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field rwkv_verbose: bool = True#
Print debug information.
field strategy: str = 'cpu fp32'#
Token context window.
field tags: Optional[List[str]] = None#
Tags to add to the run trace.
field temperature: float = 1.0#
The temperature to use for sampling.
field tokens_path: str [Required]#
Path to the RWKV tokens file.
field top_p: float = 0.5#
The top-p value to use for sampling.
field verbose: bool [Optional]#
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, **kwargs: Any) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str#
Predict text from text.
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/llms.html