INTRODUCTION

First, we need to understand the basic meaning of what LLMs are and what they are used for. LLMs which stand for Large Language Models, are deep learning models that are trained on massive pieces of textual data, which enables them to communicate, reason, and generate accurate responses to human language. Currently, there are various types of LLMs that are present in the market, which are trained on millions, billions, and even trillions of parameters.

If we talk about the largest open-source LLM currently, it is LLaMA 2-70B by Meta AI. It consists of 70 billion parameters and is mainly used for tasks like summarization, Fine-tuning, etc.

🔓Open-Source LLMs

The term open-source LLM is used to describe an open-source large language model that is available in the form of weights, architecture, and training code, released by the developers to be used, adapted, trained, or deployed at will (within the limitations of the license).

I think Open-Source LLMs are very helpful, as I have worked on several projects, and using them helps reduce the cost as they can be run on our own infrastructure. We will have a brief discussion on the topic where Open-Source LLMs fail against Closed-Source and when they do not. Here are some popular and much-used Open-Source LLMs:

LLaMA 2

This model comprises of 3 variants, i.e, 7B, 13B, and 70B Each model is trained on a different number of parameters. It is known for its efficient performance on tasks like summarization, reasoning, fine-tuning, etc. However, for commercial use, LLaMA 2 is not the right choice as it comes with some restrictions to be used for commercial apps.

Mistral 7B

This is another very popular model which is known for its efficiency and simplicity. It consists of 7.3 billion parameters, which makes it one of the widely chosen models by beginners. RAG(Retrieval Augmented Generation), Real-time applications are some main causes for which Mistral 7B is used for. One point at which it has been seen to struggle is when dealing with complex problems or complex reasoning.

DeepSeek-V2

You may have heard about DeepSeek-V2, which is a very powerful and gives tough competition to models like GPT-4. It is developed by a group of people in China. What makes it different from the rest is its excellent mathematical reasoning and multi-language support. It consists of 16 billion parameters. It is widely used for mathematical tasks and for arithmetic coding. It is still developing – new – ecosystem and tutorials.

Gemma

A model developed by Google itself. It is widely used for research purposes by college students. As it is developed by Google only, it works well in Google Cloud and Collab. It is not as powerful if we compare it to others like LLaMA or Mistral. It is mainly used for simple tasks like Educational reasoning, Chatbots, etc. It consists of 2B and 7B parametric models.

🧠 How to Choose the Right Open-Source LLM?

In this section, we will discuss how to choose the right LLM for your domain-specific task, as each model comes with its own ability in which it excels. For eg, DeepSeek-v2, as discussed above it excels in mathematical reasoning, Gemma is best for beginners as it is simple and secure. Here are some points that you can consider while choosing your Open-source LLM:

Use Case

There can be various tasks for which an individual can use LLM to solve for eg, Summarization, Mathematical Reasoning, Chatbots, etc. Particular LLMs like DeepSeek-v2, Mistral, LLaMA, etc, work brilliantly for specific tasks, while they may not perform well for others. So, choosing the right LLM for your problem will result in more efficiency as well as higher accuracy.

Model Size

Open-source LLMs are generally used by students for research or educational purposes. Generally, they don’t have good hardware, or you can say high-end GPUs to run big LLMs like LLaMA or DeepSeek, which consists of Billions of parameters. That’s why for low RAM devices, LLMs like Gemma, Phi-3, or Mistral 7B are advised. Using LLaMA or DeepSeek in low RAM devices may cause the system to crash repeatedly.

Community Support

While considering which LLM to choose should also consider of what type of community support or ecosystem he/she is getting if they choose a specific LLM. For eg, many LLMs provide GitHub repos and tutorials on how to implement them or the meaning of some complex things that might help someone. They should check whether an LLM is providing Lora/Qlora support which helps in fine-tuning of model.

What the Open-Source LLMs of Tomorrow May Look Like?

As we know, AI is evolving rapidly, so the impact of Open-Source LLMs will play a crucial role in the future and it will shape the AI prospect. Here are some points that can be looked over:

Domain-Specialized LLMs Models

Several LLMs are now being developed for specific purposes eg, Healthcare, Education, Research, etc. It results in cost reduction,,and more efficiency helps the user by not training the model completely from scratch.

MoE(mixture of experts)

In the future, more efficient models like Mixtral will come that will reduce the compute cost drastically by only activating the parts of the network per query and will give tough competition to models that are Closed-Source.

Hybrid LLMs Models

The field of AI is evolving much faster than we think it is. Let me explain it to you with the help of a real-life example, in today’s world how a Hybrid car works? It uses electricity for a certain speed, let’s say 40 km/hr. After it crosses 40, it automatically switches to petrol for transport. The same concept will be introduced in the future where for general tasks open-source LLMs will be used, whereas for complex taskss Closed-Source will be in action.

Conclusion: Why Open-Source LLMs can’t be underestimated!

In a world that is so much dependent on AI(Artificial Intelligence), one should not forget how much Open-Source Language Models contributed to this field, helping people, whether it is for research purposes, educational purposes, etc. From models like LLaMA, DeepSeek which are capable of solving complex problems, to models like Gemma, Phi-3, which are lightweight and efficient.

As the AI world changes, the question isn’t can open source models compete anymore, it’s how far they can go?

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