4d ago · 27 min read · TLDR: RAG gives LLMs access to current knowledge at inference time; fine-tuning changes how they reason and write. Use RAG when your data changes. Use fine-tuning when you need consistent style, tone, or domain reasoning. Use both for production assi...
EAli commented4d ago · 29 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, enabling 70B fine-tuning on 2× A100 80 GB instead of 8...
Join discussion6d ago · 28 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 trainable parameters. RLHF shapes its behavior using...
Join discussion
6d ago · 26 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 head, and fine-tune on your small dataset — often ...
Join discussion
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...
EAli commented
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...
Join discussion
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
Join discussion
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 pretraining and often the foundation for later RLHF. Good...
Join discussionMar 9 · 13 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 base weights. QLoRA pushes efficiency further by q...
Join discussionMar 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 added on top. 90%+ memory reduction; zero inference l...
Join discussion
