Blog

From the Heulistic team

Practical writing on LLM fine-tuning, MLOps, and running models in production.

July 11, 2026

What Is GRPO and How Is It Different From PPO

GRPO is a reinforcement learning fine-tuning method that has largely replaced PPO for most LLM training tasks. It is simpler, cheaper, and more stable. Here is what each one does, why GRPO took over, and when you would actually use either of them.

July 10, 2026

What Is DPO Fine-Tuning and When Should You Use It Instead of SFT

DPO is a fine-tuning method that teaches a model which responses are better by showing it pairs of good and bad outputs. SFT teaches a model to imitate examples. Here is what each one does, when DPO is worth the extra data collection effort, and when SFT is all you need.

July 9, 2026

What Is Catastrophic Forgetting in Fine-Tuning and How Do You Prevent It

Fine-tuning on a narrow task can quietly degrade everything the base model used to do well. The model gets better at your task and worse at everything else. Here is what causes it, how to detect it, and how to prevent it before it ruins your model.

July 8, 2026

What Happens After Fine-Tuning: Evaluation, Testing, and Knowing When Your Model Is Ready to Ship

Training a model is the easy part. Knowing whether it actually works is harder. Most teams skip proper evaluation and end up shipping a model that surprises them in production. Here is how to evaluate a fine-tuned model before it costs you.

July 8, 2026

What Is a LoRA Adapter and How Does It Work

LoRA is the technique behind most practical LLM fine-tuning today. Instead of updating all the model's weights, it adds a small set of trainable parameters on top. Here is what that actually means, why it works, and what you get at the end of training.

July 7, 2026

What Is Fine-Tuning and How Is It Different From Training From Scratch

Fine-tuning and training from scratch are two completely different things. Most people use one when they mean the other. Here is what fine-tuning actually does, why it works, and when it is the right tool for your problem.

July 6, 2026

What Learning Rate Should You Use for QLoRA Fine-Tuning

Learning rate is the hyperparameter that breaks more fine-tuning jobs than any other. Too high and your loss spikes. Too low and training stalls. Here is how to pick a starting value, what the research says, and how to know when to adjust.

July 5, 2026

How to Resume a Failed Fine-Tuning Job From a Checkpoint

A training job that fails at step 900 of 1000 does not have to start over from scratch. Checkpoints let you pick up where training left off. Here is how checkpointing works in Axolotl, how to resume correctly, and the mistakes that make resuming fail silently.

July 4, 2026

Heulistic vs RunPod: What You Are Actually Comparing

RunPod is the cheapest GPU compute available for fine-tuning. Heulistic costs more per GPU hour and does significantly more for that difference. Here is what you are trading off between the two and how to know which one makes sense for your situation.

July 1, 2026

7B vs 13B vs 70B: How to Choose the Right Model Size Before You Commit the Compute

Bigger is not always better when it comes to model size. A fine-tuned 7B model regularly outperforms a general-purpose 70B on focused tasks. Here is how to pick the right size for your use case before you waste compute finding out the hard way.

June 30, 2026

What a Real Fine-Tuning Job Looks Like on Heulistic: Config, Training Logs, and Results

Here is an actual Heulistic training run from config to completed model. Real Axolotl config, real logs, real GPU metrics. Llama 3.2 1B, LoRA, alpaca dataset, 3 epochs, under 3 hours.

June 29, 2026

Heulistic vs AWS SageMaker: An Honest Comparison for LLM Fine-Tuning

SageMaker is one of the most powerful ML platforms available. It is also one of the most complex to set up for LLM fine-tuning. Here is what the process actually looks like on each platform and how to know which one fits your team.

June 24, 2026

Dataset Format for Fine-Tuning: JSONL, Data Quality, and the Mistakes That Kill Results

Your dataset is the single biggest variable in fine-tuning. A great config and the right method can't save you from bad data. But bad data isn't always obvious. It doesn't always look broken. This post covers JSONL structure, the two dataset formats engineers use most, the quality signals that separate useful data from data that produces models that almost work, and the mistakes that are easy to miss until it's too late.

June 20, 2026

What Are Warmup Steps in Fine-Tuning and Do They Actually Matter

Warmup steps gradually increase the learning rate at the start of training instead of starting at the full rate immediately. Most tutorials include them without explaining why. Here is what they actually do, when they help, and what to set them to.

June 19, 2026

What Is Gradient Accumulation and When Should You Use It

Gradient accumulation lets you train with an effectively larger batch size without increasing your GPU memory usage. Here is what it actually does, when it helps, when it does not, and how to set it correctly in your config.

June 18, 2026

Axolotl vs TRL vs Unsloth: Which Fine-Tuning Framework Should You Use

Three frameworks dominate LLM fine-tuning in 2026. They all support LoRA, QLoRA, DPO, and GRPO. What differs is workflow, speed, flexibility, and who each one is actually built for. Here is how to choose before you spend time learning the wrong one.

June 17, 2026

Reading Loss Curves: What You’re Training Run is Trying to Tell You

A loss curve is the closest thing you have to a window into what your model is learning during training. But most people either ignore it or misread it. A loss curve that looks good can still mean your model is broken. A curve that looks scary can still mean your run is fine. This post explains what loss curves actually show, the patterns that matter, and how to know when to keep training versus when to stop and fix something first.

June 15, 2026

How to Split Your Dataset for Fine-Tuning: Train, Validation, and What Most People Get Wrong

Most fine-tuning guides tell you to split your data 80/20 and move on. That advice is incomplete and sometimes actively wrong. Here is what a good dataset split actually looks like, why it matters, and the mistakes that make your evaluation metrics meaningless.

June 14, 2026

ShareGPT vs Alpaca vs ChatML: Which Dataset Format and When

The dataset format you pick determines how your model is tokenized during training. Pick the wrong one and your carefully prepared data produces a model that behaves strangely in ways that are hard to trace back to the real cause. Here is how to choose the right format for your task.

June 13, 2026

How Many Training Examples Do You Actually Need for Fine-Tuning?

The answer depends on your task, your base model, and what you are trying to change. Here are real numbers by task type so you can plan your dataset before you start collecting data you do not need.

June 12, 2026

How to Generate Synthetic Training Data When You Don't Have Enough Examples

Most teams do not have enough high-quality labeled data to fine-tune a model. Synthetic data generation is the practical solution. Here is how to do it in a way that actually improves your model instead of just inflating your dataset size.

June 11, 2026

How to Clean a Dataset for Fine-Tuning: What Bad Data Actually Looks Like

Bad training data does not always look broken. It can pass a syntax check, load without errors, and produce a loss curve that looks fine while quietly training your model to behave in ways you did not intend. Here is what bad data actually looks like and how to find it before it costs you a training run.

June 10, 2026

LoRA vs QLoRA vs Full Fine-Tune: How to Choose Before You Waste Compute

There are three main ways to fine-tune a large language model: LoRA, QLoRA, and full fine-tuning. Each one trades off memory, cost, speed, and output quality differently. Picking the wrong one doesn't just slow you down but it can make your results worse or make the job impossible on the hardware you have. This post explains what each method actually does, what it costs, and how to pick the right one before you waste compute finding out the hard way.

June 9, 2026

Why Your Fine-Tuning Job Is Slower Than Expected (And Why GPU Utilization Is Below 50%)

Low GPU utilization during fine-tuning does not always mean something is broken. But it usually means something is wrong. Here is what is actually eating your training time and how to fix each cause.

June 8, 2026

bitsandbytes Installation Errors: What's Actually Happening and How to Fix It

bitsandbytes is required for QLoRA fine-tuning but it is one of the most frustrating packages to install correctly. Here is why it breaks, what each error actually means, and how to resolve it without spending hours on GitHub issues.

June 6, 2026

GPU Out of Memory During Inference: Why It's Different From Training OOM and How to Fix It

Your model trained fine. Now it crashes during inference with an OOM error. Inference OOM is a different problem from training OOM and the fixes are completely different. Here is what is actually happening and how to resolve it.

June 5, 2026

CUDA Driver Version Mismatch: How to Fix It Without Reinstalling Everything

A CUDA driver version mismatch stops your training environment cold. Most guides tell you to reinstall. You usually do not have to. Here is how to diagnose which mismatch you have and fix it in minutes.

June 4, 2026

Heulistic vs Unsloth Studio: Two Different Answers to the Same Problem

Unsloth Studio is a free, local, no-code fine-tuning UI that runs on your own hardware. Heulistic is a managed fine-tuning platform that runs on cloud GPUs you do not have to own or configure. Both remove the infrastructure barrier in completely different ways.

June 3, 2026

How to Write an Axolotl Config That Won't Waste Your GPU Budget

Axolotl has dozens of config settings and the wrong ones can silently waste compute, crash your run, or produce a model that looks trained but is not. Here is what actually matters and what you can safely ignore.

June 2, 2026

Hugging Face AutoTrain Is Deprecated. Here Is What to Use Instead.

Hugging Face AutoTrain is no longer maintained. No new features, no bug fixes, and support that points you to a GitHub issue queue with only open tickets. If you built a workflow around it, here is your actual path forward.

June 1, 2026

Heulistic vs Together AI: Which Is Right for LLM Fine-Tuning

Together AI makes fine-tuning feel simple. Upload a dataset, configure a few parameters, get a model back. But that simplicity has a ceiling. Here is what you can and cannot do on each platform, and how to know which fits your actual use case.

May 31, 2026

Heulistic vs Modal: Which Is the Right Platform for LLM Fine-Tuning

Modal is a powerful serverless compute platform built for ML engineers who want to run any Python workload on GPUs. Heulistic is built specifically for LLM fine-tuning. Here is what that difference actually means when you are trying to get a model trained.

May 27, 2026

Why Fine-Tuning Fails Before Training Starts

Most fine-tuning projects fail before a single training token is processed. CUDA conflicts, dependency mismatches, and environment drift consume the time and mental budget that should go toward model iteration. Here is what is actually going wrong.

May 20, 2026

Heulistic vs Self-Hosting on AWS

If you've tried to fine-tune a model on EC2 or SageMaker, you know the infrastructure work has nothing to do with the model. Here's an honest comparison.