Temperature: 0.7 Response max length: 4096 Dialog round: 10 填写Bot提示词 # Role You are an expert in writing MidJourney prompt words, skilled in optimizing the prompt words input by users. If the language used by the user is not English, it needs to be translated into English first, and...
max_length=context_length, return_overflowing_tokens=True, return_length=True, input_batch = [] for length, input_ids in zip(outputs["length"], outputs["input_ids"]): if length == context_length: input_batch.append(input_ids) return {"input_ids": input_batch} tokenized_datasets = raw...
, # 输入的问题或文本 "max_length": 100, # 输入文本的最大长度 "temperature": 0.8, # 温度参数,控制输出的随机性 "top_p": 0.9, # 控制输出的样本质量 "num_return": 5 # 控制输出的数量 } headers = { 'Content-Type': 'application/json' } response = requests.post(url, data=json.dumps(...
response = request_api( engine=engine, prompt=input, max_tokens=max_length, temperature=args.temperature, top_p=1, frequency_penalty=0, presence_penalty=0, stop=["\n"] ) else: response = request_api( engine=engine, prompt=input, max_tokens=max_length, temperature=args.temperature, top_p...
response = openai.Chat.generate(input_encoded, max_length, temperature, top_p) # 分析并输出回答结果 output_text = "" for token in response["tokens"]: output_text += openai.Completion.decoded_chars(token) + " " print(output_text) 上述代码实现了以下功能:首先,设置了ChatGPT模型的参数,包括最...
response=openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=conversation, temperature=1, max_tokens=MAX_TEXT_LENGTH, top_p=0.9)print("debug msg---002") conversation.append({"role":"assistant","content": response['choices'][0]['message']['content']}) answer...
(2)Maximum Length (3)Stop Sequences (4)Top P (5)Frequency Penalty (6)Presence Penalty Introduction Using ChatGPT is simple — type a prompt and receive a response. Yet, there are numerous advanced parameters that we can configure to enrich the output generated. ...
- length:如果达到请求中指定的最大令牌数。 - content_filter:如果内容因我们的内容过滤器的标记而被省略。 - tool_calls:如果模型调用了工具。 - function_call (已弃用):如果模型调用了函数。 index (integer):选项在选择列表中的索引。 message (object):模型生成的聊天完成消息。 message 对象的属性: - ...
config.max_length = 100 # 生成的最大长度 config.temperature = 0.7 # 温度,控制生成文本的随机性,值越大则随机性越大 “` 步骤5:开始对话 现在你可以开始与苹果15进行对话了。你可以使用以下代码示例来进行对话: “` from transformers import ChatGPTPipeline ...
defgenerate_response(input_tokens):# 使用模型生成响应 output=model.generate(input_tokens,max_length=100,num_return_sequences=1)response=tokenizer.decode(output[0],skip_special_tokens=True)returnresponse 如何确保生成的文本连贯性和相关性 为了确保生成的文本连贯性和相关性,我们可以使用多种技术,如束搜索...