README.md

---
license: llama3
datasets:
- McGill-NLP/WebLINX
language:
- en
library_name: transformers
tags:
- agents
- agent
- llm
- llama
base_model: McGill-NLP/Llama-3-8B-Web
pipeline_tag: text-generation
---


# Llama-3-8B-Web-GGUf
- This is quantized version of [McGill-NLP/Llama-3-8B-Web](https://huggingface.co/McGill-NLP/Llama-3-8B-Web) created using llama.cpp

## Model Description

Our first agent is a finetuned [`Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) model, which was recently released by Meta GenAI team. We have finetuned this model on the [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) dataset, which contains over 100K instances of web navigation and dialogue, each collected and verified by expert annotators. We use a 24K curated subset for training the data. The training and evaluation data is available on [Huggingface Hub as `McGill-NLP/WebLINX`](https://huggingface.co/datasets/McGill-NLP/WebLINX).


**It surpasses GPT-4V (zero-shot `*`) by over 18% on the [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) benchmark**, achieving an overall score of 28.8% on the out-of-domain test splits (compared to 10.5% for GPT-4V). It chooses more useful links (34.1% vs 18.9% *seg-F1*), clicks on more relevant elements (27.1% vs 13.6% *IoU*) and formulates more aligned responses (37.5% vs 3.1% *chr-F1*).

## About `WebLlama`

| `WebLlama` | The goal of our project is to build effective human-centric agents for browsing the web. We don't want to replace users, but equip them with powerful assistants. |
|:---: | :---|
| Modeling | We are build on top of cutting edge libraries for training Llama agents on web navigation tasks. We will provide training scripts, optimized configs, and instructions for training cutting-edge Llamas. |
| Evaluation | Benchmarks for testing Llama models on real-world web browsing. This include *human-centric* browsing through dialogue ([`WebLINX`](https://mcgill-nlp.github.io/weblinx/)), and we will soon add more benchmarks for automatic web navigation (e.g. Mind2Web). |
| Data | Our first model is finetuned on over 24K instances of web interactions, including `click`, `textinput`, `submit`, and dialogue acts. We want to continuously curate, compile and release datasets for training better agents. |
| Deployment | We want to make it easy to integrate Llama models with existing deployment platforms, including Playwright, Selenium, and BrowserGym. We are currently focusing on making this a reality. |


## Evaluation

We believe short demo videos showing how well an agent performs is NOT enough to judge an agent. Simply put, **we do not know if we have a good agent if we do not have good benchmarks.** We need to systematically evaluate agents on wide range of tasks, spanning from simple instruction-following web navigation to complex dialogue-guided browsing. 

<img src="assets/WebLINXTestSplits.png" style="width: 100%; max-width:800px"/>

This is why we chose [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) as our first benchmark. In addition to the training split, the benchmark has 4 real-world splits, with the goal of testing multiple dimensions of generalization: new websites, new domains, unseen geographic locations, and scenarios where the *user cannot see the screen and relies on dialogue*. It also covers 150 websites, including booking, shopping, writing, knowledge lookup, and even complex tasks like manipulating spreadsheets.

## Data

Although the 24K training examples from [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) provide a good starting point for training a capable agent, we believe that more data is needed to train agents that can generalize to a wide range of web navigation tasks. Although it has been trained and evaluated on 150 websites, there are millions of websites that has never been seen by the model, with new ones being created every day. 

**This motivates us to continuously curate, compile and release datasets for training better agents.** As an immediate next step, we will be incorporating `Mind2Web`'s training data into the equation, which also covers over 100 websites.


## Deployment

We are working hard to make it easy for you to deploy Llama web agents to the web. We want to integrate `WebLlama` with existing deployment platforms, including Microsoft's Playwright, ServiceNow Research's BrowserGym, and other partners.

## Code

The code for finetuning the model and evaluating it on the [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) benchmark is available now. You can find the detailed instructions in [modeling](https://github.com/McGill-NLP/webllama/tree/main/modeling).