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Fine-tuning a 175B-parameter model means storing another 175B-parameter copy for every task. LoRA (Hu et al., 2021, arXiv:2106.09685) avoids that with one observation: the change a model needs for a new task can be described by far fewer numbers than the model itself.
The idea
Freeze the pretrained weight matrix W. Instead of learning a full update ΔW (d × d), learn two thin matrices, B (d × r) and A (r × d), with a small rank r, and use
h = W x + B A x
Only A and B are trained. With d = 12,288 (GPT-3) and r = 8, that is about 197k numbers per matrix instead of about 151M.
Why it works
The paper's experiments suggest that the weight updates from adaptation are low-rank: most of what fine-tuning changes lies in a few directions. Even r = 1 or 2 did surprisingly well on some tasks when applied to the attention query and value matrices.
What it buys you
On GPT-3 175B, the paper reports:
- About 10,000x fewer trainable parameters than full fine-tuning with Adam.
- About 3x less GPU memory during training.
- No extra inference latency, because BA can be merged back into W after training (adapters can't do this).
- Quality on par with or better than full fine-tuning on RoBERTa, DeBERTa, GPT-2 and GPT-3, e.g. 74.0% on WikiSQL and 91.7% on MultiNLI.
Practical notes
- B is initialised to zero, so training starts from exactly the pretrained model.
- The update is scaled by alpha / r, so changing r doesn't force you to retune the learning rate much.
- Swapping tasks means swapping a few megabytes of A and B, not the whole model.
Explore it interactively
We built an interactive explainer of the paper, free with no sign-up: https://flashpapers.ai/p/axwndcargc?utm_source=devto&utm_campaign=devto_flash
If you spot an error, or want a paper explained next, tell us in the comments.
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