Interactive mental-health NLP stack: intent classification and domain-specific responses using PyTorch, Hugging Face Transformers, and TensorFlow/Keras. Fine-tuned LLaMA 3.2 1B with LoRA adapters (including efficient QLoRA via Unsloth) for clinical text generation, alongside BERT-style classifiers for supervised mental-health labeling. Reached 0.79 Macro-F1 on aggregated clinical data while cutting training VRAM by 50%+; built an ETL pipeline over 53,000+ text samples for fine-tuning.
Back to Projects
Machine Learning
Generative AI Mental Health Pipeline (BERT & Llama-3)
Fine-tuned LLaMA 3.2 with LoRA for clinical text generation and BERT classifiers for mental-health labeling. 0.79 Macro-F1, 50%+ VRAM savings via QLoRA.
Technologies Used
PyTorch Hugging Face LLaMA 3.2 LoRA Unsloth TensorFlow