[ot][spam]gpt-4 completions

[Undescribed Horrific Abuse, One Victim & Survivor of Many]

{
  "model": "gpt-4",
  "messages": [
    {
      "role": "system",
      "content": "Like codegolf is short code, do chatgolf and
dialoggolf, keeping answers as short as possible."
    },
    {
      "role": "user",
      "content": "Demonstrate how to construct a transformer model."
    },
    {
      "role": "assistant",
      "content": "1. Import libraries\n```python\nimport torch\nimport
torch.nn as nn\n```\n2. Define model\n```python\nclass
Transformer(nn.Module):\n  def __init__(self, d_model, nhead,
num_layers):\n    super(Transformer, self).__init__()\n
self.transformer = nn.Transformer(d_model, nhead, num_layers)\n\n  def
forward(self, src, tgt):\n    return self.transformer(src,
tgt)\n```\n3. Instantiate\n```python\nmodel = Transformer(512, 8,
6)\n```"
    },
    {
      "role": "user",
      "content": "Put it all together and train it to increment floats."
    },
    {
      "role": "assistant",
      "content": "This is a simplified example. In reality, you'd need
more data and fine-tuning.\n\n```python\nimport torch\nimport torch.nn
as nn\nimport torch.optim as optim\n\n# Define model\nclass
Transformer(nn.Module):\n  def __init__(self, d_model, nhead,
num_layers):\n    super(Transformer, self).__init__()\n
self.transformer = nn.Transformer(d_model, nhead, num_layers)\n\n  def
forward(self, src, tgt):\n    return self.transformer(src, tgt)\n\n#
Instantiate\nmodel = Transformer(512, 8, 6)\n\n# Data\ndata =
torch.rand(100, 1, 512)\ntarget = data + 1\n\n# Loss and
optimizer\ncriterion = nn.MSELoss()\noptimizer =
optim.Adam(model.parameters(), lr=0.001)\n\n# Training\nfor epoch in
range(100):\n  optimizer.zero_grad()\n  output = model(data, data)\n
loss = criterion(output, target)\n  loss.backward()\n
optimizer.step()\n```"
    }
  ],
  "temperature": 0,
  "max_tokens": 256,
  "top_p": 1,
  "frequency_penalty": 0,
  "presence_penalty": 0
}