Module 1: Course Introduction

• Overview of course topics and schedule
• Introduction to HuggingFace and transformers
• Discuss how LLMs can enhance enterprise applications

Module 2: Transformers and LLMs

• Introduce and motivate the transformer-style architecture from deep learning first principles
• Understand input-output processing with tokenizers, embeddings, and attention mechanisms

Module 3: Task-Specific Pipelines

• Profile encoder models for different NLP tasks where they are most useful
• Investigate the use of lightweight models for natural language embedding, classification, subsetting, and zero-shot prediction

Module 4: Seq2Seq with Decoders

• Introduce GPT-style decoder models for sequence generation and autoregressive tasks
• Apply encoder-decoder architectures for applications like machine translation and fewshot task completion

Module 5: Multimodal Architectures

• Integrate different data modalities (text, images, audio) into LLM workflows
• Explore multimodal models like CLIP for crossmodal learning, visual language models for image question-answering, and diffusion models for text-guided image generation

Module 6: Scaling Text Generation

• Explore LLM inference challenges and deployment strategies, including optimized server deployments
• Incorporate LLMs into interesting applications that can scale to larger repositories and user bases

Module 7: Orchestration and Agentics

• Introduce LangChain for LLM orchestration and agentic workflows
• Investigate use of agentics and tool-calling for integrating natural language with standard applications and data

Module 8: Final Assessment

• Build an LLM-based application integrating text generation, multimodal learning, and agentic orchestration

Module 9: Review and Wrap-up

• Review key learnings