text: Title: Planning OA v1.0
  Body: This is a call for all OA collaborators to participate in planning the work of the next 8-12 weeks with the goal to release Open-Assistant v1.0. 
  
  Mission: Deliver a great open-source assistant model together with stand-alone installable inference infrastructure.
  Release date (tentative): Aug 2023
  
  ## Organization
  
  - [x] schedule call to collect collaborator feedback and ask for developer participation/commitment
  - [ ] update vision & roadmap for v 1.0 
  - [x] schedule weekly developer meeting
  
  
  ## Feature set proposal (preliminary)
  
  ### Model
  
  - fine-tune best available base LLMs (currently LLaMA 65B & Falcon 40B) ([QLoRA](https://arxiv.org/abs/2305.14314))
  - implement long context (10k+), candidates: QLoRA+MQA+flash-attn, [BPT](https://arxiv.org/abs/2305.19370), [Landmark Attention](https://arxiv.org/abs/2305.16300)
  - add retrieval/tool-use, candidate: [Toolformer](https://arxiv.org/abs/2302.04761)
  
  ### Inference system
  - prompt preset + prompt database
  - sharing of conversations via URL
  - support for long-context & tool use
  - stand-alone installation (without feedback collection system)
  - allow editing of assistant results and message-tree submission as synthetic example for dataset for human labeling and ranking
  
  ### Classic human feedback collection
  - editing messages for moderators, submit edit-proposals for users
  - entering prompt + reply pairs
  - collecting relevant links in a separate input field
  - improve labeling: review, more guidelines, addition of further labels (e.g. robotic), labels no longer optional
  
  ### Experiments
  - Analyze whether additional fine-tuning on (synthetic) instruction datasets (Alpaca, Vicuna) is beneficial or harmful: Only OA top-1 threads (Guanaco) vs. synthetic instruction-tuning + OA top-1, potentially with system-prompt for "mode" selection to distinguish between chat and instruction following, e.g. to use instruction mode for plugin processing
  
  
  ## Perspective strategy (brain-storming)
  - Sunsetting of classic data collection after OASST2 release and transitioning towards semi-automated inference based data collection
  - Extending data collection to new domains, give users more freedom in task selection, e.g. for Code: describing code, refactoring, writing unit tests, etc.
  
  
  Please add further proposals for high-priority features and try to make a case for why they are important and should become part of v1.0. If you are a developer who wants to support OA: Let us know on what you would like to work (also if it is not yet part of the above list).
  