Generative AI | Stefano Blando

Lightweight Fine-Tuning with PEFT & LoRA

Wed, 20 Nov 2024 00:00:00 +0000

This project focuses on a practical question in NLP: how much useful adaptation can you obtain from a pretrained model without paying the full cost of fine-tuning everything?

Using LoRA on distilbert-base-uncased for sentiment analysis, the pipeline shows that a very small trainable subset of parameters can still deliver a strong performance jump over the zero-shot baseline. That makes the project less about squeezing out maximum benchmark accuracy and more about understanding the trade-off between performance and efficiency.

Built with the Hugging Face ecosystem, the implementation covers evaluation, LoRA configuration, training, and inference in a lightweight setup that remains accessible on modest hardware.

AI Photo Editor with SAM & SDXL

Sun, 10 Mar 2024 00:00:00 +0000

This project explores the intersection of precise computer vision and generative image editing by combining Segment Anything (SAM) with Stable Diffusion XL.

The core idea is straightforward: segmentation provides exact control over what should be changed, while diffusion-based inpainting provides the generative flexibility to actually change it. That makes the system useful not only as a demo, but as a concrete example of how discriminative and generative models can be combined inside the same workflow.

Built in Python with PyTorch, Diffusers, and Gradio, the project supports interactive masking, object replacement, and background generation while keeping the pipeline lightweight enough to run on consumer hardware with the right optimizations.

Custom Chatbot with RAG

Thu, 15 Feb 2024 00:00:00 +0000

This project uses a deliberately small but structured domain to explore a larger idea: how to make language-model outputs more reliable by grounding them in retrieved context.

The chatbot is built around a curated dataset of fictional characters and uses a full RAG pipeline with embeddings, retrieval, and prompt conditioning. The dataset is playful, but the methodological point is serious: retrieval changes the behavior of the model from generic completion to context-bounded reasoning.

Because the underlying data is semantically rich, the system can handle not only question answering but also character comparison, recommendation, and trait-based exploration. That makes it a useful compact example of retrieval-driven NLP design.