Open Position: Undergraduate Summer Research Student

Undergraduate Summer Research Project: Training Large Language Models from Scratch

1. Introduction to Large Language Models (LLMs)

Large Language Models (LLMs) are advanced AI systems designed to understand and generate human-like text. They power applications like chatbots, language translation, summarization, and even creative writing. This project provides students with hands-on experience in training an LLM, focusing on foundational concepts, tools, and the computational resources needed to bring these models to life. If you are interested, please send me email along with your resume and transcript.

2. Project Goals

This project aims to:

  • Provide students with practical knowledge in LLM training, from data preprocessing to resource management.
  • Teach students to use Compute Canada’s high-performance computing resources, including A100 and H100 GPUs.
  • Engage students in exploring methods for training LLM from scratch, handling large datasets, and managing training complexity.

3. Overview of Training Requirements

1. Compute Resources
We will use Compute Canada’s high-performance GPUs (A100 and H100) to handle the computational intensity of training large models. These GPUs are ideal for LLM training as they offer:

  • High memory bandwidth to process large batches and sequences.
  • Support for FP16 (half-precision) and BF16 operations, essential for training efficiency.

2. Model Training Framework

We’ll be using Megatron-LM, a framework developed by NVIDIA specifically for training large models across multiple GPUs. Megatron-LM supports distributed data parallelism and model parallelism, crucial for managing the massive model sizes in LLMs.

3. Storage and Data Pipeline

  • Dataset: Large language datasets like The Pile or Common Crawl will provide the text corpus required to train the model.
  • Data Preprocessing Tools: We’ll use tokenization techniques like Byte Pair Encoding (BPE) and SentencePiece to convert raw text into manageable tokens.
  • High-Speed Storage: SSDs or NVMe are necessary to ensure data is read and written quickly, preventing bottlenecks during training.

4. Training Setup

4.1 Distributed Training

Training an LLM requires multiple GPUs working in tandem:

  • Model Parallelism splits the model across GPUs, allowing each GPU to handle different layers or sections of the model.
  • Data Parallelism divides the data among GPUs, so each GPU processes a unique batch simultaneously.

4.2 Mixed-Precision Training

To efficiently utilize the computational power of the A100 and H100 GPUs, we use mixed-precision (FP16/BF16) training. This technique allows faster computations and reduces memory usage without compromising accuracy.

4.3 Job Scheduling with SLURM

To manage resources effectively on Compute Canada’s clusters, we use SLURM for scheduling. This allows us to allocate GPUs, manage distributed jobs, and track resource usage.

5. Monitoring and Logging

To track the progress of training and monitor resource utilization:

  • TensorBoard or Weights & Biases for visualizing training metrics, including loss, accuracy, and GPU usage.
  • NVIDIA-smi and Prometheus for GPU-specific monitoring.

6. Hyperparameter Tuning

Hyperparameter tuning is an essential part of optimizing LLM performance. Key hyperparameters include:

  • Learning Rate: Determines the step size for each update in training.
  • Batch Size: Affects how much data the model processes at once.
  • Model Size: Refers to the number of layers and attention heads in the model, impacting both performance and computation cost.

7. Checkpointing and Model Management

Checkpointing helps save the model’s progress periodically, ensuring that in case of interruptions, training can resume from a recent checkpoint:

  • Frequency of Checkpoints: Ideally, every few hours or after a set number of steps.
  • Validation Checks: To assess the model’s learning, we periodically evaluate it on a small validation set.

8. Fine-Tuning and Evaluation

Once the model is pre-trained, fine-tuning on a task-specific dataset allows it to specialize in certain tasks. For instance:

  • Text Classification: Fine-tune on a labeled dataset to classify sentiments.
  • Summarization: Fine-tune to produce concise summaries from long texts.

9. Learning Outcomes for Participants

By the end of this project, students will:

  • Understand the computational and data requirements for LLM training.
  • Gain hands-on experience with distributed training and model parallelism using GPUs.
  • Develop skills in managing and optimizing resources for high-performance training.

10. Research Opportunities

Beyond learning practical skills, this project offers insights into advanced areas of AI research, with applications across healthcare, finance, media, and more. Students can explore pathways for internships, graduate research, and industry projects related to large-scale language models and their applications.