AnyJev vs strands-decider
Nokia research: turn any open LLM into a Jev-style decision model. Typed choice, yes/no or score from one prefill, de-biased with no labels or a fitted head, served on vLLM. — versus — Strands' small decision model (2B, Qwen3.5-based): pick between options, answer yes/no or rate on a scale with calibrated confidence. CLI and /v1/systemone server on CUDA, MPS, MLX or CPU.
Same job — local typed decisions; anyjev turns any open LLM into a decision model, strands-decider is a purpose-trained small model with a CLI and server.
| AnyJev | strands-decider | |
|---|---|---|
| Stars | 1.1k | 579 |
| Forks | 138 | 77 |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | 4 days ago | 2 days ago |
| Topics | decision-models, local | decision-models, local |
| Curated connections | 6 | 5 |
AnyJev — the curator's take
The cleanest way to get decisions out of a model you already run: ask a typed question, read the next-token distribution from one prefill, nothing generated or parsed. Level L0 removes option-position bias with zero labels; L2 fits a tiny closed-form head from 100 to 300 labels and serves it from a vLLM pooling endpoint, and `anyjev.pipeline` measures accuracy, calibration and latency on your own box instead of asking you to trust their tables. One finding worth stealing: cutting Qwen2.5-7B to 18 of 28 blocks was faster with slightly better accuracy. It is a week-old research repo from two authors, so expect API churn, and it assumes you can run an open model; without a GPU, laya's small checkpoints are the lighter path.
strands-decider — the curator's take
Use it for the many small judgments inside an agent workflow — routing, triage, urgency, sentiment — where an LLM call is too slow and training a classifier too much work; answers at ≥0.9 confidence were right ~95% of the time on unseen tasks, so threshold and escalate the rest. It decides, never writes. At 2B it runs on a laptop; for agent-action ranking at scale, CLM's cached-embedding design is faster.