The Open-Source Chatbot with Exceptional Quality Based on LLaMA-13B

Large Language Models (LLMs) are advanced AI models that may process and comprehend human language, developed using deep learning techniques and trained on massive amounts of textual data. These models have gained significant popularity, with GPT-4 being a notable transformer model that was released in March 2023 and utilized in OpenAI’s ChatGPT chatbot. The chatbot’s advanced capabilities allow it to generate human-like text and answer questions.

A team from UC Berkeley, CMU, Stanford, and UC San Diego developed , an open-source chatbot with . To create Vicuna, a base model was fine-tuned using about 70K user-shared conversations collected from ShareGPT.com via public APIs. In response to initial assessments where GPT-4 is used as a reference, Vicuna-13B has achieved over 90%* quality in comparison with OpenAI ChatGPT and Google Bard, and has also demonstrated higher performance than other models akin to LLaMA and Stanford Alpaca in over of cases.

The Vicuna model is important since it is considered one of the primary open-source large language models trained with human generated data and generates coherent and artistic text. It’s an improved version of the model, based on the Transformer architecture, but fine-tuned on a dataset of human-generated conversations. This makes it a beneficial tool for creating powerful chatbots and for researchers studying large language models. The Vicuna model is an indication of progress in the sphere of natural language processing and makes large language models more accessible to the general public, which could have several advantages.

Note that the information set, training code, evaluation metrics, training cost are known for Vicuna but just isn’t known for Bard or ChatGPT.

Meta AI’s LLaMA (Large Language Model Meta AI) is a notable model that was developed in February 2023. With 13 billion parameters, it performs exceptionally well on most NLP benchmarks, even rivaling state-of-the-art models akin to PaLM and Chinchilla.

There are various kinds of LLaMA models, including the LLaMA 13B model, which is a flexible all-purpose model that might be used for a wide range of tasks, akin to generating text & translating languages, , the LLaMA 7B model, which is computationally inexpensive and suitable for less complicated tasks, and the LLaMA 65B model, which is powerful and ideal for more complex tasks. Each model is designed for various purposes, akin to generating text, translating languages, and running chatbots. Vicuna is predicated on LLaMA 13B model.

Vicuna is fine-tuned on from , a Chrome extension that permits users to share their ChatGPT conversations. Using about 70,000 conversations, the team built the chatbot upon Stanford’s Alpaca framework, with improvements akin to memory optimization, multi-round conversation handling, and value reduction.

To evaluate chatbot performance, eight query categories were created and ten questions per category were asked, and responses were collected from five chatbots: LLaMA, Alpaca, ChatGPT, Bard, and Vicuna. GPT-4 was then used to rate the standard of the chatbots’ responses based on several criteria.

With a top quality rating above 90% compared to ChatGPT and Google Bard, Vicuna outperformed LLaMA and Stanford Alpaca in over 90% of cases. The entire training cost for Vicuna was around $300, making it a cheap solution for chatbot development.

Though evaluating this using GPT-4 will not be probably the most scientific way of doing this. Developing a comprehensive and standardized evaluation system for chatbots continues to be an open query that requires further research.

Try the Vicuna demo here and the corresponding research paper here.

In conclusion, large language models (LLMs) have made significant advancements in chatbot systems, as seen in OpenAI’s ChatGPT. Nevertheless, the shortage of coaching and architecture details in ChatGPT has hindered research and innovation in the sphere. To deal with this, Vicuna-13B, an open-source chatbot with enhanced dataset and scalable infrastructure, has been developed by fine-tuning a LLaMA base model on user-shared conversations. Vicuna-13B has demonstrated competitive performance in comparison with other open-source models, and its performance and infrastructure are outlined on this blog post.