Conclusion

In this final chapter, we wrap up our journey by summarizing what we've learned from testing various AI models. We'll recap how larger models generally perform better in extracting knowledge and how adjusting the model temperature can significantly impact their performance.

We'll take a closer look at two specific models: Llama 3.2 3B and Llama 3.1 8B. While both show promise, we'll discuss why they might not be ready for production yet and highlight their strengths in generating quick responses or crafting detailed explanations.

The chapter also delves into the pros and cons of each model, such as the tendency to include internal knowledge which can sometimes lead to inaccuracies. We’ll explore how these models handle context creation and provide insights on refining prompts to enhance performance.

Finally, we'll make a decision about choosing between Llama and Qwen Coder based on specific criteria like user preferences for concise responses and the model's ability to generate code effectively. This chapter invites you to reflect on your own needs and consider which model might be best suited for your projects.