Differences between LLaMA 3 and 4 - Llama Tutorial

Contents

• Introduction Introduction

  • Developing AI models using LLaMA

    33s
• 1. Introduction to LLaMA 1. Introduction to LLaMA

  • Using LLaMA online

    1m 16s
  • Running LLaMA in a notebook

    5m 21s
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    Accessing LLaMA in an enterprise environment

    1m 50s
• 2. LLaMA Architecture 2. LLaMA Architecture

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    The LLaMA architecture

    2m 56s
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    The LLaMA tokenizer

    3m 35s
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    The LLaMA context window

    2m 40s
  • (Locked)
    Differences between LLaMA 1 and 2

    2m 20s
  • (Locked)
    Differences between LLaMA 2 and 3

    6m 51s
  • (Locked)
    Differences between LLaMA 3 and 4

    7m 5s
• 3. Fine-Tuning LLaMA 3. Fine-Tuning LLaMA

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    Fine-tuning LLaMA with a few examples

    4m 35s
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    Fine-tuning LLaMA and freezing layers

    7m 24s
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    Fine-tuning with LLaMA using LoRa

    6m 6s
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    Reinforcement learning with RLHF and DPO

    5m 49s
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    Fine-tuning larger LLaMA models

    2m 35s
• 4. Serving LLaMA 4. Serving LLaMA

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    Resources required to serve LLaMA

    4m 35s
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    Quantizing LLaMA

    4m 7s
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    Using TGI for serving LLaMA

    2m 40s
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    Using VLLM for serving LLaMA

    5m 27s
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    Using DeepSpeed for serving LLaMA

    4m 13s
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    Explaining LoRA and SLoRA

    1m 59s
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    Using a vendor for serving LLaMA

    3m 16s
• 5. Prompting LLaMA 5. Prompting LLaMA

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    Difference between LLaMA with commercial LLMs

    4m 8s
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    Few shot learning with LLaMA

    5m 7s
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    Chain of thought with LLaMA

    3m 16s
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    Using schemas with LLaMA

    3m 1s
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    Optimizing LLaMA prompts with DSPy

    4m 39s
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    Challenge: Generating product tags

    25s
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    Solution: Generating product tags

    1m
• Conclusion Conclusion

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    Continue your LlaMA AI model development journey

    30s