llama_cpp server: add some more information to fields for completions

This commit is contained in:
Lucas Doyle 2023-04-29 14:37:36 -07:00
parent a5aa6c1478
commit 978b6daf93

View file

@ -71,22 +71,70 @@ model_field = Field(
)
class CreateCompletionRequest(BaseModel):
prompt: Union[str, List[str]]
suffix: Optional[str] = Field(None)
max_tokens: int = 16
temperature: float = 0.8
top_p: float = 0.95
echo: bool = False
stop: Optional[List[str]] = []
stream: bool = False
logprobs: Optional[int] = Field(None)
prompt: Union[str, List[str]] = Field(
default="",
description="The prompt to generate completions for."
)
suffix: Optional[str] = Field(
default=None,
description="A suffix to append to the generated text. If None, no suffix is appended. Useful for chatbots."
)
max_tokens: int = Field(
default=16,
ge=1,
le=2048,
description="The maximum number of tokens to generate."
)
temperature: float = Field(
default=0.8,
ge=0.0,
le=2.0,
description="Adjust the randomness of the generated text.\n\n" +
"Temperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The default value is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run."
)
top_p: float = Field(
default=0.95,
ge=0.0,
le=1.0,
description="Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P.\n\n" +
"Top-p sampling, also known as nucleus sampling, is another text generation method that selects the next token from a subset of tokens that together have a cumulative probability of at least p. This method provides a balance between diversity and quality by considering both the probabilities of tokens and the number of tokens to sample from. A higher value for top_p (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text."
)
echo: bool = Field(
default=False,
description="Whether to echo the prompt in the generated text. Useful for chatbots."
)
stop: Optional[List[str]] = Field(
default=None,
description="A list of tokens at which to stop generation. If None, no stop tokens are used."
)
stream: bool = Field(
default=False,
description="Whether to stream the results as they are generated. Useful for chatbots."
)
logprobs: Optional[int] = Field(
default=None,
ge=0,
description="The number of logprobs to generate. If None, no logprobs are generated."
)
# ignored, but marked as required for the sake of compatibility with openai's api
model: str = model_field
# llama.cpp specific parameters
top_k: int = 40
repeat_penalty: float = 1.1
top_k: int = Field(
default=40,
ge=0,
description="Limit the next token selection to the K most probable tokens.\n\n" +
"Top-k sampling is a text generation method that selects the next token only from the top k most likely tokens predicted by the model. It helps reduce the risk of generating low-probability or nonsensical tokens, but it may also limit the diversity of the output. A higher value for top_k (e.g., 100) will consider more tokens and lead to more diverse text, while a lower value (e.g., 10) will focus on the most probable tokens and generate more conservative text."
)
repeat_penalty: float = Field(
default=1.0,
ge=0.0,
description="A penalty applied to each token that is already generated. This helps prevent the model from repeating itself.\n\n" +
"Repeat penalty is a hyperparameter used to penalize the repetition of token sequences during text generation. It helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient."
)
class Config:
schema_extra = {