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# Lily: Low-Rank Interconnected Adaptation across Layers

[Lily](https://huggingface.co/papers/2407.09946) is a parameter-efficient fine-tuning technique that introduces cross-layer weight sharing for adapter matrices. Instead of learning an independent AB pair per layer as in LoRA, Lily uses **locally shared A adapters** (each A is shared across a block of `stride_A` consecutive layers) and **globally shared B experts** (a small pool of `num_B` B adapters is shared across all layers). At each forward pass, a lightweight data-dependent router computes a softmax-weighted combination of the B experts to produce the effective B for that layer and input.

This sharing can reduce the total number of adapter matrices from `2N` (standard LoRA) to `N / stride_A + num_B`, freeing up the parameter budget to use a **much larger rank `r`** — typically `2×`–`4×` what you would use in LoRA. Higher rank and better interconnectivity increase the effective rank of the weight update `ΔW = A × combined_B`, leading to better adaptation performance.

Because the B combination is **data-dependent** (the router weights depend on the input activations at runtime), `merge` and `unmerge` are **not supported**. If weight merging is required for your deployment, consider other methods such as LoRA instead.

Lily currently has the following additional constraints:
- Only `nn.Linear` layers are supported.
- Quantized layers are not supported.

If these constraints don't work for your use case, consider other methods instead.

The abstract from the paper is:

> Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning (PEFT) method that learns weight updates ΔW = AB for pretrained weights W through low-rank adapters A and B. While LoRA ensures hardware efficiency, its low-rank weight updates limit adaptation performance. In this paper, we propose low-rank interconnected adaptation across layers (Lily), a novel PEFT method that introduces an interconnected framework with locally shared A and globally shared B experts. This structure eliminates redundant per-layer AB pairs, enabling higher-rank ΔW with equal or fewer parameters. To enhance expressiveness, we use data-dependent routers to determine A-B interconnections, preventing B experts from converging to the same behavior and improving representational power across domains. Experiments across modalities, architectures, and model sizes demonstrate Lily's superior performance and efficiency.

## Benchmark overview

<iframe
	src="https://peft-internal-testing-peft-method-comparison-embed.hf.space/?highlight[type]=LILY"
	frameborder="0"
	width="850"
	height="1000"
></iframe>

# API

## LilyConfig

[[autodoc]] tuners.lily.config.LilyConfig

## LilyModel

[[autodoc]] tuners.lily.model.LilyModel
