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AAAbstract Algorithmsabstractalgorithms.devMar 9 · 14 min read

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

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

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SDSatarupa Debai-fine-tuning-guide-beginners-2025.hashnode.devJul 9, 2025 · 9 min read

Understanding Fine-Tuning and Core AI Concepts: A Beginner's Guide

Imagine teaching a skilled chef a new cuisine. They already know how to cook, but you're showing them Italian dishes specifically. This is exactly what fine-tuning does for AI models: it takes a pre-trained model (the "chef" with general knowledge) a...

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SGSourav Ghoshtechiesourav.hashnode.devMay 27, 2025 · 11 min read

Behind the Scenes of LLM Fine-Tuning

The landscape of large language model fine-tuning has undergone a quiet but profound transformation over the past two years. What once required massive computational resources, specialized infrastructure, and substantial financial investment has evol...

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PBPronod Bharatiyadata-intelligence.hashnode.devMay 25, 2025 · 45 min read

Elevating NLP: Fine-Tuning Large Language Models for Specific Applications

Introduction to LLM Fine-Tuning Large Language Models are revolutionizing AI, but their real magic happens with fine-tuning. This isn't just about making models bigger; it's about making them smarter, faster, and perfectly aligned with the specific n...

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VSVikas Srinivasaintuitive-fine-tuning.hashnode.devApr 7, 2025 · 8 min read

A Beginner’s Guide to Parameter Efficient Fine Tuning

In this blog, we’ll explore Parameter Efficient Fine Tuning (PEFT), a technique designed to address the limitations of traditional fine-tuning methods like full fine-tuning and final layer fine-tuning. Fine-tuning only the final layer often results i...

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CChatbot-Entreprisechatbot-entreprise.frAug 20, 2024 · 5 min read

Optimisation des Modèles IA 📈 : Techniques d'Apprentissage Efficaces pour des Résultats Concrets

Les techniques d'apprentissage efficace permettent d'adapter rapidement et économiquement les grands modèles de langage (LLMs) à des tâches spécifiques. Grâce à des approches comme LoRA, qLoRA et PEFT, il est possible de personnaliser ces modèles san...

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RHRJ Honickylearning-exhaust.hashnode.devAug 11, 2024 · 4 min read

Is it the model or the data that's low rank?

I mentioned this little bit of analysis that I recently did during the Latent Space Paper Club, and got a lot of positive feedback, so I did a quick writeup. The recently released Apple Intelligence Foundation Language Models paper has spark a lot of...

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MMaximilienmaximilien.docquest.appOct 14, 2023 · 20 min read

Parameter Efficient FineTuning In Action: Finetuning LLMs Using PEFT & LoRA For Causal Language Modeling Task

Hands-on Code Generation Implementation using Codegen pre-trained model- Parameter Efficient Fine-Tuning — LoRA - CausalLM Introduction In our ever-evolving AI landscape, the excitement around Language Models is palpable. Yet, as models grow in size,...

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Trending tags this week

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

Understanding Fine-Tuning and Core AI Concepts: A Beginner's Guide

Behind the Scenes of LLM Fine-Tuning

Elevating NLP: Fine-Tuning Large Language Models for Specific Applications

A Beginner’s Guide to Parameter Efficient Fine Tuning

Optimisation des Modèles IA 📈 : Techniques d'Apprentissage Efficaces pour des Résultats Concrets

Is it the model or the data that's low rank?

Parameter Efficient FineTuning In Action: Finetuning LLMs Using PEFT & LoRA For Causal Language Modeling Task