PyTorch Implementation of Attention Prompt Tuning: Parameter-Efficient Adaptation of Pre-Trained Models for Action Recognition

This repository is doing the finetuning of the Qwen2 7B VLM for performing VQA (Visual Question Answering) on various kinds of patient radiologies or medical scans.

This repository contains code for implementing the LexGLUE benchmark using two different versions of the BERT architecture. The original BERT model is compared to a modified version that includes bottleneck adapter modules.

Build a production‑grade, modular pipeline for fine‑tuning large language models with LoRA on domain‑specific tasks (e.g., legal QA, medical summarization, financial reasoning).