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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.
11 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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What is Ollama? Advanced Guide to Local LLM Deployment & Modelfile
A comprehensive guide on what Ollama is and how to deploy large language models locally. Deep dive into advanced Ollama usage, custom Modelfiles, and API integration.
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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.
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DPO vs RLHF: The Evolution of LLM Alignment Techniques
A deep technical comparison of DPO and RLHF for LLM alignment. Covers reward model training, PPO instabilities, the Bradley-Terry framework behind DPO, compute costs, and newer variants like KTO, IPO, ORPO, and SimPO.
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Enterprise LLMOps Architecture Guide [2026]: Full Lifecycle from Development to Monitoring
A comprehensive deep dive into enterprise-grade LLMOps architecture, covering the full lifecycle from Prompt Engineering, Data Governance, and Fine-tuning to Automated Evaluation and Production Observability. Learn how to build CI/CD pipelines for LLMs to ensure consistency, security, and cost control for production-ready AI applications.
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The Rise of Small Language Models: How 2B/8B Models Are Replacing Large Models on Edge Devices
A deep dive into the rise of Small Language Models (SLMs). Compare Microsoft Phi-4, Google Gemma 3, Qwen3, Llama 3.2, and more with edge deployment strategies, INT4/INT8 quantization, LoRA fine-tuning, and complete Ollama local deployment code examples.
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The AI Inference Cost Collapse: From GPT-4 to 2B Models Efficiency Revolution [2026]
Analyze the dramatic collapse in AI inference costs. Discover how Small Language Models (2B-8B), specialized architectures, and edge deployment are driving the efficiency revolution in 2026.
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Local LLM Deployment 2026: Ollama vs vLLM Tuning
2026 benchmarks show vLLM delivers 16x throughput over Ollama at scale. Compare both with tuning strategies for PagedAttention, quantization, and multi-GPU.