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architectures/polycoder.txt
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[PolyCoder](https://github.com/VHellendoorn/Code-LMs) uses GPT2 architecture, with BPE tokenizer trained on a random 5% subset of the data (all languages), and a context mength of 2048. To study the effect of scaling of model size, the odel was trained in 3 different sizes.
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|Model | # parameters |
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| GPT2 | 160M |
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| GPT2 | 400M |
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| GPT2 | 2.7B |
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PolyCoder is currently being integrated in `transformers`. Meanwhile it can be loaded following the instructions in the original Github [repo](https://github.com/vhellendoorn/code-lms#models).
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[PolyCoder](https://github.com/VHellendoorn/Code-LMs) uses GPT2 architecture, with BPE tokenizer trained on a random 5% subset of the data (all languages), and a context mength of 2048. To study the effect of scaling of model size, the odel was trained in 3 different sizes.
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<div align="center">
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|Model | # parameters |
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| - | - |
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| GPT2 | 160M |
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| GPT2 | 400M |
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| GPT2 | 2.7B |
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</div>
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PolyCoder is currently being integrated in `transformers`. Meanwhile it can be loaded following the instructions in the original Github [repo](https://github.com/vhellendoorn/code-lms#models).
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