langchain.llms.llamacpp.LlamaCpp¶
class langchain.llms.llamacpp.LlamaCpp[source]¶
Bases: LLM
llama.cpp model.
To use, you should have the llama-cpp-python library installed, and provide the
path to the Llama model as a named parameter to the constructor.
Check out: https://github.com/abetlen/llama-cpp-python
Example
from langchain.llms import LlamaCpp
llm = LlamaCpp(model_path="/path/to/llama/model")
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param cache: Optional[bool] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
param callbacks: Callbacks = None¶
param echo: Optional[bool] = False¶
Whether to echo the prompt.
param f16_kv: bool = True¶
Use half-precision for key/value cache.
param last_n_tokens_size: Optional[int] = 64¶
The number of tokens to look back when applying the repeat_penalty.
param logits_all: bool = False¶
Return logits for all tokens, not just the last token.
param logprobs: Optional[int] = None¶
The number of logprobs to return. If None, no logprobs are returned.
param lora_base: Optional[str] = None¶
The path to the Llama LoRA base model.
param lora_path: Optional[str] = None¶
The path to the Llama LoRA. If None, no LoRa is loaded.
param max_tokens: Optional[int] = 256¶
The maximum number of tokens to generate.
param metadata: Optional[Dict[str, Any]] = None¶