LLaMA and ChatGPT: Exploring Two Major AI LLMs

Artificial Intelligence (AI) has entered a new era with a broad range of applications. One of the most crucial developments of this AI is the Large Language Model (LLM). Also, these models can generate human-like content. In recent weeks, Open AI’s ChatGPT and Meta’s LLaMA are the two most significant LLMs. 

Thus, this article will explore some common differences between LLaMA and ChatGPT (3.5, not 4 as we will compare free models) LLM models by comparing their architecture, prompt-generating capacity, and computational capability.

Brief Introduction of the LLMs

Meta and Microsoft’s AI model, LLaMA 2, is free for research since it’s an open-source model. Additionally, the pre-trained and fine-tuned LLM collection has a training dataset ranging from 7 billion to 70 billion! Hence, this is huge, increasing the size of the training parameters by 40% from its predecessor, LLaMA.

The major purpose of LLAMA is to:

• Investigate the applications like natural language comprehension, answering questions, etc.
• Develop methods and strategies for enhancing the language models.
• Help in solving complex mathematical theorems and techniques.

ChatGPT is the OpenAI-developed language model, and we already have many posts, on ChatGPT API and ChatGPT Prompts, etc. Also, OpenAI trained it trained on massive data and its main features include:

1. Conversational AI: ChatGPT is designed to engage in natural language conversations with users, responding to prompts in a human-like manner.
2. Text Generation: It can generate coherent and contextually relevant text based on the input, making it useful for content creation, explanations, and recommendations.
3. Versatility: ChatGPT’s versatility allows it to be applied across various applications, from customer support and education to creative writing and problem-solving.
4. GPT Architecture: Powered by the GPT (Generative Pre-trained Transformer) architecture, more on this in the next section, ChatGPT learns language patterns from extensive text data to produce responses.
5. Responsibility: While powerful, ChatGPT has limitations such as potential biases, inaccuracies, and the risk of generating inappropriate content, necessitating responsible usage and oversight. 

1. LLaMA and ChatGPT: Architecture of the LLMs

This is what the LLAMA Model architecture looks like, ranging from its model size (parameters) to model architecture and more.

Image Source: Official site of Meta

On the other hand, ChatGPT is based on transformer architecture and this architecture is based on the self-attention mechanism.

Image Source: Google

Then, let’s compare the computational resources of both.

2. LLaMA vs ChatGPT: Computational Resources

Regarding computational power, LLaMA uses less computational power than ChatGPT. Moreover, the major aim of the LLAMA model is to be less resource intensive and more efficient than ChatGPT. Besides, LLAMA is designed to be available and accessible to all organizations and researchers under a non-commercial license. Also, for free.

Thus, here is the LLAMA 2 Chat comparison with the other open and closed source models, including ChatGPT, by safety human evaluation.

3. LLaMA vs ChatGPT: Application Chatbot

So, now let’s see compare how to access the chatbots for both LLMs in this next section

Using API token from Replicate to build LLaMA 2 Chat

• Get the Replicate API token

First, you need to get your API token from your Replicate account. Replicate allows you to run models in the cloud at scale. Then, once you create an account, you can get it from the API token option. And you must create the token by clicking on the create token button as shown below:

These tokens are required to access the LLaMA 2 model and use it for chatbot development. 

• Set up the Coding Environment

Then set up your coding environment for cloud or local development. You can use Python or Jupyter Notebook, and any IDE of your choosing, if you want.

• Build your LLaMA 2 Chatbot

Before writing the Python code to build the LLaMA chatbot, you need to install the replicate package. In Python, you can do it as:

pip install Replicate

After that, you can write this Python code:

from getpass import getpass

import os

REPLICATE_API_TOKEN = getpass()

os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN

Then, it will prompt you to enter your API token (which we created above).

Okay, once you enter the Replicate API token, you can use its methods to import the model you want to use and the input prompt.

import replicate

data=replicate.run(

 "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1",

  input={

"prompt": "Input Prompt"}

)

for item in data:

  print(item,end='')

Here, we have used the LLAMA 2 70b chat model. The model generates text based on the input and produces a series of items as output. Additionally, these items are then printed sequentially, devoid of newline breaks between them. Essentially, the code demonstrates the interaction with the specified model, showcasing its responses to the given input prompt.

In addition to this, you can also use Hugging Face, where you can find all the LLaMA 2 models.

Making of Chatbot using ChatGPT

You can use OpenAI’s API to create your chatbot, for which you need to have your API. To do so, check our blog post for creating a chatbot using OpenAI’s API here.

4. LLaMA vs ChatGPT: Prompt Response Comparison

For simplicity, you can also access LLaMA chat using Perplexity AI’s instance here.

1. Visit LLaMA Perplexity: Access the website by navigating to llama.perplexity.ai in your web browser.
2. Input your message: The website’s interface should have a text input field to type your message or query. Then, you can choose 3 models of llama-2, including 7b-chat, 13b-chat, and 70b-chat. 
3. Generate a response: After that, click on a button or press enter to generate a response from LLaMA 2. Then, the model will process your input and generate a text-based response.

All set up? Great! So, we can now compare a series of prompts to compare the responses of LLaMA and ChatGPT.

Prompt 1: Generate a list of creative writing prompts for a short story collection

LLaMa Chat (llama-2-13b-chat) Response

Chatgpt-3.5 Response

Prompt 2: Generate a list of healthy meal ideas for a week-long meal plan

LLAMA 2-70B-chat Response

Chatgpt-3.5 Response

You should know that portion control is key when it comes to diet, and you should aim for balanced meals, including lean proteins, complex carbohydrates, and healthy fats. Also, try to limit processed foods and added sugars. Additionally, his meal plan provides approximately 1500-1700 calories per day. Adjust according to your individual needs and activity level. Moreover, consult a doctor or registered dietitian for specific dietary requirements or concerns.

In summary, below is a table that compares the responses of LLaMA and ChatGPT based on certain aspects. So, let’s explore the differences.

Comparative Analysis of the Prompt Responses of the LLMs

From the above analysis, it’s clear.; the choice between LLaMA Chat and ChatGPT depends on your specific preferences and needs. Moreover, both models offer valuable capabilities, but which one is “better” depends on what you’re looking for.

• Choose LLaMA Chat if:
  • You prefer a more formal and polished writing style.
  • You value emotionally resonant and thought-provoking prompts.
  • You’re seeking a more organized and structured approach to meal planning.

• Choose ChatGPT if:
  • You enjoy a conversational and relatable writing style.
  • You’re looking for a diverse range of creative writing prompts.
  • You appreciate a comprehensive yet friendly meal plan emphasizing variety.

At this point, it’s a good moment to draw up a table to compare the two models based on their architectures, training dataset, purpose, performance, and much more. So, you can look at the full table below:

5. LLaMA vs ChatGPT: Full Comparison

Both models generate content based on patterns and therefore require the user to critically evaluate and adapt the information presented. However, neither can be wholly relied upon for entirely accurate or fact-based content.

Conclusion: LLaMA or ChatGPT?

The comparison between LLaMA and ChatGPT reveals an interesting landscape of Large Language Models and their capabilities. Furthermore, both models exhibit strengths and weaknesses that align with different user needs and preferences. But before we wrap this article, let’s compare the writing styles and content generation of both LLaMA and ChatGPT.

Writing Style

• LLaMA: If you value a formal, polished writing style, LLaMA is your go-to model. Not only that, but it also produces precise and refined language that could be highly suitable for academic or corporate settings.
• ChatGPT: For users looking for a more conversational and relatable writing style, ChatGPT could be a better fit. Also, its tone is generally more approachable, making it ideal for casual interactions and content creation.

Content Generation

• LLaMA: Known for generating emotionally resonant and imaginative prompts, especially in creative writing. However, LLaMA might be more aligned with users seeking depth and emotional complexity in the content.
• ChatGPT: ChatGPT excels in generating a wide variety of content that sparks the imagination. Hence, it is versatile and can generate content that is as informative as it is engaging.

In conclusion, the choice between LLaMA and ChatGPT boils down to your specific requirements and the context in which you plan to deploy these models. Whether you seek formality or relatability, structured content or a variety of ideas, both models offer robust options, making them leaders in the realm of AI-based language models.

If you liked this post, please let us know and drop a comment if you learned something new or face any problems with the code or instructions. Moreover, you can let us know your experience using either LLM model.

Also, similar articles to read: Colossal AI and ChatGPT Features to 6x Your Productivity.

Edited by: Syed Umar Bukhari.