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29 lines
1.1 KiB
Bash
29 lines
1.1 KiB
Bash
#!/bin/bash
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#SBATCH --job-name=ctxlora
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#SBATCH --nodes=1
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#SBATCH --partition=a3
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#SBATCH --gpus=8
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#SBATCH --output=slurm_logs/%x-%j.out
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#SBATCH --error=slurm_logs/%x-%j.out
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port=$((10000 + ($SLURM_JOBID % 50000)))
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echo "Using port: $port"
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# port=29051
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uv run accelerate launch --config_file accelerate_config.yaml --main_process_port $port \
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--num_processes=8 --gpu_ids all train.py \
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configs/main_exp/self_gen_lv1_closed_qa_1_l2l.yaml \
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--model_name_or_path=google/gemma-2-2b-it \
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--target_modules=down_proj --lora_r=8 \
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--eval_strategy=no --max_qas_len=2048 --max_qas_per_sample=1 \
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--per_rank_gen=True --per_layer_processing=True --gen_lora_l1_reg_coef=0.1 \
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--max_steps=20000 --gradient_accumulation_steps=8 --max_packed_inp_len=4096 \
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--max_packed_ctx_len=4096 --use_per_ctx_average_loss=True --use_kl_loss=True \
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--quantize_ctx_encoder=True --ctx_encoder_model_name_or_path=google/gemma-3-4b-it \
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--max_ctx_chunk_len=512 \
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--min_ctx_chunk_len=25 \
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--num_chunk_probs='{"1":"0.5", "2":"0.125", "3":"0.0625", "4":"0.0625", "5":"0.0625", "6":"0.0625", "7":"0.0625", "8":"0.0625"}' \
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--warmup_steps=2000 \
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--learning_rate=2e-5 \
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"$@"
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