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@@ -91,5 +91,18 @@ The model is a decoder-only transformer architecture with the following modifica
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  * **ReLU Activation Function**: ReLU([Glorot et al., 2011](https://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf)) activation functions are adopted in feed-forward networks.
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  * **Tokenizer**: We use the SmolLM([Allal et al., 2024](https://huggingface.co/blog/smollm))'s tokenizer with a vocabulary size of 49,152.
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- ## LICENSE
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- * This repository is released under the [Apache-2.0](https://huggingface.co/mllmTeam/PhoneLM-1.5B-Call/blob/main/LICENSE) License.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  * **ReLU Activation Function**: ReLU([Glorot et al., 2011](https://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf)) activation functions are adopted in feed-forward networks.
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  * **Tokenizer**: We use the SmolLM([Allal et al., 2024](https://huggingface.co/blog/smollm))'s tokenizer with a vocabulary size of 49,152.
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+ ## License
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+ * This repository is released under the [Apache-2.0](https://huggingface.co/mllmTeam/PhoneLM-1.5B-Call/blob/main/LICENSE) License.
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+
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+ ## Citation
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+ ```
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+ @misc{yi2024phonelmanefficientcapablesmall,
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+ title={PhoneLM:an Efficient and Capable Small Language Model Family through Principled Pre-training},
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+ author={Rongjie Yi and Xiang Li and Weikai Xie and Zhenyan Lu and Chenghua Wang and Ao Zhou and Shangguang Wang and Xiwen Zhang and Mengwei Xu},
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+ year={2024},
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+ eprint={2411.05046},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL},
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+ url={https://arxiv.org/abs/2411.05046},
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+ }
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+ ```