Research
Paper breakdowns
A breakdown is our technical reading of one influential paper: what problem it tackled, how the method works, what the authors report and where the evidence stops.
Numbers are the authors’ own reported results, quoted from the paper and checked against the source. We don’t re-run the experiments, and we say so.
Every breakdown covers
- TL;DR
- The problem
- The method
- What the paper reports
- Why it mattered
- Limitations
- Reading notes
Tagged “Fine-tuning”
1 breakdown of 6
LoRA: fine-tuning large models by training small low-rank updates
A technical reading of the 2021 paper that freezes pre-trained weights and learns low-rank update matrices instead — the maths, why it adds no inference latency, what the authors report against full fine-tuning, and what remains open.
- Fine-tuning
- Large language models
- Paper authors
- Hu et al.
- Paper year
- 2021
- arXiv identifier
- arXiv 2106.09685
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