[Need to test] Remove max_memory usage and adapter float16 initialization by default #3610
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tgaddair
reviewed
Sep 15, 2023
| @@ -321,7 +321,6 @@ def to_device(self, device): | |||
| self.model = PeftModel.from_pretrained( | |||
| self.model, | |||
| tmpdir, | |||
| torch_dtype=torch.float16, | |||
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We probably need a way to plumb this in, otherwise it's going to load in fp32, right?
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That is true yeah, I think we actually need a way to plumb in
quantization:
bits: 16
type: bfloat16
and
quantization:
bits: 16
type: float16
and then trickle this down appropriately to both the base model and the adapter.
This will also allow users to experiment with non-deepspeed-based training if they'd like (say they have a H100 or A100)
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