Comments (2)
Hello! When the mean value is larger and positive, it indicates that the sampled initial 'z_loga' is more likely to be skewed toward a larger positive value. This, in turn, implies that the mask sampled from the hard concrete distribution tends to lean towards 1. This suggests that we get to preserve all the modules from the beginning of the pruning process. The initial mean assignment could be commented out.
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thank u for ur kind and detail answers!
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Related Issues (20)
- 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
- Open source the pruning mask. HOT 2
- Default Initialization of Lambda Parameters to Zero HOT 3
- About the NQ EM Score in Table 2
- Request for Fine-tuning Data for Continued Pre-training
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