---
license: mit
---

# Model
The Pandemic PACT Advanced Categorisation Engine (PPACE) is a fine-tuned 8B LLM designed for automatically classifying research abstracts from funded biomedical projects according to WHO-aligned research priorities. Developed as part of the GLOPID-R Pandemic PACT project, PPACE assists in tracking and analysing research funding and clinical evidence for a wide range of diseases with outbreak potential.

The model leverages a human-annotated dataset expanded with rationales generated by a larger LLM. These rationales provide explanations for the chosen labels, enhancing the model's interpretability and accuracy.

# Usage
Todo

# Model Details
PPACE is fine-tuned using Low-Rank Adaptation (LoRA) to ensure efficient training while maintaining high performance. The fine-tuning process involves training the model for 4 epochs on a dataset of 5142 projects, using 8 A100 GPUs with a batch size of 1 per GPU and 4 gradient accumulation steps.

## Hyperparameters

| Hyperparameter            | Value  |
|---------------------------|--------|
| Total Batch Size          | 2      |
| Gradient Accumulation Steps | 4    |
| Learning Rate             | 2e-4   |
| LR Scheduler              | Linear |
| Epochs                    | 2      |
| LoRA Rank                 | 128    |
| LoRA α                    | 256    |
| LoRA Dropout              | 0.05   |