wassname
b80d7778af
Replace antipasto's rotation/Cayley with a bounded 1+ELU gain and split the
S-space idea into four interpretable PiSSA-style cores (frozen U/S/Vh, small
trainable core):
- antipasto: S_eff = S*(1+ELU(coeff*g)). exp-bounded attenuation, linear
amplification (constant gradient, no runaway). g=0 -> exact identity.
- antipasto_rot: keeps the block-Cayley rotation as a separate variant for
cost comparison (its per-forward solve is the 72ms vs 36ms gap).
- antipasto_ablate: contractive (I - a c c^T) diag(S), eigenvalues in [0,1],
cannot blow up. Optional cov_orient (CorDA) basis.
- antipasto_corda: covariance-oriented oblique projector P = Vh C^{-1/2}, the
data-energy basis rather than the weight-gain basis. 1+ELU gain.
Add scripts/_cost.py + scripts/cost_report.py: one-row-per-variant cost table
(trainable params, peak GPU mem, fwd/bwd ms, added MACs/tok, group_init ms).
Wire all four into the benchmark, smoke test, and __init__ exports.
External review (DeepSeek-v4-pro, docs/reviews/) verified the math; acted on
its one real point (corda g now inits to zeros for exact identity).
Co-Authored-By: Claudypoo <noreply@anthropic.com>
lora-lite
Hackable PyTorch adapters for LoRA-family and small PEFT experiments.
Hackable code
To keep it simple and hackable we make these choices:
- Simple forward hooks, no module replacement or custom modules.
- Simple code over fast performance
- No merge/unmerge
- Single test where we train on MetaMathQA and test on GSM8K for each variant
Take a look at lora.py
Install
pip install -e git+https://github.com/wassname/lora-lite.git#egg=lora-lite
Quickstart
import torch, lora_lite as ll
model = MyTransformer()
cfg = ll.LoRAConfig(r=8, alpha=16, dtype=torch.bfloat16)
ll.attach(model, cfg)
opt = torch.optim.AdamW([p for p in model.parameters() if p.requires_grad], lr=1e-4)
# train...
ll.save(model, "adapter.safetensors")
ll.detach(model)
ll.load(model, "adapter.safetensors")
Does it work?
just check # pytest + smoke + package build + metadata check
just bnb-smoke # required CUDA bitsandbytes 4bit/8bit smoke
just qwen-probe # Qwen/Qwen3-0.6B train/save-load probe
Variants
| Variant | 4bit/8bit | GSM8K % | Params | Peak GPU (GB) |
|---|---|---|---|---|
| LoRA | yes | 63.2% | 4.59M | 11.3 |
| PiSSA | no | 63.2% | 4.59M | 11.3 |
| DoRA | no | 62.4% | 4.67M | 11.3 |
| DeLoRA | yes | 61.5% | 4.59M | 11.3 |
| AntiPaSTO | no | 61.4% | 35.8K | 11.5 |
| IA3-FF | yes | 61.4% | 86K | 11.4 |
| EVA | no | 60.3% | 4.59M | 11.3 |
| IA3 | yes | 60.0% | 57K | 11.4 |
| HRA | yes | 61.6% | 1.84M | 11.3 |
Params = trainable adapter params. Peak GPU = peak CUDA memory during train+eval (logged from this run onward; older runs predate the column).
Setup: Qwen3-0.6B-Base, MetaMathQA train (5k steps, batch 4 = 20k samples unless noted), r=32, all q/v targets, GSM8K test (1319 examples). HRA used batch 2 (10k samples) due to memory. AntiPaSTO used r=256 (default for this variant).
Reference: PEFT reports LoRA at 49.0% on Llama-3.2-3B (different model, different sample count). Our numbers are not directly comparable but suggest the adapters work.
AntiPaSTO at 59.5% with 4.5K trainable params (1000x fewer than LoRA's 4.59M). It trains singular-value deltas + block-Cayley rotation within the SVD subspace, so it can rescale and reorient existing directions but not create new ones. Higher rank (r>32) or data-driven dimension selection (from antipasto3) may close the gap further.
Developer docs
See docs/developer_guide.md for the variant API, data-calibrated init, and save/load format.
Citation
@misc{wassname2026loralite,
title = {LoRA-Lite: A Hackable Adapter Library for Research},
author = {Michael J. Clark},
year = {2026},
url = {https://github.com/wassname/lora-lite/}
}