Repository: nomic-ai/gpt4all
File: gpt4all-bindings/python/gpt4all/gpt4all.py (Python)

Code before:

        # Validate download was successful
        if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
            raise RuntimeError("An error occurred during download. Downloaded file may not work.")

        # Sleep for a little bit so OS can remove file lock
        time.sleep(2)

        if verbose:
            print("Model downloaded at: ", download_path)
        return download_path

    def generate(
        self,
        prompt: str,
        max_tokens: int = 200,
        temp: float = 0.7,
        top_k: int = 40,
        top_p: float = 0.1,
        repeat_penalty: float = 1.18,
        repeat_last_n: int = 64,
        n_batch: int = 8,
        n_predict: Optional[int] = None,
        streaming: bool = False,
        callback: pyllmodel.ResponseCallbackType = pyllmodel.empty_response_callback,
    ) -> Union[str, Iterable]:
        """
        Generate outputs from any GPT4All model.

        Args:
            prompt: The prompt for the model the complete.
            max_tokens: The maximum number of tokens to generate.
            temp: The model temperature. Larger values increase creativity but decrease factuality.
            top_k: Randomly sample from the top_k most likely tokens at each generation step. Set this to 1 for greedy decoding.
            top_p: Randomly sample at each generation step from the top most likely tokens whose probabilities add up to top_p.
            repeat_penalty: Penalize the model for repetition. Higher values result in less repetition.
            repeat_last_n: How far in the models generation history to apply the repeat penalty.
            n_batch: Number of prompt tokens processed in parallel. Larger values decrease latency but increase resource requirements.
            n_predict: Equivalent to max_tokens, exists for backwards compatibility.
            streaming: If True, this method will instead return a generator that yields tokens as the model generates them.
            callback: A function with arguments token_id:int and response:str, which receives the tokens from the model as they are generated and stops the generation by returning False.

        Returns:
            Either the entire completion or a generator that yields the completion token by token.
        """
        generate_kwargs = dict(
            temp=temp,
            top_k=top_k,
            top_p=top_p,
            repeat_penalty=repeat_penalty,
            repeat_last_n=repeat_last_n,

Review comment (suggestion):
Better:
```py
    ) -> Union[str, Iterable[str]]:
```
Leaving no doubt what comes out of the generator/ResponseCallback.

Code after:

        return config

    @staticmethod
    def download_model(
        model_filename: str,
        model_path: str,
        verbose: bool = True,
        url: Optional[str] = None,
    ) -> str:
        """
        Download model from https://gpt4all.io.

        Args:
            model_filename: Filename of model (with .bin extension).
            model_path: Path to download model to.
            verbose: If True (default), print debug messages.
            url: the models remote url (e.g. may be hosted on HF)

        Returns:
            Model file destination.
        """

        def get_download_url(model_filename):
            if url:
                return url
            return f"https://gpt4all.io/models/{model_filename}"

        # Download model
        download_path = os.path.join(model_path, model_filename).replace("\\", "\\\\")
        download_url = get_download_url(model_filename)

        response = requests.get(download_url, stream=True)
        total_size_in_bytes = int(response.headers.get("content-length", 0))
        block_size = 2**20  # 1 MB

        with tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True) as progress_bar:
            try:
                with open(download_path, "wb") as file:
                    for data in response.iter_content(block_size):
                        progress_bar.update(len(data))
                        file.write(data)
            except Exception:
                if os.path.exists(download_path):
                    if verbose:
                        print("Cleaning up the interrupted download...")
                    os.remove(download_path)
                raise

        # Validate download was successful
        if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes: