Update README.md

README.md

@@ -26,6 +26,34 @@ SmallThinker is designed for the following use cases:
 1.  **Edge Deployment:** Its small size makes it ideal for deployment on resource-constrained devices.
 2.  **Draft Model for QwQ-32B-Preview:** SmallThinker can serve as a fast and efficient draft model for the larger QwQ-32B-Preview model. From my test, in llama.cpp we can get 70% speedup (from 40 tokens/s to 70 tokens/s).
 
+## Training Details
+
+The model was trained using 8 H100 GPUs with a global batch size of 16. The specific configuration is as follows:
+
+```
+neat_packing: true
+cutoff_len: 16384
+per_device_train_batch_size: 2
+gradient_accumulation_steps: 1
+learning_rate: 1.0e-5
+num_train_epochs: 3
+lr_scheduler_type: cosine
+warmup_ratio: 0.02
+bf16: true
+ddp_timeout: 180000000
+weight_decay: 0.0
+```
+
+The SFT (Supervised Fine-Tuning) process was conducted in two phases:
+
+1. First Phase:
+   - Used only the PowerInfer/QWQ-LONGCOT-500K dataset
+   - Trained for 1.5 epochs
+
+2. Second Phase:
+   - Combined training with PowerInfer/QWQ-LONGCOT-500K and PowerInfer/LONGCOT-Refine datasets
+   - Continued training for an additional 2 epochs
+   - 
 ## Limitations & Disclaimer
 
 Please be aware of the following limitations: