| #include <iostream> |
| #include <string> |
| #include <torch/torch.h> |
| #include <transformers/generation_utils.h> |
|
|
| int main() { |
| // Load the pre-trained GPT model |
| std::string modelPath = "path/to/pretrained/model"; |
| torch::jit::script::Module model = torch::jit::load(modelPath); |
| |
| // Set the device (CPU or GPU) |
| torch::Device device(torch::kCPU); |
| model.to(device); |
| |
| // Initialize the tokenizer |
| std::string tokenizerPath = "path/to/tokenizer"; |
| transformers::GPT2Tokenizer tokenizer(tokenizerPath); |
| |
| // Start the conversation loop |
| std::string userMessage; |
| while (true) { |
| std::cout << "User: "; |
| std::getline(std::cin, userMessage); |
| |
| // Tokenize the user's message |
| std::vector<std::string> tokens = tokenizer.tokenize(userMessage); |
| |
| // Convert tokens to input tensor |
| torch::Tensor inputIds = tokenizer.convertTokensToTensor(tokens).to(device); |
| |
| // Generate a response from the GPT model |
| torch::Tensor outputIds = transformers::generate(model, inputIds); |
| |
| // Convert output tensor to tokens |
| std::vector<std::string> responseTokens = tokenizer.convertIdsToTokens(outputIds); |
| |
| // Convert tokens to text |
| std::string responseText = tokenizer.convertTokensToText(responseTokens); |
| |
| std::cout << "GPT: " << responseText << std::endl; |
| } |
| |
| return 0; |
| } |