如果使用了梯度检查点,则默认为 False,否则为 True。 ddp_bucket_cap_mb (int, optional)— 在使用分布式训练时,传递给 DistributedDataParallel 的bucket_cap_mb 标志的值。 ddp_broadcast_buffers (bool, optional)— 在使用分布式训练时,传递给 DistributedDataParallel 的broadcast_buffers 标志的值。如果使用了...
ddp_broadcast_buffers (bool, optional)— 在使用分布式训练时,传递给 DistributedDataParallel 的标志 broadcast_buffers 的值。如果使用了梯度检查点,则默认为 False,否则为 True。 dataloader_pin_memory (bool, optional, defaults to True)— 是否要在数据加载器中固定内存。默认为 True。 dataloader_persistent_wo...
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( output_dir: str overwrite_output_dir: bool = False do_train: bool = False do_eval: bool = False do_predict: bool = False evaluation_strategy: Union = 'no' prediction_loss_only: bool = False per_device_train_batch_size: int = 8 per_device_eval_batch_size: int = 8 per_gpu_tra...
COMET_LOG_ASSETS (str, 可选,默认为TRUE): 是否将训练资产(tf 事件日志、检查点等)记录到 Comet。可以是TRUE或FALSE。 有关环境中可配置项目的详细信息,请参阅此处。 class transformers.DefaultFlowCallback < source > 代码语言:javascript 复制 ( ) 处理训练循环的默认流程,包括日志、评估和检查点的 Traine...
( repo_id:struse_temp_dir:Optional=Nonecommit_message:Optional=Noneprivate:Optional=Nonetoken:Union=Nonemax_shard_size:Union='5GB'create_pr:bool=Falsesafe_serialization:bool=Truerevision:str=Nonecommit_description:str=Nonetags:Optional=None**deprecated_kwargs ) ...
COMET_LOG_ASSETS(str,可选,默认为TRUE): 是否将训练资产(tf 事件日志、检查点等)记录到 Comet。可以是TRUE或FALSE。 有关环境中可配置项目的详细信息,请参阅此处。 class transformers.DefaultFlowCallback < source > ( ) 处理训练循环的默认流程,包括日志、评估和检查点的 TrainerCallback。
if device_map is None: logger.warning_once( dacorvoSep 13, 2024 nit: for clarity I would have put this line immediately under line 46 as they are both related to the conversion from [-1, 0, 1]/int8 to [0, 1, 2]/uint8. ...
Exposeoffload_buffersparameter ofacceleratetoPreTrainedModel.from_pretrainedmethod by @notsyncing in #28755 Fix Base Model Name of LlamaForQuestionAnswering by @lenglaender in #29258 FIX [quantization/ESM] Fix ESM 8bit / 4bit with bitsandbytes by @younesbelkada in #29329 ...
If you want to dispatch the model on the CPU or the disk while keeping these modules in 32-bit, you need to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom `device_map` to `from_pretrained`. Check https://huggingface.co/docs/transformers/main/en/main_classes/quantization...