File size: 1,917 Bytes
abf246e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | #!/bin/bash
export DEBUG_MODE=true
export LOG_PATH="./debug_log_2b.txt"
export CUDA_VISIBLE_DEVICES=0
export MAIN_PROCESS_PORT=29507
export NCCL_DEBUG=INFO
export NCCL_IB_DISABLE=1
export NCCL_P2P_DISABLE=1
export NCCL_ASYNC_DISABLE=1
# options:
# - Qwen/Qwen2.5-1.5B-Instruct
# - HuggingFaceTB/SmolLM3-3B
REASONER_MODEL="Qwen/Qwen2.5-1.5B-Instruct"
WEAVER_MODEL="Qwen/Qwen2.5-1.5B-Instruct"
TRIGGER_MODEL="Qwen/Qwen2.5-1.5B-Instruct"
# Dataset configs
DATASET_NAME="kodcode" # options: gsm8k, gpqa, kodcode, triviaqa
# MemGen configs
TRAIN_METHOD="grpo" # options: sft or grpo
# Augmentation configs:
# - For gsm8k, gpqa, kodcode: MAX_PROMPT_AUG_NUM=1, MAX_INFERENCE_AUG_NUM=5
# - For triviaqa: MAX_PROMPT_AUG_NUM=6, MAX_INFERENCE_AUG_NUM=0
MAX_PROMPT_AUG_NUM=1
MAX_INFERENCE_AUG_NUM=5
PROMPT_LATENTS_LEN=8
INFERENCE_LATENTS_LEN=8
LOAD_WEAVER_PATH=""
# train
python -m accelerate.commands.launch \
--config_file=configs/zero2.yaml \
main.py \
--cfg-path configs/latent_memory/${DATASET_NAME}.yaml \
--options \
model.model_name ${REASONER_MODEL} \
model.load_model_path ${LOAD_WEAVER_PATH} \
model.max_prompt_aug_num ${MAX_PROMPT_AUG_NUM} \
model.max_inference_aug_num ${MAX_INFERENCE_AUG_NUM} \
model.weaver.model_name ${WEAVER_MODEL} \
model.weaver.prompt_latents_len ${PROMPT_LATENTS_LEN} \
model.weaver.inference_latents_len ${INFERENCE_LATENTS_LEN} \
model.trigger.model_name ${TRIGGER_MODEL} \
model.trigger.active True \
datasets.mode ${TRAIN_METHOD} \
run.mode train \
run.train_weaver False \
run.train_trigger True \
run.train_trigger_method ${TRAIN_METHOD} \
run.trigger.grpo.per_device_train_batch_size 8 \
run.trigger.grpo.per_device_eval_batch_size 8 \
run.trigger.grpo.num_train_epochs 1 \
run.trigger.grpo.num_generations 8 \
run.trigger.grpo.gradient_accumulation_steps 4 \
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