import os import argparse from dataclasses import dataclass, field from typing import List, Optional # Based on https://github.com/ggerganov/llama.cpp/blob/master/examples/common.cpp @dataclass class GptParams: seed: int = -1 n_threads: int = min(4, os.cpu_count() or 1) n_predict: int = 128 repeat_last_n: int = 64 n_parts: int = -1 n_ctx: int = 512 n_batch: int = 8 n_keep: int = 0 top_k: int = 40 top_p: float = 0.95 temp: float = 0.80 repeat_penalty: float = 1.10 model: str = "./models/llama-7B/ggml-model.bin" prompt: str = "" input_prefix: str = " " antiprompt: List[str] = field(default_factory=list) memory_f16: bool = True random_prompt: bool = False use_color: bool = False interactive: bool = False embedding: bool = False interactive_start: bool = False instruct: bool = False ignore_eos: bool = False perplexity: bool = False use_mlock: bool = False mem_test: bool = False verbose_prompt: bool = False file: str = None # If chat ended prematurely, append this to the conversation to fix it. # Set to "\nUser:" etc. # This is an alternative to input_prefix which always adds it, so it potentially duplicates "User:"" fix_prefix: str = " " output_postfix: str = "" input_echo: bool = True, # Default instructions for Alpaca # switch to "Human" and "Assistant" for Vicuna. # TODO: TBD how they are gonna handle this upstream instruct_inp_prefix: str="\n\n### Instruction:\n\n" instruct_inp_suffix: str="\n\n### Response:\n\n" def gpt_params_parse(argv = None, params: Optional[GptParams] = None): if params is None: params = GptParams() parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument("-s", "--seed", type=int, default=-1, help="RNG seed (use random seed for <= 0)",dest="seed") parser.add_argument("-t", "--threads", type=int, default=min(4, os.cpu_count() or 1), help="number of threads to use during computation",dest="n_threads") parser.add_argument("-p", "--prompt", type=str, default="", help="initial prompt",dest="prompt") parser.add_argument("-f", "--file", type=str, default=None, help="file containing initial prompt to load",dest="file") parser.add_argument("-c", "--ctx_size", type=int, default=512, help="size of the prompt context",dest="n_ctx") parser.add_argument("--memory_f32", action="store_false", help="use f32 instead of f16 for memory key+value",dest="memory_f16") parser.add_argument("--top_p", type=float, default=0.95, help="top-p samplin",dest="top_p") parser.add_argument("--top_k", type=int, default=40, help="top-k sampling",dest="top_k") parser.add_argument("--temp", type=float, default=0.80, help="temperature",dest="temp") parser.add_argument("--n_predict", type=int, default=128, help="number of model parts",dest="n_predict") parser.add_argument("--repeat_last_n", type=int, default=64, help="last n tokens to consider for penalize ",dest="repeat_last_n") parser.add_argument("--repeat_penalty", type=float, default=1.10, help="penalize repeat sequence of tokens",dest="repeat_penalty") parser.add_argument("-b", "--batch_size", type=int, default=8, help="batch size for prompt processing",dest="n_batch") parser.add_argument("--keep", type=int, default=0, help="number of tokens to keep from the initial prompt",dest="n_keep") parser.add_argument("-m", "--model", type=str, default="./models/llama-7B/ggml-model.bin", help="model path",dest="model") parser.add_argument( "-i", "--interactive", action="store_true", help="run in interactive mode", dest="interactive" ) parser.add_argument("--embedding", action="store_true", help="", dest="embedding") parser.add_argument( "--interactive-start", action="store_true", help="run in interactive mode", dest="interactive" ) parser.add_argument( "--interactive-first", action="store_true", help="run in interactive mode and wait for input right away", dest="interactive_start" ) parser.add_argument( "-ins", "--instruct", action="store_true", help="run in instruction mode (use with Alpaca or Vicuna models)", dest="instruct" ) parser.add_argument( "--color", action="store_true", help="colorise output to distinguish prompt and user input from generations", dest="use_color" ) parser.add_argument("--mlock", action="store_true",help="force system to keep model in RAM rather than swapping or compressing",dest="use_mlock") parser.add_argument("--mtest", action="store_true",help="compute maximum memory usage",dest="mem_test") parser.add_argument( "-r", "--reverse-prompt", type=str, action='append', help="poll user input upon seeing PROMPT (can be\nspecified more than once for multiple prompts).", dest="antiprompt" ) parser.add_argument("--perplexity", action="store_true", help="compute perplexity over the prompt", dest="perplexity") parser.add_argument("--ignore-eos", action="store_true", help="ignore end of stream token and continue generating", dest="ignore_eos") parser.add_argument("--n_parts", type=int, default=-1, help="number of model parts", dest="n_parts") parser.add_argument("--random-prompt", action="store_true", help="start with a randomized prompt.", dest="random_prompt") parser.add_argument("--in-prefix", type=str, default="", help="string to prefix user inputs with", dest="input_prefix") parser.add_argument("--fix-prefix", type=str, default="", help="append to input when generated n_predict tokens", dest="fix_prefix") parser.add_argument("--out-postfix", type=str, default="", help="append to input", dest="output_postfix") parser.add_argument("--input-noecho", action="store_false", help="dont output the input", dest="input_echo") args = parser.parse_args(argv) return args def gpt_random_prompt(rng): return [ "So", "Once upon a time", "When", "The", "After", "If", "import", "He", "She", "They", ][rng % 10] if __name__ == "__main__": print(GptParams(gpt_params_parse()))