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Notebook Gallery

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: 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://ai-docs.fptcloud.com/other/ai-notebook/notebook-gallery.md?ask=<question>

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.