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@@ -77,7 +77,7 @@ It turned out to be a syntax error in my arguments passed to the `Trainer` (or r
### Pitfall #2: Stability vs. Noise
The loss curve was initially extremely erratic. The batch size on my GPU was limited (Physical Batch Size = 4).
**The Fix**: I implemented **Gradient Accumulation** (accumulating over 8 steps) to simulate a batch size of 32. This smoothed out the optimization landscape significantly.
![S3 File](/images/technical-deep-dive-llm-categorization/eedb3be8259a4a70aa7029b78a029364.png)
![S3 File](http://localhost:4998/attachments/image-1b23344ea5541d156e5ac20823d12d7c6723b691.png?client=default&bucket=obsidian)
### Pitfall #3: Overfitting
With a small dataset (~2k samples), overfitting is a real risk. I employed a multi-layered defense strategy: