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This model is significantly undertrained and designed for research purposes only.
For use in transformers:

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(()))
            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))
    return model

class GPTR2Model(GPT2Model):
    def __init__(self, config):

model = GPTR2Model.from_pretrained("George-Ogden/gptr2-nano-with-momentum-without-weight-decay")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

For more details and example usage, see https://github.com/George-Ogden/residual-streams

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Dataset used to train George-Ogden/gptr2-nano-with-momentum-without-weight-decay