**🎉 YamshadowExperiment28-7B is currently the best-performing 7B model on the Open LLM Leaderboard (08 Apr 24). Use it with caution, as it is likely a sign of overfitting the benchmarks.** YamshadowExperiment28-7B is an automated merge created by Maxime Labonne using the following configuration. * automerger/YamShadow-7B * yam-peleg/Experiment28-7B This model uses a context window of 8k. I recommend using it with the Alpaca chat template (works perfectly with LM Studio).

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GEV-26B-Decide

autotrust

> [!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. > [!IMPORTANT] > **ERQA 56.8% with thinking: above Hy-Embodied-VLM-1.0 with thinking (56.3) and PhysBrain 1.5 (52.8).** No robot or > embodied data in GEV-26B-Decide's decision training (text only); System 2 is the unmodified Gemma-4-26B-A4B-it. > Thinking adds 13.3 points over one pass (43.5% → 56.8%, paired test p (+18.5) and multi-view reasoning (+13.5). **The benchmark.** ERQA (Embodied Reasoning Question Answering) is the benchmark Google DeepMind released with Gemini Robotics (March 2025) to measure whether a vision-language model understands the physical world a robot acts in. It has 400 multiple-choice questions about real robot and everyday scenes, with 1–16 images per question (287 questions have one image). 382 questions have 4 options and the rest 2–3, so chance is about 26%. ERQA is one of the standard embodied-reasoning benchmarks; Gemini Robotics-ER, PhysBrain and Hy-Embodied report it. With 400 questions the 95% interval of a score is about ±5 points.

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GLM-5.3-Flash-GGUF-DGX-Spark

autotrust

> [!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. **GLM 5.3 Flash, 12 % smaller — Sparse-Squared compression with AutoTrust SLIM-Q, resident on a single 128 GB DGX Spark with room to spare.** This repository is the llama.cpp GGUF build of the SLIM-Q-compressed GLM 5.3 Flash: 79.1 GiB instead of the stock 90 GiB Q2, with 32 of 288 routed experts removed per layer (256 retained — the **E256** member of the SLIM-Q family) and everything else untouched. It is the single-Spark companion to GLM-5.3-GGUF-DGX-Spark, the 744B SLIM-Q E192 build whose NVFP4 edition powers Guru Turbo 2.0. Runs in llama.cpp (glm5-next), so you get the OpenAI-compatible llama-server, continuous batching, tool calling and `reasoning_content` on the Spark.

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GLM-5.3-GGUF-DGX-Spark

autotrust

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JEV-27B-VL

autotrust

> [!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. > [!IMPORTANT] > **ERQA 61.5% with thinking (medium): the highest open-model score in the ranking below, tied with Claude Opus 5 and > behind only GPT 6 Astra and Gemini 3.6 Flash.** No robot or embodied data in JEV-27B-VL's decision training (text > only); System 2 is the unmodified Qwen3.8-27B. In one pass (no thinking) it scores 53.8%, above PhysBrain 1.5 (52.8), > the best open embodied model in the report. Thinking adds 7.7 points (paired test p = 0.002). **The benchmark.** ERQA (Embodied Reasoning Question Answering) is the benchmark Google DeepMind released with Gemini Robotics (March 2025) to measure whether a vision-language model understands the physical world a robot acts in. It has 400 multiple-choice questions about real robot and everyday scenes, with 1–16 images per question (287 questions have one image). 382 questions have 4 options and the rest 2–3, so chance is about 26%. ERQA is one of the standard embodied-reasoning benchmarks; Gemini Robotics-ER, PhysBrain and Hy-Embodied report it. With 400 questions the 95% interval of a score is about ±5 points.

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JEV-35B

autotrust

> [!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. **Decision Index 0.3, public suite: 59.73** (our scoring with the kit), against 53.64 for autotrust/JEV-27B-VL on the board. Approximate public vision score **71.94** (our rebuild of the vision benchmarks; JEV-27B-VL 71.67 on the same rebuild, a tie within noise). Median single-request latency 204 ms on one B200 (JEV-27B-VL 271 ms). > [!TIP] > **NVFP4 version: autotrust/JEV-35B-NVFP4**, the same model with its > routed experts in NVFP4: **25.6 GB download, 23 GiB of GPU memory** (bf16: 72 GB, 66 GiB), so it runs on one 32–48 GB > GPU. Decision Index 0.3 public 59.18 (bf16 59.73), vision rebuild 71.23 (bf16 71.94), 96.6 % of the Decision Index > answers identical to bf16, same latency, same `serve.sh` and API.

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JEV-9B

autotrust

> [!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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Qwen3.8-27B-NVFP4-DGX-SPARK

autotrust

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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AgentWorld-35B-A3B-Heretic

auttasak88

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Qwen3.8-27B-OBLITERATED

AuxGz

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Qwen3.5-4B-Uncensored-MLX-4bit

avan-ag

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Qwen2.5-7B_pyuigpt

avatargrim

- **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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