Apodex-1.1-mini-Finnish-GGUF
kataguru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。
Basic profile indexed · More details to follow
From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.
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。
Basic profile indexed · More details to follow
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。
Basic profile indexed · More details to follow
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。
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。
Basic profile indexed · More details to follow
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。
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。
Basic profile indexed · More details to follow
**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
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。
Basic profile indexed · More details to follow
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。
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。
Basic profile indexed · More details to follow
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。
Basic profile indexed · More details to follow
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。
Basic profile indexed · More details to follow
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。
Basic profile indexed · More details to follow
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。
Basic profile indexed · More details to follow
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。
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。
Basic profile indexed · More details to follow
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。
Basic profile indexed · More details to follow
Adjust provider, category or input/output filters. New models are added continuously.
Adjust monthly token usage to compare estimated API costs.
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.