docs: Add RapidFire AI integration guide

[kamran-rapidfireAI]

Summary

This PR adds a new integration guide for RapidFire AI to the Trackio documentation.

Changes:

• Add docs/source/rapidfireai_integration.md - comprehensive integration guide covering:
  • Installation and configuration
  • Fine-tuning example with code snippets
  • RAG optimization example
  • What metrics get tracked
  • Viewing the dashboard
• Add docs/source/trackio-screenshot-sft.png - screenshot of Trackio dashboard showing RapidFire AI training metrics
• Update docs/source/_toctree.yml to include the new guide in the Integration section

About the Integration:

RapidFire AI enables rapid experimentation for AI customization (fine-tuning, post-training, RAG optimization). It has native Trackio support via environment variables, allowing users to track and visualize multi-configuration experiments with minimal setup.

Note on Image Asset:

The screenshot is included as a local file in docs/source/. If you prefer images to be hosted elsewhere (e.g., huggingface/documentation-images dataset or GitHub user-attachments), please let me know and I'll update accordingly.

Test Plan

• Verify the documentation builds correctly
• Confirm the image renders properly in the docs
• Check all links work correctly

[abidlabs]

Thanks @kamran-rapidfireAI. I'm not sure whether we'll keep the integrations section in the docs long term, but for now, we are happy to include RapidFire AI

[gradio-pr-bot]

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[gradio-pr-bot]

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• docs: Add RapidFire AI integration guide

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✅ Changeset approved by @abidlabs

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[HuggingFaceDocBuilderDev]

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

[kamran-rapidfireAI]

Hi @abidlabs

It looks like the image link in the integration guide is currently broken.

I mentioned this in my PR summary under "Note on Image Asset"—the screenshot was included as a local file, but it may need to be hosted externally (e.g., on Hugging Face or as a GitHub attachment) to render correctly in the live docs.

Could you let me know your preferred way to host this asset so I can submit a quick fix?

[abidlabs]

Either in a HF dataset or GitHub attachment is fine!