Update README.md

README.md

@@ -41,12 +41,16 @@ In addition, Doge uses Dynamic Mask Attention as sequence transformation and can
 | Model | Training Data | Steps | Content Length | Tokens | LR | Batch Size | Precision |
 |---|---|---|---|---|---|---|---|
 | [Doge-20M](https://huggingface.co/JingzeShi/Doge-20M) | [HuggingFaceTB/smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | 8k  | 2048 | 4B | 8e-3 | 0.5M | bfloat16 |
+| [Doge-60M](https://huggingface.co/JingzeShi/Doge-60M) | [HuggingFaceTB/smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | 16k  | 2048 | 16B | 6e-3 | 1M | bfloat16 |
 
 **Evaluation**:
 
 | Model | MMLU | TriviaQA | ARC-E | ARC-C | PIQA | HellaSwag | OBQA | Winogrande |
 |---|---|---|---|---|---|---|---|---|
 | [Doge-20M](https://huggingface.co/JingzeShi/Doge-20M) | 25.43 | 0 | 36.83 | 22.53 | 58.38 | 27.25 | 25.60 | 50.20 |
+| [Doge-60M](https://huggingface.co/JingzeShi/Doge-60M) | 26.41 | 0 | 50.00 | 25.34 | 61.43 | 31.45 | 28.00 | 49.64 |
+
+> All evaluations are done using five-shot settings, without additional training on the benchmarks.
 
 **Procedure**: