[Paid] Hands-on AI Workshop: Building and Fine-Tuning Small Language Models

Register now: https://members.research-triangle.ai/events/7

Welcome to an AI fundamentals hands-on training! By the end of this full-day workshop, participants will be able to:

• Describe how small and large language models are structured — tokenization, embeddings, attention, and transformer blocks — and explain where SLMs fit relative to LLMs in scale, cost, and use case.
• Prepare and format their own datasets for each stage of training, from raw pre-training text to instruction pairs and preference data.
• Pre-train a small language model from scratch and interpret what it learns at this stage, along with its limits.
• Apply supervised fine-tuning (SFT) to turn a base model into an instruction-following assistant with a consistent voice and identity.
• Use Direct Preference Optimization (DPO) to align a model's responses with preferred behaviors.
• Fine-tune efficiently with Low-Rank Adaptation (LoRA), and judge when parameter-efficient methods are preferable to full fine-tuning.
• Deploy a small language model locally and on a server, including as an OpenAI-compatible API endpoint.
• Access and integrate their model from applications — including chat and function/tool calling.

Instructor: Dr. Paul Liu
Dr. Liu is a professor at NC State University and Director of the AI Hub for Science. His AI expertise spans LLM fine-tuning, AI agent and RAG system development, and large dataset processing and modeling. Dr. Liu is the author of “How to Build and Fine-Tune a Small Language Model” and “Generative AI for Science”, making him a leading voice in applying AI to scientific domains.

Register now: https://members.research-triangle.ai/events/7