Programming Tutorials

  You just pulled the Llama 3 8B weights. You have a respectable GPU rig—maybe an RTX 4090 or an A100—and a clean dataset. You fire up your training script, expecting a smooth QLoRA run. Instead, you're hit with a   CUDA out of memory   error before the first epoch completes, or worse, your training loss suddenly creates a   NaN   crater. Even if training succeeds, inference might output repetitive gibberish due to invisible tokenizer conflicts. Llama 3 is a significant architectural step up from Llama 2, but it introduces specific sensitivities regarding tokenization and numerical stability. This guide details exactly how to resolve the three most common blockers in modern fine-tuning pipelines using Unsloth, PyTorch, and QLoRA. 1. Solving "Phantom" CUDA OOM Errors Many engineers encounter OOM (Out of Memory) errors even when the calculated model size suggests they have plenty of VRAM headroom. This is rarely a capacity issue; it is usually an allocation effici...