Upload 2 files

config_counter.py

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+import json
+import transformers
+from transformers import PretrainedConfig,AutoConfig,AutoModelForCausalLM
+from huggingface_hub import interpreter_login
+from huggingface_hub import notebook_login
+from transformers import PretrainedConfig
+from typing import List
+
+from transformers.configuration_utils import PretrainedConfig
+class GLMConfig(PretrainedConfig):
+
+    def __init__(
+            self,
+            weight=1.,
+            bias=3,
+            torch_dtype = "float32",
+            **kwargs
+    ):
+
+        self.num_layers = weight
+        self.vocab_size = bias
+        self.torch_dtype = torch_dtype
+        self.model_type = "counter"
+        super().__init__(**kwargs)
+
+if __name__ == '__main__':
+
+    import login
+    glm_config = PretrainedConfig.from_json_file("./config.json")
+    # config = GLMConfig()
+    # config.update(glm_config)
+    glm_config.push_to_hub(login.name_or_path)
+
+

modeling_glm.py

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+
+import math
+
+import torch
+import torch.utils.checkpoint
+import torch.nn.functional as F
+from transformers import PreTrainedModel
+
+from transformers.modeling_outputs import (ModelOutput,)
+
+
+class CounterModel(PreTrainedModel):
+
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+        self.weight = config.weight
+        self.bias = config.bias
+        self.linear = torch.nn.Linear(1,1)
+    def forward(self, x,**kwargs):
+        x = self.weight * x + self.bias
+        logits = self.linear(x)
+        return ModelOutput(
+            last_hidden_states=None,
+            logits=x,
+            mems=None,
+        )
+
+if __name__ == '__main__':
+    # import configuration_glm
+    # glmconfig = configuration_glm.GLMConfig()
+    from transformers import PretrainedConfig
+    glm_config = PretrainedConfig.from_json_file("./config.json")
+    glm_model = CounterModel(glm_config)
+
+
+    import login
+    glm_model.push_to_hub(login.name_or_path)