Update README.md

README.md

@@ -251,15 +251,16 @@ outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
 #### Training Hyperparameters
 
 The following hyperparameters were used during training:
+- learning_rate: 3e-4
+- per_device_train_batch_size: 1
+- gradient_accumulation_steps: 4
+- eval_batch_size: Implicitly determined by the evaluation setup
+- seed: Not explicitly stated
+- optimizer: paged_adamw_8bit
+- lr_scheduler_type: Not specified, adaptive adjustments indicated
+- training_steps: 500
+- mixed_precision_training: Not explicitly mentioned
 
-- **learning_rate:** `3e-4`
-- **train_batch_size:** Effectively adjusted by `per_device_train_batch_size=1` and `gradient_accumulation_steps=4`
-- **eval_batch_size:** Implicitly determined by the evaluation setup (not explicitly defined)
-- **seed:** Not explicitly stated, crucial for ensuring reproducibility
-- **optimizer:** `paged_adamw_8bit`, designed for efficient memory utilization
-- **lr_scheduler_type:** Learning rate adjustments indicate adaptive scheduling, though specific type is not mentioned
-- **training_steps:** `500`
-- **mixed_precision_training:** Not explicitly mentioned; any applied strategy would aim at computational efficiency
 
 #### Training Results