Comments (2)
Given how hidden_z
is modeled (with hard concrete distributions), it's essentially learning a discrete binary value of 1 (retain the dimension) or 0 (prune the dimension). As the values strictly fall in side [0, 1], I don't think it will cause gradient explosion issues. For the same reason, it should not have an influence on the magnitude of the last hidden state. Let me know if it is clear!
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As the values strictly fall in side [0, 1]
Yes, the hidden state won't be too large, but will it be too small? Just plan to check it on the model without re-training.
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Related Issues (20)
- Could you provide tokenized continue-pretraining dataset for reproduction? HOT 2
- missmatch shape
- Start training but nothing continue HOT 6
- TypeError: buffer is too small for requested array
- Pruning fine-tuned model HOT 2
- save model meet problem HOT 1
- Instruction tuning dataset HOT 2
- If I can't configure Slurm on a cluster, does that mean I can't use multi-node multi-GPU setups? HOT 5
- 有没有不用Slurm跑剪枝的方法?
- None
- Start training but only output config information HOT 3
- The Project is not implemented for 70B llama? HOT 7
- LlamaRMSNorm() layer differs from original llama HOT 1
- composer model trans to pythia problem
- The dtype of tokenized data should be uint32 HOT 1
- Why the rope params are ignored while converting hf checkpoint to composer checkpoint? HOT 3
- about shearing params config HOT 1
- Can LLM-Shearing be used on ViT models? HOT 1
- Support for Llama-3 / GQA? HOT 1
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