prompter: Tell me something about how Open Assistant works, what I can do to improve it and how can I use it productively

assistant: Open Assistant is a chat-based and open-source assistant that aims to give everyone access to a great chat-based large language model. It is a project organized by LAION, a non-profit organization that wants to create "truly open AI". Open Assistant uses RLHF (Reinforcement Learning with Human Feedback) to train large language models on conversational tasks.

To improve Open Assistant, you can contribute by providing feedback, data, code, or models. You can find more details on how to contribute on their website or their GitHub repository.

To use Open Assistant productively, you can try out their web interface or their Discord bot. You can also run their models locally on your own hardware if you have a high-end consumer GPU. You can ask Open Assistant to do various tasks such as retrieving information, interacting with third-party systems, generating content, or having a chat.

prompter: What is the lowest-end GPU that will be able to run the best and worst models of open assistant. The GPUs should have a release cutoff date by Q2 2020 (They should not be younger than this date).

assistant: Open Assistant is an AI language model that is designed to understand and respond to natural language queries and commands in a conversational manner. It uses a combination of deep learning algorithms, natural language processing techniques, and large-scale training data to understand the context and intent of user input and generate relevant and informative responses.

As a user, there are several things you can do to improve the performance of Open Assistant and use it productively:

1. Be specific and clear in your queries: To get the most accurate and relevant responses from Open Assistant, it is important to be as specific and clear as possible in your queries. Use precise language, provide context and details, and avoid ambiguity or vagueness.

2. Provide feedback: If you encounter any errors or inaccuracies in Open Assistant's responses, you can provide feedback using the thumbs up/down buttons or by submitting a correction. This helps to improve the model's accuracy and relevance over time.

3. Use natural language: Open Assistant is designed to understand natural language queries and commands, so you can use conversational language and phrasing to interact with the model. You don't need to use specific keywords or syntax to get a response.

4. Explore different topics and domains: Open Assistant has been trained on a wide range of topics and domains, so you can use it to explore different areas of knowledge and expertise. Try asking questions about science, history, technology, or current events, and see what kind of responses you get.

5. Use Open Assistant as a productivity tool: Open Assistant can also be used as a productivity tool to help you with tasks such as scheduling, calculations, or research. You can use it to set reminders, convert units, or look up information, among other things.

Overall, Open Assistant is a powerful tool that can help you access information, solve problems, and be more productive. By following these tips, you can improve its performance and use it effectively in a variety of contexts.

prompter: Thanks! What's the difference between GPT4All and Open Assistant?

assistant: GPT4All is fine-tuned on synthetic instruction data extracted from OpenAI's GPT-3.5-Turbo model, whereas OpenAssistant is mainly fine-tuned on data gathered from responses written by real human volunteers. It remains to be seen whether synthetic training data can achieve results comparable to responses written by living humans, but the OpenAssistant project is testing the theory that finetuning on responses written by living humans may give language models a degree of alignment and creativity which may not be possible to replicate as effectively with data scraped purely from an existing instruct-tuned large language model.

prompter: I believe you could generate an endless supply of valuable synthetic data if you bring multiple LLM models into a conversation with each other and use a recursive process where the models criticize their own work, pretending to be different fictional and real characters (professor of physics vs. electrical engineer, etc.) who offer their independent feedback on ideas discussed. I think these can be combined with autoGPT and babyAGI with the goal of testing each other and discovering their own strengths and weaknesses. Then they could seek out new data that would bridge the gap in their understanding and maybe autonomously train new models for that purpose. I noticed chatGPT and GPT4 routinely states that they are unable to do certain things that they really can. You just have to get the model to assume the identity of someone who knows how to do the task, and suddenly it does the thing just swore it could not do. What are your thoughts?

assistant: Coming from different developers, GPT4ALL and Open Assistant can have multiple differences, most notable ones are the learning data size, as GTP4ALL is trained on 400 000 prompts, while Open Assistant is openly trained on open-assistant.io and it's training data is growing rapidly.

prompter: What sort of training data do you use?