I am finalising a research paper that relies on large-language-model experiments and I need a Python specialist to turn my methodology into clean, reproducible code. The core help I’m after is coding itself—covering the full pipeline from data preprocessing through model training to final evaluation and visualisation. I need datasets, well-documented Python scripts or notebooks that I can run end-to-end on my own machine (or a Colab instance). Expect to work with common libraries such as pandas, NumPy, PyTorch or TensorFlow, Hugging Face Transformers, plus Matplotlib or Seaborn for charts—use whichever combination best suits the objectives while keeping dependencies manageable. Deliverables • Data preprocessing module that loads the provided datasets, cleans them, applies any necessary tokenisation and splits them into train/validation/test sets. • Training script that fine-tunes the chosen LLMs, saves checkpoints, and logs metrics. • Evaluation and visualisation routines that reproduce the tables, graphs, and statistical comparisons required for the paper’s results section. Code should be readable, modular, and accompanied by concise README instructions so a reviewer can replicate the experiments without guesswork. If something in the methodology seems unclear, flag it early so we can adjust before you invest time writing code. I’m ready to start as soon as I find the right collaborator and will be responsive for quick clarifications.