Large Language Models (LLMs) have revolutionized artificial intelligence, enabling machines to understand, generate, and manipulate human-like text. For beginners, diving into LLMs can be overwhelming due to the sheer number of models available. To simplify your learning journey, we’ve curated a list of the top 5 LLMs that every beginner should start with in 2025.
These models cover a range of skills—from language understanding to text generation—and provide a solid foundation for working with more advanced AI systems. Let’s explore them one by one.
1. BERT – Mastering Language Understanding
Best for: Learning how transformers process and understand text.
Developed by Google, BERT (Bidirectional Encoder Representations from Transformers) is a groundbreaking model that reads text in both directions (left-to-right and right-to-left), allowing it to grasp context better than previous models.
Why Learn BERT?
✅ Foundation of NLP – BERT’s architecture is the basis for many modern LLMs.
✅ Pre-training & Fine-tuning – Learn how to adapt pre-trained models for specific tasks like sentiment analysis or question answering.
✅ Transformer Insight – Understand the encoder side of transformer models (as opposed to decoders like GPT).
🔗 Use Case: Ideal for search engines, chatbots, and text classification.
2. DistilBERT – Lightweight and Efficient
Best for: Running LLMs on low-resource devices (laptops, edge devices).
A distilled version of BERT, DistilBERT retains 97% of BERT’s performance while being 40% smaller and 60% faster.
Why Learn DistilBERT?
✅ Model Optimization – Learn about knowledge distillation, where a smaller model mimics a larger one.
✅ Efficiency – Perfect for deploying AI applications without heavy computational power.
✅ Real-world Applications – Great for mobile apps, lightweight chatbots, and embedded systems.
🔗 Use Case: Best for developers working with limited hardware.
3. GPT-2 – Your First Step into Text Generation
Best for: Understanding how AI generates human-like text.
Before ChatGPT took the world by storm, GPT-2 (by OpenAI) was the model that showcased the power of generative AI. Unlike BERT, GPT-2 is a decoder-only model, meaning it predicts the next word in a sequence.
Why Learn GPT-2?
✅ Text Generation Basics – Learn next-token prediction, the core mechanism behind chatbots.
✅ Fine-tuning Simplicity – Easier to experiment with than GPT-3 or GPT-4.
✅ Creative Applications – Generate stories, code, poetry, and more.
🔗 Use Case: Great for writing assistants, creative AI tools, and learning generative AI fundamentals.
4. FLAN-T5 – The Multitask Powerhouse
Best for: Learning how LLMs handle multiple tasks efficiently.
FLAN-T5 (Fine-tuned LAnguage Net-T5) is a versatile model fine-tuned on a wide range of tasks, making it excellent for instruction-based learning.
Why Learn FLAN-T5?
✅ Multitask Learning – Works well for summarization, translation, Q&A, and more.
✅ RAG (Retrieval-Augmented Generation) – Learn how to integrate external knowledge into AI responses.
✅ Balanced Performance – More efficient than massive models like GPT-4 but still powerful.
🔗 Use Case: Ideal for AI assistants that need to pull information from documents or databases.
5. LLaMA 2 / LLaMA 3 – Open-Source GPT-Scale Models
Best for: Building full-stack AI projects with open-source models.
Meta’s LLaMA 2 & 3 are open-weight models comparable to GPT-3.5 in capability. Unlike proprietary models, they allow full customization and deployment without restrictions.
Why Learn LLaMA?
✅ Open-Source Advantage – No API limits; full control over fine-tuning.
✅ GPT-Level Performance – Build chatbots, agents, and AI applications at scale.
✅ Community Support – Hugging Face and PyTorch integrations make it beginner-friendly.
🔗 Use Case: Best for developers who want to build and deploy their own AI solutions.
How to Get Started?
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Pick one model (start with BERT or GPT-2).
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Experiment with Hugging Face (a great platform for running pre-trained models).
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Fine-tune on a custom dataset (try a simple task like sentiment analysis).
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Deploy a small project (e.g., a chatbot or text summarizer).
Final Thoughts
Learning LLMs doesn’t have to be intimidating. By starting with these five models—BERT, DistilBERT, GPT-2, FLAN-T5, and LLaMA—you’ll build a strong foundation in natural language processing (NLP) and generative AI.
Which model excites you the most? Let me know in the comments!
🔗 Explore more AI guides at edtechinformative.com
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