From the source
Today we're introducing Mercury Edit 2: a purpose-built diffusion LLM (dLLM) for the most latency-sensitive component of modern development workflows: next-edit prediction.
This model upgrades our previous next-edit model and complements our existing auto-complete endpoint.
Using your recent edits and codebase context, Mercury Edit 2 predicts what you’ll change next.
Because Mercury Edit 2 uses diffusion to generate tokens in parallel, predictions arrive fast enough to feel like part of your own thinking.
Just Tab to accept.
How we trained our model To train this model, we carefully curated a high quality dataset of edits across a broad range of languages and scenarios.
These examples taught our model how to interpret a history of edits and codebase context into a targeted next edit suggestion.
While powerful, we found that the model could be overzealous in the frequency and length of its edits, distracting the user.
To combat this, we gather a high quality human preference dataset by recording the explicit feedback (accepting vs rejecting an edit) that our model receives.
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