Step Into Large Language Model (LLM) Project Overview
Introduction
The "Step Into LLM" project is part of the comprehensive MindSpore technology public course series, designed to equip developers and enthusiasts who are interested in large language models with the necessary knowledge and skills. It offers a thorough exploration of state-of-the-art models, combines theoretical knowledge with hands-on guidance, and promotes open-source sharing and community interaction.
Course Features
- Exploring Trends: This course demystifies trending technologies and models in the field of artificial intelligence, breaking down complex concepts into understandable parts.
- Practical Application: The course is practically oriented, providing a hands-on development experience guided by experts, ensuring learners can apply what they learn in real-world scenarios.
- Expert Insight: Learners gain insights from experts across various fields, offering diverse perspectives and interpretations of the material.
- Open Source and Free: All course materials, including lecture slides and code, are open source and freely accessible, promoting shared learning and collaboration.
- Competition Empowerment: The course is integrated with the ICT competition, offering specializations in large models to enhance competitive skills.
- Series Courses: While the current focus is on large model topics, additional specialized courses are planned for the future.
Registration and Participation
Participants can enroll via the provided registration link. Joining the accompanying QQ group chat is highly recommended, as it serves as the main channel for course notifications and updates.
Course Structure
First Session (Completed) & Second Session (Ongoing)
The first session spanned lectures 1 to 10 and provided foundational knowledge, beginning with the Transformer model and extending through the evolutionary path to ChatGPT. It included hands-on projects where participants built a simplified version of ChatGPT.
During the ongoing second session, lectures 11 and onwards, the course has been enhanced in all aspects. It delves deeper into advanced large model knowledge and expands its scope to cover the full development-to-application lifecycle. The broadened teaching team brings fresh insights, making this session an exciting opportunity for learners to advance their skills.
Notable Lectures from the Course
-
Lecture 1: Transformer: Discussed the theory behind multi-head self-attention and masked self-attention, and introduced machine translation tasks.
-
Lecture 2: BERT: Focused on BERT's model design based on the Transformer encoder and how to fine-tune BERT for downstream tasks.
-
Lecture 3: GPT: Explored GPT's model design, emphasizing next-token prediction and fine-tuning strategies.
-
Lecture 14: Decoding in Text Generation: Explained search and sampling techniques using MindNLP as a case example.
-
Lecture 20: Mixture of Experts (MoE): Covered the evolution of MoE, detailing its implementation and deployment using MindSpore.
Learning Support and Community Interaction
Participants are encouraged to strengthen their preparatory skills in Python and foundational AI and deep learning concepts, particularly natural language processing. Courses are recommended via open-source platforms like MindSpore and OpenI.
Feedback and suggestions are actively sought through the course repository, inviting learners to contribute to course development and improvement. Enthusiasts can submit innovative projects based on the course content to the MindSpore platform.
This comprehensive and interactive course series welcomes all curious minds to engage with cutting-edge topics in large language models and immerse themselves in this rapidly evolving field.