Are there any output samples? Would like to get a sense of how close this is to GPT-2 vs. GPT-3 levels of coherency.
It seems really good at generating code too
prompt:
class ConvBlock(nn.Module):
def __init__(self, channels, stride=1):
super(ConvBlock, self).__init__()
self.convolution = nn.Conv2d(channels//stride, channels, 3, stride=stride, padding=1)
self.normalization = nn.BatchNorm2d(channels)
#self.activation = nn.LeakyReLU()
def forward(self, x):
x =
# x = self.convolution(x)
# x = self.normalization(x)
# x = self.activation(x)
x = self.convolution(x)
x = self.normalization(x)
x = self.activation(x)
return x
def __init__(self, stride=1):
super(Resblock, self).__init__()
self.res = nn.Sequential(
nn.Conv2d(9, 9, 3, stride=stride, padding=1),
nn.BatchNorm2d(9),
nn.LeakyReLU(0.2)
)
self.output = nn.MaxPool2d(2)
def forward(self, x):
return self.res(x)
Big ugly stick.
Bowl full of chicken peas.
New York is universally inadvisable.
Long-eared seagull.
This morning I have a tumor in me.
A dog bark.
Invisible dog hides.
A crow blind.
A cat can’t see if it had a billion eyes.
Bumblebee.
Sheep herder, sheep herder.
A fawns abducts.
Two black birds are trapped.
Bottle on her finger.
Elephant sees another elephant.
Bull.
Mice in a box in the library.
A church swelter.
The door of a hotel opens.
Bosnian honey melons.
Grapes in excess.
Cat is on the loose.
Soil is shoveled into a glass jar