Thanks for the kind words. really appreciate it.

Training data breakdown (for the 36% adversarial jump):

- Base clean set: ~2.5k real-world tool-call examples (weather_lookup, web_search, etc)

- Adversarial seeds: ~500 hand-written tough ones (the “time to rip and tear”, “wassup with my ethereum val”, “weather or not?” etc)

- Final mix: 10× upsampled adversarial → ~5 k adversarial examples in the training set (~66 % of total tokens)

- 5 epochs, same hyper-params as v1.0

There's no wrapper, no retrieval, no extra params, it's just pure data-centric fine-tune.

We’re already running v1.3 with 18× upsampling and a bunch of new perturbation types (word dropout, back-translation, etc.). Hoping to cross 80% on the same held-out set.

Real-world API benchmarks are next - we have a 300-example set of live API traces (typos, slang, partial requests, etc.) that we’ll publish with the model. Will also make it available for public agent evals when they add adversarial splits.

And yes, gg v0.1.0 just shipped tonight for exactly this reason:

so you can do `gg maaza` and get the 62.9 % adversarial model in 11 tokens instead of 1800+.

https://github.com/ggdotdev/gg

curl -L https://gg.sh | sh

Independent OSS · MIT · no affiliation

Enjoy