copilot
55757e829d
V3 external review (docs/audit/variants_review_v3.md, 97KB) found 3 must-fix bugs. DoRA: bias was being scaled by m/||V|| because we operated on the full base layer output. Now subtract bias before normalization, add back after. Matches peft DoRA exactly (docs/refs/peft_lora_dora.py:157-161). New smoke dora_bias_smoke verifies identity at t=0 with bias=True. EVA load: adapter.load() called attach() which called group_init() which required calibration_data and raised. Added _skip_group_init flag to attach(); load() passes it. EVA group_init still raises loudly when called directly without data. New smoke verifies save+load WITHOUT calibration data on load path. Also tightened EVA error message. Smoke now covers 8 variants + EVA roundtrip + DoRA-bias roundtrip + bnb 4/8-bit. ALL PASS. V3 nice-to-haves (PiSSA scaling, AntiPaSTO init choice, stale GH refs) deferred -- documented as intentional in module docstrings.
lora-lite
Hackable PyTorch adapters for LoRA-family and small PEFT experiments.
lora-lite uses forward hooks instead of module replacement. Adapter parameters are plain nn.Parameters on the target layer, e.g. model.layers[5].self_attn.q_proj.lora_A.
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.LoraLiteConfig(variant="lora", 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.pt")
ll.detach(model)
ll.load(model, "adapter.pt")
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
See docs/spec/20260426_lora_lite_plan.md for verification history and exact results.
Variants
| Variant | Support | Notes |
|---|---|---|
| LoRA | yes | additive low-rank adapter |
| PiSSA | yes, fp only | mutates weight into W_res; quantized PiSSA intentionally fails |
| DeLoRA | yes | normalized additive adapter with learned scalar |
| IA3 | yes | output gate initialized to ones |
| DoRA | yes, fp only | reads dense weight for column-norm; quantized DoRA fails loudly |
| HRA | yes | output-side Householder reflection with identity gate; works on bnb |
| SSVD / OFT / ROAD | no | planned |
| S-steer / AntiPaSTO | no | should use data-calibrated group_init, not plain LoRA tests |
Targeting
By default, lora-lite targets linear-like modules with in_features, out_features, and weight, excluding lm_head and embed_tokens.
Useful LoraLiteConfig fields:
target_roles: subset of("reader", "writer", "inner");()means all.target_names: regex includes.exclude_names: regex excludes.layers: layer indices, matching.layers.<idx>.in module names.
This structural targeting is why LoRA, DeLoRA, and IA3 can run on bnb-style Linear4bit/Linear8bitLt modules. PiSSA is different because it edits the base weight.
Save format
Adapters are just:
torch.save({"cfg": cfg.to_dict(), "state": lora_state_dict}, "adapter.pt")
lora_state_dict contains full-path keys with "lora_" in the name. Missing or unexpected adapter keys fail on load.
Developer docs
See docs/developer_guide.md for the variant API, data-calibrated init, and adapter roadmap.
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/}
}