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@ -28,6 +28,8 @@ class GPT4All(LLM):
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model: str
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"""Path to the pre-trained GPT4All model file."""
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backend: str = Field("llama", alias="backend")
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n_ctx: int = Field(512, alias="n_ctx")
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"""Token context window."""
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@ -93,14 +95,11 @@ class GPT4All(LLM):
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extra = Extra.forbid
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@property
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def _default_params(self) -> Dict[str, Any]:
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def _llama_default_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {
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"seed": self.seed,
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"n_predict": self.n_predict,
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"n_threads": self.n_threads,
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"n_batch": self.n_batch,
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"repeat_last_n": self.repeat_last_n,
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"repeat_penalty": self.repeat_penalty,
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"top_k": self.top_k,
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@ -108,6 +107,16 @@ class GPT4All(LLM):
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"temp": self.temp,
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}
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def _gptj_default_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {
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"n_predict": self.n_predict,
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"n_threads": self.n_threads,
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"top_k": self.top_k,
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"top_p": self.top_p,
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"temp": self.temp,
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}
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@staticmethod
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def _llama_param_names() -> Set[str]:
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"""Get the identifying parameters."""
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@ -122,14 +131,41 @@ class GPT4All(LLM):
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"embedding",
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}
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@staticmethod
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def _gptj_param_names() -> Set[str]:
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"""Get the identifying parameters."""
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return set()
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@staticmethod
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def _model_param_names(backend: str) -> Set[str]:
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if backend == "llama":
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return GPT4All._llama_param_names()
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else:
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return GPT4All._gptj_param_names()
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def _default_params(self) -> Dict[str, Any]:
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if self.backend == "llama":
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return self._llama_default_params()
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else:
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return self._gptj_default_params()
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that the python package exists in the environment."""
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try:
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from pygpt4all.models.gpt4all import GPT4All as GPT4AllModel
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backend = values["backend"]
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if backend == "llama":
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from pygpt4all import GPT4All as GPT4AllModel
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elif backend == "gptj":
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from pygpt4all import GPT4All_J as GPT4AllModel
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else:
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raise ValueError(f"Incorrect gpt4all backend {cls.backend}")
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llama_keys = cls._llama_param_names()
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model_kwargs = {k: v for k, v in values.items() if k in llama_keys}
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model_kwargs = {
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k: v
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for k, v in values.items()
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if k in GPT4All._model_param_names(backend)
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}
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values["client"] = GPT4AllModel(
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model_path=values["model"],
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**model_kwargs,
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@ -147,11 +183,11 @@ class GPT4All(LLM):
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"""Get the identifying parameters."""
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return {
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"model": self.model,
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**self._default_params,
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**self._default_params(),
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**{
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k: v
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for k, v in self.__dict__.items()
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if k in GPT4All._llama_param_names()
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if k in self._model_param_names(self.backend)
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},
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}
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@ -181,15 +217,14 @@ class GPT4All(LLM):
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prompt = "Once upon a time, "
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response = model(prompt, n_predict=55)
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"""
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text_callback = None
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if run_manager:
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text_callback = partial(run_manager.on_llm_new_token, verbose=self.verbose)
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text = self.client.generate(
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prompt,
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new_text_callback=text_callback,
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**self._default_params,
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)
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else:
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text = self.client.generate(prompt, **self._default_params)
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text = ""
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for token in self.client.generate(prompt, **self._default_params()):
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if text_callback:
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text_callback(token)
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text += token
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if stop is not None:
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text = enforce_stop_tokens(text, stop)
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return text
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