float mirostat_eta = 0.10f; // learning rate
std::string model = "models/7B/ggml-model.bin"; // model path
+ std::string model_alias = "unknown"; // model alias
std::string prompt = "";
std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state
std::string input_prefix = ""; // string to prefix user inputs with
fprintf(stderr, " number of layers to store in VRAM\n");
fprintf(stderr, " -m FNAME, --model FNAME\n");
fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
- fprintf(stderr, " -host ip address to listen (default 127.0.0.1)\n");
- fprintf(stderr, " -port PORT port to listen (default 8080)\n");
+ fprintf(stderr, " -a ALIAS, --alias ALIAS\n");
+ fprintf(stderr, " set an alias for the model, will be added as `model` field in completion response\n");
+ fprintf(stderr, " --host ip address to listen (default 127.0.0.1)\n");
+ fprintf(stderr, " --port PORT port to listen (default 8080)\n");
fprintf(stderr, "\n");
}
}
params.model = argv[i];
}
+ else if (arg == "-a" || arg == "--alias")
+ {
+ if (++i >= argc)
+ {
+ invalid_param = true;
+ break;
+ }
+ params.model_alias = argv[i];
+ }
else if (arg == "--embedding")
{
params.embedding = true;
try
{
json data = {
+ {"model", llama.params.model_alias },
{"content", llama.generated_text },
{"tokens_predicted", llama.num_tokens_predicted}};
return res.set_content(data.dump(), "application/json");