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LLaMa for Developers

via LinkedIn Learning

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Overview

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

Syllabus

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

Taught by

Denys Linkov

Reviews

4.7 rating at LinkedIn Learning based on 84 ratings

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