Summarize below sentences.

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Background

In recent months, there has been a significant push in the development and release of chatbots. From Character.ai\'s chatbot last spring to ChatGPT in November and Bard in December, the user experience created by tuning language models for chat has been a hot topic. The emergence of open access and open-source alternatives has further fueled this interest.

The Current Environment of Open Source Chatbots

The success of these chat models is due to two training paradigms: instruction finetuning and reinforcement learning through human feedback (RLHF). While there have been significant efforts to build open source frameworks for helping train these kinds of models, such as trlX, trl, DeepSpeed Chat and ColossalAI, there is a lack of open access and open source models that have both paradigms applied. In most models, instruction finetuning is applied without RLHF training because of the complexity that it involves.

Recently, Open Assistant, Anthropic, and Stanford have begun to make chat RLHF datasets readily available to the public. Those datasets, combined with the straightforward training of RLHF provided by trlX, are the backbone for the first large-scale  instruction fintuned and RLHF model we present here today: StableVicuna.


Introducing the First Large-Scale Open Source RLHF LLM Chatbot

We are proud to present StableVicuna, the first large-scale open source chatbot trained via reinforced learning from human feedback (RLHF). StableVicuna is a further instruction fine tuned and RLHF trained version of Vicuna v0 13b, which is an instruction fine tuned LLaMA 13b model. For the interested reader, you can find more about Vicuna here. 


Similarly, here are a number of benchmarks showing the overall performance of StableVicuna compared to other similarly sized open source chatbots.


In order to achieve StableVicuna’s strong performance, we utilize Vicuna as the base model and follow the typical three-stage RLHF pipeline outlined by NAME_1 et NAME_2. and NAME_3 et NAME_2. Concretely, we further train the base Vicuna model with supervised finetuning (SFT) using a mixture of three datasets:

OpenAssistant Conversations Dataset (OASST1), a human-generated, human-annotated assistant-style conversation corpus comprising 161,443 messages distributed across 66,497 conversation trees, in 35 different languages;

GPT4All Prompt Generations, a dataset of 437,605 prompts and responses generated by GPT-3.5 Turbo;

And Alpaca, a dataset of 52,000 instructions and demonstrations generated 