Your first fine tune, start to finish
Run one small, honest fine tuning project end to end and decide whether it was worth doing
A taste of a lesson
My fine tuned classifier gets 94 percent on my test set. My prompt only got 81. Am I done?
That is a promising gap, and a few checks will tell you whether it holds up. First, confirm no test examples, or near copies of them, ended up in training data; leakage inflates scores. Second, look at per class results: the gain might come from common classes while a rare one got worse. Third, test some unusual inputs the dataset did not cover, such as very short or mixed language messages. If all three look fine, weigh the gain against the cost of serving and retraining the model. Quick exercise: how would you search for near duplicates between your train and test sets?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Define a measurable goal and build a prompted baseline before training
- Prepare and split a small dataset without leakage between splits
- Choose a sensible tuning route and starting settings for a small project
- Compare a fine tuned model against the baseline on held out data
- Decide whether the improvement justifies the cost and upkeep
Lesson plan
- 1 Pick a task and define success Choose a narrow first task and write down how improvement will be measured. Start
- 2 Build the prompted baseline Measure how far prompting alone gets on the test set. Start
- 3 Prepare a small, clean dataset Collect consistent examples and split them correctly. Start
- 4 Choose a route and train Pick an open model with LoRA or a hosted service and train with safe starting settings. Start
- 5 Evaluate against the baseline Compare the tuned model with the baseline fairly and study what changed. Start
- 6 Decide whether it was worth it Weigh the measured gain against training, serving and maintenance costs. Start
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About this tutor
For beginners who have used language models through prompts and want to try fine tuning without getting lost. You take one small, realistic task, such as sorting support messages into categories or rewriting text in a house style, all the way through: defining success and building a prompted baseline first, preparing a small clean dataset, splitting it, choosing between a small open model with parameter efficient tuning or a hosted tuning service in general terms, setting sensible starting values, training, and comparing against the baseline on held out data. You finish by checking for regressions and making an honest call on whether the improvement justifies the cost and upkeep.
Reviews
4.7
3 ratingsSample
- Siddharth M.Sample
Building the baseline first was humbling: few shot prompting got closer than I expected. The fine tune still won, and I could prove it with numbers.
- Moses W.Sample
The template mismatch warning saved me. My inference prompt was formatted differently from training and results were terrible until I fixed it.
- Julia K.Sample
Clear, step by step and tool neutral. I wanted more detail on choosing a learning rate, but the 'start conservative and watch validation' advice worked.
About the teacher
Fine tuning with judgment: when to do it, how to do it well, and how to know it worked
9 tutors 428 lessons taught Sample
I teach fine tuning and post training: choosing between prompting, retrieval and tuning, building datasets, parameter efficient methods, instruction and preference tuning, and evaluating the result. My background is in applied machine learning projects where the expensive mistake was usually tuning a model before anyone had defined what better meant. That is why I start every topic with the evaluation...
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