_mark_only_lora_as_trainable 的作用是仅将Lora 参数设为可训练,其余参数冻结;_find_and_replace 会根据 config 中的参数从基础模型的 named_parameters 中找出包含指定名称的模块 (默认为“q”、“v”,即注意 力模块的 Q 和 V 矩阵),创建一个新的自定义类 Linear 模块,并替换原来的。 创建Linear 模块时...
module.query_key_value = qkv_proj lora.mark_only_lora_as_trainable(model) model_parameters = filter(lambda p: p.requires_grad, model.parameters()) trainable_params = sum([np.prod(p.size()) for p in model_parameters]) model_parameters = filter(lambda p: not p.requires_grad, model.pa...
3)训练开始之前,只标记LoRA参数为可训练 import loralib as lora model = BigModel() # This sets requires_grad to False for all parameters without the string “lora_” in their names lora.mark_only_lora_as_trainable(model) # Training loop for batch in dataloader: … 4)当存储一个checkpoint的...
lora_alpha=16, target_modules=["query", "value"], # 定义需要做低秩适配的部分 lora_dropout=0.1, bias="none", modules_to_save=["classifier"], ) lora_model = get_peft_model(model, config) print_trainable_parameters(lora_model) "trainable params: 667493 || all params: 86466149 || traina...
lora.mark_only_lora_as_trainable(model) # Training loop for batch in dataloader: 1. 2. 3. 4. 5. 6. 保存模型时也可以只储存LoRA所训练的权重,这特性将方便大家分享自己的权重 # === Before === # torch.save(model.state_dict(), checkpoint_path) # ==...
mark_only_LLM_lora_as_trainable(model, LLM_name='bert') 实战分析 本文采用IMDB影评情感分析数据集测试训练集各25000条来进行实验。 因为bert才1.1B,可能在bert上使用这个东西有点小题大做了,但是一屋不扫何以扫天下,现在的大模型架构基本都是基于transformer架构的(bert可以说是第一个),其实本质上都是差不多...
Before the training loop begins, mark only LoRA parameters as trainable. 代码语言:javascript 复制 importloralibaslora model=BigModel()# This sets requires_grad to Falseforall parameters without the string"lora_"intheir names lora.mark_only_lora_as_trainable(model)# Training loopforbatchindataloade...
importloralibaslora model=YourNeuralNetwork()# This sets requires_grad to False for all parameterslora.mark_only_lora_as_trainable(model)# Training loopforbatchindataloader:... When saving a checkpoint, you can generate astate_dicthat only contains LoRA parameters. ...
Before the training loop begins, mark only LoRA parameters as trainable. importloralibasloramodel=BigModel()# This sets requires_grad to False for all parameters without the string "lora_" in their nameslora.mark_only_lora_as_trainable(model)# Training loopforbatchindataloader: ... ...
Mark only LoRA parameters as trainable before the training loop. model = Model() # This sets requires_grad to False for all parameters without the string "lora_" in their names lora.mark_only_lora_as_trainable(model) # Training loop for batch in dataloader: model.train() ... Save LoRA...