Fine-Tune an LLM for Text Classification, Socratic

prerequisites
Intermediate Python • Beginner Hugging Face • Basic NLP (tokenization, text classification)
skills learned
Fine-tuning an LLM for lead classification • Preparing and cleaning real-world marketing text data • Evaluating classification performance
Tobias Zwingmann
1 week · 4-6 hours per week · INTERMEDIATE
Included with a Manning Online subscription
catalog / Data Science / Machine Learning / Natural Language Processing
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Socratic liveProjects pair you with an AI tutor to help you learn by doing, in real time.

X Education gets thousands of potential leads every day—so why do the sales team keep chasing cold leads instead of promising prospects? In this liveProject, your boss has tasked you to solve this problem by creating a powerful lead-scoring model using LLM technology. You’ll take a base purpose model and train it on X Education’s data to become a focused machine lead evaluating machine! Begin by setting up a resource-efficient environment and load the pre-trained model, then transform X Education’s 9,000-lead dataset into tokenized prompts. You’ll fine-tune Llama 3.2 to classify leads for conversion potential, and finally evaluate its performance against a few-shot baseline.

This project is a part of the series Build Your LLM and Fine-Tune it for Real Tasks.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Tobias Zwingmann
Tobias Zwingmann is a leading AI expert and advisor. As Managing Partner at Germany-based RAPYD.AI, he helps companies adopt AI and machine learning faster while achieving meaningful business impact. Leveraging his background as a data scientist and over 15 years of experience at the intersection of business and technology, Tobias has led AI initiatives across a wide range of industries, from early-stage prototyping to full-scale deployment.

prerequisites

This liveProject is for intermediate Python programmers who understand both data processing and machine learning, and are keen to adapt these skills to creating and tuning Large Language Models.


TOOLS
  • Intermediate Python
  • Basics of Google Colab
  • Basics of Pandas
  • Basics of PyTorch
  • Basics of Huggingface
TECHNIQUES
  • Basics of data preprocessing
  • Basics of machine learning
  • Basics of Text Data Handling

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