Comments (2)
Hi @LameloBally, thanks for your interest of our work.
Unfortunately the answer is no. LoftQ aims to provide a data-free initialization for all downstream task finetuning, so quantization with data calibration is out of our scope. However, we welcome you to explore more possibilities of our method: you can obtain a quantized backbone (Q) from GPTQ or AWQ and then initialize LoRA adapters, A and B, by SVD(W-Q), where W is the full-precision pre-trained weight.
Let me know if you have further questions.
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@yxli2123 Thanks for your kind answer. It was very helpful for me!!
But, I have another question. How Can I initialize LoRA Adapter by SVD(W-Q) s.t. Q is GPTQ model & W is full precision.
I thought LoftQ is very innovative idea so I want to apply it to other cases to develop it further.
It will be very helpful for me if you let me know How can I find any guide or the way I can do it.
Thanks!
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Related Issues (20)
- Can we use LoftQ to optimize vision foundation models like OWL-ViT v2 and Grounding Dino? HOT 1
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- A question from a novice. HOT 2
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- Why are the full models, and not just adapters, pushed to hub? HOT 2
- Failing to converge when using some random seeds HOT 2
- Performance worsens versus QLoRA with TinyLlama
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- quick question about the Llama-3 results HOT 1
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