LLaMa for Developers

Get an introduction to the architecture, process of fine tuning, deploying, and prompting in the popular open source LLaMa model.

Introduction

• Developing AI models using LLaMA

1. Introduction to LLaMA

• Using LLaMA online
• Running LLaMA in a notebook
• Accessing LLaMA in an enterprise environment

2. LLaMA Architecture

• The LLaMA architecture
• The LLaMA tokenizer
• The LLaMA context window
• Differences between LLaMA 1 and 2

3. Fine-Tuning LLaMA

• Fine-tuning LLaMA with a few examples
• Fine-tuning LLaMA and freezing layers
• Fine-tuning with LLaMA using LoRa
• Reinforcement learning with RLHF and DPO
• Fine-tuning larger LLaMA models

4. Serving LLaMA

• Resources required to serve LLaMA
• Quantizing LLaMA
• Using TGI for serving LLaMA
• Using VLLM for serving LLaMA
• Using DeepSpeed for serving LLaMA
• Explaining LoRA and SLoRA
• Using a vendor for serving LLaMA

5. Prompting LLaMA

• Difference between LLaMA with commercial LLMs
• Few shot learning with LLaMA
• Chain of thought with LLaMA
• Using schemas with LLaMA
• Optimizing LLaMA prompts with DSPy
• Challenge: Generating product tags
• Solution: Generating product tags

Conclusion

• Continue your LlaMA AI model development journey