Introduction to the Transformers-code Project
The "Transformers-code" project serves as the code repository for a course entitled "Hands-On Practice with Transformers." This initiative offers a comprehensive guide designed to help participants gain practical skills and understanding of transformers—a powerful tool in natural language processing. The course is structured in multiple phases, each crafted to provide insights and hands-on experience with different aspects of transformers.
Code Compatibility
The project uses the following technology stack to ensure seamless functionality:
- PyTorch version 2.2.1 with CUDA 11.8 support
- Transformers library version 4.42.4
- PEFT library version 0.11.1
- Datasets library version 2.20.0
- Accelerate library version 0.32.1
- Bitsandbytes library version 0.43.1
- FAISS-CPU version 1.7.4
- Tensorboard version 2.14.0
Course Outline
The course is divided into several key sections, each building on the previous to deliver thorough training on transformers.
Basic Introduction
Participants start with the basics, exploring initial setup and key components of transformers. This phase includes detailed segments on Pipelines, Tokenizers, Models, Datasets, Evaluation, and Trainers, all demonstrated through a simple text classification example.
Practical Exercises
In the practical exercises section, learners delve into real-world applications of transformers in NLP tasks such as:
- Named Entity Recognition
- Machine Reading Comprehension
- Multiple-Choice Tasks
- Text Similarity
- Retrieval-Based Dialogue Bots
- Masked and Causal Language Models
- Text Summarization
- Generative Dialogue Robots
Efficient Fine-Tuning
The course proceeds to efficient fine-tuning strategies, where the PEFT library is central to the learning. Various techniques such as BitFit, Prompt-Tuning, P-Tuning, Prefix-Tuning, LoRA, and IA3 are unpacked in depth.
Low-Precision Training
Low-precision training is explored using the bitsandbytes library, with practical exercises on models like LLaMA2-7B and ChatGLM2-6B. This section includes half-precision, 8-bit, and 4-bit (QLoRA) training methods.
Distributed Training
Finally, distributed training techniques are introduced using the accelerate library, detailing solutions to scale transformers training. This includes integration with DeepSpeed and understanding the principles of distributed data parallelism.
Course Availability
Course materials and video content are published on Bilibili and YouTube. Updates are primarily available on Bilibili, with YouTube updates planned subsequently. Learners can access various modules through platform-specific links provided.
Additional Skills
In addition to the core content, the course offers bonus lessons on skills such as automated hyperparameter tuning of transformers models using Optuna, presenting an opportunity to deepen technical proficiency.
Conclusion
The "Transformers-code" project is a diverse and thorough training program that equips learners with both the theoretical knowledge and practical skills to leverage transformers in a wide array of NLP applications. Whether you're starting with the basics or advancing to distributed and low-precision training, this resource provides the essential guidance and expertise needed in the field of transformers.