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- A Reddit user reported that reducing the learning rate from 2e-4 to 1e-4 and increasing the number of training epochs from 3 to 5 improved their Qlora fine-tuning results significantly. They were previously struggling with poor evaluation metrics despite trying various data preprocessing techniques.
- Specifically, they noted that a learning rate of 2e-4 was too high for their dataset size (8k samples), leading the model to overfit in just one epoch and performing poorly thereafter. By lowering this to 1e-4, they allowed the model more time to converge without overshooting the optimal solution.
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### Takeaways
– Lowering learning rate from 2e-4 to 1e-4 can significantly improve Qlora fine-tuning results for smaller datasets.
– Increasing training epochs helped stabilize and enhance the model’s performance.
– Careful tuning of hyperparameters like learning rate is crucial, especially with limited data.
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