AI Weekly Malaysia

Jeff is an independent open-source project offering fine-tunes of Qwen3.5 (0.8B and 2B) and Gemma 4 (E2B) as tiny zero-shot classification models that reuse Jev's request format and return a calibrated probability per option from a single forward pass instead of generated text. The README reports about 22 ms per decision on an RTX PRO 6000 and 28 ms on an Apple M4 Max via MLX, with the 0.8B training in roughly 2 hours and the 2B in about 3.5 hours on one RTX PRO 6000, using synthetic data written by an open model on two DGX Sparks. It is explicitly not affiliated with or endorsed by TypeSafe, the makers of Jev, and the repo shows 298 stars, 8 forks and 6 commits; the Hacker News thread drew 222 points and 71 comments.

Why: If you currently route simple label decisions — support queues, moderation labels, intents, game moves — through a hosted LLM API, this is a concrete alternative: ~22-28 ms per decision on a single GPU or an M4 Max MacBook, no per-token billing and no data leaving the machine. The reported fine-tune result (held-out accuracy 31.7% to 95.8% for voice navigation in under 30 minutes on one GPU) is the number to test against your own labels, since zero-shot accuracy at 0.8B is the stated weak point and the README itself says reasoning will not match a much larger model. For teams in Malaysia, running this on local or consumer hardware removes cloud GPU spend and cross-border data transfer for classification tasks, though you still need to verify the models' licensing and Jev's own terms before swapping them in.

Cloudflare released two Cloudflare-trained "decision models" — Clef and Clef-flash — hosted on Workers AI, open-sourced on Hugging Face under Apache 2.0, and made Jev-API compatible with Typesafe AI's Jev System One. Decision models return bounded, typed outputs with probabilities (e.g. 95% fashion, 85% ecommerce, <1% phishing) instead of open-ended text, and Cloudflare says Clef currently leads the Jev Decision Index. Cloudflare also debuted an RL product for fine-tuning Clef, and reported its own Threat Intelligence workflow classified a domain in 2.2s with Clef versus 4.7s for gpt-oss-120b, which returned only two classifications.

Why: If you are routing tickets, escalations, or domain/page categories inside an agent loop, a classifier that returns typed labels plus probabilities lets your code branch deterministically instead of parsing LLM prose — and since Clef is Apache 2.0 on Hugging Face you can self-host and test it without committing to Workers AI billing. Treat the 2.2s vs 4.7s figure as vendor-reported on Cloudflare's own Threat Intelligence workflow, so benchmark it on your own inputs before swapping out a prompt-based classifier. The new RL fine-tuning option is the piece to evaluate if your label set is domain-specific and you don't want to retrain a full classifier each time categories change.