classLlamaPreTrainedModel(PreTrainedModel):config_class=LlamaConfigbase_model_prefix="model"supports_gradient_checkpointing=True_no_split_modules=["LlamaDecoderLayer"]_skip_keys_device_placement="past_key_values"_supports_flash_attn_2=Truedef_init_weights(self,module):std=self.config.initializer_rangei...
classLlamaPreTrainedModel(PreTrainedModel):config_class = LlamaConfigbase_model_prefix ="model"supports_gradient_checkpointing =True_no_split_modules = ["LlamaDecoderLayer"]_skip_keys_device_placement ="past_key_values"_supports_flash_attn_2 =Truedef_init_weights(self, module):std = self.config....
_cache=True`is incompatible with gradient checkpointing. Setting`use_cache=False`...`use_cache=True`is incompatible with gradient checkpointing. Setting`use_cache=False`... Traceback (most recent call last): File"/home/username/.pyenv/versions/3.8.18/lib/python3.8/runpy.py", line 194,in_...
base_model_prefix="model"supports_gradient_checkpointing=True _no_split_modules=["LlamaDecoderLayer"]_skip_keys_device_placement="past_key_values"_supports_flash_attn_2=True def_init_weights(self,module):std=self.config.initializer_rangeifisinstance(module,nn.Linear):module.weight.data.normal_(me...
--gradient_checkpointing \\ --zero_stage 3 \\ --deepspeed \\ --output_dir $OUTPUT_PATH \\ &> $OUTPUT_PATH/training.log 可以加上LoRA deepspeed --num_gpus 1 main.py \\ --data_path Dahoas/rm-static \\ --data_split 2,4,4 \\ ...
save_on_each_node=True, gradient_checkpointing=True, report_to="none", ) 数据预处理 data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True) Trainer 配置 trainer = Trainer( model=model, args=args, train_dataset=train_dataset, ...
from transformers import AutoModelForCausalLM, TraininArgumentsmodel = AutoModelForCausalLM.from_pretrained( model_id, use_cache=False, # False if gradient_checkpointing=True **default_args)model.gradient_checkpointing_enable()LoRA LoRA是微软团队开发的一种技术，用于加速大型语言模型的微调。他...
supports_gradient_checkpointing = True _no_split_modules = ["LlamaDecoderLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True def _init_weights(self, module): std = self.config.initializer_range if isinstance(module, nn.Linear): ...
to True with an A100)fp16 = Falsebf16 = True# Batch size per GPU for trainingper_device_train_batch_size = 4# Number of update steps to accumulate the gradients forgradient_accumulation_steps = 1# Enable gradient checkpointinggradient_checkpointing = True# Maximum gradient normal (gradient ...
--lora_dropout_p 0.05 \ --lora_target_modules ALL \ --gradient_checkpointing true \ ...