> [!IMPORTANT] > **Try AutoTrust/GuruSearch live (new, 11 October 2026).** A recommendation and search demo in which every ranking is a > calibrated System 1 decision, made in about half a second: > * **Recommendation:** the latest headlines from 6 news feeds, ranked by importance. > * **Search:** results from Google News, Bing News and Yahoo News re-ranked by relevance, side by side with the > search engines' own order, plus a short answer with citations. **Fast, calibrated System 1 decisions that are indistinguishable from the closed TypeSafe Jev 1.13 by KL, and deliberate System 2 generation and reasoning from an untouched Qwen3.5-9B — one set of weights, one vLLM engine, routed per request. The fastest model of the family: it answers a single decision in about a third of the time the hosted API takes. Its successor, autotrust/JEV-27B, is closer to Jev, transfers better to unseen tasks and has a stronger System 2.** JEV-9B now takes images. Every step below is **one System 1 decision**: camera image or screenshot in, a probability for every action out, in a single forward pass (about 0.2 s on one GPU). Run it with `bash vl/serve.sh` (see Images: quick start).
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> [!IMPORTANT] > **Try AutoTrust/GuruSearch live (new, 11 October 2026).** A recommendation and search demo in which every ranking is a > calibrated System 1 decision, made in about half a second: > * **Recommendation:** the latest headlines from 6 news feeds, ranked by importance. > * **Search:** results from Google News, Bing News and Yahoo News re-ranked by relevance, side by side with the > search engines' own order, plus a short answer with citations. This bundle serves two systems from one set of base weights. **System 2** is the unmodified `google/gemma-4-26B-A4B-it` (a 26 B-parameter mixture-of-experts model with ≈ 4 B active parameters per token, text and image input): generation and reasoning, bit-identical to the base model. **System 1** adds a LoRA adapter and a 24-slot decision head and returns calibrated probabilities for three typed primitives in one prefill pass: * `noul`: true / false * `choice`: 2–16 options (wider questions are read in groups of ≤ 16 plus a final) * `score`: 0–5
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A **lossless speed distribution** of `nvidia/Qwen3.8-27B-NVFP4`: the exact same weights, shipped with a fully benchmark-tuned MTP speculative decoding setup. Single-stream decode on a DGX Spark (GB10) goes from **12.5 → 48.2 tok/s (3.9×)**. Screen recording on a DGX Spark: a coding benchmark script streams the model's reasoning and code, then executes the generated program and checks its output (dgx_spark_demo.mov, 4 min 49 s). The same weights and drafter recipe on one NVIDIA B200 (vLLM 0.31.0 OpenAI server, single stream, temperature 0, MTP k=7 — the best setting on B200; k=8 is best on Spark). Four coding tasks: the model streams its thinking and code, then the generated program is executed against tests (all pass). The video is a real-time replay of the measured token arrival times (no speed-up or cuts); token counts include thinking + answer.
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A compact build of nvidia/Qwen3.8-Flash-Next-FP4-Local found by **Neural Architecture Search (NAS)** over the routed-expert configuration: it keeps **384 of the 512 routed experts** in every decoder MoE layer and in the MTP layer. Unchanged from the original: - top-10 routing and the shared experts; - attention, the n-gram PLE table, the vision tower and the tokenizer. **Checkpoint format.** The checkpoint keeps NVIDIA's exact format: - Every kept tensor is a byte-for-byte copy: packed NVFP4 weights, CSF-coded E4M3 block scales, global and input scales, MXFP8 linears and the NVFP4 PLE table. - Only the 49 router matrices change: they keep the rows of the experts chosen by the search. - `text_config.num_experts` is 384.
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- **Finetuned from model:** Qwen/Qwen-Image-Edit-2509 - **Model type:** LoRA - **License:** MIT This model was made for 🔥AutoWeeb, the AI anime creator. Works well with simple prompts and high-resolution photos. Try **"transform into anime"**.
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- **Developed by:** avatargrim - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
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