{ "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 }