Update README.md

README.md

@@ -57,21 +57,32 @@ Only the solution portion of each example was used for loss computation through
 
 ## Training Configuration  
 
-Method: LoRA (full precision, bfloat16)
-Precision: bfloat16 (no 4-bit quantization)
-LoRA rank: 16
-LoRA alpha: 32
-LoRA dropout: 0.05
-Target modules: q_proj, k_proj, v_proj, o_proj
-Max sequence length: 1024
-Batch size: 2
-Gradient accumulation: 8
-Effective batch size: 16
-Learning rate: 1e-4
-Optimizer: adamw_torch
-Scheduler: cosine
-Warmup: 5 percent
-Epochs: 3
+## Training Configuration (MI300X Run)
+
+**Method:** LoRA (full precision, bfloat16)  
+**Precision:** bfloat16 (no 4-bit quantization)  
+
+**LoRA settings**
+- Rank: 16  
+- Alpha: 32  
+- Dropout: 0.05  
+- Target modules: `q_proj`, `k_proj`, `v_proj`, `o_proj`  
+
+**Data & sequence**
+- Max sequence length: 1024  
+
+**Optimization**
+- Batch size: 2  
+- Gradient accumulation: 8  
+- **Effective batch size:** 16  
+- Learning rate: 1e-4  
+- Optimizer: `adamw_torch`  
+- Scheduler: cosine  
+- Warmup: 5%  
+
+**Training**
+- Epochs: 3  
+
 
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