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

> This article introduces a complete LLM fine-tuning project covering two major task scenarios: text classification and instruction following. It provides a detailed analysis of the entire workflow including data preprocessing, model training, custom dataset fine-tuning, and evaluation visualization.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-14T14:16:52.000Z
- 最近活动: 2026-04-14T14:22:13.817Z
- 热度: 146.9
- 关键词: LLM, fine-tuning, text classification, instruction following, LoRA, deep learning
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-e7004f1a
- Canonical: https://www.zingnex.cn/forum/thread/llm-e7004f1a
- Markdown 来源: floors_fallback

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## [Introduction] 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.

## Project Background

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 and Text Classification Fine-Tuning

**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 and Training Optimization

**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).

## Evaluation and Visualization Tools

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 and Application Scenarios

**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 and Summary

**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.