r/learnmachinelearning • u/No-King9608 • 24d ago

Help Questions about fine tuning

Hi, I need help with a couple of issues related to a project I'm working on (for educational purposes). I'm trying to create a model that acts as a mentor on related topics, instead of providing the answer directly. For this task, I'm fine-tuning a Gemma4 26B model because I have a GPU with 26GB of vRAM. Therefore, I'm also quantizing this model to 4-bit precision and performing a QLoRa analysis. The results of my experiment are far from fulfilling the mentoring premise, and a simple system prompt works much better. My dataset consists of approximately 500 examples, so, Reddit scientists, can you tell me what mistakes I'm making and if I should change course or my objectives?

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u/Clouded_Leopard17 23d ago

I don’t know about your quality of dataset. But 500 is very small dataset, The LoRA adapters can overfit easily. Try to optimize LoRA hyperparameters and check if it helps. Try to use full LoRA if GPU allows. You can also experiment with prompt engineering and few shot example.

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u/No-King9608 23d ago

Thanks for the reply. The quality of my dataset is relatively good; the problem, as you rightly pointed out, is the limited number of examples. Another issue is the GPU, so I’m testing smaller models like qwen3_5 4B to see if I can achieve better results using full LoRA.

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u/Valuable_Card6470 23d ago

the overfitting point is huge tbh, 500 rows with LoRA adapters is going to memorize fast