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LLM Fine-Tuning: Full, LoRA & QLoRA Methods Compared
Fine-tune large language models with full fine-tuning, LoRA, or QLoRA. Includes Hugging Face code, data preparation, and when to choose fine-tuning vs RAG.
LLM Fine-tuning & Deployment
Master LLM fine-tuning (LoRA/RLHF), quantization, and best practices for local and server-side production deployment.
6 Articles in This Series · 创建于 2026-02-21
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LoRA Fine-Tuning: QLoRA Setup & PEFT Guide
Fine-tune LLMs efficiently with LoRA and QLoRA. Step-by-step PEFT setup, key hyperparameters, and memory optimization for Hugging Face model customization.
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What is RLHF? How ChatGPT Learns from Human Feedback
RLHF aligns AI with human preferences through reward modeling and PPO. Learn the technique behind ChatGPT, InstructGPT, and compare RLHF vs DPO approaches.
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What is Model Quantization? INT8, GPTQ & AWQ Explained
Model quantization reduces LLM size by 75% with minimal quality loss. Learn INT8/INT4, GPTQ, AWQ, GGUF methods with practical code examples using llama.cpp.
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Ollama Advanced Practical Guide: Running and Fine-tuning Open Source LLMs Locally
With increasing demands for data privacy and offline computing, running Large Language Models (LLMs) locally has become a top choice for many enterprises and developers. This article delves into the advanced usage of Ollama, including custom Modelfiles, REST API integration, and lightweight fine-tuning with external data.
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WebLLM Practical Guide: Engineering Architecture for Running Large Language Models in the Browser
Explore the execution mechanism of browser-based Large Language Models (LLMs) based on WebGPU. This article details the WebLLM architecture and guides you in building an offline AI application with zero server inference costs, complete with model caching and VRAM optimization strategies.