🧑‍💻 Language Model Engineering refers to the evolving set of best practices for training, fine-tuning, and aligning LLMs to optimize their use and function to balance performance with efficiency.

These best practices have formed the basis for the LLMs, or Large Language Models (a.k.a. Foundation Models) and Small Language Models (SLMs) of today.

🤖 Whether you’re looking at OpenAI’s GPT series, Anthropic Claude, Grok, Google Gemini, Mistral, or any other model provider, the core underlying transformer architectures are similar, as are the training and tuning methods.

When chasing after high scores on LLM benchmarks or creating state-of-the-art SLMs, techniques like Model Merging and Distillation are important as well.

🏫 This course will provide you with the foundational concepts and code to train, fine-tune, and align state-of-the-art SLMs and LLMs using industry-standard and emerging approaches from the open-source edge heading into 2025.

🤓 Become the expert in your organization on all things training (pretraining, post-training), fine-tuning (supervised fine-tuning, instruction tuning, chat tuning, etc.), alignment (PPO, DPO, etc.), Small Language Models (SLMs), Model Merging, Distillation, and more!