', name=None, function_call=None)], 'temperature': 0.8, 'top_p': 0.8, 'max_tokens': 1024, 'max_length': None, 'echo': False, 'stream': False, 'repetition_penalty': 1.1, 'functions': [{'name': 'send_message', 'description': 'Sends a message to the human user', 'parameters...
生成式人工智能是能够生成文本、图像和其他类型内容的人工智能。 它之所以成为一项出色的技术,是因为它使人工智能更通用化,任何人都可以使用它, 只需简单文本提示,使用自然语言编写的提示词。 你不需要学习 Java 或 SQL 这样的语言来完成一些有价值的事情,你只需要使用你的日常语言,描述需要的东西,然后LLMs就会给出...
-2.00 ~ 2.00 frequency penalty frequency_penalty this setting adjusts the frequency at which the model reuses specific vocabulary that has already appeared in the input. higher values reduce the likelihood of such repetition, while negative values have the opposite effect. vocabulary penalties do not...
repetition_penalty: Optional[float] = 1.1 class ChatCompletionResponseChoice(BaseModel): index: int message: ChatMessage finish_reason: Literal["stop", "length", "function_call"] # no need for logprobs class ChatCompletionResponseStreamChoice(BaseModel): ...
Additionally, you might want to consider the frequency_penalty, which controls the repetition of words, and presence_penalty, which influences the likelihood of introducing new topics. With the assistance of GPT-4, I’ve develop...
class CustomRepetitionPenaltyLogitsProcessorRepeat: Expand All @@ -17,34 +18,41 @@ def __init__(self, penalty: float, max_input_ids: int, past_window: int): def __call__( self, input_ids: torch.LongTensor, scores: torch.FloatTensor ) -> torch.FloatTensor: input_ids = input_ids...
# example token count from the OpenAI APIresponse=client.chat.completions.create(model=model,messages=example_messages,temperature=0,max_tokens=1)token=response.usage.prompt_tokensprint(f'{token}prompt tokens counted by the OpenAI API.')print()...
repetition_penalty:Optional[float]=1.1 classChatCompletionResponseChoice(BaseModel): index:int message:ChatMessage finish_reason:Literal["stop","length","function_call"] classChatCompletionResponseStreamChoice(BaseModel): index:int delta:DeltaMessage ...
"repetition_penalty": repetition_penalty, @@ -29,10 +43,35 @@ def chatglm_generate_stream( hist.append((messages[i][1], messages[i + 1][1])) query = messages[-2][1] for response, new_hist in model.stream_chat(tokenizer, query, hist, **gen_kwargs): input_echo_len = stream...
repetition_penalty:Optional[float]=1.1 classChatCompletionResponseChoice(BaseModel): index:int message:ChatMessage finish_reason:Literal["stop","length","function_call"] classChatCompletionResponseStreamChoice(BaseModel): index:int delta:DeltaMessage ...