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_10

WANDB_MODE=disabled run python hyperlora/intx_sft.py configs/context_numbers_10.yaml --model_name_or_path=meta-llama/Llama-3.2-1B-Instruct --num_train_epochs=100 --per_device_train_batch_size=64 --per_device_eval_batch_size=64 --exp_setup=hyper_lora --aggregator_type=perceiver --target_modules=down_proj,up_proj

HyperLoRA w/ context_numbers_128

WANDB_MODE=disabled run python hyperlora/intx_sft.py configs/context_numbers_128.yaml --model_name_or_path=meta-llama/Llama-3.2-1B-Instruct --num_train_epochs=10 --per_device_train_batch_size=64 --per_device_eval_batch_size=8 --exp_setup=hyper_lora --aggregator_type=perceiver --target_modules=down_proj

Generate fineweb qa

# this might take several days...
# python process_fineweb.py
# python generate_fw_qa.py
# python post_process_fw_qa.py
conda activate vllm;
vllm_model=mistralai/Mistral-Small-3.1-24B-Instruct-2503 run python generate_fw_edu_qa_vllm.py "0*_*" 3

GSM8k LoRA

run python intx_sft.py configs/gsm8k.yaml --model_name_or_path=google/gemma-2-2b-it --num_train_epochs=10 --per_device_train_batch_size=16 --gradient_accumulation_steps=1 --per_device_eval_batch_size=32 --exp_setup=lora --target_modules=up_proj,down_proj --eval_steps=5000 --save_steps=5000 --learning_rate=1e-4 --neftune_noise_alpha=5 --load_best_model_at_end=True --metric_for_best_model=gsm8k_loss

Multitask LoRA

run python intx_sft.py configs/math_and_code.yaml --model_name_or_path=google/gemma-2-2b-it --num_train_epochs=1 --per_device_train_batch_size=4 --gradient_accumulation_steps=1 --per_device_eval_batch_size=32 --exp_setup=lora --target_modules=up_proj,down_proj --eval_steps=5000 --save_steps=5000 --learning_rate=2e-5 --neftune_noise_alpha=5 --load_best_model_at_end=True --metric_for_best_model=gsm8k_loss

HyperLoRA finetune on GSM8K

run python intx_sft.py configs/gsm8k.yaml --model_name_or_path=google/gemma-2-2b-it --num_train_epochs=5 --per_device_train_batch_size=16 --gradient_accumulation_steps=1 --per_device_eval_batch_size=8 --exp_setup=hyper_lora --aggregator_type=perceiver --target_modules=down_proj --num_blocks=1 --num_self_attends_per_block=16 --self_attention_widening_factor=1 --eval_steps=5000 --save_steps=5000 --learning_rate=2e-5 --neftune_noise_alpha=5 --use_light_weight_lora=True --light_weight_latent_size=512 --load_best_model_at_end=True --metric_for_best_model=pwc_loss --add_negative_prompt=False --add_repeat_prompt=False --ctx_encoder_model_name_or_path=meta-llama/Llama-3.2-11B-Vision-Instruct

Continue from a checkpoint

run python intx_sft.py configs/...yaml ... --from_pretrained_checkpoint=train_outputs/runs/May09_16-25-35_slurm0-a3nodeset-4_59459_ea85a571/checkpoint-10000/pytorch_model.bin --resume_from_checkpoint=train_outputs/runs/May09_16-25-35_slurm0-a3nodeset-4_59459_ea85a571/checkpoint-10000

Evaluation

LongBench

# generative
run python src/ctx_to_lora/eval.py --checkpoint_path train_outputs/runs/.../pytorch_model.bin --datasets negative_nq triviaqa_retrieved hotpot_qa squad longbench_e -
-split test

# run python src/ctx_to_lora/eval.py --checkpoint_path train_outputs/runs/May08_13-56-31_slurm0-a3nodeset-5_59383_906acb28/checkpoint-105000/pytorch_model.bin

# # benchmark
# cd LongBench/LongBench

# run python pred_ctx_to_lora.py  --checkpoint_path ../../train_outputs/runs/Mar16_12-38-01_slurm0-a3nodeset-12_54818_32426662/checkpoint-136782/pytorch_model.bin

# run python eval_ctx_to_lora.py --model_name Mar16_12-38-01_slurm0-a3nodeset-12_54818_32426662/checkpoint-136782 --checkpoint_path ../../train_outputs/runs/Mar16_12-38-01_slurm0-a3nodeset-12_54818_32426662/checkpoint-136782/pytorch_model.bin