Comments (2)
Hi, thank you for your feedback! In this case, it's better to use double()
precision rather than the float()
precision to run cholesky decomposition like the following way:
scaling_diag_matrix = torch.linalg.cholesky(raw_scaling_diag_matrix.double())
I have updated the code to fix this potential problem. You can compress LLaMA-13b using the following command:
python SVDLLM.py --model jeffwan/llama-13b-hf --step 1 --save_path "./"
If you still met the same problem when running the new code, please reopen this issue.
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I still got the same problem.
Traceback (most recent call last):
File "/home/xxx/SVD-LLM/SVDLLM_new.py", line 193, in whitening
scaling_matrix_inv = torch.linalg.inv(scaling_diag_matrix)
torch._C._LinAlgError: linalg.inv: The diagonal element 6940 is zero, the inversion could not be completed because the input matrix is singular. "
My python environment is built on requirements.txt. And I run the code on 2 3090 GPUs
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Related Issues (11)
- Request for Code Integration of SVD-LLM with GPTQ HOT 2
- recommended hardware resources HOT 1
- Instead of ratio-compress to specific layer size HOT 1
- Incorrect Model Responses after compression HOT 4
- still fail to apply on llama-13b HOT 2
- compresssed checkpoints HOT 1
- Compressed Model Produces Random and Repetitive Output - Request for Compressed Weights HOT 1
- Question about C4 dataset HOT 4
- Question about ASVD and FWSVD HOT 1
- Converting Compressed LLaMA2 Model to Hugging Face-Compatible Format HOT 1
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