model
Open-weight Gemma-family model; a 26B-parameter mixture-of-experts variant with ~4B active parameters, often run locally in 4-bit form.
No evidence-backed relationships are recorded.
llama-server b11430 reports logprob 0.0 for every speculatively decoded token, dragging one test's mean logprob from -0.48 to -0.0011. Nothing in the response or the server log flags the fill-ins, so evals and calibration built on those numbers go wrong quietly.
Reality
Nine LLMs completed a Kaggle benchmark of 120 URLs, 45 of which Python's urlsplit and fetch() resolve to different hosts. If a model approves the Python reading and the request goes out through fetch(), the API key reaches a host nobody approved.
Reality
A pre-registered run reads Gemma's label logits on one 24 GB GPU and scores it against the hosted API on the same 3,880 records, where it is level on yes/no questions and 4.5 points behind on multiple choice. One temperature fitted on 50 labels closes the calibration gap.
Reality
An agent swarm's Arbiter refused nearly everything and finished PII governance at zero. Its four-case adversarial suite handed that behaviour partial credit.
Reality
A dev.to build log finds Gemma 4 26B holds deep single-artifact work but loses the plan after one or two hand-offs, while the coordinators that can plan will not fit in memory.
Reality