| from __future__ import annotations |
|
|
| import json |
| import os |
| from llama_cpp import Llama |
|
|
| from ..index_func import * |
| from ..presets import * |
| from ..utils import * |
| from .base_model import BaseLLMModel, download |
|
|
| SYS_PREFIX = "<<SYS>>\n" |
| SYS_POSTFIX = "\n<</SYS>>\n\n" |
| INST_PREFIX = "<s>[INST] " |
| INST_POSTFIX = " " |
| OUTPUT_PREFIX = "[/INST] " |
| OUTPUT_POSTFIX = "</s>" |
|
|
|
|
| class LLaMA_Client(BaseLLMModel): |
| def __init__(self, model_name, lora_path=None, user_name="") -> None: |
| super().__init__(model_name=model_name, user=user_name) |
|
|
| self.max_generation_token = 1000 |
| if model_name in MODEL_METADATA: |
| path_to_model = download( |
| MODEL_METADATA[model_name]["repo_id"], |
| MODEL_METADATA[model_name]["filelist"][0], |
| ) |
| else: |
| dir_to_model = os.path.join("models", model_name) |
| |
| path_to_model = None |
| for root, dirs, files in os.walk(dir_to_model): |
| for file in files: |
| if file.endswith(".gguf"): |
| path_to_model = os.path.join(root, file) |
| break |
| if path_to_model is not None: |
| break |
| self.system_prompt = "" |
|
|
| if lora_path is not None: |
| lora_path = os.path.join("lora", lora_path) |
| self.model = Llama(model_path=path_to_model, lora_path=lora_path) |
| else: |
| self.model = Llama(model_path=path_to_model) |
|
|
| def _get_llama_style_input(self): |
| context = [] |
| for conv in self.history: |
| if conv["role"] == "system": |
| context.append(SYS_PREFIX + conv["content"] + SYS_POSTFIX) |
| elif conv["role"] == "user": |
| context.append( |
| INST_PREFIX + conv["content"] + INST_POSTFIX + OUTPUT_PREFIX |
| ) |
| else: |
| context.append(conv["content"] + OUTPUT_POSTFIX) |
| return "".join(context) |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| def get_answer_at_once(self): |
| context = self._get_llama_style_input() |
| response = self.model( |
| context, |
| max_tokens=self.max_generation_token, |
| stop=[], |
| echo=False, |
| stream=False, |
| ) |
| return response, len(response) |
|
|
| def get_answer_stream_iter(self): |
| context = self._get_llama_style_input() |
| iter = self.model( |
| context, |
| max_tokens=self.max_generation_token, |
| stop=[SYS_PREFIX, SYS_POSTFIX, INST_PREFIX, OUTPUT_PREFIX, OUTPUT_POSTFIX], |
| echo=False, |
| stream=True, |
| ) |
| partial_text = "" |
| for i in iter: |
| response = i["choices"][0]["text"] |
| partial_text += response |
| yield partial_text |
|
|