Update README.md

README.md

@@ -89,26 +89,37 @@ optimizer: AdamW (β₁=0.9, β₂=0.999, ε=1e-8)
 neftune_noise_alpha: 5
 per_device_train_batch_size: 32
 gradient_accumulation_steps: 2
-
-# Steps & Efficiency
-total_training_steps: 2,960
 packing_enabled: true (~5x efficiency gain)
 eval_steps: 100
 save_steps: 200
 logging_steps: 10
-
-# Final Metrics
-final_train_loss: 1.127
-final_eval_loss: 1.041
 ```
 
 ### Hardware & Distributed Training
 - **GPUs**: 4× AMD MI300X (192GB total VRAM)
-- **Training Time**: 6 hours 47 minutes (2.0 epochs, 2,960 steps)
 - **FSDP**: Full sharding with activation checkpointing
-- **Peak Memory**: 71GB per GPU
 - **Effective Batch Size**: 256 (32 × 4 GPUs × 2 gradient accumulation)
 - **Packing**: Enabled for efficient sequence batching
+- **Total Training Steps**: 2,960
+- **Training Time**: 6 hours 47 minutes (2.0 epochs, 2,960 steps)
+- **Peak Memory**: 71GB per GPU
+
+
+### Training Progress
+
+The following charts show the training dynamics over 2,960 steps (2 epochs with sequence packing):
+
+![Training Loss](./train_loss_noot_eurollm.png)
+*Training loss showing rapid initial convergence from 2.0 to ~1.3 within 500 steps, stabilizing at 1.127*
+
+![Evaluation Loss](./eval_loss_noot_eurollm.png)
+*Evaluation loss measured every 100 steps, final value: 1.041*
+
+**Key observations:**
+- Rapid convergence in first 500 steps due to effective initialization from base model
+- Smooth cosine learning rate schedule with 200 warmup steps
+- Consistent improvement throughout both epochs
+- Stable training with no signs of overfitting (eval loss < train loss)
 
 ### Dataset Engineering