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Fine-Tuning LLMs with LoRA and QLoRA: A Practical Deep-Dive

Apr 19 · 31 min read · TLDR: LoRA freezes the base model and trains two tiny matrices per layer — 0.1 % of parameters, 70 % less GPU memory, near-identical quality. QLoRA adds 4-bit NF4 quantization of the frozen base, enab

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RAG vs Fine-Tuning: When to Use Each (and When to Combine Them)

Apr 19 · 31 min read · 📌 TL;DR Summary Use RAG when facts change frequently and answers must be source-grounded. Use fine-tuning when you need stable behavior: tone, format, and domain-specific reasoning. Use RAG + fine-t

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Fine-Tuning LLMs: The Complete Engineer's Guide to SFT, LoRA, and RLHF

Apr 18 · 30 min read · TLDR: A pretrained LLM is a generalist. Fine-tuning makes it a specialist. Supervised Fine-Tuning (SFT) teaches it your domain's language through labeled examples. LoRA does the same with 99% fewer tr

Transfer Learning Explained: Standing on the Shoulders of Pretrained Models

Apr 18 · 28 min read · TLDR: You don't need millions of labeled images or months of GPU time to build a great model. Transfer learning lets you borrow a pretrained network's hard-won feature detectors, plug in a new output

JKJangwook Kimeffloow.hashnode.dev

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Fine-Tune LLMs with LoRA and QLoRA: 2026 Guide

Apr 17 · 12 min read · Two years ago, fine-tuning a large language model required a rack of A100s, a machine learning team, and a five-figure cloud bill. In 2026, a single RTX 4070 Ti is enough to specialize a 7B model on your domain data — in an afternoon. That shift happ...

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RSRahul Sehrawatai-zero-to-hero.hashnode.dev

Fine-Tuning in 2026: What It Is Still Good For

Apr 13 · 11 min read · Every team I've worked with has asked this question at some point: "Should we fine-tune our own model?" The question almost always comes wrapped in optimism — a PM or engineer has read an article about fine-tuning, talked to a vendor, seen a benchmar...

RSRAMAKRISHNAN SAKTHIVELlearnwithramaaiml.hashnode.dev

Reinforcement Fine-Tuning on Amazon Bedrock: Train Smarter, Not Harder

Apr 3 · 6 min read · AWS · Amazon Bedrock · April 2026 · 🕒 7 min read You've been fine-tuning models the hard way. Collecting thousands of labeled examples. Running expensive annotation pipelines. Managing GPU infras

SFT for LLMs: A Practical Guide to Supervised Fine-Tuning

Mar 9 · 12 min read · TLDR: Supervised fine-tuning (SFT) is the stage where a pretrained model learns task-specific response behavior from curated input-output examples. It is usually the first alignment step after pretrai

PEFT, LoRA, and QLoRA: A Practical Guide to Efficient LLM Fine-Tuning

Mar 9 · 14 min read · TLDR: Full fine-tuning updates every model weight, which is expensive in memory, compute, and storage. PEFT methods update only a small trainable slice. LoRA learns low-rank adapters on top of frozen

LoRA Explained: How to Fine-Tune LLMs on a Budget

Mar 9 · 13 min read · TLDR: Fine-tuning a 7B-parameter LLM updates billions of weights and requires expensive GPUs. LoRA (Low-Rank Adaptation) freezes the original weights and trains only tiny adapter matrices that are add