模型微调与训练

Fine-Tuning GPT-4o Mini: OpenAI Fine-Tuning API Complete Guide

When and how to fine-tune LLMs for domain-specific tasks

Fine-Tuning LLMs for Domain-Specific Applications

Adapt large language models to your specific use case

Fine-Tuning GPT-4 and Claude: When to Fine-Tune vs RAG 2026

Make the right architectural decision: fine-tuning or RAG for your LLM application

Fine-Tuning LLMs with LoRA and QLoRA: Complete Guide 2026

Train custom AI models from Llama 3 and Mistral using LoRA/QLoRA fine-tuning on a single consumer GPU with less than 24GB VRAM

AI Model Compression: Pruning, Quantization, and Knowledge Distillation

Deploy smaller, faster AI models without sacrificing accuracy

AI Model Quantization (GPTQ, AWQ): Complete Developer Guide 2026

Master AI Model Quantization (GPTQ, AWQ) with practical examples and production patterns

Fine-Tuning LLMs in 2025: When to Do It and How to Do It Right

The practical guide to fine-tuning language models for specific tasks and domains

LLM Fine-Tuning in 2025: When to Fine-Tune vs. RAG vs. Prompting (With Cost Analysis)

Senior AI engineers explain the decision framework for choosing between fine-tuning, RAG, and prompt engineering

Multi-task Fine-tuning: Hands-On Tutorial

Training on multiple tasks simultaneously for generalization — step-by-step implementation guide

Data Synthesis for Fine-tuning: Hands-On Tutorial

Using GPT-4 to generate fine-tuning data synthetically — step-by-step implementation guide

Instruction Fine-tuning: Hands-On Tutorial

Fine-tuning LLMs to follow instructions with supervised learning — step-by-step implementation guide

Continual Learning for LLMs: Hands-On Tutorial

Preventing catastrophic forgetting during fine-tuning — step-by-step implementation guide

Adapters vs LoRA Comparison: Hands-On Tutorial

Comparing adapter-based methods for LLM customization — step-by-step implementation guide

Fine-tuning Llama Models: Hands-On Tutorial

End-to-end Llama 3 fine-tuning with custom datasets — step-by-step implementation guide

Unsloth Fast Fine-tuning: Hands-On Tutorial

2x faster fine-tuning with Unsloth optimization library — step-by-step implementation guide

RLHF Training Pipeline: Hands-On Tutorial

Reward modeling and PPO for RLHF fine-tuning — step-by-step implementation guide

AI Knowledge Distillation Pipeline: Advanced Guide

Distilling large model knowledge into smaller models

Deployment of Fine-tuned Models: Hands-On Tutorial

Serving custom fine-tuned models with vLLM and TGI — step-by-step implementation guide

Fine-tuning Mistral Models: Hands-On Tutorial

Mistral 7B fine-tuning for domain specialization — step-by-step implementation guide

QLoRA: Quantized LoRA: Hands-On Tutorial

Combining quantization with LoRA for 4-bit fine-tuning — step-by-step implementation guide

Llama 3.3 70B Fine-tuning

Fine-tuning Llama 3.3 70B for domain specialization

Fine-tuning Evaluation: Hands-On Tutorial

Evaluating fine-tuned models with domain benchmarks — step-by-step implementation guide

Hugging Face Transformers: Custom Training Pipelines and Advanced Fine-Tuning

Trainer API, custom callbacks, gradient checkpointing, and deployment with Inference Endpoints

Merging Fine-tuned Models: Hands-On Tutorial

Combining multiple LoRA adapters with model merging — step-by-step implementation guide

LLM Fine-tuning with LoRA: Complete Developer Guide 2026

Master LLM Fine-tuning with LoRA with practical examples and production patterns

Dataset Preparation for Fine-tuning: Hands-On Tutorial

Building high-quality fine-tuning datasets from scratch — step-by-step implementation guide

PEFT: Parameter-Efficient Methods: Hands-On Tutorial

Overview of all parameter-efficient fine-tuning approaches — step-by-step implementation guide

AI Model Merging: SLERP, TIES, DARE, and Model Soup Techniques

Combine multiple fine-tuned models without additional training to create superior models

Full Fine-tuning with FSDP: Hands-On Tutorial

Full model fine-tuning using Fully Sharded Data Parallel — step-by-step implementation guide

Fine-Tuning LLMs with LoRA and QLoRA: Complete Practical Guide 2025

Hugging Face PEFT, dataset preparation, training and deployment on consumer hardware

LoRA Fine-tuning Guide: Hands-On Tutorial

Low-Rank Adaptation for efficient LLM fine-tuning — step-by-step implementation guide

Fine-tuning for Code Generation: Hands-On Tutorial

Domain-specific fine-tuning for code completion and generation — step-by-step implementation guide

Hugging Face SFT Trainer: Hands-On Tutorial

Supervised fine-tuning with Hugging Face TRL SFTTrainer — step-by-step implementation guide

DPO: Direct Preference Optimization: Hands-On Tutorial

Simplified alignment using Direct Preference Optimization — step-by-step implementation guide

Fine-tune Llama 3.2 on Your Data

End-to-end Llama 3.2 fine-tuning on custom dataset — hands-on project tutorial

LLM Fine-Tuning for Production: LoRA, QLoRA & RLHF in 2025

Adapt foundation models to your domain efficiently with parameter-efficient fine-tuning techniques

LLM微调实战：LoRA和QLoRA参数高效微调完全指南

用消费级GPU微调大语言模型，实现专业领域定制化

Quantization for Production

Reducing model size and latency through quantization techniques

LLM Inference Optimization: vLLM, TensorRT-LLM & Quantization in 2025

Achieve 10-50x throughput improvements for LLM serving through batching, quantization, and GPU optimization

AI Model Distillation: Technical Deep Dive

How smaller models learn from larger teacher models

Quick Tip: The difference between RAG and fine-tuning (quick guide)

Practical guide to the difference between rag and fine-tuning (quick guide)

LLM Pretraining vs Fine-tuning: Technical Deep Dive

Understanding the two-stage training process for LLMs

Fine-tuning LLMs Best Practices: 2026 Developer Guide

Essential practices every AI developer should follow for fine-tuning llms

RLHF Step-by-Step Guide

Reinforcement Learning from Human Feedback implementation tutorial

Quantization Explained: Technical Deep Dive

INT4/INT8 quantization for faster, smaller model inference

LoRA Mathematics: Technical Deep Dive

The mathematical foundation of Low-Rank Adaptation

How to Fine-tune Llama 3 on Custom Data: Complete Guide for Developers 2026

Build a specialized AI model step by step

Production NER Systems: Fine-Tuning spaCy and Transformers for Custom Entities

Training custom NER models, handling low-resource scenarios, and deployment patterns

Knowledge Distillation: Train Small, Fast AI Models from Large Teacher Models

Task-specific distillation, intermediate layer matching, and deployment tradeoffs

Introduction to Fine-tuning LLMs: Beginner's Complete Guide

Everything a beginner needs to know about introduction to fine-tuning llms

RLHF Explained Simply: Technical Deep Dive

Reinforcement Learning from Human Feedback demystified

AI Recipe: Fine-tune Llama 3.2 in Google Colab

Step-by-step implementation: fine-tune llama 3.2 in google colab

RLHF vs DPO: Training LLMs from Human Feedback - Technical Guide 2025

Reinforcement Learning from Human Feedback, Direct Preference Optimization, and alternatives

Understanding RAG vs Fine-tuning: Beginner's Complete Guide

Everything a beginner needs to know about understanding rag vs fine-tuning

RAG vs Fine-tuning: Side-by-Side Comparison

When to use retrieval augmentation vs fine-tuning — comparing accuracy and cost across openai and langchain