Built a lane-based dataset bundle explorer for LLM training — would love feedback from the HF community

Hi everyone! I’ve been building DinoDS , a modular dataset system for LLM training built around lane-based dataset bundles.

The idea is simple: instead of treating training data like one giant premade dump, I’m organizing it into capability-focused bundles that map to specific assistant behaviors and failure types — things like:

• retrieval grounding

• workflow / tool routing

• memory and continuity

• structured outputs

• identity and behavior shaping

I’ve started publishing some of these dataset bundle previews on Hugging Face, and I also made a Space that helps people explore which dataset bundle might actually be useful for their use case.

So the current flow is:

• explore the DinoDS concept

• identify what kind of assistant behavior you want to improve

• see which bundle / lane family fits

• check out the related dataset previews

I’d really love feedback from the HF community on a few things:

1. Does this bundle-first / lane-based way of presenting datasets make sense?

2. Is the Space + dataset bundle flow intuitive?

3. What would make these previews more useful for people evaluating training data?

4. Would you rather explore by failure type , capability , or use case?

You can check out the bundles, the Space, and the website here:

• Hugging Face Space: Dinodataset Failure Mapper - a Hugging Face Space by DinoDS

• Dataset bundles: DinoDS (DinoDS Labs)

• Website: www.dinodsai.com

Would love thoughts, criticism, and suggestions — especially from people building assistants, copilots, routing systems, or structured-output workflows.