AI 开发 教程
系统化的 AI 开发学习路径。从提示词工程到模型微调，从 RAG 到 Agent 开发，覆盖 AI 应用开发全栈知识。共 142 篇教程，18 个分类，入门到高级全覆盖。
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DeepSeek Model Evaluation and Benchmarking
Complete evaluation guide for DeepSeek models: results on mainstream benchmarks such as MMLU, HumanEval, GSM8K, and MT-Bench. Comprehensive comparison of DeepSeek-V3 vs GPT-4o vs Claude 3.5, including evaluation code and model selection recommendations.
阅读教程 →Practical Guide to QLoRA Fine-Tuning
Full-parameter fine-tuning is expensive; QLoRA uses 4-bit quantization and low-rank adaptation to allow ordinary developers to fine-tune large models on consumer-grade GPUs. This article provides a hands-on guide to the complete QLoRA fine-tuning process from data preparation to model deployment, using DeepSeek as the base model.
阅读教程 →The Art of Building Fine-Tuning Datasets
The upper limit of fine-tuning effectiveness is determined by data quality, not the model or algorithm. This article delves into the methodology for building fine-tuning datasets, covering data sources, quality control, diversity assurance, and data mixing strategies, helping you build high-quality fine-tuning datasets.
阅读教程 →Comprehensive Comparison of LoRA vs QLoRA vs Full Fine-tuning
A comprehensive comparison of LoRA, QLoRA, and full fine-tuning, analyzing from the perspectives of principles, memory usage, training speed, and final performance, to help you make the right technology choice.
阅读教程 →Practical Construction of Instruction Fine-Tuning Data
Data quality determines the upper limit of fine-tuning effectiveness. This article explains the construction methods for instruction fine-tuning data, including data sources, cleaning strategies, quality evaluation, data augmentation, and mixing design.
阅读教程 →Practical Model Quantization and Deployment
Quantization is a key technology to reduce model inference costs. This article systematically explains the principles, toolchains, and deployment practices of model quantization from INT8 to INT4, enabling your model to run efficiently on consumer-grade hardware.
阅读教程 →Complete Guide to Large Model Evaluation and Benchmarking
How to objectively evaluate the capabilities of large models? This article systematically explains mainstream benchmark tests such as MMLU, HumanEval, and C-Eval, as well as evaluation methods like LLM-as-Judge and arena rankings, to help you establish a scientific model evaluation system.
阅读教程 →QLoRA Quantized Fine-Tuning in Practice
QLoRA reduces the memory requirements for fine-tuning large models to the extreme through 4-bit quantization. This article provides an in-depth explanation of QLoRA's technical principles, NF4 quantization, double quantization, and other key technologies, along with complete practical code.
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