Source code for langchain_community.llms.ctransformers

from functools import partial
from typing import Any, Dict, List, Optional, Sequence

from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.language_models.llms import LLM
from langchain_core.pydantic_v1 import root_validator


[docs]class CTransformers(LLM): """C Transformers LLM models. To use, you should have the ``ctransformers`` python package installed. See https://github.com/marella/ctransformers Example: .. code-block:: python from langchain_community.llms import CTransformers llm = CTransformers(model="/path/to/ggml-gpt-2.bin", model_type="gpt2") """ client: Any #: :meta private: model: str """The path to a model file or directory or the name of a Hugging Face Hub model repo.""" model_type: Optional[str] = None """The model type.""" model_file: Optional[str] = None """The name of the model file in repo or directory.""" config: Optional[Dict[str, Any]] = None """The config parameters. See https://github.com/marella/ctransformers#config""" lib: Optional[str] = None """The path to a shared library or one of `avx2`, `avx`, `basic`.""" @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return { "model": self.model, "model_type": self.model_type, "model_file": self.model_file, "config": self.config, } @property def _llm_type(self) -> str: """Return type of llm.""" return "ctransformers" @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that ``ctransformers`` package is installed.""" try: from ctransformers import AutoModelForCausalLM except ImportError: raise ImportError( "Could not import `ctransformers` package. " "Please install it with `pip install ctransformers`" ) config = values["config"] or {} values["client"] = AutoModelForCausalLM.from_pretrained( values["model"], model_type=values["model_type"], model_file=values["model_file"], lib=values["lib"], **config, ) return values def _call( self, prompt: str, stop: Optional[Sequence[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Generate text from a prompt. Args: prompt: The prompt to generate text from. stop: A list of sequences to stop generation when encountered. Returns: The generated text. Example: .. code-block:: python response = llm.invoke("Tell me a joke.") """ text = [] _run_manager = run_manager or CallbackManagerForLLMRun.get_noop_manager() for chunk in self.client(prompt, stop=stop, stream=True): text.append(chunk) _run_manager.on_llm_new_token(chunk, verbose=self.verbose) return "".join(text) async def _acall( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Asynchronous Call out to CTransformers generate method. Very helpful when streaming (like with websockets!) Args: prompt: The prompt to pass into the model. stop: A list of strings to stop generation when encountered. Returns: The string generated by the model. Example: .. code-block:: python response = llm.invoke("Once upon a time, ") """ text_callback = None if run_manager: text_callback = partial(run_manager.on_llm_new_token, verbose=self.verbose) text = "" for token in self.client(prompt, stop=stop, stream=True): if text_callback: await text_callback(token) text += token return text