Comments (4)
I'm also hitting some CUDA out of memory errors on models + data that I expect to more easily fit on a 40GB A100 MiG.
I'm not familiar with the lit-llama codebase, so I'm not sure what's potentially different in lit-parrot but wanted to note that I'm seeing something similar.
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Do you still see this behaviour, and if so, can you share exactly the code you ran and the arguments passed?
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This is because LLaMA fine-tuning is hardcoded to use 256
max_seq_length:
https://github.com/Lightning-AI/lit-llama/blob/main/scripts/prepare_alpaca.py#L26
https://github.com/Lightning-AI/lit-llama/blob/main/finetune/adapter.py#L52
Whereas this repository is configured to use the longest sequence length in alpaca: 1037
. If you override it to 256
in https://github.com/Lightning-AI/lit-gpt/blob/main/finetune/adapter.py#L30, you should see the times match.
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Actually I was using the pretrain script, and I think the max token length is fixed in both lit-llama and lit-gpt?
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Related Issues (20)
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