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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)
# look for nay .gguf file in the dir_to_model directory and its subdirectories
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)
# for conv in self.history:
# if conv["role"] == "system":
# context.append(conv["content"])
# elif conv["role"] == "user":
# context.append(
# conv["content"]
# )
# else:
# context.append(conv["content"])
# return "\n\n".join(context)+"\n\n"
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
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