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Any QLoRA adapters trained on large checkpoints (e.g., 70B) are unusable as we cannot use TP>1 to shard the model over multiple GPUs. Therefore, resolving this would enable models that were trained with quantization, rather than having to rely on GPTQ and AWQ, which are applied post-hoc after training.
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🚀 The feature, motivation and pitch
Any QLoRA adapters trained on large checkpoints (e.g., 70B) are unusable as we cannot use TP>1 to shard the model over multiple GPUs. Therefore, resolving this would enable models that were trained with quantization, rather than having to rely on GPTQ and AWQ, which are applied post-hoc after training.
Alternatives
No response
Additional context
No response
Before submitting a new issue...
The text was updated successfully, but these errors were encountered: