🔥 Want to make your LLMs smarter? Discover the truth about fine-tuning - it's not what most people think! Learn when to use it, when to avoid it, and how to prepare for success with popular tools like Axolotl, Unsloth, and MLX.

🎯 In this video, you'll learn:
• What fine-tuning actually does (hint: it's not about new knowledge)
• The key differences between full fine-tuning, LoRA, and QLoRA
• How to prepare high-quality training data
• Which base model to choose for your project
• Common pitfalls to avoid

⏱️ Timestamps:
00:00 - Intro
00:29 - The first tip
00:44 - The second tip...what it does
01:17 - But wait
02:49 - The next one
03:02 - The approaches
04:33 - The first example
05:04 - The parameters
06:40 - Another tip
07:12 - Second great use case
07:39 - This tip is a mistake to avoid
07:55 - Another mistake to avoid
08:12 - RAG is often better
09:09 - Patterns
09:31 - The most crucial piece
10:13 - Another tip
10:31 - Good Data
11:01 - High quality data
11:34 - What your training data must have
12:29 - Foundations
12:55 - Model size
13:47 - Something often overlooked
14:56 - But what do i DO


💡 Want to implement fine-tuning yourself? Subscribe and hit the notification bell to catch our upcoming tutorials on Axolotl, unsloth, and MLX!

Drop a comment below: Which fine-tuning tool should we cover first? 👇

#MachineLearning #LLM #ArtificialIntelligence

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