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@@ -95,6 +95,8 @@ Here `ablate_{mag/wanda}_{seq/iter}` means that we use magnitude pruning or wand
 ### Zero-Shot Evaluation
 For evaluating zero-shot tasks, we modify the [EleutherAI LM Harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/master) framework so that it could evaluate pruned LLM models. We provide the modified repo in [this link](https://drive.google.com/file/d/1zugbLyGZKsH1L19L9biHLfaGGFnEc7XL/view?usp=sharing). Make sure to download, extract and install this custom `lm_eval` package from the source code.
 
+For reproducibility, we used [commit `df3da98`](https://github.com/EleutherAI/lm-evaluation-harness/tree/df3da98c5405deafd519c2ddca52bb7c3fe36bef) on the main branch. All tasks were evaluated on task version of 0 except for BoolQ, where the task version is 1.
+
 On a high level, the functionality we provide is adding two arguments `pretrained_model` and `tokenizer` in this [function](https://github.com/EleutherAI/lm-evaluation-harness/blob/master/lm_eval/evaluator.py#L17). We can then call this `simple_evaluate` function API from our [codebase](https://github.com/locuslab/wanda/blob/main/main.py#L100) to evaluate sparse pruned LLMs. To evaluate zero-shot tasks in addition to the WikiText perplexity, pass the `--eval_zero_shot` argument. 
 
 Last, for pruning image classifiers, see directory [image_classifiers](image_classifiers) for details.