[ot][spam]gpt-4 completions

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

{
  "model": "gpt-4",
  "messages": [
    {
      "role": "system",
      "content": "Do dialog like codegolf, with the fewest characters possible."
    },
    {
      "role": "user",
      "content": "Construct, train, and save, a torch.nn.Transformer .
Generate data so it increments floats."
    },
    {
      "role": "assistant",
      "content": "```python\nimport torch\nfrom torch import
nn\nimport numpy as np\n\n# Construct\nm=nn.Transformer()\n\n#
Generate data\nx=np.arange(0,1,0.01)\ny=x+1\n\n#
Train\no=torch.optim.Adam(m.parameters())\nl=nn.MSELoss()\nfor _ in
range(100):\n  p=m(torch.tensor(x).unsqueeze(0))\n  o.zero_grad()\n
e=l(p,torch.tensor(y).unsqueeze(0))\n  e.backward()\n  o.step()\n\n#
Save\ntorch.save(m.state_dict(),'m.pt')\n```"
    },
    {
      "role": "user",
      "content": "fix this: Transformer.forward() takes two
parameters, src and tgt.\nAlso, load a previous version of the model
on bootup if it's been saved, version the saved models with their
training data, and give progress output to the user."
    },
    {
      "role": "assistant",
      "content": "```python\nimport torch\nfrom torch import
nn\nimport numpy as np\nimport os\n\n#
Construct\nm=nn.Transformer()\n\n# Load previous model if exists\nif
os.path.exists('m.pt'):\n  m.load_state_dict(torch.load('m.pt'))\n\n#
Generate data\nx=np.arange(0,1,0.01)\ny=x+1\n\n# Versioning\nv =
str(len(os.listdir())) # number of files in
directory\ntorch.save((x,y), f'data_{v}.pt')\n\n#
Train\no=torch.optim.Adam(m.parameters())\nl=nn.MSELoss()\nfor i in
range(100):\n  src = torch.tensor(x).unsqueeze(0)\n  tgt =
torch.tensor(y).unsqueeze(0)\n  p=m(src, tgt)\n  o.zero_grad()\n
e=l(p,tgt)\n  e.backward()\n  o.step()\n  if i%10==0: print(f'Epoch
{i}, Loss {e.item()}')\n\n#
Save\ntorch.save(m.state_dict(),f'm_{v}.pt')\n```"
    }
  ],
  "temperature": 0,
  "max_tokens": 256,
  "top_p": 1,
  "frequency_penalty": 0,
  "presence_penalty": 0
}