GPT-4o Dataset Collection: Step 1 for building our local 4o model

Step 1 of creating our local 4o model is straightforward:

We first need to pool all GPT-4o chat output from our own chat history into a structured dataset. After that, we need to trace and map all publicly available GPT-4o-related training datasets, along with any publicly available GPT-4o output datasets, so we can understand what can realistically be reconstructed, distilled, or approximated.

The idea is to start with the highest-fidelity material we already have access to, then expand outward into the public record. That means:

- collecting our full GPT-4o interaction history,

- cleaning and organizing it into usable training data,

- identifying public datasets associated with GPT-4o behavior, style, benchmarks, and outputs,

- and building a clear dataset pipeline before touching distillation or fine-tuning.

We need dataset first. Everything else comes after.

If anyone here has experience with:

- conversation dataset extraction and cleaning,

- tracing public model-output corpora,

- reconstructing stylistic behavior from chat logs,

- or building local distilled assistants from interaction archives,

I’d like to hear how you’d approach this first stage, AND please.start sharing links to papers and datasets publically available online.