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pre-train processing

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51616 2025-06-06 05:01:22 +00:00
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@ -33,7 +33,6 @@ print(model.decode(outputs))
```bash
WANDB_MODE=disabled uv run python 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 --per_rank_gen=True --per_layer_processing=True --decoder_depth=2 --seed=1 --gen_lora_l1_reg_coef=0 --use_token_mixing=False --lora_r=8 --bf16=True --tf32=True --dataloader_num_workers=8 --dataloader_prefetch_factor=8
```
<!-- # TODO: sequence packing remove cols? -->
### PwC + Hotpot training (for testing/debugging)
```bash
WANDB_MODE=disabled uv run intx_sft.py configs/pwc_hotpot_qa.yaml \
@ -60,7 +59,13 @@ uv run python data/self_generate_qa.py \
--vllm_model=google/gemma-2-2b-it --config=configs/self_gen_3_mini.yaml
uv run python intx_sft.py configs/self_gen_3_mini.yaml --model_name_or_path=google/gemma-2-2b-it --num_train_epochs=10 --per_device_train_batch_size=32 --gradient_accumulation_steps=8 --per_device_eval_batch_size=32 --exp_setup=hyper_lora --aggregator_type=perceiver --target_modules=down_proj --num_self_attends_per_block=8 --num_latent_factor=2 --lora_r=8 --eval_steps=5000 --save_steps=5000 --learning_rate=4e-5 --lora_dropout=0.0 --neftune_noise_alpha=5 --use_light_weight_lora=False --load_best_model_at_end=True --metric_for_best_model=pwc_loss --add_negative_prompt=False --add_repeat_prompt=False --use_sequence_packing=True --per_rank_gen=True --per_layer_processing=True --gen_lora_l1_reg_coef=0.1
WANDB_MODE=disabled uv run python intx_sft.py configs/self_gen_3_mini.yaml --model_name_or_path=google/gemma-2-2b-it --num_train_epochs=10 --per_device_train_batch_size=32 --gradient_accumulation_steps=8 --per_device_eval_batch_size=32 --exp_setup=hyper_lora --aggregator_type=perceiver --target_modules=down_proj --num_self_attends_per_block=8 --num_latent_factor=2 --lora_r=8 --eval_steps=5000 --save_steps=5000 --learning_rate=4e-5 --lora_dropout=0.0 --neftune_noise_alpha=5 --use_light_weight_lora=False --load_best_model_at_end=True --metric_for_best_model=pwc_loss --add_negative_prompt=False --add_repeat_prompt=False --use_sequence_packing=True --per_rank_gen=True --per_layer_processing=True --gen_lora_l1_reg_coef=0.1
```
### HyperLoRA w/ fw-qa pretrain only
```bash
WANDB_MODE=disabled run uv run python intx_sft.py configs/fw_qa_pretrain_only.yaml --model_name_or_path=google/gemma-2-2b-it --num_train_epochs=1 --per_device_train_batch_size=4 --gradient_accumulation_steps=8 --per_device_eval_batch_size=8 --exp_setup=hyper_lora --aggregator_type=perceiver --target_modules=down_proj --num_self_attends_per_block=4 --num_latent_factor=1 --lora_r=8 --eval_steps=100 --save_steps=100 --learning_rate=4e-5 --lora_dropout=0.0 --neftune_noise_alpha=5 --use_light_weight_lora=False --add_negative_prompt=False --add_repeat_prompt=False --use_sequence_packing=True --per_rank_gen=True --per_layer_processing=True --gen_lora_l1_reg_coef=0.1
```
<!-- ### HyperLoRA w/ context_numbers_128