Simple Explaination-What is GPT?

GPT refers to-Generative Pretrained Transformers

LLM refers to Large Language Models.

Key points about GPTs:

Generative: They can create new text formats, such as summaries, translations, scripts, code, musical pieces, emails, letters, etc.

Pre-trained: They are trained on massive amounts of text data, allowing them to learn patterns and relationships between words and sentences.

Transformer: They use a transformer architecture, which is particularly effective at processing and understanding sequential data like text.

LLMs are a type of artificial intelligence (AI) that can understand, process, and generate human-like text.

They are trained on massive amounts of text data, allowing them to learn patterns and relationships between words and sentences.

Key characteristics of LLMs:

Understanding: They can comprehend the meaning of text, including context and nuances.

Generation: They can create new text formats, such as summaries, translations, scripts, code, musical pieces, email, letters, etc.

Learning: They can continuously learn and improve their abilities through exposure to more data.

How do they work? LLMs are built upon neural networks, a type of machine learning model inspired by the human brain. These networks process information in layers, gradually learning complex patterns.

Examples of LLMs:

GPT-3 (OpenAI)

LaMDA (Google AI)

Bard (Google AI)

Jurassic-1 Jumbo (AI21 Labs)

Applications of LLMs:

Customer service chatbots

Language translation

Content generation

Summarization

Code generation

All AI tools which are used commonly are

Open-Source LLMs

GPT-J-6B: A powerful open-source model capable of generating human-quality text.

Llama: Developed by Meta AI, Llama is another strong open-source LLM with various sizes.

Falcon: This open-source model from TII has shown impressive performance in various benchmarks.

Proprietary LLMs

GPT-3 and GPT-4: Developed by OpenAI, these models are renowned for their ability to generate human-quality text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

LaMDA: Google AI's language model for dialogue applications, designed to engage in conversation with users.

Bard: Google's large language model chatbot, built on LaMDA, designed to be informative and comprehensive.

Jurassic-1 Jumbo: Developed by AI21 Labs, this model focuses on generating human-quality text.

PaLM: Google's Pathways Language Model, capable of handling a wide range of tasks.

LLM’s are special transformers which are called GPT

LLM’s are neural networks which contain billions of Digital Neurons.

Biological Neuron

Biological neurons are the fundamental building blocks of the human nervous system. These specialized cells receive, process, and transmit information through electrical and chemical signals. They consist of three main parts: the dendrites, which receive signals; the cell body, where information is processed; and the axon, which transmits signals to other neurons. This intricate network of neurons underlies complex cognitive functions, from sensation and movement to thought and emotion.

Digital Neuron

Digital neurons, or artificial neurons, are the computational units inspired by their biological counterparts.

These mathematical functions process and transmit information within artificial neural networks. Unlike biological neurons, they operate on numerical data and do not have physical form. Digital neurons learn from data and can be adjusted to improve their performance over time, enabling complex tasks like image recognition, natural language processing, and decision making.

Tokenization

Tokenization is a process of breaking down the sentence into smaller units called token.which can be words ,phrases and characters.

Every LLM has its own vocabulary any word outside of its vocabulary cannot be understood by him.

Every LLM has its own capacity of input tokens

GPT- 3 has 4k input tokens capacity

GPT- 4 has 32k tokens of input capacity

If a LLM has given a command of more than its capacity it will fail to understand and fail to give proper answer.

LLM

Approximate Token Limit

GPT-3

2048 - 4096 tokens

GPT-4

Higher than GPT-3 (exact limit undisclosed)

Jurassic-1 Jumbo

Around 4096 tokens

LaMDA

Similar to GPT-3 range

Important Notes:

These values are approximations and can vary depending on the specific model version and implementation.

Newer models often have higher token limits than older ones.

Some LLMs offer different token limits for different tasks or use cases.

Keep following for more information.