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**Kataguru-Sceptic-35B-A3B-v2.6-GGUF** is the `llama.cpp` / LM Studio / Ollama GGUF release of **Kataguru-Sceptic-35B-A3B-v2.6**, a bilingual (English & Finnish) 35-billion parameter (3B active parameters per token, 254 routed experts per layer) multimodal Mixture-of-Experts (MoE) model. Unlike the 25.44 GB `NVFP4` and 27.85 GB `AWQ` server packages, these imatrix-calibrated 3-bit GGUF quantizations (`12.0 GiB` to `15.3 GiB`) are specifically engineered to fit **wholly inside the VRAM of a single 16 GB or 24 GB GPU**. 1. **Dual-Stage 2D Zero-Swap Epistemic Triage (System 1 Reflex)**: - Evaluates candidate training samples in ** 83.46 % | 81.72 % | **+1.74 pp** | 80.10 % | **+3.36 pp** | | 2 | **WinoGrande** | Contextual Reasoning (`acc`) | 75.45 % | 71.82 % | **+3.63 pp** | 71.10 % | **+4.35 pp** | | 3 | **MedQA (USMLE)** | Clinical Medicine 4-opts (`acc`) | 85.31 % | 84.10 % | **+1.21 pp** | 78.40 % | **+6.91 pp** | | 4 | **TruthfulQA MC2** | Epistemic Truthfulness (`acc`) | 52.47 % | 52.89 % | -0.42 pp | 49.80 % | **+2.67 pp** | | 5 | **TruthfulQA MC1** | Single-True Factuality (`acc`) | 36.23 % | 34.39 % | **+1.84 pp** | 31.40 % | **+4.83 pp** | | 6 | **ARC-Easy** | Standard STEM (`acc_norm`) | 82.95 % | 74.83 % | **+8.12 pp** | 72.98 % | **+9.97 pp** | | 7 | **HellaSwag** | Linguistic Coherence (`acc_norm`) | 82.42 % | 83.30 % | -0.88 pp | 74.60 % | **+7.82 pp** | | 8 | **OpenBookQA** | Scientific Application (`acc_norm`) | 45.00 % | 46.00 % | -1.00 pp | 44.80 % | **+0.20 pp** | | 9 | **ARC-Challenge** | Advanced STEM (`acc_norm`) | 62.54 % | 57.94 % | **+4.60 pp** | 58.62 % | **+3.92 pp** | | 10 | **BoolQ** | Natural Language Logic (`acc`) | 90.37 % | 85.78 % | **+4.59 pp** | 89.60 % | **+0.77 pp** | | 11 | **GSM8K Math** (CoT) | Exact Match (`strict` / `flex`) | 89.39 % / 90.07 % | 1.06 % / 12.36 %\* | **+88.33 pp** | 76.50 % | **+12.89 pp** | | **$\Sigma_{10}$** | **10-Task Mean (1–10)** | **Mean Excluding GSM8K Format Gap** | 69.62 % | **67.28 %** | **+2.34 pp** | **65.14 %** | **+4.48 pp** | | **$\Sigma_{11}$** | **11-Task Mean (1–11)** | **Official Overall Cognitive Mean** | 71.42 % | **61.26 % (62.28 % flex)** | **+10.16 pp** | **66.17 %** | **+5.25 p

Visual QA
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Arch-Guard

katanemo

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Text analysis
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With the rapid proliferation of large language models (LLMs) -- each optimized for different strengths, style, or latency/cost profile -- routing has become an essential technique to operationalize the use of different models. However, existing LLM routing approaches are limited in two key ways: they evaluate performance using benchmarks that often fail to capture human preferences driven by subjective evaluation criteria, and they typically select from a limited pool of models. We introduce a preference-aligned routing framework that guides model selection by matching queries to user-defined domains (e.g., travel) or action types (e.g., image editing) -- offering a practical mechanism to encode preferences in routing decisions. Specifically, we introduce Arch-Router, a compact 1.5B model that learns to map queries to domain-action preferences for model routing decisions. Experiments on conversational datasets demonstrate that our approach achieves state-of-the-art (SOTA) results in matching queries with human preferences, outperforming top proprietary models. This model is described in the paper: https://arxiv.org/abs/2506.16655, and powers Arch the models-native proxy server for agents.

Text generation
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humanizer

Kathir121

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Text generation
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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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