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...
( 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...
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...
( save_log_history: bool = True sync_checkpoints: bool = True ) 参数 save_log_history (bool, 可选, 默认为 True)— 当设置为 True 时,训练日志将保存为 Flyte Deck。 sync_checkpoints (bool, 可选, 默认为 True)— 当设置为 True 时,检查点将与 Flyte 同步,并可用于在中断的情况下恢复训练。
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Since the BitNetLinear is composed of buffers essentially, make sure to use with init_empty_weights(include_buffers=True) in _replace_with_bitnet_linear + device_map_kwargs["offload_buffers"] = True in modeling_utils.py. Check fbgemm-fp8 code. Otherwise, it won't offload the buffers ...
(imgs):returnFalsereturnTruedefis_batched(img):ifisinstance(img, (list,tuple)):returnis_valid_image(img[0])# 如果是列表或元组,且第一个元素是有效图像,则认为是批量数据returnFalse# 检查图像是否已经被重新缩放到 [0, 1] 范围内defis_scaled_image(image: np.ndarray) ->bool:ifimage.dtype ==...
"""_configure_library_root_logger()# 配置库的根记录器_get_library_root_logger().propagate =True# 将根记录器的传播设置为 True# 启用明确的格式化方式用于每个 HuggingFace Transformers 的记录器defenable_explicit_format() ->None:""" Enable explicit formatting for every HuggingFace Transformers's logger...
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...
"input_ids=tokenizer(input_text,return_tensors="pt").to("cuda")output=quantized_model.generate(**input_ids,max_new_tokens=10)print(tokenizer.decode(output[0],skip_special_tokens=True)) @slow @require_torch_gpu