def METHOD_NAME(g, messages: List[ChatMessage], model: Any):
    try:
        from gpt4all import GPT4All
    except ModuleNotFoundError as e:
        logger.info("There was an error importing GPT4All. Please run pip install gpt4all in order to install it.")
        raise e
    assert isinstance(model, GPT4All), "model should be of type GPT4All"
    user_message = messages[-1]
    system_message = messages[0]
    conversation_history = messages[1:-1]
    formatted_messages = [
        prompts.chat_history_llamav2_from_assistant.format(message=message.content)
        if message.role == "assistant"
        else prompts.chat_history_llamav2_from_user.format(message=message.content)
        for message in conversation_history
    ]
    stop_words = ["<s>"]
    chat_history = "".join(formatted_messages)
    templated_system_message = prompts.system_prompt_llamav2.format(message=system_message.content)
    templated_user_message = prompts.general_conversation_llamav2.format(query=user_message.content)
    prompted_message = templated_system_message + chat_history + templated_user_message
    state.chat_lock.acquire()
    response_iterator = model.generate(prompted_message, streaming=True, max_tokens=500, n_batch=512)
    try:
        for response in response_iterator:
            if any(stop_word in response.strip() for stop_word in stop_words):
                logger.debug(f"Stop response as hit stop word in {response}")
                break
            g.send(response)
    finally:
        state.chat_lock.release()
    g.close()