AutoJev：用 agent 集群自主训练 Jev 类模型
Denis Yarats 用 agent 集群在 20 小时、3.1k 美元预算内，以 qwen3.8-27b 为底座训练出对标 Jev 的模型，准确率和校准更好，并提供 Jev 兼容 API 与代码。
fun weekend project: AutoJev. i was curious to see if i could train a competitive Jev-like model completely autonomously with a swarm of agents using our internal system. turns out you can get quite far! some details: - gave the swarm a devbox with an h200 gpu - the swarm is a mix of astra and fable; used sol and luna for synthetic data and filtering; ran for 20 hours - spent $3.1k in total ($1.9k on agents, $1.2k on data) - used qwen3.8-27b as the backbone. it underperforms Jev initially, but after training it is competitive/better, and it also supports 260k context and multimodal input - the agents tried RL, but it hasn't worked out so far, so they fell back to SFT on high-quality synthetic data - both accuracy and calibration are better - jev-compatible api, playground, and inference. on an h200 it gets around 120ms p95. that will be higher under contention, but still quite fast for an unoptimized version code: https://github.com/denis-pplx/autojev model: https://huggingface.co/denis-pplx/autojev-27b going to let it run longer, and will also bring up inference so folks can try it out.