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When fine-tuning a common thing to happen is that samples have very different length, particularly when we chain longer conversations together. It is a regular practice to pack sequences together in a batch, to fill up the vailable size.
The current solution is inefficient particularly in multi-gpu setups.
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When fine-tuning a common thing to happen is that samples have very different length, particularly when we chain longer conversations together. It is a regular practice to pack sequences together in a batch, to fill up the vailable size.
The current solution is inefficient particularly in multi-gpu setups.
I have not fully explored what the best strategy is here. Can also have a look how other libraries are doing it, such as:
https://github.com/OpenAccess-AI-Collective/axolotl/blob/450e04d3c460828be66937426a91cfd161973a87/src/axolotl/utils/samplers/multipack.py#L105
Additionally, we should explore if scaling the loss based on actual tokens per sample trained on is something to look into.
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