peft/pr_3293/en/package_reference/hira.md

HiRA

High-Rank Adaptation (HiRA) is a PEFT method that extends the LoRA approach by applying an element-wise modulation on the original weight matrix. Instead of adding a low-rank update directly, HiRA computes:

$$W^{\mathrm{\prime }}=W_0+W_0\odot (BA)W\text{'}\; =\; W\_0\; +\; W\_0\; \backslash odot\; (B\; A)$$

where $W_0$ is the base weight, and $A, B$ are low-rank factors with rank $r \ll \min( \text{in_features}, \text{out_features})$. This formulation allows HiRA to adapt existing weights with a multiplicative, input-dependent modulation, often improving fine-tuning efficiency on downstream tasks.

The abstract from the HiRA paper is:

We propose Hadamard High-Rank Adaptation (HiRA), a parameter-efficient fine-tuning (PEFT) method that enhances the adaptability of Large Language Models (LLMs). While Low-rank Adaptation (LoRA) is widely used to reduce resource demands, its low-rank updates may limit its expressiveness for new tasks. HiRA addresses this by using a Hadamard product to retain high-rank update parameters, improving the model capacity. Empirically, HiRA outperforms LoRA and its variants on several tasks, with extensive ablation studies validating its effectiveness.

Examples

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import get_peft_model
from peft.tuners.hira import HiraConfig

# Example 1: HiRA on opt-125m for causal language modeling
model_id = "facebook/opt-125m"
base_model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Define HiRA configuration: apply to the MLP dense layers in each transformer block
hira_config = HiraConfig(
    r=32,
    target_modules=["k_proj", "q_proj", "v_proj", "fc1", "fc2"],
    hira_dropout=0.0,
    init_weights=True,
)
peft_model = get_peft_model(base_model, hira_config)

peft_model.print_trainable_parameters()
# trainable params: 4,718,592 || all params: 129,957,888 || trainable%: 3.6309

Benchmark overview