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https://github.com/wassname/lora-lite.git
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wassname
89 lines
2.7 KiB
Markdown
89 lines
2.7 KiB
Markdown
# lora-lite
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Hackable PyTorch adapters for LoRA-family and small PEFT experiments.
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`lora-lite` uses forward hooks instead of module replacement. Adapter parameters are plain `nn.Parameter`s on the target layer, e.g. `model.layers[5].self_attn.q_proj.lora_A`.
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## Install
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```bash
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pip install -e git+https://github.com/wassname/lora-lite.git#egg=lora-lite
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```
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## Quickstart
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```python
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import torch, lora_lite as ll
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model = MyTransformer()
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cfg = ll.LoraLiteConfig(variant="lora", r=8, alpha=16, dtype=torch.bfloat16)
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ll.attach(model, cfg)
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opt = torch.optim.AdamW([p for p in model.parameters() if p.requires_grad], lr=1e-4)
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# train...
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ll.save(model, "adapter.pt")
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ll.detach(model)
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ll.load(model, "adapter.pt")
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```
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## Does it work?
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```bash
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just check # pytest + smoke + package build + metadata check
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just bnb-smoke # required CUDA bitsandbytes 4bit/8bit smoke
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just qwen-probe # Qwen/Qwen3-0.6B train/save-load probe
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```
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See [docs/spec/20260426_lora_lite_plan.md](docs/spec/20260426_lora_lite_plan.md) for verification history and exact results.
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## Variants
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| Variant | Support | Notes |
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| LoRA | yes | additive low-rank adapter |
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| PiSSA | yes, fp only | mutates `weight` into `W_res`; quantized PiSSA intentionally fails |
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| DeLoRA | yes | normalized additive adapter with learned scalar |
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| IA3 | yes | output gate initialized to ones |
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| DoRA | no | next small candidate |
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| SSVD / OFT / HRA / ROAD | no | planned after the hook-only invariant is clear |
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| S-steer / AntiPaSTO | no | should use data-calibrated `group_init`, not plain LoRA tests |
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## Targeting
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By default, `lora-lite` targets linear-like modules with `in_features`, `out_features`, and `weight`, excluding `lm_head` and `embed_tokens`.
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Useful `LoraLiteConfig` fields:
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- `target_roles`: subset of `("reader", "writer", "inner")`; `()` means all.
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- `target_names`: regex includes.
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- `exclude_names`: regex excludes.
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- `layers`: layer indices, matching `.layers.<idx>.` in module names.
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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.
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## Save format
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Adapters are just:
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```python
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torch.save({"cfg": cfg.to_dict(), "state": lora_state_dict}, "adapter.pt")
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```
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`lora_state_dict` contains full-path keys with `"lora_"` in the name. Missing or unexpected adapter keys fail on load.
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## Developer docs
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See [docs/developer_guide.md](docs/developer_guide.md) for the variant API, data-calibrated init, and adapter roadmap.
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## Citation
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```bibtex
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@misc{wassname2026loralite,
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title = {LoRA-Lite: A Hackable Adapter Library for Research},
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author = {Michael J. Clark},
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year = {2026},
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url = {https://github.com/wassname/lora-lite/}
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}
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```
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