The settings for conducting batch editing on LLaMA2-7B.

[XZhang00]

Hello! Thanks for your outstanding work!
I wanted to edit LLaMA2 with PMET, but the experiment failed. Can you share the settings for conducting batch editing on LLaMA2-7B?
By the way, when will the SWEAOS code be open source? I am very excited and thank you very much for your new SOTA approach.

Looking forward to your reply!

[xpq-tech]

Thank you for your interest in our work. We utilized the following hyperparameter settings for LLama-2 editing with PMET:
{ "layers": [ 4, 5, 6, 7, 8 ], "clamp_norm_factor": 1, "layer_selection": "all", "fact_token": "subject_last", "v_num_grad_steps": 20, "v_lr": 0.5, "v_loss_layer": 31, "v_weight_decay": 0.5, "kl_factor": 0.0625, "rewrite_module_tmp": "model.layers.{}.mlp.down_proj", "rewrite_module_tmps": ["model.layers.{}.mlp.down_proj"], "layer_module_tmp": "model.layers.{}", "mlp_module_tmp": "model.layers.{}.mlp.down_proj", "attn_module_tmp": "model.layers.{}.self_attn.o_proj", "ln_f_module": "model.norm", "lm_head_module": "lm_head", "mom2_adjustment": true, "mom2_update_weight": 6000, "mom2_dataset": "wikipedia", "mom2_n_samples": 100000, "mom2_dtype": "float32", "nll_loss_factor": 1 }

Please note that there is a slight difference between the tokenizer of LLama-2 and that of GPT-J. The latter requires a space before each word for correct encoding, whereas the former does not. Our SWEAOS code will be released soon.

[XZhang00]

Thanks for your reply!