[center]
Learn the fundamentals of LLM and LLM Fine tuning. Hands-On sessions to Fine tune OpenAI GPT model with custom data.

What you'll learn:
Explore the fundamentals of LLM and LLM Fine tuning including - Why Fine tuning is needed, How it works, Applications, etc.
Learn the complete workflow, architecture, essential steps involved for Fine tuning Large language models (LLMs).
Different types of LLM Fine tuning techniques including RLHF, PEFT, LoRA, QLoRA, Standard, Sequential and Instructions based LLM Fine tuning.
Get a comprehensive walkthrough of OpenAI Dashboard and Playground to get a holistic understanding of wide range of tools that OpenAI offers for Generative AI.
Prepare the dataset as per OpenAI accepted JSONL format for Fine-tuning GPT models, while also calculating the fine-tuning cost in advance.
Learn step-by-step through Hands-on sessions on how to fine tune OpenAI's GPT model on custom dataset using Python.
Evaluate and compare the fine tune model with the base pre-trained GPT model to to assess improvements in accuracy and performance.
Discover Best practices for LLM fine-tuning.
Requirements:
Basic Python knowledge.
Description:
"Large Language Models (LLMs) have revolutionized the AI industry, providing unprecedented precision that are reshaping industries and expanding the possibilities of artificial intelligence. However the pre-trained LLMs may not always meet the specific requirements of an organization, hence there is always a need to fine tune the LLMs to tailor these models to your unique tasks and requirements."This comprehensive course is designed to equip you with the skills to enhance OpenAI's GPT models capabilities for specialized tasks using Fine tuning technique. It starts with a thorough introduction to LLMs and the critical role of fine-tuning to make the LLM models adapt to your specific data. Dive into Hands-on sessions covering the entire fine-tuning workflow to fine tune OpenAI GPT model. Through practical sessions, you'll step-by-step learn to prepare & format datasets, execute fine tuning processes, and evaluate model outcomes.By the end of this course, you will be proficient in fine tuning the OpenAI's GPT model to meet specific organizational needs, ensuring optimal performance and relevance in real-world applications.____________________________________________________________________________________________What in nutshell is included in the course ?[Theory]We'll start with LLM and LLM Fine tuning's core basics and fundamentals.Discuss Why is Fine tuning needed, How it works, Workflow of Fine tuning and the Steps involved in it.Different types of LLM Fine tuning techniques including RLHF, PEFT, LoRA, QLoRA, Standard, Sequential and Instructions based LLM Fine tuning.Best practices for LLM Fine tuning.[Practicals]Get a detailed walkthrough of OpenAI Dashboard and Playground to get a holistic understanding of wide range of tools that OpenAI offers for Generative AI.Follow the OpenAI Fine tuning workflow in practical sessions covering Exploratory Data Analysis (EDA), Data preprocessing, Data formatting, Creating fine tuning job, Evaluation.Understand the OpenAI specialized JSONL format that it accepts for training & test data, and learn about 3 important roles - System, User, Assistant.Calculate the Token count and Fine tuning cost in advance using Tiktoken library.Gain Hands-on experience in fine tuning OpenAI's GPT model on a custom dataset using Python through step-by-step practical sessions.Assess the accuracy and performance of the fine-tuned model compared to the base pre-trained model to evaluate the impact of fine-tuning.
Who this course is for:
Data scientists who want to learn fundamentals of LLM Fine-tuning.
OpenAI users who want to Fine tune OpenAI GPT models with custom data.
Machine learning engineers who want to enter into LLM domain.
Generative AI engineers.

https://rapidgator.net/file/f5c0a4fd575acd8133e91b18ae82cbe4/LLM_Fine_tuning_Fundamentals___Fine_tune_OpenAI_GPT_model.rar.html