Comments (1)
jerry-chee
commented on August 11, 2024
Hi, please see our updated repo QuIP# which does save weights in the correct format. For this project we were primarily testing the effect on model quality (ie perplexity) as a result of quantization, and therefore employed "fake quantization" where we restricted to the correct number of unique points, but kept things in fp16 for ease of engineering.
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Related Issues (10)
- Why only class Quant3Linear(nn.Module)? Does this work for 2bit, 3bit, and 4bit? I understand it is only for bit3 . Thanks HOT 1
- gptq reorder canceled in quip? HOT 1
- Evaluation on LLama HOT 4
- How to use quantized model on inference HOT 1
- error computing is not correct of covariant of pre and post process HOT 1
- group-size for quip HOT 1
- Do we still need to store U, V for each W HOT 2
- support for llama HOT 1
- Thank you
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