Create README.md

README.md

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+---
+license: mit
+datasets:
+- wikipedia
+language:
+- en
+tags:
+- research
+---
+This model is significantly undertrained and designed for research purposes only.  
+For use in transformers:
+```python
+from transformers import AutoTokenizer, GPT2Model
+
+import torch.nn as nn
+import torch
+
+class RMSLayerNorm(nn.Module):
+    def __init__(self, normalized_shape, eps=1e-8, affine=True):
+        super(RMSLayerNorm, self).__init__()
+        self.normalized_shape = normalized_shape
+        self.eps = eps
+        self.affine = affine
+
+        if self.affine:
+            self.weight = nn.Parameter(torch.ones(()))
+        else:
+            self.register_parameter('weight', None)
+            self.register_parameter('bias', None)
+
+    def forward(self, x):
+        rms = torch.sqrt(torch.mean(x**2, dim=-1, keepdim=True) + self.eps)
+        x_normalized = x / rms
+        if self.affine:
+            x_normalized = x_normalized * self.weight
+        return x_normalized
+
+
+def replace(model):
+    for name, child in model.named_children():
+        if isinstance(child, nn.modules.normalization.LayerNorm):
+            setattr(model, name, RMSLayerNorm(child.normalized_shape, eps=child.eps, affine=True))
+        else:
+            replace(child)
+    return model
+
+
+class GPTR2Model(GPT2Model):
+    def __init__(self, config):
+        super().__init__(config)
+        replace(self)
+
+model = GPTR2Model.from_pretrained("George-Ogden/gptr2-nano-without-momentum-without-weight-decay")
+tokenizer = AutoTokenizer.from_pretrained("gpt2")
+```
+For more details and example usage, see https://github.com/George-Ogden/residual-streams