Welcome to my blog post where I'll share my journey and insights into working with the LLama2 model. LLama2 is a fantastic AI model developed by Meta, and it's exciting to explore its capabilities that are reminiscent of GPT-3. In this post, we'll delve into different facets of LLama2, including its setup, prerequisites, applications, significance, and even a peek into how we can train it ourselves. I'm thrilled to take you through my learning experience with LLama2, all gained while working on my HackBot project.

Topics to cover

What is LLama2?

LLama2 is a cutting-edge technology created by Meta that is classified as an AI model at its heart. Think of it as a very intelligent assistant that can comprehend human language and produce meaningful responses, almost human-like. The goal of LLama2 is to improve the ease and naturalness of interactions between people and computers.

Consider how you express yourself verbally when you talk to friends or compose emails; the people you are communicating with understand and react. Similar in operation, LLama2 can process enormous volumes of text data and learn from it. This makes it possible for LLama2 to assist with a variety of tasks, such as delivering information and answering queries, as well as writing content and assisting with problem-solving.

The unique feature of LLama2 is that it was created with accessibility in mind. It's like having a flexible instrument that can be used by anyone with different levels of technical ability. LLama2 provides a simple approach to accessing the potential of artificial intelligence, whether you're a developer, writer, student, or someone just interested in it.

In essence, LLama2 creates a realm of possibilities where computers may interact more easily and effectively with human language. Your interactions with technology become much more productive and efficient since it's like having a virtual buddy who is constantly there to help you with activities involving text and language.

How to get started?

Let's get started with the first steps to get you going. The following are the tasks you must consider to get the code to work.

Choosing Your Language:

Python was my first choice for a reliable travelling companion. It's a great option for interacting with LLama2 due to its adaptability and extensive usage in the programming community. You're in good shape if you're already familiar with Python.

Setting Up the Essentials:

Logging In and Getting Ready:

How is it used in Python code?

Now the main question is how is it used?

To answer that the integration part can be divided into steps.

Now by implementing this, we have created a chatbot backbone or a connecter and using this we can start an active conversation with the AI model.

How did I use it in HackBot?

HackBot was my attempt at creating a cybersecurity-specific chat bot and this tool has features such as scan data, log data analysis tools and code analysis capabilities.

You can view and try out the entire chatbot from my Github repo: Link

How to train the model?

Training an AI model is a transformational process that calls for planning and accuracy. Here is a step-by-step guide to completing the process.

Prerequisites:

The process of training:

The Dataset:

The dataset can be both one which is pre-built like the ones available in huggingface datasets under the text generation specific datasets available. For custom datasets make sure you follow these steps:

Here is a sample dataset format you can use: data.csv

Name

Description

Prompt

Greeting

Basic greetings and responses

###HUMAN: Hi there

###Analyst: Hello!

Weather

Asking about the weather

###HUMAN: How's the weather today

###Analyst: It's sunny and warm.

Restaurant

Inquiring about a restaurant recommendation

###HUMAN: Can you suggest a good restaurant

###Analyst: Sure! I recommend trying...

Technology

Discussing the latest tech trends

###HUMAN: What are the tech trends this year

###Analyst: AI and blockchain are prominent trends...

Travel

Seeking travel advice and tips

###HUMAN: Any travel tips for visiting Paris

###Analyst: Absolutely! When in Paris...

This is just 1 type.

Once you have your dataset it’s time to train your AI based on how much GPU power you have and how big is your dataset the time also corresponds accordingly. Now we can use autotrain advanced modules from huggingface to train the AI.

We can install autotrain-advanced using this command:

pip install autotrain-advanced

And this command to train the AI:

autotrain llm --train --project_name your_project_name --model TinyPixel/Llama-2-7B-bf16-sharded --data_path your_data_set --use_peft --use_int4 --learning_rate 2e-4 --train_batch_size 2 --num_train_epochs 3 --trainer sft --model_max_length 2048 --push_to_hub --repo_id your_repo_id -

You can change the project_name from your_project_name to your actual project name, the model from TinyPixel/Llama-2-7B-bf16-sharded to the llama model you're interested to train and the data_path to . if it is a custom dataset or huggingface/dataset the repo ID of the dataset if it is from huggingface.

I aim to train a Llama model or any LLM model I can get my hands on to be a complete cybersecurity assistant to automate the majority of our tasks as hackers that would be a blessing to have.

Capabilities and Advantages

So talking about capabilities Meta has released research papers for Llama with several benchmarks. According to the papers, the Llama models range from 7B to 65B parameters and have competitive performance compared to other large language models. For example, Llama-13B outperforms GPT-3 on most benchmarks despite being 10 times smaller. The 65B-parameter model of Llama is also competitive with other large language models such as Chinchilla or PaLM-540B. The papers also mention that a smaller model trained longer can ultimately be cheaper at inference. It states that the performance of a 7B model continues to improve even after 1T tokens. However, the document does not provide specific numerical values for the performance differences between the Llama models.

Other sources claim Llama models are more versatile and fast compared to the GPT models and also PaLM models making it one of the best AI models out there to use. But for hacking or any security-specific task, this needs a lot of training or personal inputs It’s not easy to generate a training model for it but once trained this can be a game changer.

Final Keynotes

The voyage into the world of artificial intelligence has been incredibly illuminating, demonstrating the amazing impact of automation and integration. I have a deep appreciation for how AI is changing how we work and interact after seeing its capabilities in a variety of industries. My learning experience has been a revelation, from observing the seamless automation of regular operations to experiencing the incorporation of AI into daily life. I've learned that automation and integration are more than just technical ideas as I learn more about the complexities of AI; rather, they act as catalysts for innovation. With this newfound insight, I can now see a world in which AI's potential to improve efficiency and collaboration is limitless.

Sources

Contacts

You can reach out to me over LinkedIn. If you have any concerns you can comment below.

Thanks for reading.