Training LLMs
Goals: Provide a hands-on coding experience for training an LLM from scratch in the cloud. Introduce domain-specific LLMs and guide students on benchmarking their custom LLMs.
As we transition into the practical aspects, the focus shifts to training LLMs in the cloud and the significance of efficient scaling techniques. With a focus on benchmarking LLMs and the strategic application of domain-specific models in various sectors, it provides a meticulous understanding of tools like Deep Lake, their role in refining LLM training, and the importance of dataset curation.
- When to Train an LLM from Scratch: This lesson will help with the decision of when to train an LLM from scratch versus utilizing a pre-trained model and the tradeoffs of using both proprietary and open-source models. Specific references to models like BloombergGPT, FinGPT, and LawGPT will be highlighted, offering real-world context. We also address the ongoing debate about the advantages and challenges of training domain-specific LLMs.
- LLMOps: This lesson touches upon LLMOps, a specialized practice catering to the operational needs of Large Language Models. It is imperative to have dedicated operations for LLMs to streamline deployment, maintenance, and scaling. We will underscore the significance of tools like Weights & Biases in managing and optimizing LLMs, emphasizing their role in modern LLMOps practices.
- Overview of the Training Process: This lesson provides sequential steps essential to an LLM training process. We will gather and refine data, then move to model initialization and set training parameters, often using the Trainer class. The lesson continues with monitoring via an evaluation dataset.
- Deep Lake and Data Loaders: This lesson covers Deep Lake and its affiliated data loaders and their role in the LLM training and finetuning process. We will discuss the utility of these tools and gain insights into how they streamline data handling and model optimization.
- Datasets for Training LLMs: This lesson dives into diverse datasets for LLM training in text and coding. Students learn the intricacies of curating specialized datasets, with an example of storing data in Deep Lake. We emphasize on data quality, referencing the "Textbooks Are All You Need" research.
- Train an LLM in the Cloud: The lesson lays out the process of training LLMs in the cloud using the Hugging Face Accelerate library and Lambda. We offer practical insights into integrating these platforms with hands-on guidance on leveraging data from a Deep Lake dataset.
- Tips for Training LLMs at Scale: This lesson offers students valuable strategies for efficiently scaling LLM training. It emphasizes advanced techniques and optimizations.
- Benchmarking your own LLM: The lesson centers around the importance of benchmarking LLMs. We will discuss tools such as InstructEval and Eleuther’s Language Model Evaluation Harness. We also provide hands-on experience in assessing their LLM's performance against recognized benchmarks.
- Domain-specific LLMs: The lesson covers the strategic use of domain-specific LLMs. We explore scenarios where these specialized models are most effective, with a spotlight on popular instances like FinGPT and BloombergGPT. By analyzing these cases, we will understand the nuances of utilizing domain-specific LLMs to cater to unique industry demands.
Upon completing this comprehensive module, participants have gained insights into the multifaceted landscape of training LLMs. From understanding the tradeoffs between training from scratch versus leveraging pre-existing models to LLMOps, students are well-equipped to benchmark their LLMs effectively and understand the nuanced value of domain-specific models in meeting specific industry requirements. The following section will introduce learners to the complexities that come with finetuning techniques and practical hands-on projects.