> For the complete documentation index, see [llms.txt](https://ai-docs.fptcloud.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ai-docs.fptcloud.com/other/ai-notebook/notebook-gallery.md).
# Notebook Gallery
#### **Discover the Power of Notebook Gallery in Ai Notebook**
Welcome to **Notebook Gallery**, your ultimate gateway to hands-on AI and data science! This feature brings a curated collection of ready-to-run notebooks directly into your JupyterLab environment, sourced from **Hugging Face** and other leading repositories. Whether you’re exploring **text classification**, **image recognition**, **entity linking**, or **translation tasks**, these notebooks are designed to help you hit the ground running.
**Why Notebook Gallery?**
* **Instant Access to Expert Workflows**\
No need to start from scratch—get pre-built, well-structured notebooks tailored for real-world machine learning tasks.
* **Diverse Topics at Your Fingertips**\
From **fine-tuning LLaMA-3 models** to advanced **image classification**, every notebook is optimized for clarity and performance.
* **Seamless Integration with JupyterLab**\
Launch, modify, and execute notebooks effortlessly within your familiar environment.
* **GPU-Ready for High Performance**\
Accelerate your experiments with one-click GPU scaling options—perfect for deep learning and large-scale computations.
**What Can You Do?**
* **Diverse Model Ecosystem**\
From **LLaMA 3 (70B)** for advanced language tasks, to **Mixtral 8x7B** for efficient mixture-of-experts performance, and giants like **Qwen 2 (72B)** and **Gemma 2 (27B)**—you have the power of multiple state-of-the-art LLMs at your fingertips.\
Need vision? **Kosmos-2** and **Vision Transformer** bring multimodal and image-focused capabilities into the mix.
* **Pre-Built, Hugging Face-Integrated Notebooks**\
Explore fine-tuning workflows for **text classification**, **entity linking**, **translation**, **image recognition**, and more—without wasting time on setup.
* **GPU-Ready for High-Speed Training**\
Scale effortlessly with one-click GPU options for deep learning and large-scale inference.
**Why You’ll Love It**
* **Instant Productivity**: No boilerplate, no guesswork—just launch and start experimenting.
* **Learn from the Best**: Each notebook reflects industry best practices and optimized pipelines.
* **Flexibility for Innovation**: Modify, extend, and adapt notebooks to fit your unique use case.
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.
Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:
```
GET https://ai-docs.fptcloud.com/other/ai-notebook/notebook-gallery.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.
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.
