LLM Fine-Tuning Practice: A Complete Workflow from Text Classification to Instruction Following

The open-source project introduced in this article provides a complete workflow for LLM fine-tuning, covering two major scenarios: text classification and instruction following. It includes the entire process of data preprocessing, model training (e.g., LoRA efficient fine-tuning), evaluation visualization, etc., and offers reproducible technical solutions for researchers and engineers.

Fine-tuning of Large Language Models (LLMs) is a key technology to adapt general models to specific tasks, with lower costs compared to training from scratch. This project provides a systematic fine-tuning framework that supports two mainstream application scenarios: text classification and instruction following.

Data Preprocessing: The standardized process includes text cleaning, format conversion, tokenization, and dataset splitting. It supports multiple input formats and automatically handles label encoding and alignment.

Text Classification Fine-Tuning: Fine-tuning the classification head based on pre-trained models, supporting multi-label and hierarchical classification. By freezing the underlying parameters and only training the classification layer, it retains general capabilities while improving classification accuracy.

Instruction Following Fine-Tuning: Supports mainstream instruction formats such as Alpaca and ShareGPT. Uses LoRA to implement efficient parameter fine-tuning, significantly reducing memory usage.

Training Optimization: Integrates LoRA/QLoRA, gradient accumulation, learning rate scheduling (warmup + cosine annealing), and mixed-precision training (FP16/BF16 acceleration).

Comprehensive evaluation is provided after training: automatically calculates accuracy, F1-score, and confusion matrix; supports training curve visualization (loss, learning rate) and comparative analysis of generated results.

Technical Highlights: Modular design (components can be replaced), configuration-driven (YAML for experiment management), multi-model support (compatible with Hugging Face), efficient training (DeepSpeed + FSDP acceleration).

Application Scenarios: Vertical domain adaptation (law/medical/finance), specific task optimization (sentiment analysis/intent recognition), dialogue system construction, multi-language support (low-resource language transfer).

Practical Recommendations: Ensure data quality and perform sufficient cleaning; tune learning rate and batch size; use early stopping and dropout to prevent overfitting; retain an independent test set.

Summary: This project provides a complete reproducible solution for LLM fine-tuning, suitable for research experiments and business implementation. As model scales grow, efficient fine-tuning techniques will become more important.