Hypernetworks that update LLMs to remember factual information https://arxiv.org/abs/2602.15902
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Project doc

Finetuning the base model with LoRA adaptor

WANDB_MODE=disabled python hyperlora/intx_sft.py configs/default.yaml --model_name_or_path=meta-llama/Llama-3.2-1B-Instruct --num_train_epochs=50 --per_device_train_batch_size=128 --per_device_eval_batch_size=128 --exp_setup=lora --train_ds_name=data/raw_datasets/context_numbers_small

HyperLoRA w/ context_numbers_small

WANDB_MODE=disabled python hyperlora/intx_sft.py configs/default.yaml --model_name_or_path=meta-llama/Llama-3.2-1B-Instruct --num_train_epochs=50 --per_device_train_batch_size=128 --per_device_eval_batch_size=128 --exp_setup=hyper_lora --train_ds_name=data/raw_datasets/context_numbers_small

HyperLoRA w/ Perceiver w/ context_numbers_medium

WANDB_MODE=disabled python hyperlora/intx_sft.py configs/default.yaml --model_name_or_path=meta-llama/Llama-3.2-1B-Instruct --num_train_epochs=1 --per_device_train_batch_size=128 --per_device_eval_batch_size=128 --exp_setup=hyper_lora --train_ds_name=data/raw_datasets/context_numbers_medium --aggregator_type=perceiver