LoRA Fine-Tuning

Enhance virtual animal models with parameter-efficient fine-tuning

Low-Rank AdaptationPEFT Framework

What is LoRA Fine-Tuning for Animal Models?

Low-Rank Adaptation (LoRA) injects small trainable weight matrices into frozen large language model layers, enabling species-specific and disease-specific customization with only 0.1–1% of the original parameters. This dramatically improves DART prediction accuracy for your specific experimental context without the cost of full fine-tuning.

⚡ 10–100× fewer trainable params🎯 Species-specific adaptation🔬 Disease-context specialization📊 +8–20% accuracy improvement

LoRA Architecture

W' = W₀ + BA

W₀: frozen pre-trained weights

B ∈ ℝ^d×r, A ∈ ℝ^r×k

r ≪ min(d,k) — the rank

Only B and A are trained

Hu et al. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv:2106.09685

Base Model

Training Context

Target Species / Strain

Disease Area

LoRA Hyperparameters

Rank (r)16

4 (minimal)64 (balanced)128 (maximal)

Higher rank = more capacity but more parameters. r=16 is standard for most tasks.

Alpha (α)32

Scaling factor for LoRA updates. α/r controls the effective learning rate of the adapter.

Dropout0.05

Regularization applied to LoRA layers. 0.05–0.1 is typical.

Learning Rate1.0e-4

1e-51e-41e-1

Epochs5

Target Modules

Select which transformer layers to inject LoRA adapters into:

Parameter Efficiency

Base model params7B

LoRA trainable params0.59M

% of total params0.008%

Target modules2

Rankr=16

Alphaα=32