🚀 Fine-Tuning AI Models for Better Results 🚀
In the ever-evolving world of Artificial Intelligence, mastering the basics is crucial to success. My recent work with Qwen3-VL (8B) highlighted just how pivotal this process is. Here’s a snapshot of my journey:
- Initial Challenge: Discovered that Qwen3-VL generated invalid SVGs 50% of the time.
- Solution Implemented: Conducted a Self-Fine-Tuning (SFT) run.
- Started with 200 examples from Claude.
- Augmented this to a robust dataset of 1,200 entries.
- Results Achieved: Reduced the invalid generation rate to just ~5%.
By refining our models and datasets, we can unlock significant improvements in AI outputs.
🔍 Interested in AI advancements? Dive in and explore how fine-tuning can transform your projects!
💬 Share your thoughts and let’s spark a conversation on optimizing AI performance!
