---
title: "Discussion on \"Fine-Tuning LLMs: The Complete Engineer's Guide to SFT, LoRA, and RLHF\" | Hashnode"
description: "Discussion on \"Fine-Tuning LLMs: The Complete Engineer's Guide to SFT, LoRA, and RLHF\". Supervised fine-tuning, parameter-efficient LoRA, and reinforcement learning from human feedback — when to use each and how to implement them"
url: "https://hashnode.com/posts/fine-tuning-llms-complete-guide-sft-lora-rlhf/69e30446c1f5802e1dbdb589"
---

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# [Fine-Tuning LLMs: The Complete Engineer's Guide to SFT, LoRA, and RLHF](https://abstractalgorithms.hashnode.dev/fine-tuning-llms-complete-guide-sft-lora-rlhf?utm_source=hashnode&utm_medium=feed)

Supervised fine-tuning, parameter-efficient LoRA, and reinforcement learning from human feedback — when to use each and how to implement them

Apr 18 · 30 min read · abstractalgorithms.hashnode.dev  
Updated Jun 29  
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-   [#llm](https://hashnode.com/tag/llm)
-   [#fine-tuning](https://hashnode.com/tag/fine-tuning)
-   [#lora](https://hashnode.com/tag/lora)
-   [#rlhf](https://hashnode.com/tag/rlhf)
-   [#machine-learning](https://hashnode.com/tag/machine-learning)
-   [#pytorch](https://hashnode.com/tag/pytorch)
-   [#huggingface](https://hashnode.com/tag/huggingface)

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