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9 / 200AI 开发实战教程，从零到一构建你的 AI 应用。
All200
Beginner32
Intermediate92
Advanced76
All
Prompt Engineering12
RAG Retrieval Augmented17
Agent Development34
Model Fine-tuning21
Deployment and Operations25
AI Application Development40
DeepSeek API16
Multimodal Development6
AI Safety6
Model Inference3
Retrieval Systems1
RAG Retrieval1
Model Architecture3
API Development1
Agent Development10
Model Deployment1
Multimodal1
Model Evaluation2
Tutorial
Date
QLoRA Quantized Fine-Tuning in Practice
Model Fine-tuning
Intermediate
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.
Practical Guide to QLoRA Fine-Tuning
Model Fine-tuning
Intermediate
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.
The Art of Building Fine-Tuning Datasets
Model Fine-tuning
Intermediate
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.
模型后训练
Model Fine-tuning
Intermediate
本书的核心公式是 Agent = LLM + 上下文 + 工具。本章聚焦于优化 LLM 这个“大脑”——先用 Mid-training 补齐目标领域的知识与基础能力，再用 SFT/RL 等后训练方法塑造模型利用上下文和工具的行为。第七章结尾指出，评估体系与仿真环境是后训练的两块基石：评估环境为训练提供练习场，评估指标为…
DeepSeek Model Evaluation and Benchmarking
Model Fine-tuning
Intermediate
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.
Practical Model Quantization and Deployment
Model Fine-tuning
Intermediate
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.
Comprehensive Comparison of LoRA vs QLoRA vs Full Fine-tuning
Model Fine-tuning
Intermediate
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.
Complete Guide to Large Model Evaluation and Benchmarking
Model Fine-tuning
Intermediate
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.
Practical Construction of Instruction Fine-Tuning Data
Model Fine-tuning
Intermediate
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.
