# Vicuna-13B ## **Vicuna-13B** MSYS ORG has made a significant mark in the realm of open-source LLMs with the introduction of Vicuna-13B. This open-source chatbot has been meticulously trained by fine-tuning LLaMA on user-shared conversations sourced from ShareGPT. Preliminary evaluations, with GPT-4 acting as the judge, indicate that Vicuna-13B achieves more than 90% quality of renowned models like OpenAI ChatGPT and Google Bard. Impressively, Vicuna-13B outperforms other notable models such as LLaMA and Stanford Alpaca in over 90% of cases. The entire training process for Vicuna-13B was executed at a cost of approximately $300. For those interested in exploring its capabilities, the code, weights, and an online demo have been made publicly available for non-commercial purposes. The Vicuna-13B model has been fine-tuned with 70K user-shared ChatGPT conversations, enabling it to generate more detailed and well-structured responses. The quality of these responses is comparable to ChatGPT. Evaluating chatbots, however, is a complex endeavor. With the advancements in GPT-4, there's a growing curiosity about its potential to serve as an automated evaluation framework for benchmark generation and performance assessments. Initial findings suggest that GPT-4 can produce consistent ranks and detailed assessments when comparing chatbot responses. Preliminary evaluations based on GPT-4 show that Vicuna achieves 90% capability of models like Bard/ChatGPT. **Key Features Overview of Vicuna-13B:** * **Open-Source Nature:** Vicuna-13B is available for public access, promoting transparency and community involvement. * **Extensive Training Data:** The model has been trained on 70K user-shared conversations, ensuring a comprehensive understanding of diverse interactions. * **Competitive Performance:** Vicuna-13B's performance is on par with industry leaders like ChatGPT and Google Bard. * **Cost-Effective Training:** The entire training process for Vicuna-13B was executed at a low cost of around $300. * **Fine-Tuning on LLaMA:** The model has been fine-tuned on LLaMA, ensuring enhanced performance and response quality. * **Online Demo Availability:** An interactive online demo is available for users to test and experience the capabilities of Vicuna-13B. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://metaverse-imagen.gitbook.io/ai-tools-research/large-language-models-llms/open-source-llms/vicuna-13b.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.