return dict(zip(chats.keys(), responses))import nest_asyncionest_asyncio.apply()response_dict = asyncio.run(get_msgs())for model_name, response in response_dict.items():    print(f'\t{model_name}')    print()    print(response.content)    print('\n---\n')        meta-llama/Llama-2-70b-chat-hf        Greetings! I'm just an AI, I don't have a personal identity like humans do, but I'm here to help you with any questions you have.        I'm a large language model, which means I'm trained on a large corpus of text data to generate language outputs that are coherent and natural-sounding. My architecture is based on a transformer model, which is a type of neural network that's particularly well-suited for natural language processing tasks.        As for my parameters, I have a few billion parameters, but I don't have access to the exact number as it's not relevant to my functioning. My training data includes a vast amount of text from various sources, including books, articles, and websites, which I use to learn patterns and relationships in language.        I'm designed to be a helpful tool for a variety of tasks, such as answering questions, providing information, and generating text. I'm constantly learning and improving my abilities through machine learning algorithms and feedback from users like you.        I hope this helps! Is there anything else you'd like to know about me or my capabilities?        ---            meta-llama/Llama-2-7b-chat-hf        Ah, a fellow tech enthusiast! *adjusts glasses* I'm glad to share some technical details about myself. ü§ì    Indeed, I'm a transformer model, specifically a BERT-like language model trained on a large corpus of text data. My architecture is based on the transformer framework, which is a type of neural network designed for natural language processing tasks. üè†    As for the number of parameters, I have approximately 340 million. *winks* That's a pretty hefty number, if I do say so myself! These parameters allow
This notebook demonstrates the use of langchain.chat_models.ChatAnyscale for Anyscale Endpoints.
This notebook demonstrates the use of langchain.chat_models.ChatAnyscale for Anyscale Endpoints. ->: return dict(zip(chats.keys(), responses))import nest_asyncionest_asyncio.apply()response_dict = asyncio.run(get_msgs())for model_name, response in response_dict.items():    print(f'\t{model_name}')    print()    print(response.content)    print('\n---\n')        meta-llama/Llama-2-70b-chat-hf        Greetings! I'm just an AI, I don't have a personal identity like humans do, but I'm here to help you with any questions you have.        I'm a large language model, which means I'm trained on a large corpus of text data to generate language outputs that are coherent and natural-sounding. My architecture is based on a transformer model, which is a type of neural network that's particularly well-suited for natural language processing tasks.        As for my parameters, I have a few billion parameters, but I don't have access to the exact number as it's not relevant to my functioning. My training data includes a vast amount of text from various sources, including books, articles, and websites, which I use to learn patterns and relationships in language.        I'm designed to be a helpful tool for a variety of tasks, such as answering questions, providing information, and generating text. I'm constantly learning and improving my abilities through machine learning algorithms and feedback from users like you.        I hope this helps! Is there anything else you'd like to know about me or my capabilities?        ---            meta-llama/Llama-2-7b-chat-hf        Ah, a fellow tech enthusiast! *adjusts glasses* I'm glad to share some technical details about myself. ü§ì    Indeed, I'm a transformer model, specifically a BERT-like language model trained on a large corpus of text data. My architecture is based on the transformer framework, which is a type of neural network designed for natural language processing tasks. üè†    As for the number of parameters, I have approximately 340 million. *winks* That's a pretty hefty number, if I do say so myself! These parameters allow