Update README.md

README.md

@@ -23,8 +23,8 @@ In addition, Doge uses Dynamic Mask Attention as sequence transformation and can
 ```python
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM
 
->>> tokenizer = AutoTokenizer.from_pretrained("JingzeShi/Doge-60M")
->>> model = AutoModelForCausalLM.from_pretrained("JingzeShi/Doge-60M", trust_remote_code=True)
+>>> tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-60M")
+>>> model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-60M", trust_remote_code=True)
 >>> inputs = tokenizer("Hey how are you doing?", return_tensors="pt")
 
 >>> out = model.generate(**inputs, max_new_tokens=100)
@@ -36,9 +36,9 @@ In addition, Doge uses Dynamic Mask Attention as sequence transformation and can
 
 We build the Doge by doing Per-Training on [Smollm-Corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus).
 
-> NOTE: If you want to continue pre-training this model, you can find the unconverged checkpoint [here](https://huggingface.co/JingzeShi/Doge-60M-checkpoint).
+> NOTE: If you want to continue pre-training this model, you can find the unconverged checkpoint [here](https://huggingface.co/SmallDoge/Doge-60M-checkpoint).
 
-> NOTE: These models has not been fine-tuned for instruction, the instruction model is [here](https://huggingface.co/JingzeShi/Doge-60M-Instruct).
+> NOTE: These models has not been fine-tuned for instruction, the instruction model is [here](https://huggingface.co/SmallDoge/Doge-60M-Instruct).
 
 > TODO: The larger model is under training and will be uploaded soon.
 
@@ -46,15 +46,15 @@ We build the Doge by doing Per-Training on [Smollm-Corpus](https://huggingface.c
 
 | 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 |
+| [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 |
+| [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 |
 
 **Evaluation**:
 
 | Model | MMLU | TriviaQA | ARC-E | ARC-C | PIQA | HellaSwag | OBQA | Winogrande | tokens / s on CPU |
 |---|---|---|---|---|---|---|---|---|---|
-| [Doge-20M](https://huggingface.co/JingzeShi/Doge-20M) | 25.43 | 0.03 | 36.83 | 22.78 | 58.38 | 27.25 | 25.60 | 50.20 | 142 |
-| [Doge-60M](https://huggingface.co/JingzeShi/Doge-60M) | 26.41 | 0.18 | 50.46 | 25.34 | 61.43 | 31.45 | 28.00 | 50.75 | 62 |
+| [Doge-20M](https://huggingface.co/SmallDoge/Doge-20M) | 25.43 | 0.03 | 36.83 | 22.78 | 58.38 | 27.25 | 25.60 | 50.20 | 142 |
+| [Doge-60M](https://huggingface.co/SmallDoge/Doge-60M) | 26.41 | 0.18 | 50.46 | 25.34 | 61.43 | 31.45 | 28.00 | 50.75 | 62 |
 
 > All evaluations are done using five-shot settings, without additional training on the benchmarks.