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LLM Ecosystem explained: Your ultimate Guide to AI
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LLM Ecosystem explained: Your ultimate Guide to AI

Introduction

The speaker introduces the topic of large language models and their ecosystem in April 2023.

Large Language Models Ecosystem

  • GPT 3.5 was released in 2022 with two access paths, ChatGPT and OpenAI API.
  • GPT 4 is expected to have about 500 billion trainable parameters and can handle text, images, and code.
  • ChatGPT PLUS costs $20 per month while OpenAI provides an API for professional use.
  • Google's T5 model had a size of 3 billion trainable parameters in 2020.
  • In 2022, Google came out with Flan T5 models that were fine-tuned on hundreds and thousands of tasks.

Fine-Tuning

The speaker explains how fine-tuning works.

Fine-Tuning

  • Fine-tuning involves training a system for one particular task using pre-trained models like GPT-3 or GPT-4.
  • Google discovered fine-tuning and called it Flan T5 models which were fine-tuned on hundreds and thousands of tasks.

Special Data

The speaker discusses special data that are not available on the internet.

Special Data

  • There is special data that companies keep secret such as private medical records or research data.
  • Google found out that fine-tuning can be used for special data that are not available on the internet.

Conclusion

The speaker concludes the talk by summarizing the main points discussed.

Main Points

  • Large language models ecosystem includes GPT 3.5, GPT 4, ChatGPT, and OpenAI API.
  • Fine-tuning involves training a system for one particular task using pre-trained models like GPT-3 or GPT-4.
  • Special data that are not available on the internet can be used with fine-tuning.

Pre-training GPT-4 and In-context Learning

The speaker discusses the hardware configuration required to pre-train GPT-4, the black box nature of the system, and how in-context learning can be used to prompt the system without changing its learnable parameters.

Pre-training GPT-4

  • To pre-train GPT-4, a hardware configuration of 1000 GPUs to 10,000 GPUs is required.

In-context Learning

  • In-context learning is an advanced prompt engineering methodology that involves slicing up a non-public dataset into tiny chunks and feeding them into the system one at a time.
  • This method does not change any of the weights or learnable parameters of the GPT-4 system because they are top secret.
  • In-context learning allows for interaction with a small surface area of the system where input data can be processed and results obtained without training on new data.

Meta LLama Models

The speaker discusses Meta's LLama models, their sizes, availability, and access requirements.

LLama Model Sizes

  • Meta's LLama model comes in four sizes: small, medium, large XL or if you want 7 billion trainable parameter 13 33 and 65 billion trainable parameter.
  • Compared to GPT-4, these models are relatively small but still require significant compute infrastructure.

Access Requirements

  • Access to Meta's LLama model requires filling out a form with personal information such as name, address, email address etc., as well as scientific publications.
  • Without access to the weights of the model provided by Meta themselves, the model is useless.

Risks of Leaking Information

The speaker discusses the risks associated with leaked information about Meta's LLama models.

  • There is a risk associated with using Meta's LLama models due to the fact that they are trained on top-secret data and access to their weights requires personal information.
  • Leaked information about these models could pose a significant risk to scientific communities working with them.

Using Intelligent Data to Train Small AI Models

In this section, the speaker discusses how intelligent data can be used to train small AI models.

Intelligent Data for Training

  • The speaker emphasizes the need for a lot of training data but suggests that intelligently configured data with inherent information can be used instead.
  • Synthetic artificially generated data is created by asking GPT to create thousands of similar instructions based on one human instruction. This self-instructed data is then used as training data for the small model.
  • Stanford University fine-tuned their mini system using 50,000 self-instructed data sets and called it ALPACA.

Fine-Tuning Methods

  • Two options are available for fine-tuning: parameter efficient fine-tuning (PEFT) and classical fine-tuning.
  • Classical fine-tuning involves reducing trainable mathematical objects to less than one percent of what it really is due to limited compute infrastructure.

Classical Fine-Tuning

In this section, the speaker explains classical fine-tuning and how it works.

How Classical Fine-Tuning Works

  • All weights of all tensor operations in all layers of a transformer architecture are set to trainable and will be included in the mathematical update.
  • 100% of tensors and AI is tensor operation, matrix multiplication, will be updated.
  • This method was used by Stanford University for ALPACA after generating synthetic data using GPT-4.

Instruction Fine-Tuning

In this section, the speaker explains instruction fine-tuning and how it can be used for complex data sets.

What is Instruction Fine-Tuning?

  • It is a way to fine-tune models on specific data sets.
  • It can be used for complex data sets that have a hidden pattern structure.
  • It involves feeding information into a highly dedicated system with a strict focus on one or two tasks.

LLM Ecosystem of April 2023

In this section, the speaker provides an overview of the LLM ecosystem as of April 2023.

The LLM Ecosystem

  • The speaker provides an overview of the LLM ecosystem as of April 2023.
  • He explains that companies can invest in fine-tuning for their particular corporate interests and goals.
  • The speaker explains that ALPACA from Stanford University paid $600 for computer infrastructure, but had to pay $500 to generate data and $100 to run it on the supercomputer center by Microsoft.

Specific Intelligence: 5 to 1 in the case of ALPACA

In this section, the speaker discusses specific intelligence and how it relates to ALPACA.

Understanding Specific Intelligence

  • Specific intelligence refers to a person's ability to excel in a particular area or skill.
  • The speaker notes that in the case of ALPACA, their specific intelligence is 5 to 1, meaning they have a high level of expertise in a particular area compared to other areas.

Conclusion and Call for Questions

In this section, the speaker concludes the video and invites viewers to ask questions.

  • The speaker concludes by thanking viewers for watching and sharing their knowledge.
  • Viewers are invited to leave comments with any questions they may have.
Video description

Introduction to the world of LLM (Large Language Models) in April 2023. With detailed explanation of GPT-3.5, GPT-4, T5, Flan-T5 to LLama, Alpaca and KOALA LLM, plus dataset sources and configurations. Including ICL (in-context learning), adapter fine-tuning, PEFT LoRA and classical fine-tuning of LLM explained. When to choose what type of data set for what LLM job? Addendum: Beautiful, new open-source "DOLLY 2.0" LLM was not published at time of recording, therefore a special link to my video explaining DOLLY 2: https://youtu.be/kZazs6V3314 A comprehensive LLM /AI ecosystem is essential for the creation and implementation of sophisticated AI applications. It facilitates the efficient processing of large-scale data, the development of complex machine learning models, and the deployment of intelligent systems capable of performing complex tasks. As the field of AI continues to evolve and expand, the importance of a well-integrated and cohesive AI ecosystem cannot be overstated. A complete overview of today's LLM and how you can train them for your needs. #naturallanguageprocessing #LargeLanguageModels #chatgpttutorial #finetuning #finetune #ai #introduction #overview #chatgpt