通过AutoDL 的自定义服务启动页面后,依次点击“LoRA”->“Training”。 在“Configuration file”这里输入我提前预置好的训练配置文件地址:/root/autodl-tmp/train/dudu/config.json ; 然后点击“Load”加载训练参数; 最后点击“Start training”开始训练。 训练的进度需要去 JupterLab 中查看,大约需要8分钟,当看到 s...
LoRA network weights 输入框 Path to an existing LoRA network weights to resume training from :如果你想使用已经成型的 LoRA 模型文件进行额外的训练,请在此处指定 LoRA 文件所在位置。这里指定的 LoRA 会在学习开始时加载,并且训练的进程会从这个 LoRA 的现有状态开始。 训练后的 LoRA 模型会另存为一个新文...
通过AutoDL 的自定义服务启动页面后,依次点击“LoRA”->“Training”。 在“Configuration file”这里输入我提前预置好的训练配置文件地址:/root/autodl-tmp/train/dudu/config.json ; 然后点击“Load”加载训练参数; 最后点击“Start training”开始训练。 训练的进度需要去 JupterLab 中查看,大约需要8分钟,当看到 s...
通过AutoDL 的自定义服务启动页面后,依次点击“LoRA”->“Training”。 在“Configuration file”这里输入我提前预置好的训练配置文件地址:/root/autodl-tmp/train/dudu/config.json ; 然后点击“Load”加载训练参数; 最后点击“Start training”开始训练。 训练的进度需要去 JupterLab 中查看,大约需要8分钟,当看到 s...
--train_batch_size = 1, 2,3, 4-repeats = 1,2-learning_rate = 1.0 (Prodigy), 1e-4 (AdamW)-text_encoder_lr = 1.0 (Prodigy), 3e-4, 5e-5 (AdamW)-snr_gamma = None, 5.0-max_train_steps = 1000, 1500, 1800-text_encoder_training = regular finetuning, pivotal tuning (textual...
size mismatch for mid_block.attentions.0.proj_out.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]). 00:50:24-776948 ERROR Training failed / 训练失败 ...
"down_lr_weight":"","enable_bucket":true,"epoch":50,"factor":-1,"flip_aug":false,"full_bf16":false,"full_fp16":false,"gradient_accumulation_steps":"1","gradient_checkpointing":true,"keep_tokens":"0","learning_rate":0.0001,"logging_dir":"/dataset/train-log/","lora_network_...
"stop_text_encoder_training":0,"use_8bit_adam":true,"xformers":true,"save_model_as":"safetensors","shuffle_caption":false,"save_state":false,"resume":"","prior_loss_weight":1.0,"text_encoder_lr":"5e-5","unet_lr":"0.0001","network_dim":128,"lora_network_weights":"","color...
self.base_layer.weight = self.base_layer.weight - self.lora_B.weight @ self.lora_A.weight 对比高中低奇异值微调效果实验 为了验证使用不同大小奇异值、奇异向量初始化适配器对模型的影响,研究人员分别使用高、中、低奇异值初始化 LLaMA 2-7B、Mistral-7B-v0.1、Gemma-7B 的适配器,然后在 MetaMathQA 数...
return F.linear(input, self.weight, self.bias) RuntimeError: expected scalar type Float but found BFloat16 Expected behavior No response System Info No response Others No response