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+--- Thread: Custom Fine-Tuning GPT-2 & StarCoder in PyTorch for Chatbots (/Thread-Custom-Fine-Tuning-GPT-2-StarCoder-in-PyTorch-for-Chatbots--488394)
Custom Fine-Tuning GPT-2 & StarCoder in PyTorch for Chatbots - BaDshaH - 07-25-2024
![[Image: e6ada747e4f63651d59e20acfa5d5f77.jpg]](https://i123.fastpic.org/big/2024/0725/77/e6ada747e4f63651d59e20acfa5d5f77.jpg)
Published 6/2024
Created by Shadi Ghaith
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 17 Lectures ( 2h 15m ) | Size: 1.1 GB
Use basic PyTorch training loop to fine-tune pretrained GPT-2 & StarCoder transformers as basic ChatGPT-like assistants.
What you'll learn:
Master basic PyTorch to fine-tune GPT-2 and StarCoder 2 for chat applications.
Design and prepare custom datasets for training conversational models.
Implement training loops, manage datasets, and optimize model performance.
Utilize trained models to generate real-time, context-aware dialogues.
Apply fine-tuning techniques across multiple domains and models.
Requirements:
Basic Knowledge of Python: Students should be comfortable with Python programming basics, as the course involves scripting in Python using PyTorch.
Understanding of Machine Learning Basics: A fundamental understanding of machine learning concepts like models, datasets, and training loops is necessary.
Familiarity with PyTorch: Prior experience with PyTorch is required.
No Specialized Hardware Required: While a GPU can speed up training, it is not mandatory as the models can be trained with CPU-only setups.
Description:
Embark on a comprehensive journey into the realm of AI-driven chatbots with our detailed course focused on fine-tuning transformer models like GPT-2 and StarCoder 2 using PyTorch. This course is meticulously designed for both beginners and experienced practitioners who wish to leverage the power of advanced AI models to develop sophisticated chat assistants tailored to a variety of uses, from everyday conversational interfaces to specialized coding assistants.Throughout this course, you will gain hands-on experience with the essentials of transformer technology, starting with the basics of fine-tuning techniques and progressing through the intricate process of preparing custom datasets. You will learn to fine-tune and configure models effectively, ensuring that they can handle real-world conversational flows and engage users with contextually aware interactions. The course also covers the crucial aspects of training loop implementation, optimization of model parameters, and bringing your chatbot to life in a real-time environment.This course is ideally suited for aspiring AI developers, data scientists keen on NLP, software developers looking to integrate AI functionalities into applications, tech educators seeking to expand their academic offerings, and hobbyists passionate about the cutting-edge of technology. By the end of this course, participants will be equipped with the know-how to not only comprehend the functionalities of GPT-2 and StarCoder 2 but to also innovate and implement their own AI chat solutions, pushing the boundaries of what conversational AI can achieve.Join us to transform your understanding of artificial intelligence and take your skills in building and deploying AI-driven chatbots to the next level. Whether you are looking to enhance your professional skills or simply explore a fascinating aspect of AI, this course will provide you with the knowledge and tools necessary to succeed.
Who this course is for:
Data Scientists and Machine Learning Enthusiasts: Professionals who have foundational knowledge in machine learning and wish to expand their expertise into the realm of natural language processing and conversational AI.
Software Developers: Coders and developers who want to integrate AI-driven chat functionalities into their applications and need to understand the underlying technology to customize it effectively.
Students in Computer Science: University or college students studying computer science, artificial intelligence, or related fields who are eager to apply theoretical knowledge in real-world projects, particularly in enhancing user interaction through AI.
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