Memory-Efficient AI: How PEFT and PyTorch Enable Accessible LLM Fine-Tuning
summary
Full fine tuning of a 7B model needs roughly 112 GB of GPU RAM and multi‑GB checkpoints per task, while PEFT trains only 0.1–2% of parameters and cuts memory ~3–4× with per‑task checkpoints down to 2–10 MB. LoRA replaces big weight matrices with low‑rank factors (example: 16.7M → ~131k params, ~128× smaller) and generally yields better quality; prefix tuning uses virtual tokens and is even lighter. Implementation relies on PyTorch features like module wrapping, nn.ModuleDict for dynamic adapters, autograd requires_grad=False to avoid optimizer state bloat, and optional custom CUDA extensions for speed.
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chapters (13)
- 0:00Introduction
- 0:53Full tuning costs and limits
- 1:58PEFT with LoRA and prefix tuning
- 2:20LoRA reduces memory with low-rank matrices
- 3:27LoRA configuration keys
- 4:30LoRA architecture and weight merging
- 5:12Prefix tuning memory gains
- 6:39PyTorch’s PEFT role
- 7:21LoRA layer injection wrapper
- 8:03Autograd memory savings
- 9:30Dynamic graphs for multi-task training
- 10:12PEFT setup with PyTorch
- 11:22Summary
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