Large Language Models & Famous AI Models

Exploring the revolutionary systems that defined modern AI

What Are Large Language Models?

Large Language Models (LLMs) are deep learning algorithms that can recognize, summarize, translate, predict, and generate text and other content based on knowledge gained from massive datasets. These models have revolutionized natural language processing and become the foundation for most modern AI applications.

The "large" in LLMs refers to both the massive amount of training data (often terabytes of text) and the enormous number of parameters (ranging from billions to trillions). These parameters are the "knobs" that the model adjusts during training to learn patterns in language.

How LLMs Work

1

Tokenization

Text is broken down into tokens (words, parts of words, or characters) that the model can process. Each token is converted to a numerical representation.

2

Embedding

Tokens are transformed into dense vectors in a high-dimensional space, capturing semantic meaning and relationships between words.

3

Transformer Processing

The transformer architecture uses self-attention mechanisms to process all tokens simultaneously, understanding context and relationships across the entire input.

4

Prediction

The model predicts the most likely next token (or tokens) based on the input and learned patterns, generating coherent text output.

Famous Language Models

GPT Series

OpenAI

The Generative Pre-trained Transformer series represents the breakthrough in large language models.

GPT-1 (2018): 110M parameters - First to demonstrate effective unsupervised pre-training
GPT-2 (2019): 1.5B parameters - Showed impressive text generation capabilities
GPT-3 (2020): 175B parameters - Demonstrated few-shot learning at scale
GPT-3.5 / GPT-4 (2023): Multimodal capabilities, improved reasoning, tool use

Impact: Revolutionized AI accessibility and spawned the conversational AI revolution with ChatGPT.

BERT

Google AI

Bidirectional Encoder Representations from Transformers introduced bidirectional context understanding.

BERT (2018): 110M-340M parameters - First bidirectionally trained transformer
RoBERTa (2019): Optimized BERT with more data and training
ALBERT (2019): Lite BERT with parameter reduction techniques

Impact: Established new state-of-the-art in NLP benchmarks, particularly for question answering and sentiment analysis.

Claude

Anthropic

Claude emphasizes safety, helpfulness, and honest responses through Constitutional AI.

Claude 1 (2023): First release, focusing on harmless and helpful AI
Claude 2 (2023): Improved context window (100K tokens)
Claude 3 (2024): Haiku, Sonnet, Opus - Multimodal capabilities

Impact: Pushed AI safety to the forefront, demonstrating that powerful AI can be both capable and aligned.

Llama Series

Meta AI

LLaMA (Large Language Model Meta AI) democratized access to open-source LLMs.

LLaMA (2023): 7B-65B parameters - Released to researchers
LLaMA 2 (2023): Open commercial license, improved performance
LLaMA 3 (2024): 400B+ parameters, multilingual support

Impact: Sparked an open-source AI revolution, enabling researchers and companies to build upon Meta's work.

Gemini

Google DeepMind

Google's flagship multimodal model designed to compete with GPT-4.

Gemini Pro (2023): Competitive with GPT-3.5, available in Bard
Gemini Ultra (2024): Claims to exceed GPT-4 on many benchmarks
Gemini 1.5 (2024): Massive 2M token context window

Impact: Demonstrated Google's AI capabilities and integrated multimodal understanding natively.

Code-Specific Models

Various

Specialized models for code understanding and generation.

Codex (OpenAI): powers GitHub Copilot, trained on public code
CodeLlama (Meta): LLaMA fine-tuned for code generation
StarCoder (BigCode): Open-source model trained on 80+ languages
DeepSeek Coder (2024): Open-source code model with competitive performance

Impact: Transformed software development with AI-assisted coding tools.

Vision and Multimodal Models

DALL-E

OpenAI

Generative image models that create images from text descriptions. DALL-E 2 and 3 demonstrated photorealistic and artistic image generation capabilities.

Midjourney

Independent Research

An independent research lab producing an image generator known for artistic and aesthetic outputs, popular in creative communities.

Stable Diffusion

Stability AI

Open-source image generation model that democratized high-quality image synthesis and spawned countless derivatives and tools.

CLIP

OpenAI

Contrastive Language-Image Pre-training connecting text and images, enabling zero-shot image classification and robust vision representations.

Model Architecture Comparison

Model Parameters Context Training Data Open Source?
GPT-4 ~1.76T (estimated) 128K 13T tokens No
Claude 3 Opus ~2T (estimated) 200K Unknown No
Gemini Ultra ~1.5T (estimated) 2M Unknown No
LLaMA 3 70B 70B 128K 15T tokens Yes
Mixtral 8x7B 12.9B (effective) 32K 12T tokens Yes
DeepSeek-V2 236B total 128K 20T tokens Yes

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