Parameter Tuning Strategies for Large Language Models

⚙️ Fine-Tuning Large Language Models: What Works, When, and Why Fine-tuning is not a magic wand. It's a design decision balancing specificity and generality, control and cost, performance and pragmatism. Let's break down the engineering tradeoffs. 🔧 1. Full Fine-Tuning Full Fine Tuning updates all model weights, offering the best performance but at the highest cost and lowest modularity. When to use: → High-stakes domains (medical, legal, aerospace) → When training data diverges from pre-trained distribution → When interpretability matters more than generality Pros: ✅ State-of-the-art performance in specialized domains ✅ Complete behavioral control—no surprises ✅ Enables deep internal shifts in model representations Cons: ⚠️ Requires 3-4x the base model's memory during training ⚠️ High risk of catastrophic forgetting ⚠️ Unwieldy checkpoints (dozens of GBs) ⚠️ Computationally intensive 🧠 2. Parameter-Efficient Fine-Tuning (PEFT) PEFT adds minimal learnable components into a frozen pre-trained model. A. LoRA (Low-Rank Adaptation) LoRA introduces low-rank matrices into specific layers, achieving high efficiency and performance close to full fine tuning, with no inference overhead after merging. Why it works: Transformer weights are often over-parameterized. Low-rank deltas steer behavior without disrupting the base. Pros: ✅ Trains just ~0.2% of parameters ✅ Reduces cost by 70-80% ✅ Works with off-the-shelf models ✅ Compatible with consumer GPUs (16-24GB VRAM) Cons: ⚠️ Slight performance dip for outlier tasks ⚠️ Managing multiple adapters increases complexity B. Adapters Adapters add small modules between layers, providing modularity and efficiency, but with a minor inference cost since adapters remain in the model. Why it works: Creates isolated "learning compartments" letting you swap behaviors without retraining. Pros: ✅ Strong modularity for multi-task settings ✅ Easier governance: version and audit per adapter ✅ Widely supported in open-source Cons: ⚠️ Increased inference latency ⚠️ Requires architectural support C. Prefix Tuning Prefix Tuning adds trainable vectors to the model’s input or transformer blocks, making it the most parameter-efficient and fastest to train, but generally with lower performance on complex tasks and best for scenarios where preserving the pre-trained model’s representation is critical Why it works: Initial LLM layers are sensitive to context. Prefix vectors steer activations like tuning a radio. Pros: ✅ Trains <0.1% of parameters ✅ Fast training and inference ✅ Ideal for personalization and low-resource devices Cons: ⚠️ Less stable in models >30B unless regularized ⚠️ Struggles with deep reasoning tasks In 2025, switch from "Can I fine-tune?" to "What am I optimizing for?" If you need control? Full fine-tuning- at a cost. If you need agility? LoRA or adapters. If you need speed? Prefix tuning. Share it with your network ♻️ Follow me(Aishwarya Srinivasan) for more no-fluff AI insights

Understanding LLM Adaptation: Full Training vs Fine-Tuning vs LoRA/QLoRA 🚀 Which approach really moves the needle in today’s AI landscape? As large language models (LLMs) become mainstream, I frequently get asked: “Should we train from scratch, full fine-tune, or use LoRA/QLoRA adapters for our use case?” Here’s a simple breakdown based on real-world considerations: 🔍 1. Full Training from Scratch What: Building a model from the ground up with billions of parameters. Who: Only major labs/Big Tech (OpenAI, Google, etc.) Cost: 🏦 Millions—requires massive clusters and huge datasets. Why: Needed ONLY if you want a truly unique model architecture or foundation. 🛠️ 2. Full Fine-Tuning What: Take an existing giant model and update ALL its weights for your task. Who: Advanced companies with deep pockets. Cost: 💰 Tens of thousands to millions—need multiple high-end GPUs. Why: Useful if you have vast domain data and need to drastically “re-train” the model’s capabilities. ⚡ 3. LoRA/QLoRA (Parameter-Efficient Tuning) What: Plug low-rank adapters into a model, updating just 0.5-5% of weights. Who: Startups, researchers, almost anyone! Cost: 💡 From free (on Google Colab) to a few hundred dollars on cloud GPUs. Why: Customize powerful LLMs efficiently—think domain adaption, brand voice, or private datasets, all without losing the model’s general smarts. 🤔 Which one should YOU use? For most organizations and projects, LoRA/QLoRA is the optimal sweet spot Fast: Results in hours, not weeks Affordable: Accessible to almost anyone Flexible: Update or revert adapters with ease Full fine-tuning and from-scratch training make sense only for the biggest players—99% of AI innovation today leverages parameter-efficient tuning! 💬 What’s your experience? Are you using full fine-tunes, or has LoRA/QLoRA met your business needs? Share your project (or frustrations!) in comments.

𝘍𝘰𝘳𝘨𝘦𝘵 𝘸𝘩𝘢𝘵 𝘺𝘰𝘶 𝘵𝘩𝘪𝘯𝘬 𝘺𝘰𝘶 𝘬𝘯𝘰𝘸 𝘢𝘣𝘰𝘶𝘵 𝘧𝘪𝘯𝘦-𝘵𝘶𝘯𝘪𝘯𝘨 𝘓𝘓𝘔𝘴. 𝘛𝘩𝘪𝘴 𝘱𝘢𝘱𝘦𝘳 𝘴𝘩𝘰𝘸𝘴 𝘢 𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦𝘭𝘺 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘱𝘢𝘵𝘩 - 𝘰𝘯𝘦 𝘸𝘩𝘦𝘳𝘦 𝘢𝘯 𝘰𝘭𝘥𝘦𝘳 𝘈𝘐 𝘵𝘦𝘤𝘩𝘯𝘪𝘲𝘶𝘦, 𝘌𝘷𝘰𝘭𝘶𝘵𝘪𝘰𝘯 𝘚𝘵𝘳𝘢𝘵𝘦𝘨𝘪𝘦𝘴, 𝘰𝘶𝘵𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘴 𝘮𝘰𝘥𝘦𝘳𝘯 𝘙𝘦𝘪𝘯𝘧𝘰𝘳𝘤𝘦𝘮𝘦𝘯𝘵 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨. We all know that the instability, sample inefficiency, and tendency for "reward hacking" in RL are persistent bottlenecks in deploying aligned and reliable AI. We need a more robust path forward. A new paper from Cognizant AI Lab and Massachusetts Institute of Technology, "𝐄𝐕𝐎𝐋𝐔𝐓𝐈𝐎𝐍 𝐒𝐓𝐑𝐀𝐓𝐄𝐆𝐈𝐄𝐒 𝐀𝐓 𝐒𝐂𝐀𝐋𝐄: 𝐋𝐋𝐌 𝐅𝐈𝐍𝐄𝐓𝐔𝐍𝐈𝐍𝐆 𝐁𝐄𝐘𝐎𝐍𝐃 𝐑𝐄𝐈𝐍𝐅𝐎𝐑𝐂𝐄𝐌𝐄𝐍𝐓 𝐋𝐄𝐀𝐑𝐍𝐈𝐍𝐆" directly tackles the assumption that Evolution Strategies (ES) - the method that directly perturbs model parameters - is too inefficient to work on models with billions of weights. Using a memory-efficient, parallelized approach, the researchers scaled ES to fine-tune the full parameters of modern LLMs. The results: - On a symbolic reasoning benchmark (the Countdown task), ES-tuned models consistently and significantly outperformed state-of-the-art RL methods. For example, ES boosted a 7B parameter model's accuracy to 66.8%, crushing PPO's 55.1%. - ES was also far more sample-efficient, requiring less than 20% of the training data RL needed to achieve similar performance. This is a very cool experiment. It suggests direct parameter-space exploration could be the future for building more stable and capable models. #AI #MachineLearning #LLM #FineTuning #EvolutionaryAlgorithms

Confession: At a conference a few months ago, I ended up in a conversation with two engineers who kept talking about “LoRA” and “DORA.” I genuinely thought they were referring to founders. Like… Laura and Dora. Maybe they’d raised a big round? Spoiler: they’re not people. They’re fine-tuning methods. That night, I sat on my bed typing: “LoRA DORA AI explained like I’m five and socially humiliated” Anyway, welcome to Day 29 of learning about AI in public. A few years ago, if you wanted to fine-tune a large language model, you needed millions of dollars, hundreds of GPUs, and a serious infrastructure team. Today? Thanks to a class of methods called parameter-efficient fine-tuning, you can do it with a single GPU. Let’s break down how. “Full fine-tuning” means updating all of a model’s internal settings (parameters) to adapt it to a new task. That’s what big labs used to do. But it has big downsides: - Training a 65 billion–parameter model requires clusters of GPUs, each costing thousands of dollars - Just loading a 16-bit version of the model requires over 130GB of VRAM - Every experiment means re-training from scratch - If your dataset is small or narrow, the model might “memorize” instead of learning useful generalizations PEFT flips this: it freezes most of the model and only updates a small, smart subset of parameters. Part 1: Low-Rank Adaptation LoRA’s idea is simple: instead of updating an entire matrix of weights, just learn a small patch that captures what’s different about your new task. Take the pretrained model. Don’t touch its main weights. Insert two small matrices (A and B) inside certain layers, typically the attention and feedforward layers These matrices learn a low-rank approximation of the change you would have made Think of it like adding removable “task-specific plugins” to a general-purpose brain. Part 2: Quantized LoRA LoRA is great but it still assumes you can fit the base model in memory. That’s where QLoRA comes in: - It compresses the base model into 4-bit precision - Keeps LoRA adapters in normal precision - Uses tricks like offloading optimizer states to your CPU - Still allows backpropagation (i.e., learning) through the adapter The result? You can fine-tune a 65B model on a single consumer-grade 48GB GPU. And you lose almost no performance. Part 3: Dynamic Rank Adaptation LoRA requires you to set a “rank” (basically: how big your low-rank patch is). QLoRA keeps that fixed. But different tasks and different model layers need different levels of expressiveness. DORA fixes that: - It lets each layer learn its own optimal rank automatically - Uses smart techniques (like matrix decomposition) to identify which layers need more or less capacity - Keeps performance high while reducing waste In a nutshell: PEFT makes LLM customization democratized, cheap, and modular. 👉 This is Day 29 of my 30-day deep dive into AI. Follow Justine Juillard to go from AI-curious to AI-confident.

Evolution strategies at scale: A new way to fine tune LLMs For a while now, reinforcement learning has been the standard way to fine tune LLMs. It works in the action space, i.e. exploring alternative actions to maximize reward, which is difficult especially with long action sequences. If we could explore LLM parameters directly, it might be possible to find more systematic and creative changes. Indeed parameter-space fine tuning is possible, and in a surprising way: evolution strategies, i.e. population-based search, can be scaled up to optimize billions of parameters in LLMs. It outperforms PPO and GRPO in a symbolic reasoning task, for example, while being more consistent and sample-efficient. Its gradient-free nature makes it a promising alternative in environments where RL is fragile or costly to apply. Read the full paper: https://lnkd.in/gGtyddty Read the blog (with a video and a conceptual illustration): https://lnkd.in/gZtWccrJ Explore the code: https://lnkd.in/gtJ95Nvb #AIResearch #EvolutionStrategies #LLM

Day 10/30 of LLMs/SLMs - Pre-training, Fine-Tuning and PEFT Pretraining is where it all begins. The model learns language itself i.e. syntax, semantics, reasoning, by predicting the next word across vast amounts of data - books, code, Wikipedia, and the web. This phase is heavy: hundreds of billions of tokens, thousands of GPUs, weeks of training.  Models like GPT, LLaMA, and Mistral all start this way. Fine-tuning is where the model gets focused. You take that pretrained generalist and train it on your data - medical records, product descriptions, customer chats - so it speaks your domain’s language. Now the problem? Full fine-tuning can be computationally expensive. Especially when models have billions of parameters. Also updating all of the parameters for every task is wasteful. Enter PEFT. Instead of retraining the entire model, PEFT techniques tweak only a small subset of parameters, like <1%, while keeping the rest frozen. Three popular methods are - 👉 LoRA (Low-Rank Adaptation): Adds tiny trainable matrices to attention layers. It’s like giving your model small adjustment knobs rather than rewiring the whole circuit. Hugely popular and performant compared to the rest for domain adaptation (e.g., making a general LLM fluent in healthcare or finance jargon). 👉 Adapters: Small bottleneck layers inserted between transformer blocks. The are quick to switch out and this makes it possible to maintain multiple domain-specialized variants efficiently. 👉 Prefix-Tuning: Prepends learned “soft prompts” to the model’s inputs, teaching it how to behave differently without changing any internal weights. It’s also lightweight and ideal for few-shot or multitask scenarios. Takeaway ✅ Pretraining helps LLM builds general intelligence and Fine-tuning makes the model useful for your task. ✅ PEFT makes it efficient to fine-tune LLMs. LoRA for performance and adapters/prefix-finetuning for lightweight multi-task settings. Tune in tomorrow for more SLM/LLMs deep dives. -- 🚶➡️ To learn more about LLMs/SLMs, follow me - Karun! ♻️ Share so others can learn, and you can build your LinkedIn presence! (Img Src: https://lnkd.in/gUEG4_nM)

3 Ways to Fine-Tune an AI Model 👇 Fine-tuning involves adapting a model to a particular use case through additional training. Before doing this, however, we must ask ourselves a fundamental question: which parameters do I want to fine-tune? While there are countless ways to do this, I like to split them into 3 buckets. Bucket 1: All Parameters 🌎 The simplest approach is to retrain all the parameters in your base model, also called full parameter fine-tuning. This works well when you have a lot of data and want to make fundamental changes to the model’s behavior. The downside, of course, is that this is the most computationally expensive option and risks the model “forgetting” key abilities. Bucket 2: Some Parameters 🪛 To mitigate the compute requirements and training risks of full fine-tuning, one can pick a subset of parameters to retrain. This is called transfer learning, which typically consists of retraining the last few layers of a model (and maybe adding a new one). The key upside of this is that you can significantly improve a model's performance on a novel task without major data and compute costs. However, even this approach can be impractical for very large base models. Bucket 3: New Parameters (i.e. adapters) 💻 This final bucket consists of adding a (relatively) small number of trainable parameters to key parts of a model while keeping its original parameters frozen. The technical term for this approach is Parameter Efficient Fine-Tuning (PEFT). A popular PEFT approach is Low-Rank Adaption (LoRA), which has made fine-tuning large language models practical for GPU-poor practitioners. Additionally, this is a handy approach when working with a relatively small training data set (which is the biggest unlock IMO).

🎯 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐭𝐨 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐞 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (𝐋𝐋𝐌𝐬) Fine-tuning Large Language Models is transforming the way we customize AI for specific tasks. These cutting-edge techniques ensure precision, efficiency, and adaptability for various use cases. Let’s dive in! 1️⃣ 𝑳𝒐𝑹𝑨 (𝑳𝒐𝒘-𝑹𝒂𝒏𝒌 𝑨𝒅𝒂𝒑𝒕𝒂𝒕𝒊𝒐𝒏): - Instead of fine-tuning all model weights, LoRA freezes the pre-trained weights and learns small, low-rank matrices to adapt the model to a specific task. - Why it’s great: Drastically reduces computational costs while maintaining performance! 2️⃣ 𝑫𝒆𝒍𝒕𝒂 𝑳𝒐𝑹𝑨: - An improvement on standard LoRA, this method introduces dropout layers to enhance generalization and robustness. - Why it’s useful: Perfect for tasks that require diverse contextual understanding or face data variability. 3️⃣ 𝑷𝒓𝒆𝒇𝒊𝒙 𝑻𝒖𝒏𝒊𝒏𝒈: - Optimizes a small set of task-specific parameters (prefix vectors) while keeping the model weights untouched. - Where it shines: Tasks like translation, summarization, and other NLP applications where minimal tuning is essential. 4️⃣ 𝑳𝒐𝑹𝑨-𝑭𝑨 (𝑭𝒆𝒂𝒕𝒖𝒓𝒆 𝑨𝒖𝒈𝒎𝒆𝒏𝒕𝒂𝒕𝒊𝒐𝒏): - Extends LoRA by decomposing weight matrices and adding directional features to boost the model’s ability to learn task-specific nuances. - Why it’s innovative: Improves both adaptability and performance, especially for domain-specific tasks. 5️⃣ 𝑽𝒆𝒓𝒂: - A unique fine-tuning framework that ensures output relevance and coherence by refining outputs for downstream applications. - Best for Complex tasks requiring high levels of accuracy and contextual alignment. 𝑾𝒂𝒏𝒕 𝒕𝒐 𝒄𝒐𝒏𝒏𝒆𝒄𝒕 𝒘𝒊𝒕𝒉 𝒎𝒆? 𝘍𝒊𝒏𝒅 𝒎𝒆 𝒉𝒆𝒓𝒆 --> https://lnkd.in/dTK-FtG3 Follow Shreya Khandelwal for more such content. ************************************************************************ #DataScience #artificialintelligence #MachineLearning #LargeLanguageModels #LLMs #GenerativeAI #LanguageModels #NLP #RAG #DataEngineering #Analytics #VectorDB #AI #FineTuning #LoRA

If you fine-tune large language model without understanding LoRA’s hyperparameters, you’re likely wasting compute, time, or both. LoRA fine-tuning is controlled by three hyperparameters that balance expressiveness, training stability, and computational cost. Rank R sets the dimensionality of adapter matrices. Values as low as 4-8-16 can match full fine-tuning performance, with 8 being the most common starting point. The choice is primarily constrained by available memory rather than model quality. Alpha acts as a scaling factor, typically set to 2×R, that maintains training stability when experimenting with different rank values. Without it, changing R would require manually adjusting learning rates each time. Target modules specify which neural network components get adapted. The default recommendation is the query and value projection matrices in attention blocks. This video is part of our 𝗟𝗟𝗠 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 program. 𝗟𝗲𝗮𝗿𝗻 𝗺𝗼𝗿𝗲 𝗵𝗲𝗿𝗲: https://lnkd.in/gM7jrinp Follow for more practical insights into real-world AI engineering and development.