How I Fine-Tuned an AI Model for Chemical Engineering

Welcome to the new blog. This post demonstrates the MDX setup with headings, code, and quotes.

Why fine-tune for ChemE?

General LLMs don't know domain-specific abbreviations and symbols well enough. Fine-tuning bridges that gap.

# example: simple regressor baseline
import numpy as np
from sklearn.linear_model import LinearRegression
X = np.array([[1, 3], [2, 5], [3, 9]])
y = np.array([2.1, 3.6, 7.3])
model = LinearRegression().fit(X, y)
print(model.coef_)

Fine-tuning is not magic — data quality and evaluation matter most.

Next up

• Dataset curation
• Tokenization strategy
• Evaluation checklist