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efrantar avatar efrantar commented on August 18, 2024

Hi, what do you mean by "very bad results"? As also discussed in #7, pruning LLaMa seems to be more challenging than pruning e.g. OPT, possibly because it is more parameter efficient. I just ran --sparsity .5 on the 7B model with pretty recent package versions (transformers==4.31.0, datasets==2.13.1 and torch==2.0.1) and got 7.20 PPL for Wiki and 9.29 for C4 PPL (some package version newer than the ones we list in the README seems to have broken PTB numbers in general, not sure why). What numbers do you get?

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MrGranddy avatar MrGranddy commented on August 18, 2024

Hello, I make evaluations on some standart LLM evaluation tasks, using "LLM Evaluation Harness":
https://github.com/EleutherAI/lm-evaluation-harness

I get the following results for LLaMA:

LLaMA-7B Dense Magnitude 50% SparseGPT 50% SparseGPT 2:4
arc_challenge (acc_norm) 0.4138 0.302 0.2833 0.291
arc_easy (acc_norm) 0.5248 0.2702 0.2588 0.266
boolq (acc) 0.7315 0.6214 0.6193 0.3823

Normally I would expect some performance drop yet for comparison here are the results for BLOOM-7B:

BLOOM-7B1 Dense Magnitude 50% SparseGPT 50% SparseGPT 2:4
arc_challenge (acc_norm) 0.3336 0.3072 0.3055 0.2722
arc_easy (acc_norm) 0.5728 0.5261 0.5316 0.4945
boolq (acc) 0.6291 0.6064 0.6303 0.6226

So probably there is something wrong with the implementation, as I mentioned my own implementation also get the same results, so I would like to compare it with your results. Can you please do the experiments with the latest version of transformers so we can validate?

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MrGranddy avatar MrGranddy commented on August 18, 2024

Sorry I've closed the issue by accident, I would be glad if you can re-open so we can solve the issue. I also tried the experiment with multiple torch, python and transformers versions, if your results are better, I would expect that it works for a very spesific version of libraries for some reason.

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