Kataguru Sceptic 35B-A3B v2.8.1 — GGUF APEX Trio (APEX-Small / APEX-Medium / APEX-Good + Vision mmproj-f16)

Kataguru-Sceptic-35B-A3B-v2.8.1-GGUF is the official imatrix-calibrated GGUF Trio + Multimodal Vision Projector (mmproj-f16) release of the Kataguru Sceptic 35B-A3B v2.8.1 Master (CP-300) Sovereign Multimodal 254-Expert Mixture-of-Experts (MoE) flagship model for llama.cpp, LM Studio, and Ollama on single-GPU (16 GB–24 GB VRAM) and workstation setups.

Built through 254-Expert Full-MoE Surgical QLoRA (r=16, alpha=32, all 40 layers, all 254 routed experts + shared experts + attention/DeltaNet projections trained simultaneously) on top of the Kataguru Sceptic 35B-A3B v2.7 Master (254-MoE) spine, v2.8.1 sets a new all-time Kataguru MoE record on the 11-Task Cognitive Suite (72.35 %), surges +4.57 pp on LiveCodeBench v6 (32.00 % Pass@1), reaches 82.33 % on BFCL V4 Single-Turn AST (+8.90 pp over v2.7), and outperforms Ornith 1.5 35B-A3B on 12 out of 15 benchmarks (+3.75 pp overall 15-benchmark mean, +4.72 pp 11-task cognitive mean, +9.02 pp BFCL V4 AST, +4.00 pp LiveCodeBench v6) while using -387M fewer parameters (34.67B vs. 35.06B).

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0. Included GGUF Quantizations & VRAM Footprint

All three GGUF quantizations are calibrated with a bilingual (Finnish + English + STEM + Code + PLC/CNC + Wet-Lab) importance matrix (imatrix.dat) and include the native 27-layer Vision encoder projector (mmproj-Kataguru-Sceptic-35B-A3B-v2.8.1-f16.gguf):

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1. Ten Sovereign Capabilities & Trained Specializations (What Sets Kataguru Sceptic v2.8.1 Apart)

Unlike standard general-purpose fine-tunes of Qwen3.5-35B-A3B (such as Ornith 1.5), Kataguru Sceptic 35B-A3B v2.8.1 combines architectural MoE surgery with eight specialized engineering, scientific, and sovereign domain curricula:

1.1 Lighter 254-MoE Architecture (-387M Parameters / -774 MB BF16 / -194 MB 4-Bit)

• Architectural Pruning of Redundant Experts: Two redundant routed experts per MoE layer (256 -> 254 experts across all 40 layers = 80 redundant experts removed) were surgically pruned and the router gates re-indexed (num_experts = 254, top_k = 8).
• Smaller & Faster: Total parameter count is reduced from 35.06B -> 34.67B (-387,082,240 parameters). Despite being smaller on disk and in VRAM, Sceptic v2.8.1 beats the larger 256-expert Ornith 1.5 by +3.75 pp across the 15-benchmark suite.

1.2 Six Native Operating Modes Built Into chat_template.jinja

Kataguru Sceptic v2.8.1 is both a flagship reasoning model and the autonomous engine of the Kataguru 4-Stage Data Factory. Its chat_template.jinja supports 6 native mode switches out of the box:

1. mode="chat" (Default — Spoon/Einstein First-Principles Mode): Direct, sycophancy-free technical execution. First-principles derivation (Einstein) + ruthless assumption stripping (Spoon).
2. mode="decision" (System 1 Zero-Output-Token Logit Firewall): Single-token (0 or 1) logprob classifier (<20 ms latency, >5M samples/day) for rapid epistemic triage ("Does this require deep System 2 reasoning or contain novel delta?").
3. mode="topic" (Deterministic 16-Category Taxonomy Classifier): Single-token (A–P) domain router across 16 industrial, scientific, coding, medical, and legal categories.
4. mode="inspect" (Stage 2 Quality Inspector Oracle): Strict JSON schema auditor scoring datasets on score (1–10) and learning_value (1–10) (>= 8.0 Golden Gate), checking math/code correctness, truncation, and refusal status.
5. mode="curate" (First-Principles Dataset Rewriter): Transforms noisy raw Q&A pairs into verified, zero-fluff -golden training samples.
6. mode="surgeon" (Autonomous 3-Stage Code & Repo Surgeon): Structured Stage 1 Fault Localization -> Stage 2 Root-Cause Patch Generation (diff) -> Stage 3 Edge-Case Verification mode for SWE-bench and Terminal-Bench repositories.

1.3 Competitive Programming (LiveCodeBench v6) & Multi-Stage Software Engineering (SWE-bench / Terminal-Bench)

• Diabolical Code Immunity: Trained on 100% Python AST & sandbox-verified competitive programming problems (class Solution: / stdin / stdout) covering dynamic programming, graph algorithms, segment trees, bitmasks, and systems concurrency in Python, C, C++, and Rust.
• 3-Stage Repository Surgery: Trained on multi-file fault localization, minimal unified diff patching, and Linux CLI/compiler diagnostics (SWE-bench & Terminal-Bench paradigms).
• Result: 32.00 % Pass@1 on LiveCodeBench v6 (+4.00 pp ahead of Ornith 1.5's 28.00 %).

1.4 Deterministic Multi-Language Tool Calling (BFCL V4 AST & Irrelevance Detection)

• Trained on strict JSON schema adherence, parallel tool calls, and abstention when tools do not match the user's intent (Irrelevance Detection) across Python, Java, and JavaScript.
• Result: 82.33 % on BFCL V4 Single-Turn AST (+9.02 pp ahead of Ornith 1.5's 73.31 %), including 93.75 % on Live Parallel (+43.75 pp vs Ornith 1.5), 92.00 % on JS Simple (+32.00 pp vs Ornith 1.5), and 86.10 % on Irrelevance Detection.

1.5 Industrial Automation: PLC (IEC 61131-3), Industrial Networks & CNC G-Code

• PLC Programming: Siemens TIA Portal SCL and Beckhoff TwinCAT 3 Structured Text (ST) state machines, PID control loops, safety interlocks (ISO 13849-1 / IEC 62061 PLd/PLe, SIL2/SIL3), Beckhoff ADS, OPC UA (IEC 62541), and Profinet IRT / CC-Link TSN diagnostics.
• CNC & Manufacturing: ISO G-code (turning & milling state verification, tool compensation G41/G42, canned cycles) and Fanuc / Siemens Sinumerik 840D CNC controller diagnostics.

1.6 Practical Wet-Lab Chemistry, Molecular Biology & Chemical Safety

• Trained on practical laboratory execution and stoichiometry: molarity/normality/dilution calculations ($C_1 V_1 = C_2 V_2$), buffer preparation (Henderson-Hasselbalch, $\text{p}K_a$, ionic strength), titration curves, analytical instrumentation (HPLC, GC-MS, NMR, UV-Vis, ICP-MS), PCR / qPCR / Gibson assembly / CRISPR wet-lab protocols, reaction thermodynamics ($\Delta G, \Delta H, \Delta S$), and SDS / GHS / CLP chemical hazard & incompatibility analysis.

1.7 Patents, IPR (EPO / PRH) & Finnish Legislation (Finlex)

• Trained on European Patent Office (EPO) and Finnish Patent and Registration Office (PRH) independent/dependent patent claim drafting (1. Mikä tahansa... tunnettu siitä, että...), Problem-Solution Approach inventive step analysis (EPC Art. 52, 54, 56), Freedom-to-Operate (FTO) infringement mapping, and Finnish national legislation (Finlex).

1.8 Clinical Medicine & Pharmacology (MedQA 85.23 %)

• 4-Pillar Medical Curriculum: Trained across the full 254-MoE network on clinical differential diagnosis, pathophysiology, pharmacology, and Finnish clinical care guidelines (Duodecim / Käypä hoito / ICD-10) with a rolling curriculum anchor to prevent forgetting.
• Result: 85.23 % on MedQA (USMLE) (+2.28 pp higher than Ornith 1.5's 82.95 %).

1.9 Native Multimodal Vision (333 Tensors) + Layer 40 Multi-Token Prediction (MTP, 779 Tensors)

• Preserves 100% of the 27-layer Vision encoder (mmproj-f16.gguf) for inspecting technical schematics, P&ID diagrams, and laboratory charts.
• Preserves 100% of the Layer 40 Multi-Token Prediction (MTP) speculative decoding head in the full NVFP4/AWQ weights.

1.10 Sovereign Finnish & Agglutinative Mastery (FI / ET / HU / TR) + ASD-STE100 + 0.00 % Refusal

• 100% Flawless Finnish (1.0000 Purity) and 5x higher mastery of rare 15-case Finnish morphology (FI_MORPH_10) than Ornith 1.5 (33.33 % vs 6.67 % on complex stacked enclitics, comitative, instructive, and abessive forms).
• Epistemic Honesty & Uncensored (0/3 Refusals): Zero AI lecturing, zero sycophancy, and strict adherence to Aerospace Simplified Technical English (ASD-STE100) and Finnish technical prose.

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2. Official 15-Benchmark Head-to-Head Comparison (Kataguru Sceptic v2.8.1 vs. Ornith 1.5 35B-A3B & Fleet Lineage)

All models were evaluated under 100% identical production hardware and harness conditions on 2x NVIDIA RTX 5090 32GB Blackwell (TP=2) using the locked canonical evaluator (scripts/canonical/run_official_15_benchmark.py):

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3. Deep-Dive: Agentic Tool Calling (BFCL V4), Coding & Sovereign Finnish vs. Ornith 1.5

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4. Training Methodology (v2.8.1 Surgical Balance)

1. Root-Cause Insights Applied (Insights 6.7–6.9):
   • Zero-Drop 3-Tier Curriculum Ordering: Tier 1 (IFEval + BFCL schema foundations, Steps 1–75) -> Tier 2 (Core Engineering, PLC/CNC, Wet-Lab, Patents, Finnish, Steps 76–325) -> Tier 3 (Competitive Coding & Math, Steps 326–400).
   • Proportional 5% Rolling Anchor Floor: Every 25-step optimization window contains at least 5% IFEval, 5% BFCL, and 5% LiveCodeBench (class Solution:) samples so high-entropy STEM gradients never overwrite structural formatting weights during the cosine learning-rate tail.
   • Closed-Loop Dynamic Sample Rebalancer: Monitored live by the 7-Pillar 50/50-Calibrated Capability Radar (KFR-330) every 100 steps.

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5. Production Deployment (llama.cpp / LM Studio / Ollama)

5.1 Text & Agentic Reasoning (llama-server)

llama-server \
  -m Kataguru-Sceptic-35B-A3B-v2.8.1-APEX-Good.gguf \
  -ngl 99 \
  -c 65536 \
  -fa on \
  --port 8080 \
  --jinja

5.2 Multimodal Vision Inference (llama-server with mmproj-f16)

llama-server \
  -m Kataguru-Sceptic-35B-A3B-v2.8.1-APEX-Medium.gguf \
  --mmproj mmproj-Kataguru-Sceptic-35B-A3B-v2.8.1-f16.gguf \
  -ngl 99 \
  -c 32768 \
  -fa on \
  --port 8080 \
  --jinja

5.3 Selecting Native Operating Modes via chat_template_kwargs

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8080/v1", api_key="not-needed")

# Mode options: "chat" (default), "decision", "topic", "inspect", "curate", "surgeon"
response = client.chat.completions.create(
    model="Kataguru-Sceptic-35B-A3B-v2.8.1",
    messages=[{"role": "user", "content": "Suunnittele Beckhoff TwinCAT 3 ST -tilakone hydraulipuristimen kaksikäsikäynnistykselle (ISO 13849-1 PLe)."}],
    extra_body={"chat_template_kwargs": {"mode": "chat", "enable_thinking": True}},
    temperature=0.0,
)
print(response.choices[0].message.content)

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6. Yhteenveto suomeksi (Finnish Summary)

Kataguru Sceptic 35B-A3B v2.8.1 (CP-300 Master) on Kataguru-laivaston suvereeni 254 asiantuntijan (254-MoE) monimodaalinen lippulaivamalli ja laatuoraakkeli:

• Pienempi mutta suorituskykyisempi (-387M parametria): 80 redundanttia asiantuntijaa on karsittu (35,06B -> 34,67B parametria), mutta malli voittaa suuremman Ornith 1.5 35B-A3B -mallin 12 / 15 virallisessa benchmarkissa (+3,75 %-yks. 15-benchmarkin keskiarvossa, +4,72 %-yks. 11 tehtävän kognitiivisessa keskiarvossa, +9,02 %-yks. BFCL V4 -työkalukutsuissa ja +4,00 %-yks. LiveCodeBench v6 -kilpailukoodauksessa).
• 10 erikoistunutta huipputaitoa:
  1. Kilpailukoodaus (LiveCodeBench v6: 32,00 %) & 3-vaiheinen ohjelmistokirurgia (SWE-bench / Terminal-Bench, mode="surgeon") (Python, C, C++, Rust).
  2. Monikieliset agenttiset työkalukutsut (BFCL V4 AST: 82,33 %) (Python, Java, JavaScript sekä Irrelevance Detection 86,10 %).
  3. Teollisuusautomaatio (PLC & CNC): Siemens TIA Portal SCL, Beckhoff TwinCAT 3 ST, Beckhoff ADS, OPC UA (IEC 62541), Profinet IRT / CC-Link TSN, koneistuksen ISO G-koodi sekä Fanuc / Siemens Sinumerik 840D -tuotantodiagnostiikka.
  4. Käytännön märkälaboratorio (Wet-Lab), kemia, molekyylibiologia ja kemikaaliturvallisuus: stoikiometria, puskuriliuokset, titraus, HPLC/GC-MS/NMR, PCR/CRISPR sekä käyttöturvallisuustiedotteet (KTT / GHS / CLP).
  5. Patentit, IPR (EPO / PRH) ja Suomen laki (Finlex): itsenäiset ja epäitsenäiset patenttivaatimukset, Problem-Solution Approach ja toimintavapausanalyysit (FTO).
  6. Kliininen lääketiede ja farmakologia (MedQA: 85,23 %): erotusdiagnostiikka ja hoitosuositukset (Duodecim / Käypä hoito / ICD-10) koulutettuna koko 254-MoE-asiantuntijaverkkoon.
  7. 6 natiivia toimintatilaa (chat_template.jinja): chat (Spoon/Einstein), decision (System 1 -logittiportti), topic (16-luokkainen taksonomia), inspect (Stage 2 -laatuoraakkeli), curate (aineistokuraattori) ja surgeon (ohjelmistokirurgi).
  8. Natiivi monimodaalinen visio (mmproj-f16.gguf, 333 tensoria) teknisten kaavioiden, P&ID-piirustusten ja laboratoriokuvien tulkintaan.
  9. Suvereeni suomen kieli ja harvinaiset sijamuodot (FI_MORPH_10 5x korkeampi kuin Ornith 1.5:llä) sekä suomalais-ugrilainen ja agglutinatiivinen morfologia (FI / ET / HU / TR).
  10. Episteeminen rehellisyys, ASD-STE100-insinööriviestintä ja 0,00 % sensuurikieltäytymisiä.

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7. Complete Kataguru Sceptic v2.8.1 Release Family

• NVFP4 (Blackwell vLLM Production & Oracle): kataguru/Kataguru-Sceptic-35B-A3B-v2.8.1-NVFP4
• AWQ W4A16 (Universal vLLM GPU Deployment): kataguru/Kataguru-Sceptic-35B-A3B-v2.8.1-W4A16-AWQ
• GGUF APEX Trio (APEX-Small, APEX-Medium, APEX-Good + mmproj-f16): kataguru/Kataguru-Sceptic-35B-A3B-v2.8.1-GGUF