LlaMA3 vs LLaMA3: Some Issues of Worry or Concern

In the realm of nowadays AI, LlaMA3 is a brand new and fantastic AI engine. As a .NET ML developer, you’re likely familiar with the power of machine learning in building intelligent applications.

LLaMA3 is a cutting-edge AI model developed by Meta AI that leverages this same power to revolutionize natural language processing.

It’s a large-scale language model trained on an enormous dataset of text from the internet, books, and other sources.

How does LLaMA3 work?

From a .NET ML developer’s perspective, understanding how LLaMA3 works is crucial for integrating it into your projects.

At its core, LLaMA3 uses transformer-based architectures to process input text through multiple neural networks that analyze syntax, semantics, and pragmatics.

This allows the model to identify entities, extract relevant information, and generate responses based on context.

How can you use LLaMA3 in everyday .NET ML development?

Here are some practical ways to harness the power of LLaMA3 as a .NET ML developer:

Integrate with your existing projects

LLaMA3’s language capabilities can be seamlessly integrated into your existing .NET applications, allowing you to enhance user interactions, automate text-based workflows, or even build chatbots and conversational interfaces.

Enhance NLP capabilities in .NET

By leveraging LLaMA3’s advanced language understanding, you can improve the overall NLP capabilities within your .NET projects. This includes tasks like sentiment analysis, named entity recognition, and text classification.

Develop AI-powered language tools

LLaMA3’s strengths lie in its ability to generate human-like text responses. As a .NET ML developer, you can use this capability to build AI-powered language tools, such as grammar checkers, spell correctors, or even automated writing assistants.

Research and experimentation

As a .NET ML developer, you’re likely always looking for new ways to push the boundaries of machine learning.

LLaMA3’s capabilities make it an excellent tool for researching and experimenting with new NLP techniques and applications.

My own Experience with LlaMA3

Recently Meta (formerly, Facebook) has propagated to announce a brand-new LlaMA AI engine with vast number of options such is being most recent, having a powerful engine and many other features (which you can find in the LlaMA project website).

The brilliant feature was that all the engine and the interface are free and open to download and/or manipulate as well as train.  The more fabulous option is that you can download several varied engines and each engine itself has several different types.

Like many other AI fanatics, I have downloaded the OLlaMA program (Windows version) and then LlaMA3 engines, namely LlaMA:8b and LlaMA:70b (both with lots of trouble; because after 300MB of download TLS handshake timeout error would appear and I shall re-run the command).

However, unfortunately, nothing was like what Meta advertised and OLlaMA website said. My Problems are the Following:

1. Parameters of LlaMA3:

While in the website it has been written that LlaMA3:70b has 70 billion parameters, the engine responding that it is LlaMA3:80b and has 80 billion. I have asked it from Llama3:70 and it answered so!

2. Ability to Train LlaMA3:

In my Opinion, one of the most important abilities of each AI network is its ability to train (or be trained). While both LlaMA3:8b and LlaMA3:70b said same way to save chats in order to train them, but there were no python/ LlaMA3 file in the destination they have told to run the train. I have even downloaded and installed python package for LlaMA, and it has not worked yet! Even there is no code to train like what both LlaMA3s said!

3. Being Up-To-Date:

I have asked both LlaMA3:8b and LlaMA3:70b (or like what itself said 80b) and both told me their information are up to mid-2022!

Although one might think this is more than OK, I shall emphasize that at least Microsoft have gone 3 Versions of .NET Framework forward, let alone Java, Android Studio, JavaScript (in ReactJs, GraphQL, NextJs, etc.) and so on.

What’s more, even in neuroscience we have achieved many progresses within these 2 years. Hence, it might be a bit hard to advertising for LlaMA3’s newness.

Comparing to Microsoft Copilot which is updated until 2021, it may look fabulous, but keeping in mind that copilot can perform web search with its tools, everything would be way different!

4. Level of Accuracy

Despite the images within the website of LlaMA3, I have experienced that the accuracy level of LlaMA3:8b is much more than LlaMA3:70b. In my last instance, I have asked same question from both versions.

For LlaMA3:8b it took only 3 corrective consecutive questions to give me the desirable answer! But, LlaMA3:70b gave the desired answer after 8 corrective consecutive questions!(I have started both and gave the same question).

5. Level of Answer Integration and Coherence

In LlaMA3:8b answers were more coherent and relevant to the question than LlaMA3:70b. In various instances, LlaMA3:70b forgot to give the full answer and leaved its answer incomplete.

In some other instances, it took several times to emphasize on what I want to take that point into consideration. This latter issue was just emerged 2 times in LlaMA3:8b.

Conclusion:

LlaMA3:8b and LlaMA3:70b in intellectual and rational matters are greater companions than human. However, I have made my mental images upon Meta’s advertisements which were far from the facts!

Considering the vast effort to download these two super huge databases, expectation about being able to train them in local machine, being updated and giving accurate answers appear to be a fair demand, which I hope to reach them soon.