Comments (2)
iopwsy
commented on May 23, 2024
I have the same problem, but I fix it.
It seems you need set loss.requires_grad = True and it seems normal.
However, the results is not as good. I don't know why.
Maybe the loss here is for basic model rather than LoRA?
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HelloWorldLTY
commented on May 23, 2024
Hi, thanks for your exaplaniation. I use another approch to address it.
But my training time is longer.
After using LoRA, it is:
No Lora, it is:
I have no ideas.
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Related Issues (20)
- Error in MergedLinear HOT 5
- RuntimeError: CUDA error: CUBLAS_STATUS_INVALID_VALUE when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc)` HOT 4
- Pre-trained conv weight is not same as that self.conv.weight HOT 3
- matmul ordering in MergedLinear HOT 1
- How does this paper search the hyperparameters on GLUE datasets with Roberta? HOT 1
- PyPi version and GitHub version give different results HOT 1
- Embedding reset_parameters() implement wrong HOT 3
- Conv1d and Conv3d are not working HOT 2
- fine tuning RoBERTa-base with LoRA (ValueError: Classification metrics can't handle a mix of binary and multilabel-indicator targets)
- The content on pypi does not seem to be updated HOT 3
- Using gradient checkpoint with LoRA
- Probably a bug in the lora embeding class in loralib/layers.py HOT 2
- Some questions about LoRA for pre-trained model
- Question about reproducing RoBERTa base Fine-tune HOT 1
- T(w) problem
- batch=1,why adapter latency so much vs. LoRA in paper??? HOT 3
- AttributeError: 'lora.Conv2d' object has no attribute 'weight' HOT 3
- Support multi-lora fine tune in the same GPU
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