Comprehensive Analysis of LoRA, QLoRA, RLHF, PPO, DPO, and Flash Attention

With the rapid development of large models, there has been significant technological iteration and updates in just a year, from LoRA, QLoRA, AdaLoRa, ZeroQuant, Flash Attention, KTO, distillation techniques to model incremental learning, data processing, and understanding new open-source models, almost every day brings new developments.

As algorithm engineers, do you feel like your learning pace is a bit behind the rapid technological advancements? And do you find that your understanding of these emerging technologies only stays at the application level, without a specific analysis of the underlying principles? If you wish to maintain a competitive edge in the large model race, a deeper understanding of the technology itself may be a necessary option.

In light of these pain points and in response to technological development, Greedy Technology has once again launched the “Large Model Fine-Tuning Algorithm Practical Camp” at this critical moment, allowing participants to fully grasp the mainstream technologies in the field of large models and their essence over a three-month period, significantly saving learning costs.

Below is the 7-stage learning schedule, with core technologies accompanied by representative project explanations. Interested parties are welcome to scan the code for consultation.

Discounts have been applied for users of this account!

The first 20registered students enjoyearly bird benefits!

Please contact the course consultant for inquiries~

Detailed Outline

Stage 1: Fundamentals of Large Models

Chapter 1: Opening Ceremony

• Introduce course objectives, arrangements, and expected outcomes

• Clarify requirements and expectations for students

• Overview of projects and technologies that will be explored in the course

• Discuss the current industry status of large model technologies

• Recommend tools and open-source projects to pay attention to

Chapter 2: How Large Models Are Trained

• Definition and importance of large models

• Development history and key milestones of large models

• Basic concepts of pre-training and fine-tuning

• Pre-training, data processing, fine-tuning, alignment of large models

• Infrastructure and resource requirements for training large models

• Challenges faced and future development directions

Chapter 3: Analysis of Transformer Model Principles (1)

• Basic architecture of the Transformer model

• Principles and computational process of the Self-Attention mechanism

• Design and role of Multi-Head Attention

• Calculation and visualization of attention weights

• Role and advantages of Self-Attention in the model

Chapter 4: Analysis of Transformer Model Principles (2)

• Concept and implementation methods of Positional Encoding

• Rotary Positional Embedding

• BPE tokenizer, SentencePiece Encoding

• Feed-Forward Networks in Transformer

• Principles and importance of Layer Normalization

• Residual connections in the Transformer model

• Structural differences between encoder and decoder

Chapter 5: Analysis of Transformer Model Principles (3)

• Training strategies and optimization methods for Transformers

• Parameter initialization and learning rate scheduling

• Regularization techniques for Transformer models

• Variants and improvements of the Attention mechanism

• Greedy Decoding, Beam-search

• Top-K Sampling, Top-p Sampling

• Source code interpretation of Transformer

Chapter 6: Full Fine-tuning and Efficient Fine-tuning of Transformer Models

• Differences between full fine-tuning and efficient fine-tuning

• Common strategies for fine-tuning Transformer models

• Selecting appropriate fine-tuning tasks and datasets

• Challenges and best practices in fine-tuning

• Standards and tools for evaluating fine-tuning effectiveness

Chapter 7: [Project Practice 1] Fine-tuning Project for Large Model PEFT

• Installation of PEFT

• Usage instructions for PEFT, explanation of core modules

• Techniques for preparing and preprocessing instruction data

• Detailed steps for implementing fine-tuning

• Performance evaluation and analysis of the fine-tuning project

Chapter 8: Analysis of the GPT Model Family

• Development history of the GPT series models

• Analysis of models from GP1 to GPT4, GPT3

• Interpretation of GPT code

• Analysis of the InstructGPT model

• Zero-shot Prompting

• Few-shot Prompting

• Limitations and challenges of GPT models

Chapter 9: Analysis of the LLaMA Model Family

• Features and technological innovations of the LLaMA model

• Principles of the LLaMA model

• Source code interpretation of LLaMA

• Comparison of LLaMA with other large models

• Training and fine-tuning strategies for the LLaMA model

• Future development directions for the LLaMA model

Chapter 10: Analysis of the ChatGLM Model Family

• Architecture and design philosophy of ChatGLM

• Interpretation of the ChatGLM model

• Technical iterations from ChatGLM1 to ChatGLM3

• Advantages and application areas of the ChatGLM model

• Practical guide for fine-tuning and deploying the ChatGLM model

• Evaluation and performance optimization of the ChatGLM model

Chapter 11: Analysis of the Baichuan Model Family

• Overview and core technologies of the Baichuan model

• Principles and source code interpretation of Baichuan

• Comparison of the Baichuan model with other models

• Application of the Baichuan model in specific tasks

• Strategies and techniques for fine-tuning the Baichuan model

• Limitations of the Baichuan model

Stage 2: Instruction Fine-tuning of Large Models – LoRA

Chapter 12: Basics of Instruction Fine-tuning

• Definition and application background of instruction fine-tuning

• Comparison of instruction fine-tuning with traditional fine-tuning

• Importance of instruction fine-tuning in large models

• Overview of the instruction fine-tuning process

• Challenges and strategies in instruction fine-tuning

Chapter 13: Necessary Matrix Knowledge

• Basic concepts of matrices and vectors

• Matrix operations and properties

• Eigenvalues and eigenvectors

• Introduction to matrix decomposition (SVD) techniques

• Application of matrices in the LoRA algorithm

Chapter 14: Analysis of the LoRA Algorithm

• Principles and motivations of the LoRA algorithm

• Low-rank assumptions in LoRA

• Key technical components of LoRA

• Implementation steps of the LoRA algorithm

• Optimization and debugging of the LoRA algorithm

• Source code interpretation of the LoRA algorithm

Chapter 15: Instruction Data Collection and Generation

• Importance and sources of instruction data

• Methods for automated and manual collection of instruction data

• Preprocessing and standardization of instruction data

• Techniques for generating high-quality instruction data

• Maintenance and updating of instruction datasets

• Manual quality assessment and automated quality assessment of instruction data

Chapter 16: [Project Practice 2] Fine-tuning Large Models with Alpaca

• Design and objectives of the Alpaca fine-tuning project

• Preparing instruction data needed for Alpaca fine-tuning

• Detailed steps for implementing Alpaca fine-tuning

• Methods for evaluating the effectiveness of Alpaca fine-tuning

• Analyzing and solving problems encountered in Alpaca fine-tuning

• Interpreting the source code of the Alpaca project

Chapter 17: Analysis of the AdaLoRA Algorithm

• Comparison of AdaLoRA and LoRA

• Significance of dynamically changing matrix weights

• SVD and AdaLoRA

• Training AdaLoRA

• Source code interpretation of AdaLoRA

• Explanation of an AdaLoRA case study

Chapter 18: [Project Practice 3] Fine-tuning Large Models with Vicuna

• Background and application scenarios of the Vicuna fine-tuning project

• Data collection for ShareGPT

• Implementation process and technical details of Vicuna fine-tuning

• Evaluation and analysis of the effects of Vicuna fine-tuning

• Experience summary and outlook based on the Vicuna fine-tuning project

Stage 3: Instruction Fine-tuning of Large Models – Quantization

Chapter 19: Basics of Model Quantization

• Role and principles of quantization in deep learning

• Common quantization techniques and their classifications

• Impact of model quantization on performance and accuracy

• Practical steps and tools for quantization

• Challenges and solutions for model quantization

Chapter 20: Analysis of the QLoRA Algorithm

• Definition and background of the QLoRA algorithm

• Key differences and improvements of QLoRA compared to LoRA

• Detailed implementation process of the QLoRA algorithm

• 4bit NormalFloat, double quantization

• Optimization and debugging techniques for the QLoRA algorithm

• Source code interpretation of QLoRA

Chapter 21: [Project Practice 4] Fine-tuning the LLaMA Model with QLoRA

• Design of the technical solution

• Collection and preprocessing of instruction data

• Fine-tuning the QLoRA large model based on PEFT

• Evaluating the effects after QLoRA fine-tuning

• Analyzing problems encountered during QLoRA fine-tuning and their solutions

Chapter 22: Model Compression Techniques

• Necessity and technical background of model compression

• Overview of common model compression methods

• Relationship between model compression and quantization

• Steps and precautions for implementing model compression

• Latest research progress in model compression techniques

Chapter 23: Exploration of Model Distillation Techniques

• Basic concepts and working principles of model distillation

• Application of model distillation in model optimization

• Comparison and selection of different distillation techniques

• Specific methods for implementing model distillation

• Challenges and solutions faced by model distillation techniques

Chapter 24: Analysis of the ZeroQuant Algorithm

• Basic principles and application background of the ZeroQuant algorithm

• Innovations of ZeroQuant in model quantization

• Key steps and technical requirements for implementing ZeroQuant

• Source code interpretation of ZeroQuant

• Limitations and future directions of ZeroQuant technology

Chapter 25: Analysis of the SmoothQuant Algorithm

• Design philosophy and core technologies of the SmoothQuant algorithm

• Differences between SmoothQuant and traditional quantization methods

• Specific process for implementing the SmoothQuant algorithm

• Source code interpretation of SmoothQuant

• Technical challenges and improvement paths for SmoothQuant

Stage 4: Alignment of Large Models – RLHF

Chapter 26: Overview of the RLHF Algorithm

• Origins and background of RLHF

• Role and importance of RLHF in artificial intelligence

• Advantages of combining reinforcement learning with human feedback

• Main application areas and case studies of RLHF

• From InstructGPT to GPT-4

Chapter 27: Integration of Human Feedback

• Role of human feedback in reinforcement learning

• Different forms of human feedback: annotations, preferences, guidance

• Learning from human feedback: methods and strategies

• Collection and processing of human feedback data

• Challenges and solutions for human feedback reinforcement learning

Chapter 28: Overview of the PPO Algorithm

• Origins and motivations of PPO

• Comparison of PPO with other policy gradient methods

• Core concepts and principles of the algorithm

• Advantages and limitations of PPO

• Application areas and cases of PPO

Chapter 29: Basics of Reinforcement Learning and Data

• Introduction to basic concepts of reinforcement learning

• Role and importance of data in reinforcement learning

• Data structures of states, actions, and rewards

• Methods for data collection, processing, and utilization

• Generating and testing data using simulation environments

Chapter 30: Basics of Policy Optimization

• Introduction to policy gradient methods

• Advantage functions and returns

• Concept and role of baselines

• Cumulative returns and discounted returns

• Trade-offs between exploration and exploitation

Chapter 31: Core Technical Details of PPO

• Objective functions and KL divergence

• Principles of clipping the objective function

• Multiple iterations of optimization strategies

• Generalized Advantage Estimation (GAE)

• Importance sampling and policy updates

Chapter 32: Implementing the PPO Algorithm from Scratch Based on Open Source Large Models

• Building a neural network model

• Implementing the optimization loop of PPO

• Adaptive learning rate adjustment

• Debugging and performance analysis techniques

• Evaluating the aligned large model

Chapter 33: Advanced PPO Techniques and Reinforcement Learning Progressions

• Variants and improvement strategies of PPO

• Handling high-dimensional inputs and model generalization

• PPO applications in multi-agent environments

• Transfer learning and multi-task learning in reinforcement learning

• Safety and interpretability in reinforcement learning

Chapter 34: [Project Practice 5] Fine-tuning Medical Large Models with RLHF

• Project requirement analysis and technical solution design

• Environment setup and task definition

• Collection and preprocessing of alignment data

• Implementing the PPO training process

• Result analysis and performance optimization

Stage 5: Alignment of Large Models – DPO

Chapter 35: Overview of the DPO Algorithm

• Introduction to DPO (Direct Preference Optimization)

• Comparison with the PPO algorithm

• Application scenarios and importance of DPO

• Basic principles and working mechanisms

• Advantages and challenges of the DPO algorithm

Chapter 36: Basics of Ranking and Preferences

• Role of preferences and ranking problems in AI

• Data representation: pairwise comparisons and preference matrices

• Challenges in preference learning

• Evaluation metrics for ranking and preference prediction

• Overview of classic preference learning algorithms

Chapter 37: Core Technical Details of DPO

• Mathematical framework for preference modeling

• Comparison of direct and indirect preference optimization

• Key algorithm components in DPO

• Methods for processing pairwise comparison data

• Loss functions and optimization strategies in DPO

Chapter 38: Implementing the DPO Algorithm from Scratch

• Data organization and preprocessing

• Steps for building a preference learning model

• Using Python to implement a basic DPO model

• Testing DPO performance on benchmarks

• Advantages and disadvantages of DPO

Chapter 39: [Project Practice 6] Application of DPO in Recommendation Systems

• Preference learning in recommendation systems

• Designing DPO-driven recommendation algorithms

• Handling real-time user feedback

• Implementing DPO for fine-tuning recommendation models

• Evaluating the performance of recommendation systems

Chapter 40: Advanced DPO Techniques

• Combining multi-task learning with DPO

• Application of DPO in unsupervised learning

• Deep learning methods and DPO

• Interactive preference learning

• Variants of DPO technology

Stage 6: Other Fine-tuning Techniques for Large Models

Chapter 41: Analysis of the Prefix Tuning Algorithm

• Basic principles of Prefix Tuning

• Key steps for implementing Prefix Tuning

• Source code interpretation of Prefix Tuning

• Comparison of Prefix Tuning with other fine-tuning methods

• Case studies of applying Prefix Tuning in NLP tasks

• Limitations and challenges of Prefix Tuning

Chapter 42: Analysis of the Adaptor Tuning Algorithm

• Basic principles of Adaptor Tuning

• How to insert Adaptor layers in large models

• Advantages and application scenarios of Adaptor Tuning

• Source code interpretation of Adaptor Tuning

• Practical case: Application of Adaptor Tuning in classification tasks

• Efficiency and scalability issues of Adaptor Tuning

Chapter 43: Analysis of the Flash Attention Algorithm

• Design philosophy and algorithm principles of Flash Attention

• Optimizing the attention mechanism in Transformer models

• Role of Flash Attention in improving processing speed and efficiency

• Case analysis of improving large models with Flash Attention

• Challenges and solutions for implementing Flash Attention

Chapter 44: Analysis of Flash Attention 2 Algorithm

• Introduction to differences between Flash Attention 2 and previous versions

• In-depth exploration of technical improvements in Flash Attention 2

• Application examples of Flash Attention 2 in complex task processing

• Evaluating the performance and applicability of Flash Attention 2

• Implementation details and tuning suggestions for Flash Attention 2

Chapter 45: Analysis of the Kahneman-Tversky Optimization (KTO) Algorithm

• Background and theoretical foundation of KTO algorithm

• Application of Kahneman-Tversky optimization in fine-tuning

• Key technical steps for implementing KTO

• Role of KTO in improving decision quality

• Application cases and performance analysis of KTO

Chapter 46: [Project Practice 7] Fine-tuning Large Models with QLoRA and Flash Attention

• Fine-tuning strategy combining QLoRA and Flash Attention

• Task selection and data preparation

• Detailed fine-tuning process: from preprocessing to model evaluation

• Analyzing performance improvements of the model after fine-tuning

• Challenges faced and solutions shared

Stage 7: Incremental Learning of Large Models

Chapter 47: Overview of Incremental Learning for Large Models

• Importance of incremental learning (Continual learning)

• Comparison with traditional zero-training

• Application scenarios of incremental learning

• Task selection and data preparation

• Detailed fine-tuning process: from preprocessing to model evaluation

Chapter 48: Catastrophic Forgetting in Incremental Learning

• What is catastrophic forgetting

• Ideas for solving catastrophic forgetting

• Regularization, dynamic network architecture, meta-learning

• Mixed training of general data and vertical data

• Information analysis in data

• Adjusting learning rates

Chapter 49: Advanced Topics in Incremental Learning

• Application of incremental learning on large-scale datasets

• Multimodal and cross-domain incremental learning

• Adaptive learning and online learning techniques

• Combination of reinforcement learning and incremental learning

• Future directions for incremental learning

Category	Description
Course Format	Online live sessions + Q&A in course study group
Course Schedule	13 live sessions, once a week, each lasting 3-3.5 hours
Course Services	Study group with less than 25 people, teaching assistants for Q&A, ensuring problems are solved quickly Dedicated consulting advisors and class teachers accompanying throughout the course Live explanations and demonstrations throughout + ability to watch course videos repeatedly

Main Instructor

Teacher Zheng

Expert in artificial intelligence and large models

• Postdoctoral researcher in Computer Science and Artificial Intelligence at Tsinghua University
• Long-term engagement in the development and commercialization of dialogue systems and pre-trained language models at large companies
• Mainly engaged in pioneering research and commercialization in natural language processing and dialogue fields
• Published over ten high-level papers in international conferences and journals such as AAAI, NeurIPS, ACM, EMNLP

Li Wenzhe

Founder and CEO of Greedy Technology

Expert in artificial intelligence and large models

• Technical strategy advisor for several listed companies
• Former chief scientist at a fintech unicorn company
• Former chief scientist at a quantitative investment startup
• Former recommendation system engineer at Amazon USA
• Deeply engaged in artificial intelligence for over ten years, trained tens of thousands of AI students

Registration Consultation

Discounts have been applied for users of this account!

The first 20registered students enjoyearly bird benefits!!

Please contact the course consultant for inquiries~