## Problem
When running SFT with small device-batch-size (≤8), fully-masked micro-batches
cause NaN loss from step 1, corrupting gradients permanently. This happens when
a micro-batch contains only 'User' tokens (all targets=-1), especially common
with small batch sizes on consumer GPUs.
Root cause: torch.nn.functional.cross_entropy with reduction='mean' returns NaN
when all labels are -1 (division by zero in mean computation).
## Solution
Added validation in the training loop to detect and skip fully-masked batches:
- Check (y != -1).any() before computing loss
- Skip backward() for batches with no valid targets (zero gradient contribution)
- Track skipped batches and warn user if >5% in first 100 steps
- Log skipped batches as loss=0 for transparency
## Testing
- Added comprehensive test suite (test_sft_masked_batches.py)
- Tests cover: fully masked, partially masked, and unmasked batches
- Documents cross_entropy behavior with ignore_index=-1
- Validates the fix logic
## Impact
- Fixes#590: NaN loss with small batch sizes
- No performance impact for normal batches
- Helps users on consumer GPUs (RTX 3060, etc.)
- Prevents silent gradient corruption
Resolves#590
* printing steps count
* adding reply only loss for chat
* using the mask by render_conversation function of tokeniser
* undoing some changes
* putting back the comment which got removed accidently, no functionality change
