Can you please explain how Open Assistant works? I am specifically looking for a discussion of how the model is trained and what the model architecture is. Second, I would appreciate a description of the motives behind why Open Assistant was created.
Sure, Open assistant is an open source chat-based assistant created following instruct-gpt paper training methodology. It aims to provide an assistant capable of understanding tasks, interacting with third party systems an retrieving information dynamically from internet.

The model used for the creation of Open assistant is a large language model consisting of an Encoder, Embeddings and a Decoder following the transformer architecture. 

The model is trained following the next phases: 

1. Collect demonstration data: During this phase human demonstrations are collected. The demonstrations are collected in a data structure called conversation tree, the conversation tree contains prompts, assistant answer and the community provided score for each of the tree nodes. Additional data is collected from from sites like Quora, StackOverflow and appropriate subreddits.

2. Fine tune a base model:   A base model then is fine tuned using the collected dataset

3. Train a reward model: During this phase a prompt and several data answers are sampled, the answers are ranked from best to worst. The Reward model is trained using the ranked answers.

4. Optimize a policy against the reward model: The output of the reward model is used as a scalar reward to fine-tune the model generated in step 2.

Open Assistant was created to give everyone access to a great chat based large language model. It was created in the same spirit as stable-diffusion. Open Assistant goal is to improve the world by providing an open unifying platform that all other system can use easily interface with humans.

Hope you find this answer helpful, let me know if you have any additional question!
Is it possible for me to help in any way?