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License: Apache License 2.0
Code for "Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes"
License: Apache License 2.0
I am trying to run your code and encounter this error in lib/scoring_model.py
in the run_epoch
function, n_batches
is zero and leads to float division by zero error in running_loss_avg /= n_batches
file main.py
line 65
seed_ = random.randint(0, 1e4)
Gives error
TypeError: 'float' object cannot be interpreted as an integer
Solution:
seed_ = random.randint(0, int(1e4) )
The current installation from the original /lm-evaluation-harness does not work with the no_cache and pretrained model arguments
cd lm-evaluation-harness
pip install -e .
'''
May I ask if this tool is currently unable to perform pruning on GQA models? Llama2-70B or Llama3
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