README.md

---
license: mit
datasets:
- glue
language:
- en
metrics:
- accuracy
- f1
- spearmanr
- pearsonr
- matthews_correlation
base_model: google-bert/bert-base-uncased
pipeline_tag: text-classification
tags:
- adapter
- low-rank
- fine-tuning
- LoRA
- DiffLoRA
eval_results: "Refer to GLUE experiments in the examples folder"
view_doc: "https://huggingface.co/nozomuteruyo14/Diff_LoRA"
---

# Model Card for DiffLoRA

<!-- Provide a quick summary of what the model is/does. -->

DiffLoRA is an innovative adapter architecture that extends conventional low-rank adaptation (LoRA) by fine-tuning a pre-trained large-scale model using differential low-rank matrices. Instead of updating all model parameters, DiffLoRA updates only a small set of low-rank matrices, which allows for efficient fine-tuning with reduced trainable parameters.

## Model Details

### Model Description

DiffLoRA is an original method developed by the author and is inspired by the conceptual ideas from the Differential Transformer paper (https://arxiv.org/abs/2410.05258). It decomposes the weight update into two components—positive and negative contributions—enabling a more fine-grained adjustment than traditional LoRA. The output of a single layer is computed as:

$$
y = W x + \Delta y
$$


- \(x \in \mathbb{R}^{d_{in}}\) is the input vector (or each sample in a batch).  
- \(W \in \mathbb{R}^{d_{out} \times d_{in}}\) is the fixed pre-trained weight matrix.  
- \(\Delta y\) is the differential update computed as:


$$
\Delta y = \frac{\alpha}{r} \Big( x' A_{\text{pos}} B_{\text{pos}} - \tau \, x' A_{\text{neg}} B_{\text{neg}} \Big)
$$

with:  
- $x'$ being the input after dropout (or another regularization).  
- $A_{\text{pos}} \in \mathbb{R}^{d_{in} \times r}$ and $B_{\text{pos}} \in \mathbb{R}^{r \times d_{out}}$ capturing the positive contribution.  
- $A_{\text{neg}} \in \mathbb{R}^{d_{in} \times r}$ and $B_{\text{neg}} \in \mathbb{R}^{r \times d_{out}}$ capturing the negative contribution.  
- $\tau \in \mathbb{R}$ is a learnable scalar that balances the two contributions.  
- $\alpha$ is a scaling factor and $r$ is the chosen rank.

For computational efficiency, the two low-rank components are fused via concatenation:

$$
\text{combined\_A} = \big[ A_{\text{pos}}, A_{\text{neg}} \big] \in \mathbb{R}^{d_{in} \times 2r}
$$

$$
\text{combined\_B} = \begin{bmatrix} B_{\text{pos}} \\ -\tau \, B_{\text{neg}} \end{bmatrix} \in \mathbb{R}^{2r \times d_{out}}
$$

The update is then calculated as:

$$
\text{update} = x' \cdot \text{combined\_A} \cdot \text{combined\_B}
$$

resulting in the final output:

$$
y = W x + \frac{\alpha}{r} \, \text{update}.
$$

- **Developed by:** Nozomu Fujisawa in Kondo Lab
- **Model type:** Differential Low-Rank Adapter (DiffLoRA)
- **Language(s) (NLP):** en
- **License:** MIT
- **Finetuned from model [optional]:** bert-base-uncased

### Model Sources

- **Repository:** [https://huggingface.co/nozomuteruyo14/Diff_LoRA](https://huggingface.co/nozomuteruyo14/Diff_LoRA)
- **Paper:** DiffLoRA is inspired by ideas from the Differential Transformer (https://arxiv.org/abs/2410.05258), but it is an original method developed by the author.

## Uses

### Direct Use

DiffLoRA is intended to be integrated as an adapter module into pre-trained transformer models. It allows efficient fine-tuning by updating only a small number of low-rank parameters, making it ideal for scenarios where computational resources are limited.

### Out-of-Scope Use

DiffLoRA is not designed for training models from scratch, nor is it recommended for tasks where full parameter updates are necessary. It is optimized for transformer-based NLP tasks and may not generalize well to non-NLP domains. Also, there are only a limited number of base models that can be used.

## Bias, Risks, and Limitations

While DiffLoRA offers a parameter-efficient fine-tuning approach, it inherits limitations from its base models (e.g., BERT, MiniLM). It may not capture all domain-specific nuances when only a limited number of parameters are updated. Users should carefully evaluate performance and consider potential biases in their applications.

### Recommendations

Users should:
- Experiment with different rank ($r$) and scaling factor ($\alpha$) values.
- Compare DiffLoRA with other adapter techniques.
- Be cautious about over-relying on the adapter when full model adaptation might be necessary.

## How to Get Started with the Model

To integrate DiffLoRA into your fine-tuning workflow, check the example script in the `examples/run_glue_experiment.py` file.

## Training Details

### Training Data

This implementation has been demonstrated on GLUE tasks using the Hugging Face Datasets library.

### Training Procedure

DiffLoRA is applied by freezing the base model weights and updating only the low-rank adapter parameters. The procedure involves:
- Preprocessing text inputs (concatenating multiple text columns if necessary).
- Injecting DiffLoRA adapters into target linear layers.
- Fine-tuning on a downstream task while the base model remains frozen.

#### Training Hyperparameters

- **Training regime:** Fine-tuning with frozen base weights; only adapter parameters are updated.
- **Learning rate:** 2e-5 (example)
- **Batch size:** 32 per device
- **Epochs:** 3 (example)
- **Optimizer:** AdamW with weight decay

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

GLUE validation sets are used for evaluation.

#### Factors

Evaluations are performed across multiple GLUE tasks to ensure comprehensive performance analysis.

#### Metrics

Evaluation metrics include accuracy, F1 score, Pearson correlation, and Spearman correlation, depending on the task.

### Results

For detailed evaluation results, please refer to the GLUE experiment script in the `examples` directory.

#### Summary

DiffLoRA achieves faster convergence and competitive performance on GLUE tasks compared to other parameter-efficient fine-tuning methods.

## Citation

paper: Writing

## Model Card Contact

For any questions regarding this model card, please contact: [nozomu_[email protected]]