# Welcome to Alpaca ## Overview Here are a couple of example overviews from Stanford Alpaca > **Stanford Alpaca GPT will be available Offline where it can be a useful tools for all.** > The current Alpaca model is fine-tuned from a 7B LLaMA model \[1] on 52K instruction-following data generated by the techniques in the Self-Instruct \[2] paper, with some modifications that we discuss in the next section. In a preliminary human evaluation, we found that the Alpaca 7B model behaves similarly to the `text-davinci-003` model on the Self-Instruct instruction-following evaluation suite \[2]. Our initial release contains the data generation procedure, dataset, and training recipe. We intend to release the model weights if we are given permission to do so by the creators of LLaMA. For now, we have chosen to host a live demo to help readers better understand the capabilities and limits of Alpaca, as well as a way to help us better evaluate Alpaca's performance on a broader audience. {% content-ref url="/pages/9uSz9U3Vq7XaHvfq3gcR" %} [Our Features](/everything-you-need-to-know/overview/our-features.md) {% endcontent-ref %} --- # 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://stanford-alpaca.gitbook.io/everything-you-need-to-know/welcome-to-alpaca.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.