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Update 7B_sft.py

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Guoteng 2023-09-27 22:07:53 +08:00 committed by GitHub
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configs/7B_sft.py
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@ -30,14 +30,14 @@ ckpt = dict(
# 2. the 'content‘ means what states will be loaded, support: "model", "sampler", "optimizer", "scheduler", "all"
# 3. the ’ckpt_type‘ means the type of checkpoint to be loaded, now only 'normal' type is supported.
load_ckpt_info=dict(path=MODEL_ONLY_FOLDER, content=("model",), ckpt_type="internlm"),
# 'auto_resume' is designed to automatically load the latest checkpoint from 'save_ckpt_folder' when encountering
# training interruptions/hangs caused by hardware failures, using a scheduling system (such as k8s/slurm)
# 'auto_resume' is designed to automatically load the latest checkpoint from 'save_ckpt_folder' when encountering
# training interruptions/hangs caused by hardware failures, using a scheduling system (such as k8s/slurm)
# with an automatic restart mechanism upon training reboot.
# Please be aware that if `auto_resume` is not set (its default value is True), it will not load the checkpoint
# path specified in `load_ckpt_info` by default.
# Please be aware that if `auto_resume` is not set (its default value is True), it will not load the checkpoint
# path specified in `load_ckpt_info` by default.
# If you want to initialize your model weights from another model, you must set `auto_resume` to False.
# If you want to train from scratch, please set `auto_resume` to False and 'load_ckpt_info' to None.
auto_resume=False,
auto_resume=True,
checkpoint_every=CHECKPOINT_EVERY,
async_upload=True, # async ckpt upload. (only work for boto3 ckpt)
async_upload_tmp_folder="/dev/shm/internlm_tmp_ckpt/", # path for temporarily files during asynchronous upload.