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微调对话裁剪

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binary-husky 2023-04-23 17:45:56 +08:00
parent 676fe40d39
commit 0785ff2aed
2 changed files with 11 additions and 8 deletions
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2
request_llm/bridge_chatgpt.py
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@ -200,7 +200,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
if "reduce the length" in error_msg:
if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'],
max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])//2) # history至少释放二分之一
max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
# history = [] # 清除历史
elif "does not exist" in error_msg:

17
toolbox.py
View File

@ -555,23 +555,26 @@ def run_gradio_in_subpath(demo, auth, port, custom_path):
def clip_history(inputs, history, tokenizer, max_token_limit):
"""
reduce the length of input/history by clipping.
reduce the length of history by clipping.
this function search for the longest entries to clip, little by little,
until the number of token of input/history is reduced under threshold.
通过剪辑来缩短输入/历史记录的长度。
until the number of token of history is reduced under threshold.
通过裁剪来缩短历史记录的长度。
此函数逐渐地搜索最长的条目进行剪辑，
直到输入/历史记录的标记数量降低到阈值以下。
直到历史记录的标记数量降低到阈值以下。
"""
import numpy as np
from request_llm.bridge_all import model_info
def get_token_num(txt):
return len(tokenizer.encode(txt, disallowed_special=()))
input_token_num = get_token_num(inputs)
if input_token_num < max_token_limit * 3 / 4:
# 当输入部分的token占比小于限制的3/4时，在裁剪时把input的余量留出来
if input_token_num < max_token_limit * 3 / 4:
# 当输入部分的token占比小于限制的3/4时，裁剪时
# 1. 把input的余量留出来
max_token_limit = max_token_limit - input_token_num
# 2. 把输出用的余量留出来
max_token_limit = max_token_limit - 128
# 3. 如果余量太小了，直接清除历史
if max_token_limit < 128:
# 余量太小了，直接清除历史
history = []
return history
else: