Fine-tune Llama 2 with DPO

dpo is fine, but don't get blinded by the simplicity. it's just a cleaner way to alignment training. if you're fine-tuning llama 2, you're still wrestling with massive VRAM requirements and data quality. dpo just makes the data presentation smoother, which is useful. it doesn't magically fix poor training data or GPU bottlenecks.

we're seeing faster alignment cycles, sure, but the fundamental problem is still data curation and computational cost. don't treat dpo as a silver bullet; it's an optimization layer on an already expensive ML pipeline. we still need serious compute, and that hasn't changed.