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clean up + configs + scripts

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51616 2025-06-24 21:51:33 +09:00
parent 1ec757f43a
commit 816377b4ac
37 changed files with 216 additions and 1091 deletions
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35
scripts/fw_qa_pretrain/gemma_pretrain.sh
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#!/bin/bash
#SBATCH --job-name=ctxlora_medium
#SBATCH --partition=a3
#SBATCH --nodes=1
#SBATCH --gpus=4
#SBATCH --output=outputs/%x-%j.out
#SBATCH --error=outputs/%x-%j.out
# module load
# module load cuda/12.1
# module load cudnn/8.9.7
# module load nccl/cuda-12.1/2.18.3
# module load hpcx/2.20
# export OMP_NUM_THREADS=24
# export TRITON_CACHE_DIR=/tmp/.triton/
# . ~/miniconda3/etc/profile.d/conda.sh
# conda activate /home/rujikorn_sakana_ai/.conda/envs/ctx-to-lora
# eval "$@"
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=32 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29560 intx_sft.py configs/fw_qa_pretrain_small.yaml \
--model_name_or_path=google/gemma-2-2b-it --num_train_epochs=2 --per_device_train_batch_size=4 \
--gradient_accumulation_steps=32 --per_device_eval_batch_size=8 --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 \
--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 \

35
scripts/fw_qa_pretrain/gemma_pretrain_and_pwc.sh
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#!/bin/bash
#SBATCH --job-name=ctxlora_medium
#SBATCH --partition=a3
#SBATCH --nodes=1
#SBATCH --gpus=4
#SBATCH --output=outputs/%x-%j.out
#SBATCH --error=outputs/%x-%j.out
# module load
# module load cuda/12.1
# module load cudnn/8.9.7
# module load nccl/cuda-12.1/2.18.3
# module load hpcx/2.20
# export OMP_NUM_THREADS=24
# export TRITON_CACHE_DIR=/tmp/.triton/
# . ~/miniconda3/etc/profile.d/conda.sh
# conda activate /home/rujikorn_sakana_ai/.conda/envs/ctx-to-lora
# eval "$@"
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=16 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29560 intx_sft.py configs/fw_qa_pretrain_small_and_pwc.yaml \
--model_name_or_path=google/gemma-2-2b-it --num_train_epochs=1 --per_device_train_batch_size=4 \
--gradient_accumulation_steps=16 --per_device_eval_batch_size=8 --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 \
--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 \

25
scripts/gemma_data_exp/gemma_qa.sh
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#!/bin/bash
#SBATCH --job-name=ctxlora_medium
#SBATCH --partition=a3
#SBATCH --nodes=1
#SBATCH --gpus=4
#SBATCH --output=outputs/%x-%j.out
#SBATCH --error=outputs/%x-%j.out
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=16 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29561 intx_sft.py configs/qa.yaml \
--model_name_or_path=google/gemma-2-2b-it --num_train_epochs=2.2 --per_device_train_batch_size=-1 \
--gradient_accumulation_steps=16 --per_device_eval_batch_size=4 --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=1000000000000000 --do_eval=no --do_predict=no --eval_on_start=False \
--save_steps=5000 --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 --max_packed_inp_len=8192 --max_packed_ctx_len=16384 \
--per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \

25
scripts/gemma_data_exp/gemma_qa_8_gpu.sh
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#!/bin/bash
#SBATCH --job-name=ctxlora_medium
#SBATCH --partition=a3
#SBATCH --nodes=1
#SBATCH --gpus=8
#SBATCH --output=outputs/%x-%j.out
#SBATCH --error=outputs/%x-%j.out
uv run accelerate launch --num_processes=8 --gradient_accumulation_steps=8 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29561 intx_sft.py configs/qa.yaml \
--model_name_or_path=google/gemma-2-2b-it --num_train_epochs=2.2 --per_device_train_batch_size=-1 \
--gradient_accumulation_steps=8 --per_device_eval_batch_size=4 --exp_setup=hyper_lora --aggregator_type=perceiver \
--target_modules=down_proj \
--num_self_attends_per_block=8 --num_latent_factor=2 \
--lora_r=16 \
--eval_steps=1000000000000000 --do_eval=no --do_predict=no --eval_on_start=False \
--save_steps=5000 --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 --max_packed_inp_len=8192 --max_packed_ctx_len=16384 \
--per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \

24
scripts/gemma_data_exp/gemma_self_gen_qa.sh
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#!/bin/bash
#SBATCH --job-name=ctxlora_medium
#SBATCH --partition=a3
#SBATCH --nodes=1
#SBATCH --gpus=4
#SBATCH --output=outputs/%x-%j.out
#SBATCH --error=outputs/%x-%j.out
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=16 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29562 intx_sft.py configs/self_gen_3_small.yaml \
--model_name_or_path=google/gemma-2-2b-it --num_train_epochs=2.2 --per_device_train_batch_size=-1 \
--gradient_accumulation_steps=16 --per_device_eval_batch_size=4 --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=1000000000000000 --do_eval=no --do_predict=no --eval_on_start=False \
--save_steps=5000 --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 --max_packed_inp_len=8192 --max_packed_ctx_len=16384 \
--per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \

24
scripts/gemma_data_exp/gemma_self_gen_qa_mini.sh
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#!/bin/bash
#SBATCH --job-name=ctxlora_medium
#SBATCH --partition=a3
#SBATCH --nodes=1
#SBATCH --gpus=4
#SBATCH --output=outputs/%x-%j.out
#SBATCH --error=outputs/%x-%j.out
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=16 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29562 intx_sft.py configs/self_gen_3_mini.yaml \
--model_name_or_path=google/gemma-2-2b-it --num_train_epochs=3 --per_device_train_batch_size=8 \
--gradient_accumulation_steps=16 --per_device_eval_batch_size=4 --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 \
--add_negative_prompt=False \
--add_repeat_prompt=False \
--use_sequence_packing=True --max_packed_inp_len=8192 --max_packed_ctx_len=16384 \
--per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \

26
scripts/gemma_data_exp/gemma_self_gen_qa_repeat.sh
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#!/bin/bash
#SBATCH --job-name=ctxlora_medium
#SBATCH --partition=a3
#SBATCH --nodes=1
#SBATCH --gpus=4
# NO ! #SBATCH --begin=now+6hour # delay scheduling
#SBATCH --output=outputs/%x-%j.out
#SBATCH --error=outputs/%x-%j.out
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=16 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29563 intx_sft.py configs/self_gen_3_small.yaml \
--model_name_or_path=google/gemma-2-2b-it --num_train_epochs=2 --per_device_train_batch_size=-1 \
--gradient_accumulation_steps=16 --per_device_eval_batch_size=4 --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=1000000000000000 --do_eval=no --do_predict=no --eval_on_start=False \
--save_steps=5000 --learning_rate=4e-5 --lora_dropout=0.0 \
--neftune_noise_alpha=5 --use_light_weight_lora=False \
--add_negative_prompt=False \
--add_repeat_prompt=True --repeat_prob=0.1 \
--use_sequence_packing=True --max_packed_inp_len=8192 --max_packed_ctx_len=16384 \
--per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \

26
scripts/gemma_data_exp/gemma_self_gen_qa_repeat_and_pretrain.sh
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#!/bin/bash
#SBATCH --job-name=ctxlora_medium
#SBATCH --partition=a3
#SBATCH --nodes=1
#SBATCH --gpus=4
# NO ! #SBATCH --begin=now+6hour # delay scheduling
#SBATCH --output=outputs/%x-%j.out
#SBATCH --error=outputs/%x-%j.out
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=16 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29564 intx_sft.py configs/data_exp/self_gen_3_and_pretrain_small.yaml \
--model_name_or_path=google/gemma-2-2b-it --num_train_epochs=1.5 \
--gradient_accumulation_steps=16 --per_device_eval_batch_size=4 --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=1000000000000000 --do_eval=no --do_predict=no --eval_on_start=False \
--save_steps=5000 --learning_rate=4e-5 --lora_dropout=0.0 \
--neftune_noise_alpha=5 --use_light_weight_lora=False \
--add_negative_prompt=False \
--add_repeat_prompt=True --repeat_prob=0.1 \
--use_sequence_packing=True --max_packed_inp_len=8192 --max_packed_ctx_len=16384 \
--per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \

26
scripts/gemma_data_exp/gemma_self_gen_qa_repeat_and_pretrain_augment.sh
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#!/bin/bash
#SBATCH --job-name=ctxlora_medium
#SBATCH --partition=a3
#SBATCH --nodes=1
#SBATCH --gpus=4
# NO ! #SBATCH --begin=now+6hour # delay scheduling
#SBATCH --output=outputs/%x-%j.out
#SBATCH --error=outputs/%x-%j.out
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=16 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29565 intx_sft.py configs/data_exp/self_gen_3_and_pretrain_small_aug.yaml \
--model_name_or_path=google/gemma-2-2b-it --num_train_epochs=1 \
--gradient_accumulation_steps=16 --per_device_eval_batch_size=4 --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=1000000000000000 --do_eval=no --do_predict=no --eval_on_start=False \
--save_steps=5000 --learning_rate=4e-5 --lora_dropout=0.0 \
--neftune_noise_alpha=5 --use_light_weight_lora=False \
--add_negative_prompt=False \
--add_repeat_prompt=True --repeat_prob=0.1 \
--use_sequence_packing=True --max_packed_inp_len=8192 --max_packed_ctx_len=16384 \
--per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \

24
scripts/gemma_rank_exp/gemma_self_gen_qa_rank_16.sh
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#!/bin/bash
#SBATCH --job-name=ctxlora_medium
#SBATCH --partition=a3
#SBATCH --nodes=1
#SBATCH --gpus=8
#SBATCH --output=outputs/%x-%j.out
#SBATCH --error=outputs/%x-%j.out
uv run accelerate launch --num_processes=8 --gradient_accumulation_steps=16 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29561 intx_sft.py configs/self_gen_3_small.yaml \
--model_name_or_path=google/gemma-2-2b-it --num_train_epochs=2.2 --per_device_train_batch_size=-1 \
--gradient_accumulation_steps=16 --per_device_eval_batch_size=4 --exp_setup=hyper_lora --aggregator_type=perceiver \
--target_modules=down_proj \
--num_self_attends_per_block=8 --num_latent_factor=1 \
--lora_r=16 \
--eval_steps=1000000000000000 --do_eval=no --do_predict=no --eval_on_start=False \
--save_steps=5000 --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 --max_packed_inp_len=4096 --max_packed_ctx_len=8192 \
--per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \

26
scripts/gemma_rank_exp/gemma_self_gen_qa_rank_16_lightweight.sh
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#!/bin/bash
#SBATCH --job-name=ctxlora_medium
#SBATCH --partition=a3
#SBATCH --nodes=1
#SBATCH --gpus=4
#SBATCH --output=outputs/%x-%j.out
#SBATCH --error=outputs/%x-%j.out
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=32 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29563 intx_sft.py configs/self_gen_3_small.yaml \
--model_name_or_path=google/gemma-2-2b-it --num_train_epochs=1 --per_device_train_batch_size=-1 \
--gradient_accumulation_steps=32 --per_device_eval_batch_size=4 --exp_setup=hyper_lora --aggregator_type=perceiver \
--target_modules=down_proj \
--num_self_attends_per_block=8 --num_latent_factor=1 \
--lora_r=16 \
--eval_steps=1000000000000000 --do_eval=no --do_predict=no --eval_on_start=False \
--save_steps=5000 --learning_rate=4e-5 --lora_dropout=0.0 \
--num_pre_head_layers=1 \
--latent_size=256 \
--neftune_noise_alpha=5 --use_light_weight_lora=True --light_weight_latent_size=512 \
--add_negative_prompt=False \
--add_repeat_prompt=False \
--use_sequence_packing=True --max_packed_inp_len=4096 --max_packed_ctx_len=8192 \
--per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \

24
scripts/gemma_rank_exp/gemma_self_gen_qa_repeat_rank_16.sh
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@ -1,24 +0,0 @@
#!/bin/bash
#SBATCH --job-name=ctxlora_medium
#SBATCH --partition=a3
#SBATCH --nodes=1
#SBATCH --gpus=8
#SBATCH --output=outputs/%x-%j.out
#SBATCH --error=outputs/%x-%j.out
uv run accelerate launch --num_processes=8 --gradient_accumulation_steps=16 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29562 intx_sft.py configs/self_gen_3_small.yaml \
--model_name_or_path=google/gemma-2-2b-it --num_train_epochs=2.2 --per_device_train_batch_size=-1 \
--gradient_accumulation_steps=16 --per_device_eval_batch_size=4 --exp_setup=hyper_lora --aggregator_type=perceiver \
--target_modules=down_proj \
--num_self_attends_per_block=8 --num_latent_factor=1 \
--lora_r=16 \
--eval_steps=1000000000000000 --do_eval=no --do_predict=no --eval_on_start=False \
--save_steps=5000 --learning_rate=4e-5 --lora_dropout=0.0 \
--neftune_noise_alpha=5 --use_light_weight_lora=False \
--add_negative_prompt=False \
--add_repeat_prompt=True --repeat_prob=0.1 \
--use_sequence_packing=True --max_packed_inp_len=4096 --max_packed_ctx_len=8192 \
--per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \

26
scripts/gemma_rank_exp/gemma_self_gen_qa_repeat_rank_16_lightweight.sh
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@ -1,26 +0,0 @@
#!/bin/bash
#SBATCH --job-name=ctxlora_medium
#SBATCH --partition=a3
#SBATCH --nodes=1
#SBATCH --gpus=4
#SBATCH --output=outputs/%x-%j.out
#SBATCH --error=outputs/%x-%j.out
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=32 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29564 intx_sft.py configs/self_gen_3_small.yaml \
--model_name_or_path=google/gemma-2-2b-it --num_train_epochs=1 --per_device_train_batch_size=-1 \
--gradient_accumulation_steps=32 --per_device_eval_batch_size=4 --exp_setup=hyper_lora --aggregator_type=perceiver \
--target_modules=down_proj \
--num_self_attends_per_block=8 --num_latent_factor=1 \
--lora_r=16 \
--eval_steps=1000000000000000 --do_eval=no --do_predict=no --eval_on_start=False \
--save_steps=5000 --learning_rate=4e-5 --lora_dropout=0.0 \
--num_pre_head_layers=1 \
--latent_size=256 \
--neftune_noise_alpha=5 --use_light_weight_lora=True --light_weight_latent_size=512 \
--add_negative_prompt=False \
--add_repeat_prompt=True --repeat_prob=0.1 \
--use_sequence_packing=True --max_packed_inp_len=4096 --max_packed_ctx_len=8192 \
--per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \

35
scripts/self_gen_fw_qa_3_mini/medium_gemma_per_rank_fac2_per_layer_4_gpus_gen_lora_reg.sh
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@ -1,35 +0,0 @@
#!/bin/bash
#SBATCH --job-name=ctxlora_medium
#SBATCH --partition=a3
#SBATCH --nodes=1
#SBATCH --gpus=4
#SBATCH --output=outputs/%x-%j.out
#SBATCH --error=outputs/%x-%j.out
# module load
# module load cuda/12.1
# module load cudnn/8.9.7
# module load nccl/cuda-12.1/2.18.3
# module load hpcx/2.20
# export OMP_NUM_THREADS=24
# export TRITON_CACHE_DIR=/tmp/.triton/
# . ~/miniconda3/etc/profile.d/conda.sh
# conda activate /home/rujikorn_sakana_ai/.conda/envs/ctx-to-lora
# eval "$@"
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=8 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29564 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=24 \
--gradient_accumulation_steps=8 --per_device_eval_batch_size=24 --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=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 \

35
scripts/self_gen_fw_qa_3_mini/medium_gemma_per_rank_fac2_per_layer_4_gpus_gen_lora_reg_repeat.sh
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@ -1,35 +0,0 @@
#!/bin/bash
#SBATCH --job-name=ctxlora_medium
#SBATCH --partition=a3
#SBATCH --nodes=1
#SBATCH --gpus=4
#SBATCH --output=outputs/%x-%j.out
#SBATCH --error=outputs/%x-%j.out
# module load
# module load cuda/12.1
# module load cudnn/8.9.7
# module load nccl/cuda-12.1/2.18.3
# module load hpcx/2.20
# export OMP_NUM_THREADS=24
# export TRITON_CACHE_DIR=/tmp/.triton/
# . ~/miniconda3/etc/profile.d/conda.sh
# conda activate /home/rujikorn_sakana_ai/.conda/envs/ctx-to-lora
# eval "$@"
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=8 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29560 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=24 \
--gradient_accumulation_steps=8 --per_device_eval_batch_size=24 --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=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=True --repeat_prob=0.1 \
--use_sequence_packing=True --per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \

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#!/bin/bash
#SBATCH --job-name=ctxlora
#SBATCH --nodes=1
#SBATCH --partition=sakura-gpu
#SBATCH --gpus=4
#SBATCH --output=slurm_logs/%x-%j.out
#SBATCH --error=slurm_logs/%x-%j.out
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=2 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29562 intx_sft.py configs/fw_qa_v2_level_0_tiny.yaml \
--model_name_or_path=google/gemma-3-1b-it \
--num_train_epochs=5 \
--per_device_train_batch_size=-1 \
--gradient_accumulation_steps=2 \
--per_device_eval_batch_size=64 \
--target_modules=down_proj \
--num_self_attends_per_block=8 \
--num_latent_factor=1 \
--num_pre_head_layers=1 \
--lora_r=8 \
--eval_steps=1000 \
--save_steps=1000 \
--learning_rate=4e-5 \
--lora_dropout=0.0 \
--neftune_noise_alpha=5 \
--add_negative_prompt=False \
--add_repeat_prompt=False \
--use_sequence_packing=True \
--max_packed_inp_len=16000 \
--max_packed_ctx_len=32000 \
--per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \
--logging_steps=50

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#!/bin/bash
#SBATCH --job-name=ctxlora
#SBATCH --nodes=1
#SBATCH --partition=sakura-gpu
#SBATCH --gpus=4
#SBATCH --output=slurm_logs/%x-%j.out
#SBATCH --error=slurm_logs/%x-%j.out
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=2 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29564 intx_sft.py configs/fw_qa_v2_level_3.yaml \
--model_name_or_path=google/gemma-3-1b-it \
--num_train_epochs=5 \
--per_device_train_batch_size=-1 \
--gradient_accumulation_steps=2 \
--per_device_eval_batch_size=64 \
--target_modules=down_proj \
--num_self_attends_per_block=8 \
--num_latent_factor=1 \
--num_pre_head_layers=1 \
--lora_r=8 \
--eval_steps=1000 \
--save_steps=1000 \
--learning_rate=4e-5 \
--lora_dropout=0.0 \
--neftune_noise_alpha=5 \
--add_negative_prompt=False \
--add_repeat_prompt=False \
--use_sequence_packing=True \
--max_packed_inp_len=16000 \
--max_packed_ctx_len=32000 \
--per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \
--logging_steps=50

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#!/bin/bash
#SBATCH --job-name=ctxlora
#SBATCH --nodes=1
#SBATCH --partition=sakura-gpu
#SBATCH --gpus=4
#SBATCH --output=slurm_logs/%x-%j.out
#SBATCH --error=slurm_logs/%x-%j.out
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=2 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29563 intx_sft.py configs/fw_qa_v2_level_3_tiny.yaml \
--model_name_or_path=google/gemma-3-1b-it \
--num_train_epochs=5 \
--per_device_train_batch_size=-1 \
--gradient_accumulation_steps=2 \
--per_device_eval_batch_size=64 \
--target_modules=down_proj \
--num_self_attends_per_block=8 \
--num_latent_factor=1 \
--num_pre_head_layers=1 \
--lora_r=8 \
--eval_steps=1000 \
--save_steps=1000 \
--learning_rate=4e-5 \
--lora_dropout=0.0 \
--neftune_noise_alpha=5 \
--add_negative_prompt=False \
--add_repeat_prompt=False \
--use_sequence_packing=True \
--max_packed_inp_len=16000 \
--max_packed_ctx_len=32000 \
--per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \
--logging_steps=50

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#!/bin/bash
#SBATCH --job-name=ctxlora
#SBATCH --nodes=1
#SBATCH --partition=sakura-gpu
#SBATCH --gpus=4
#SBATCH --output=slurm_logs/%x-%j.out
#SBATCH --error=slurm_logs/%x-%j.out
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=2 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29561 intx_sft.py configs/squad.yaml \
--model_name_or_path=google/gemma-3-1b-it \
--num_train_epochs=5 \
--per_device_train_batch_size=-1 \
--gradient_accumulation_steps=2 \
--per_device_eval_batch_size=64 \
--target_modules=down_proj \
--num_self_attends_per_block=8 \
--num_latent_factor=1 \
--num_pre_head_layers=1 \
--lora_r=8 \
--eval_steps=1000 \
--save_steps=1000 \
--learning_rate=4e-5 \
--lora_dropout=0.0 \
--neftune_noise_alpha=5 \
--add_negative_prompt=False \
--add_repeat_prompt=False \
--use_sequence_packing=True \
--max_packed_inp_len=16000 \
--max_packed_ctx_len=32000 \
--per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \
--logging_steps=50

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#!/bin/bash
#SBATCH --job-name=ctxlora
#SBATCH --nodes=1
#SBATCH --partition=sakura-gpu
#SBATCH --gpus=4
#SBATCH --output=slurm_logs/%x-%j.out
#SBATCH --error=slurm_logs/%x-%j.out
uv run accelerate launch --num_processes=4 --gradient_accumulation_steps=2 --gradient_clipping=1.0 \
--gpu_ids all --main_process_port 29565 intx_sft.py configs/squad.yaml \
--model_name_or_path=google/gemma-3-1b-it \
--num_train_epochs=5 \
--per_device_train_batch_size=-1 \
--gradient_accumulation_steps=2 \
--per_device_eval_batch_size=64 \
--target_modules=down_proj \
--num_self_attends_per_block=8 \
--num_latent_factor=1 \
--num_pre_head_layers=1 \
--lora_r=8 \
--eval_steps=1000 \
--save_steps=1000 \
--learning_rate=4e-5 \
--lora_dropout=0.0 \
--neftune_noise_alpha=5 \
--add_negative_prompt=False \
--add_repeat_prompt=False \
--use_sequence_packing=True \
--max_packed_inp_len=16000 \
--max_packed_ctx_len=32000 \
--per_rank_gen=True \
--per_layer_processing=True \
--gen_lora_l1_reg_coef=0.1 \
--logging_steps=50