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update gpt

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Ivan Office
2023-09-22 20:47:18 +08:00
parent 6ef2c27b4a
commit 8db7fabe56
11 changed files with 91 additions and 100 deletions
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6
app_chatgpt/models/ai_robot.py
View File

@@ -52,7 +52,7 @@ GPT-3 A set of models that can understand and generate natural language
# 3. top_p：0-1，语言连贯性，与temperature有些类似，也是控制回复的“新颖度”。不同的是，top_p控制的是回复中概率最高的几个可能性的累计概率之和，值越小，生成的回复越保守，值越大，生成的回复越新颖。
# 4. frequency_penalty：-2~2，用于控制聊天机器人回复中出现频率过高的词汇的惩罚程度。聊天机器人会尝试避免在回复中使用频率较高的词汇，以提高回复的多样性和新颖度。
# 5. presence_penalty：-2~2与frequency_penalty相对，用于控制聊天机器人回复中出现频率较低的词汇的惩罚程度。聊天机器人会尝试在回复中使用频率较低的词汇，以提高回复的多样性和新颖度。
max_tokens = fields.Integer('Max response', default=600,
max_tokens = fields.Integer('Max Response', default=600,
help="""
Set a limit on the number of tokens per model response.
The API supports a maximum of 4000 tokens shared between the prompt
@@ -66,7 +66,7 @@ GPT-3 A set of models that can understand and generate natural language
Increasing the temperature will result in more unexpected or creative responses.
Try adjusting temperature or Top P but not both.
""")
top_p = fields.Float('Top probabilities', default=0.6,
top_p = fields.Float('Top Probabilities', default=0.6,
help="""
Similar to temperature, this controls randomness but uses a different method.
Lowering Top P will narrow the model’s token selection to likelier tokens.
@@ -74,7 +74,7 @@ GPT-3 A set of models that can understand and generate natural language
Try adjusting temperature or Top P but not both
""")
# 避免使用常用词
frequency_penalty = fields.Float('Frequency penalty', default=1,
frequency_penalty = fields.Float('Frequency Penalty', default=1,
help="""
Reduce the chance of repeating a token proportionally based on how often it has appeared in the text so far.
This decreases the likelihood of repeating the exact same text in a response.

24
app_chatgpt/models/mail_channel.py
View File

@@ -27,32 +27,32 @@ class Channel(models.Model):
domain=[('gpt_id', '!=', None), ('is_chat_private', '=', True)])
description = fields.Char('Ai Character', help="Ai would help you act as the Character set.")
set_max_tokens = fields.Selection([
('300', '简短'),
('600', '标准'),
('1000', '中等'),
('2000', '长篇'),
('3000', '超长篇'),
('300', 'Short'),
('600', 'Standard'),
('1000', 'Medium'),
('2000', 'Long'),
('3000', 'Overlength'),
('32000', '32K'),
], string='响应篇幅限制', default='600', help="越大返回内容越多，计费也越多")
], string='Max Response', default='600', help="越大返回内容越多，计费也越多")
set_chat_count = fields.Selection([
('none', 'Ai自动判断'),
('none', 'Ai Auto'),
('1', '1标准'),
('3', '3强关联'),
('5', '5超强关联'),
], string="上下文相关", default='1', help="0-5，设定后，会将最近n次对话发给Ai，有助于他更好的回答，但太大费用也高")
], string="History Count", default='1', help="0-5，设定后，会将最近n次对话发给Ai，有助于他更好的回答，但太大费用也高")
set_temperature = fields.Selection([
('2', '天马行空'),
('1.5', '创造性'),
('1', '标准'),
('0.6', '理性'),
('0.1', '保守'),
], string="创造性", default='1', help="0-21，值越大越富有想像力，越小则越保守")
], string="Set Temperature", default='1', help="0-21，值越大越富有想像力，越小则越保守")
set_top_p = fields.Selection([
('0.9', '严谨惯性思维'),
('0.6', '标准推理'),
('0.4', '跳跃性'),
('0.1', '随便'),
], string="思维连贯性", default='0.6', help="0-1，值越大越倾向大众化的连贯思维")
], string="Top Probabilities", default='0.6', help="0-1，值越大越倾向大众化的连贯思维")
# 避免使用常用词
set_frequency_penalty = fields.Selection([
('2', '老学究-晦涩难懂'),
@@ -61,7 +61,7 @@ class Channel(models.Model):
('0.1', '少常用词'),
('-1', '通俗易懂'),
('-2', '大白话'),
], string='语言风格', default='1', help="-2~2，值越大越少使用常用词")
], string='Frequency Penalty', default='1', help="-2~2，值越大越少使用常用词")
set_presence_penalty = fields.Selection([
('2', '多样强迫症'),
('1.5', '新颖化'),
@@ -69,7 +69,7 @@ class Channel(models.Model):
('0.1', '允许常规重复'),
('-1', '允许较多重复'),
('-2', '更多强调重复'),
], string='用词多样性', default='1', help="-2~2，值越大越少重复词")
], string='Presence penalty', default='1', help="-2~2，值越大越少重复词")
# todo: 这里用 compute?
max_tokens = fields.Integer('最长响应Token', default=600, help="越大返回内容越多，计费也越多")