···# 使用 get_peft_model 封装 model(顺便添加一个 adapter 名为 "memory_encoder")model = get_peft_model(model, peft_config, adapter_name="memory_encoder")# 然后再添加两个 adapters "get_memory_slot_num" 和 "memory_merge"model.add_adapter(peft_config=peft_config, adapter_name="get_memo...
loaded_adapter_weights=get_adapter_state_dict(loaded_peft_model,ADAPTER_NAME)# Assertion fails due to adapter weights been newly intializedforkeyinadapter_weights:asserttorch.isclose(adapter_weights[key],loaded_adapter_weights[key]).all() Expected behavior A clear error or warning message indicating ...
lora_dropout=0.1 ) # 初始化PeftModel并添加多个LoRA模块 model = PeftModel(model, peft_config, adapter_name="0") for LoRA_index in range(1, LoRA_amount): model.add_adapter(str(LoRA_index), peft_config) # 激活特定的LoRA模块 model.set_adapter('2') # 查看当前激活的LoRA模块 print(model....
peft_config, PromptLearningConfig ):returnPeftModel(model, peft_config, adapter_name=adapter_name)ifisinstance(peft_config, PromptLearningConfig): peft_config = _prepare_prompt_learning_config(peft_config, model_config)returnMODEL_TYPE_TO_PEFT_MODEL_MAPPING[peft_config.task_type](model, peft_config...
这只会保存经过训练的增量 PEFT 权重。例如，您可以在此处的 twitter_complaints raft 数据集上找到使用 LoRA 调整的 bigscience/T0_3B : smangrul/twitter_complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM。请注意，它只包含 2 个文件: adapter_config.json 和 adapter_model.bin，后者只有 19MB。模型地址:...
utils import get_root_path, FGM task_name = "POI-Name" assert task_name in ['POI-Name',...
self._disable_adapters =Falseelse:# disable grads on all adapter layersforlayer_nameinself.adapter_layer_names: layer = getattr(self, layer_name) layer.requires_grad_(False) self._disable_adapters =True复制 函数接收一个布尔值enabled,这个值标志了调节器的开闭状态。
Adapter Tuning在预训练模型中插入设计的适配器模块,仅训练这些模块参数以微调模型,同时保持预训练模型参数不变,并在特定任务数据集上评估性能。 准备环境:安装并配置好深度学习框架(如 PyTorch 或 TensorFlow)和相关的库,这些框架和库将用于模型的训练、评估和部署。
This pull request addresses an issue where adapter weights are incorrectly initialized when the adapter_name conflicts with the tuner_prefix during model loading. Specifically, it introduces a war...
例如,这个在RAFT数据集的twitter_complaints子集上使用LoRA训练的bigscience/T0_3B模型只包含两个文件:adapter_config.json和adapter_model.bin,后者仅有19MB! 使用from_pretrained函数轻松加载模型进行推理: from transformers import AutoModelForSeq2SeqLM from peft import PeftModel, PeftConfig peft...