Update README.md

README.md

@@ -51,17 +51,17 @@ Doge uses Dynamic Mask Attention as sequence transformation and can use Multi-La
 
 ## Model Details
 
-We build the Doge by doing Per-Training on [Smollm-Corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus). If you want to continue pre-training this model, you can find the unconverged checkpoint [here](https://huggingface.co/SmallDoge/Doge-320M-checkpoint). These models has not been fine-tuned for instruction, the instruction model is [here](https://huggingface.co/SmallDoge/Doge-320M-Instruct).
+We build the Doge by doing Per-Training on [Smollm-Corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus). If you want to continue pre-training this model, you can find the unconverged checkpoint [here](https://huggingface.co/SmallDoge/Doge-60M-checkpoint). These models has not been fine-tuned for instruction, the instruction model is [here](https://huggingface.co/SmallDoge/Doge-60M-Instruct).
 
 
 **Pre-Training**:
 
 | Model | Training Data | Steps | Content Length | Tokens | LR | Batch Size | Precision | RTX 4090 GPU hours |
 |---|---|---|---|---|---|---|---|---|
-| [Doge-20M](https://huggingface.co/SmallDoge/Doge-20M) | [HuggingFaceTB/smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | 8k  | 2048 | 4B | 8e-3 | 0.5M | bfloat16 | 14 |
-| [Doge-60M](https://huggingface.co/SmallDoge/Doge-60M) | [HuggingFaceTB/smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | 16k  | 2048 | 16B | 6e-3 | 1M | bfloat16 | 128 |
-| [Doge-160M](https://huggingface.co/SmallDoge/Doge-160M) | [HuggingFaceTB/smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | 24k  | 2048 | 32B | 4e-3 | 1.5M | bfloat16 | 522 |
-| [Doge-320M](https://huggingface.co/SmallDoge/Doge-320M) | [HuggingFaceTB/smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | 32k  | 2048 | 64B | 2e-3 | 2M | bfloat16 | 1856 |
+| [Doge-20M](https://huggingface.co/SmallDoge/Doge-20M) | [smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | 8k  | 2048 | 4B | 8e-3 | 0.5M | bfloat16 | 14 |
+| [Doge-60M](https://huggingface.co/SmallDoge/Doge-60M) | [smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | 16k  | 2048 | 16B | 6e-3 | 1M | bfloat16 | 128 |
+| [Doge-160M](https://huggingface.co/SmallDoge/Doge-160M) | [smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | 24k  | 2048 | 32B | 4e-3 | 1.5M | bfloat16 | 522 |
+| [Doge-320M](https://huggingface.co/SmallDoge/Doge-320M) | [smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | 32k  | 2048 | 64B | 2e-3 | 2M | bfloat16 | 1856 |
 
 **Evaluation**:
 
@@ -70,17 +70,12 @@ We build the Doge by doing Per-Training on [Smollm-Corpus](https://huggingface.c
 | [Doge-20M](https://huggingface.co/SmallDoge/Doge-20M) | 25.4 | 0.03 | 29.8 | 58.4 | 27.3 | 25.6 | 50.2 | 142 |
 | [Doge-60M](https://huggingface.co/SmallDoge/Doge-60M) | 26.4 | 0.2 | 37.9 | 61.4 | 31.5 | 28.0 | 50.8 | 62 |
 | [Doge-160M](https://huggingface.co/SmallDoge/Doge-160M) | 29.2 | 4.8 | 44.4 | 70.1 | 43.4 | 34.4 | 52.2 | 28 |
-| [Doge-320M](https://huggingface.co/SmallDoge/Doge-320M) | 33.8 | 9.4 | 52.1 | 73.9 | 52.7 | 37.9 | 55.3 | 16 |
-
-> [!NOTE]
-> All evaluations are done using five-shot settings, without additional training on the benchmarks.
-
+| [Doge-320M](https://huggingface.co/SmallDoge/Doge-320M) | 35.6 | 9.4 | 55.4 | 73.9 | 52.7 | 37.9 | 59.3 | 16 |
 
 **Procedure**:
 
 [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/loser_cheems/huggingface/runs/p8x93v5l) 
 
-
 **Environment**:
 
 - Image: nvcr.io/nvidia/pytorch:24.12-py3