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README.md
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The training data contains approximately 400 million tokens which from high-quality code dataset on HuggingFace.
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In addition, we applied `memory_efficient_attention` to the pre-training, which saves us a lot of GPU memory space.
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Our model can be used for SFT, and we hope to contribute more valuable work in the Chinese field.
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The training data contains approximately 400 million tokens which from high-quality code dataset on HuggingFace.
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In addition, we applied `memory_efficient_attention` to the pre-training, which saves us a lot of GPU memory space. If you want to quickly use this technology in your LLaMA model, you can refer to my GitHub: https://github.com/FrankMinions/memory_efficient_adapter.
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Our model can be used for SFT, and we hope to contribute more valuable work in the Chinese field.
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