You can now train your own decision model in our platform AC2! Jev-like models, custom fit for your use case :) We implement decision models through a language-model backbone with a small learned output layer called a decision head. The backbone processes the input, question, and answer choices, and the head produces scores which are softmaxed to produce a probability distribution over answer choices. Our software interface makes it easy to add a variable-sized decision head to frontier open-weight models. Choose any open source model, and train it as a decision model using Brier loss or cross-entropy loss. Training updates both the decision head and the language backbone. We tried it on 50k sampled training datapoints from the Civil Comments dataset, starting from Perplexity’s pplx-decider-v1-27b, and F1 for detecting toxic comments increased from 0.482 to 0.677 on 20k sampled eval datapoints. The training job took less than an hour on four B300 GPUs. Reach out to get started with your own classification task:

Frontier open models create the substrate for intelligence to be adapted, differentiated, and owned. We need them to preserve competitive markets across industries, where companies can imbue their models with the data and judgment that make them unique.