Step 10: Compare LoRA vs QLoRA

Now repeat training without quantization:

Remove BitsAndBytesConfig and load in FP16:

model = AutoModelForCausalLM.from_pretrained(    model_name,    torch_dtype=torch.float16,    device_map="auto")

COPY

Code Breakdown

• The only change here is that you load the base model in full FP16 instead of 4-bit.
• You still attach LoRA adapters (Step 4) and train with the same setup (Steps 6–7).
• This gives you a clean A/B comparison:
• QLoRA: lower VRAM, sometimes slightly noisier optimization.
• LoRA (FP16 base): higher VRAM, often a bit more stable.

Train with same LoRA config.

Then compare:

Memory Usage

• QLoRA significantly lower

Training Stability

• FP16 slightly more stable in some cases

Final Quality

• Often very similar for moderate tasks

This experiment teaches you something critical:

QLoRA usually provides 90–99% of full LoRA performance at a fraction of memory cost.