LocalOptimum

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chinese-crypto-importance

like 1

• "train.NewsImportanceModel",
• "peft.tuners.lora.layer.Linear",
• "torch._utils._rebuild_parameter",
• "torch.nn.modules.activation.ReLU",
• "torch.nn.modules.container.Sequential",
• "torch.nn.modules.sparse.Embedding",
• "torch.nn.modules.container.ParameterDict",
• "transformers.models.bert.modeling_bert.BertEncoder",
• "torch.nn.modules.dropout.Dropout",
• "collections.OrderedDict",
• "torch.nn.modules.container.ModuleDict",
• "transformers.models.bert.modeling_bert.BertModel",
• "torch.FloatStorage",
• "transformers.models.bert.modeling_bert.BertIntermediate",
• "torch.nn.modules.linear.Linear",
• "transformers.models.bert.modeling_bert.BertEmbeddings",
• "transformers.models.bert.modeling_bert.BertLayer",
• "torch._C._nn.gelu",
• "transformers.models.bert.modeling_bert.BertPooler",
• "transformers.models.bert.modeling_bert.BertAttention",
• "torch.nn.modules.activation.Tanh",
• "torch.LongStorage",
• "torch._utils._rebuild_tensor_v2",
• "__builtin__.set",
• "peft.utils.peft_types.PeftType",
• "peft.tuners.lora.model.LoraModel",
• "torch._utils._rebuild_parameter_with_state",
• "transformers.models.bert.modeling_bert.BertSelfAttention",
• "peft.tuners.lora.config.LoraRuntimeConfig",
• "transformers.models.bert.modeling_bert.BertOutput",
• "peft.peft_model.PeftModel",
• "torch.nn.modules.container.ModuleList",
• "transformers.models.bert.configuration_bert.BertConfig",
• "torch.nn.modules.normalization.LayerNorm",
• "transformers.models.bert.modeling_bert.BertSelfOutput",
• "transformers.activations.GELUActivation",
• "peft.tuners.lora.config.LoraConfig",
• "torch.nn.modules.activation.Sigmoid"

How to fix it?