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  01 - Introduction

   01 - Developing AI models using LLaMA.srt

  02 - 1. Introduction to LLaMA

   01 - Using LLaMA online.srt

   02 - Running LLaMA in a notebook.srt

   03 - Accessing LLaMA in an enterprise environment.srt

  03 - 2. LLaMA Architecture

   01 - The LLaMA architecture.srt

   02 - The LLaMA tokenizer.srt

   03 - The LLaMA context window.srt

   04 - Differences between LLaMA 1 and 2.srt

  04 - 3. Fine-Tuning LLaMA

   01 - Fine-tuning LLaMA with a few examples.srt

   02 - Fine-tuning LLaMA and freezing layers.srt

   03 - Fine-tuning with LLaMA using LoRa.srt

   04 - Reinforcement learning with RLHF and DPO.srt

   05 - Fine-tuning larger LLaMA models.srt

  05 - 4. Serving LLaMA

   01 - Resources required to serve LLaMA.srt

   02 - Quantizing LLaMA.srt

   03 - Using TGI for serving LLaMA.srt

   04 - Using VLLM for serving LLaMA.srt

   06 - Explaining LoRA and SLoRA.mp4

   07 - Using a vendor for serving LLaMA.mp4

  06 - 5. Prompting LLaMA

   01 - Difference between LLaMA with commercial LLMs.srt

   02 - Few shot learning with LLaMA.srt

   03 - Chain of thought with LLaMA.srt

   04 - Using schemas with LLaMA.srt

   05 - Optimizing LLaMA prompts with DSPy.srt

   06 - Challenge Generating product tags.srt

   07 - Solution Generating product tags.srt

  07 - Conclusion

   01 - Continue your LlaMA AI model development journey.srt

  Bonus Resources.txt