Project
GPT-2 Fine-Tuning
Tools & technologies
PyTorch
Hugging Face
NLP
Fine-Tuning
PyTorch
Hugging Face
NLP
Fine-Tuning
Teaching a general-purpose model to speak one domain fluently.
An end-to-end fine-tuning pipeline that adapts a pretrained GPT-2 model to a specialized domain, from corpus curation and tokenization through mixed-precision training and evaluation.
Adapted a pretrained GPT-2 large language model to generate high-quality text for a specialized domain by training it on custom datasets. Developed an end-to-end pipeline for domain adaptation using PyTorch and the Hugging Face Transformers framework. Curated and preprocessed custom training corpora, implementing tokenization, dataset construction, and sequence optimization to enable efficient large-scale language model training. Fine-tuned the transformer architecture using supervised learning, mixed-precision optimization, and validation-based checkpointing to improve domain-specific text generation. Evaluated model performance through perplexity analysis and qualitative generation benchmarks, leveraging modern NLP techniques to enhance coherence, contextual understanding, and generation quality.
More Projects