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fix chatgpt 参数设定与说明

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ivan deng
2023-04-27 20:06:27 +08:00
parent a7e19354a6
commit adbf5edcf5
3 changed files with 16 additions and 16 deletions
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app_chatgpt/models/ai_robot.py
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@@ -39,10 +39,10 @@ GPT-3 A set of models that can understand and generate natural language
openapi_api_key = fields.Char(string="API Key", help="Provide the API key here")
# begin gpt 参数
# 1. stop：表示聊天机器人停止生成回复的条件，可以是一段文本或者一个列表，当聊天机器人生成的回复中包含了这个条件，就会停止继续生成回复。
# 2. temperature：控制回复的“新颖度”，值越高，聊天机器人生成的回复越不确定和随机，值越低，聊天机器人生成的回复会更加可预测和常规化。
# 3. top_p：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：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：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。
# 2. temperature：0-2，控制回复的“新颖度”，值越高，聊天机器人生成的回复越不确定和随机，值越低，聊天机器人生成的回复会更加可预测和常规化。
# 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,
help="""
Set a limit on the number of tokens per model response.
@@ -50,7 +50,7 @@ GPT-3 A set of models that can understand and generate natural language
(including system message, examples, message history, and user query) and the model's response.
One token is roughly 4 characters for typical English text.
""")
temperature = fields.Float(string='Temperature', default=0.8,
temperature = fields.Float(string='Temperature', default=1,
help="""
Controls randomness. Lowering the temperature means that the model will produce
more repetitive and deterministic responses.
@@ -65,13 +65,13 @@ 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=0.5,
frequency_penalty = fields.Float('Frequency penalty', default=0.1,
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.
""")
# 越大模型就趋向于生成更新的话题，惩罚已经出现过的文本
presence_penalty = fields.Float('Presence penalty', default=0.5,
presence_penalty = fields.Float('Presence penalty', default=0.1,
help="""
Reduce the chance of repeating any token that has appeared in the text at all so far.
This increases the likelihood of introducing new topics in a response.
@@ -313,11 +313,11 @@ GPT-3 A set of models that can understand and generate natural language
pdata = {
"model": self.ai_model,
"prompt": data,
"temperature": 0.8,
"temperature": 1,
"max_tokens": max_tokens,
"top_p": 1,
"frequency_penalty": 0.0,
"presence_penalty": 0.6,
"top_p": 0.6,
"frequency_penalty": 0.1,
"presence_penalty": 0.1,
"stop": stop
}
response = requests.post(o_url, data=json.dumps(pdata), headers=headers, timeout=R_TIMEOUT)