Demystifying Large Language Models (LLMs)
Learn about what LLMs are, how they work and are trained, explore market-leading models, their capabilities, open vs. closed-source options, and how to build one from scratch.
The previous lesson showed how generative AI can create images, music, and code. But have you wondered whatās powering these magical creations? Behind the scenes,Ā large language models (LLMs)Ā do the heavy lifting!
What are LLMs?
Large language models (LLMs) are the foundation for AI to generate text, answer questions, and engage in conversations. They are trained on vast amounts of text data and learn language patterns, allowing them to produce human-like responses.
Fun fact:
Did you know that some LLMs like GPT-4 and Claude have been trained onĀ trillionsĀ of words? Thatās like reading thousands of libraries worth of text!
Letās dive into the fascinating world of LLMs!
How do large language models work?
Think of LLMs as super-smart predictive text systems. They donāt just know the next wordāthey understand context, grammar, and the overall meaning of what theyāre generating. Hereās how it works:
Input: You give the LLM a
prompt a sentence, a question, or an incomplete idea Processing: The model uses patterns learned during training to predict the next word or phrase.
Output: It generates a response that fits naturally with the input, often making it seem like it truly understands the conversation.
LLMs break down the structure of sentences, keep track of topics, and make sense of words in relation to each other. Thatās why they can hold coherent conversations or write essays with surprising accuracy!
Fun fact:
An LLM doesnāt think like humansāitās just really good at figuring out what comes next based on patterns in text. Think of it as a language wizard predicting the future of sentences!
How are large language models trained?
Training an LLM is like teaching it to understand all the information on the Internet. Hereās the process in simple steps:
Collect massive data: LLMs are trained on enormous datasets that include books, articles, websites, and more. This is why they know about so many topics.
Learning patterns: The model studies this data, learning the relationships between words, sentences, and ideas. It looks at grammar, context, and the structure of language.
Fine-tuning: After initial training, LLMs are fine-tuned on more specific tasks, like answering questions or summarizing text. This fine-tuning improves their performance on real-world applications.
During training, large language models (LLMs) utilize a deep learning approach centered around the transformer architecture. This involves a network of interconnected units called attention heads, which mimic how the human brain processes information. By adjusting the connections within these units, the model enhances its ability to predict and generate more accurate and contextually appropriate responses over time.
Key players in the LLM space
Different tech companies are leading the way in developing large language models (LLMs), each offering unique contributions to AI. Hereās an overview of LLMs from major companies:
OpenAI: GPT series
OpenAI is known for its groundbreaking models, particularly theĀ GPT seriesĀ (likeĀ GPT-3Ā andĀ GPT-4). These models are widely used for tasks like content creation, coding, and virtual assistants, making them a cornerstone of generative AI.
Meta: LLaMA
Llama 3 significantly outperforms its predecessor, Llama 2. It features a larger training dataset, more parameters, and supports up to 30 languages with a 128,000-token context length, enhancing its performance in handling complex tasks.
Fun fact: Tiny models, big impact
Small language models (SLMs) are like the mini-mes of AI. Models likeĀ DistilBERTĀ are small enough to run on your smartphone yet powerful enough to handle tasks like summarizing a news article or classifying emailsāall while beingĀ 60% smallerĀ than their bigger counterparts like BERT!
Google: Gemini
Gemini is a multimodal LLM capable of processing text, images, audio, video, and code simultaneously. It aims to surpass existing models like GPT by integrating advanced capabilities from DeepMindās AlphaGo program.
Microsoft: Phi-2
Microsoft has introducedĀ Phi-2, a highly efficient LLM that balances performance and resource usage. Itās designed for real-world applications like text generation and question-answering, making it suitable for various tasks.
Anthropic: Claude 3.5
Anthropic has developed the Claude series of AI assistants, with Claude 3.5 Sonnet being the latest iteration as of October 2024. Claude 3.5 Sonnet introduces a computer use capability, allowing the AI to perform tasks akin to human computer use, such as moving cursors, typing, and browsing the internet. This feature has been adopted by companies like Canva, and DoorDash.
Mistral AI: Mistral
Mistral AI developed efficient open-weight models likeĀ Mistral 7B, which performs well even with fewer parameters than larger models. Their recent model, Pixtral, handles both text and images, making it useful for tasks like image captioning and multimodal content generation.
xAI: Grok
Founded by Elon Musk,Ā xAIĀ focuses on developing AI systems that prioritize human alignment and safety. Its main model,Ā Grok, enhances conversational AI by delivering accurate, context-aware responses while emphasizing ethical considerations.
Discover more about the various types of LLMs through our specialized courses.
Capabilities of LLMs
Large language models (LLMs) are versatile tools capable of generating text, code, and images, as well as answering questions and translating languages. Their capabilities extend to creating speech and videos and engaging in dialogue, making them valuable across various applications in AI.
Multimodal magic:
Vision language models (VLMs) likeĀ CLIPĀ andĀ DALLā¢EĀ bridge the gap between text and images, enabling machines to understand and generate content that combines both, such as creating art from textual descriptions.
Closed-source vs. Open-source LLMs
Closed-source LLMsĀ are proprietary models developed by companies that do not share their source code or training data with the public. For example, OpenAIāsĀ GPT-4Ā is a closed-source model, meaning users can access it through API services but cannot modify or examine the underlying architecture. XAI's Grok is also a closed-source and paid model.
Fun fact: Is ChatGPT a large language model?
Yes!Ā ChatGPTĀ is powered by a large language model (LLM) from theĀ GPT seriesĀ developed by OpenAI. It uses deep learning to understand and generate human-like text, making it capable of holding conversations, answering questions, and even writing stories. So, when you chat with ChatGPT, youāre interacting with an advanced LLM!
In contrast,Ā open-source LLMsĀ allow developers and researchers to access, modify, and distribute the modelās code. A prime example isĀ LLaMAĀ from Meta, which is openly available for experimentation and innovation, encouraging collaboration within the AI community. This open approach often leads to faster advancements and tailored applications in various fields.
Test your knowledge
Imagine you have an LLM trained on a dataset with many grammatical errors. What might happen if you ask it to generate text for a new task?
It will generate flawless text. The model automatically corrects errors.
It may mimic errors. The model might produce text with similar issues due to the training data.
It will become confused. The model cannot generate any meaningful content.
It will always fail the task. LLMs are incapable of adapting.
How to create a large language model from scratch
Creating a large language model (LLM) from scratch involves gathering vast amounts of text data, building a neural network (usually based on transformers), and training it on powerful hardware like GPUs or TPUs. Itās a highly resource-intensive process that requires expertise in machine learning, data handling, and model fine-tuning.
To learn more aboutĀ how LLMs are used and evaluated, check out our course onĀ Ā large language models, where we break down real-world applications and hands-on deployment strategies. For those eager to dive deeper into howĀ LLMs are developed, explore our skill path on Developing Large Language Models , where we guide you through the data collection, model architecture, and training process.
For more hands-on experience, check out these amazing projects: