llama8b-nnetnav-wa

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llama8b-nnetnav-wa
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---
license: apache-2.0
metrics:
- accuracy
base_model:
- meta-llama/Llama-3.1-8B-Instruct
---

# Model Card for Llama8b-NNetNav-WA

<!-- Provide a quick summary of what the model is/does. [Optional] -->
LLama8b-NNetNav-WA is a [LLama-3.1-8B]() model that is instruct-tuned with [NNetNav]() data collected via unsupervised exploration on WebArena websites, with a larger [LLama-3.1-70B]() model.

Most details about this model along with details can be found in our paper: [NNetNav: Unsupervised Learning of Browser Agents Through Environment Interaction in the Wild](https://arxiv.org/abs/2410.02907).


![show an example trajectory from NNetNav-WA](TODO)

##  Table of Contents

- [Model Card for Llama8b-NNetNav-WA](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
  - [Model Description](#model-description)
- [Uses](#uses)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
- [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)
- [Citation](#citation)
- [Model Card Authors [optional]](#model-card-authors-optional)
- [Model Card Contact](#model-card-contact)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is/does. -->


## Uses



## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

## How to Get Started with the Model

```python


```

## 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 [NNetnav-WA]() corpus.


### Training Procedure

This model was trained for 2 epochs (roughly 4k gradient steps) with a batch size of 128, and a maximum sequence length of 20000.

### Environmental Impact

- **Hardware Type:** 4 H100 GPUs (80G)
- **Hours used:** Roughly 2 days.
- **Cloud Provider:** Stanford compute.
- **Compute Region:** Stanford energy grid.

### Model Architecture and Objective


### Compute Infrastructure

This model was trained on a slurm cluster.

### Hardware

This model was trained on 4 H100s.

### Software

This model was fine-tuned with [Open-Instruct](https://github.com/allenai/open-instruct/tree/main)

## Citation

**BibTeX:**

```

```


## 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. -->

Shikhar Murty

## Model Card Contact

smurty@cs.stanford.edu
shikhar.murty@gmail.com