08 Open AI LLM

-

 OpenAI LLM Families

🔹 1. GPT Series (Core LLMs)

These are the main Generative Pre-trained Transformer (GPT) models:

  • GPT-1 (2018) – First transformer-based LLM from OpenAI
  • GPT-2 (2019) – Much larger, strong text generation
  • GPT-3 (2020) – Massive leap (175B parameters)
  • GPT-3.5 (2022) – Improved GPT-3 (used in early ChatGPT)
  • GPT-4 (2023) – Multimodal (text + image understanding)
  • GPT-4 Turbo (2023–2024) – Faster & cheaper GPT-4 variant
  • GPT-4o (2024) – “Omni” model (text, image, audio)
  • GPT-4o-mini – Smaller, cheaper version of 4o

 

🔹 2. Reasoning Models (Newer Specialized LLMs)

Designed for deeper reasoning and step-by-step thinking:

  • o1 – Advanced reasoning model
  • o1-mini – Lightweight version
  • o3 – Improved reasoning (stronger than o1)
  • o3-mini – Faster/cheaper reasoning model

👉 These are optimized for:

  • Math
  • Coding
  • Complex logic problems

🔹 3. Embedding Models

Used for semantic search, retrieval, and RAG systems:

  • text-embedding-ada-002 (older, widely used)
  • text-embedding-3-small
  • text-embedding-3-large

🔹 4. Older / Legacy Models

These were part of earlier APIs:

  • davinci-002
  • babbage-002
  • curie (deprecated)
  • ada (deprecated)

⚖️ Simple Classification

Type

Models

General LLMs

GPT-1 → GPT-4o

Reasoning-focused

o1, o3 series

Embeddings

text-embedding-*

Legacy

davinci, curie, ada

🧠 Key Insight

  • GPT models → General-purpose (chat, coding, writing)
  • o-series → Deep reasoning (like “thinking models”)
  • Embeddings → Power search & retrieval systems

⚠️ Important Note

OpenAI evolves models frequently, so:

  • Some older models are deprecated
  • Newer models (like GPT-4o, o3) are current and preferred

 

Comments

Post a Comment

Popular posts from this blog

19 Google ADK Tutorial

-
Image
  Google ADK Tutorial 1️ ⃣ What is Google ADK? Simple explanation: Google ADK (Agent Development Kit) is a framework to build smart AI agents that can: Work together (multi-agent systems) Call tools or APIs (like databases, web services) Evaluate their own outputs Be deployed into production easily Why use it: Easier to scale complex tasks Modular: break jobs into smaller specialized agents Built-in evaluation avoids hallucinations           2️ ⃣ Core Concepts Concept Simple Explanation Agent A single AI “worker” that can perform a task Multi-Agent System Multiple agents working together on one problem Orchestrator Brain that tells agents when to act and in what order Tools / MCP External functions your agent can call (like a skill database or API) Evaluation Bu...
Read more

15 Agent AI vs Agentic AI

-
Image
 Agent AI vs Agentic AI The difference between Agent AI and Agentic AI is subtle but important—it’s mostly about capabilities, autonomy, and how they interact with goals . Let’s break it down clearly: 1. Agent AI Definition: An AI system designed to perform tasks or act in an environment based on instructions or pre-programmed rules. Characteristics: Usually goal-directed but limited in autonomy. Operates within a constrained environment . Often relies on human input to decide what actions to take next. Examples: A chatbot that answers FAQs. A recommendation engine that suggests movies based on user history. A robot vacuum following pre-set rules to clean rooms. Key Point: It’s “an agent” in the sense that it can take actions, but it doesn’t really plan or self-direct beyond its instructions .   2. Agentic AI Definition: An AI system that demonstrates agency , ...
Read more

16 Build a Free Chat App on Google Colab using RAG

-
 16 Build a Free Chat App on Google Colab using RAG Note: This code has been corrected and the model used is not the best model. The model selection will depend on colab GPU and that can be explored and changed but this is a good starting point nonetheless to understand how things really work.  🔹 Step 1: Install Dependencies 👉 Paste this in a Colab cell: ! pip install - q transformers accelerate sentence - transformers faiss - cpu gradio pypdf 🔹 Step 2: Import Libraries from transformers import AutoTokenizer , AutoModelForCausalLM , pipeline from sentence_transformers import SentenceTransformer import faiss import numpy as np import gradio as gr from pypdf import PdfReader 🔹 Step 3: Load Free LLM (Lightweight) model_name = "google/flan-t5-base" tokenizer = AutoTokenizer . from_pretrained( model_name ) model = AutoModelForCausalLM . from_pretrained( model_name ) generator = pipeline ( "text2text-generation" , model = mode...
Read more