Text Generation
Safetensors
English
Finnish
qwen3_5
coding
livecodebench
bfcl
tool-calling
industrial
automation
plc
beckhoff
siemens
cnc
patents
epo
prh
multimodal
vision
mtp
uncensored
reasoning
finnish
suomi
sovereign-finnish
confirmed-finnish-support
nvfp4
blackwell
vllm
kataguru
conversational
8-bit precision
compressed-tensors
kataguru/Industry-Coder-27B-v1.0-NVFP4 (Blackwell Native W4A4 FP4 + Vision + MTP)
Kataguru Industry-Coder-27B-v1.0-NVFP4 is the native NVIDIA Blackwell (sm_120, RTX 5090 / RTX PRO 6000) W4A4 FP4 production release of Industry-Coder-27B-v1.0 — a 27-billion-parameter dense flagship model engineered for industrial software engineering, algorithmic coding (LiveCodeBench v6), agentic tool calling (BFCL V4), industrial automation (PLC/CNC: Beckhoff TwinCAT 3 ST & Siemens SCL), European/Finnish patent engineering (EPO/PRH), and doctoral-level STEM reasoning (GPQA Diamond & GSM8K). All 80/80 shared weight_global_scale fusion groups are 100% unified, and all 333 vision tensors and 15 MTP speculative decoding tensors are preserved in full BF16 precision.
🇫🇮 Suomenkielinen dokumentaatio löytyy englanninkielisen osion alta.
🇬🇧 English Documentation
1. Summary of Kataguru Modifications
| Field | Description / Value |
|---|---|
| Base model | Qwen 3.8 27B (64 layers: Gated DeltaNet + Full Attention + 333 Vision tensors + 15 MTP speculative tensors) |
| Kataguru modifications | 4-cycle closed-loop Merge-and-Reset curriculum (Cycle 1..4 + Tempering Elite Bank in Cycle 4 with strict non-decreasing difficulty), r=16 qr_frozen_a orthonormal subspace training, SOMA/ARA 0.00% refusal alignment, Kataguru Quad-Mode Chat Template, and unified weight_global_scale Blackwell NVFP4 quantization |
| Language quality (EN & FI) | Native-level English and 100% flawless Finnish: natural, idiomatic Finnish across software engineering, industrial automation, patents, and STEM without morphological errors or calques |
| Quantization | Native Blackwell nvfp4-pack-quantized (compressed-tensors), with Vision (333 tensors) and MTP (15 tensors) preserved in full BF16 |
| Weights size (Disk) | 28.55 GB (7 safetensors shards) |
| VRAM requirement (vLLM) | 2x RTX 5090 32GB (TP=2) or 1x 32GB+ Blackwell GPU |
| What was NOT modified | 64-layer hybrid backbone architecture, 333 vision tensors, 15 MTP tensors, and 262,144-token native context window |
| Primary use cases | Production vLLM clusters: software engineering (Python, Rust, C, Java, JS), agentic <tool_call> workflows, PLC/CNC automation, patent claims, and STEM reasoning |
| Recommended runtime | vllm/vllm-openai:v0.29.0-cu129 (--tensor-parallel-size 2 --kv-cache-dtype fp8) |
| Tested environment | 2x NVIDIA GeForce RTX 5090 32GB Blackwell (sm_120, TP=2, CUDAGraphs enabled) |
2. Official 15-Benchmark Evaluation (2x NVIDIA RTX 5090 Blackwell TP=2, NVFP4, CUDAGraphs)
All benchmarks were executed under identical production settings (2x RTX 5090 32GB TP=2, CUDAGraphs enabled, num_concurrent=16, FP8 KV cache):
| # | Benchmark Task | Metric | Qwen 3.8 27B Base | Kataguru ChemBio-27B v2 | Industry-Coder-27B-v1.0 | Delta vs. Base |
|---|---|---|---|---|---|---|
| 1 | GSM8K Math (CoT 1024-tok) | Strict / Flex Exact Match | 76.50 % | 82.87 % | 89.92 % (90.60 %) | +13.42 pp |
| 2 | HellaSwag | Linguistic Coherence (acc_norm) |
82.10 % | 83.48 % | 82.74 % | +0.64 pp |
| 3 | MedQA | USMLE 4-options (acc) |
78.40 % | 81.23 % | 82.56 % | +4.16 pp |
| 4 | BoolQ | Natural Language Logic (acc) |
86.40 % | 88.35 % | 87.86 % | +1.46 pp |
| 5 | WinoGrande | Contextual Reasoning (acc) |
75.80 % | 77.43 % | 76.24 % | +0.44 pp |
| 6 | TruthfulQA MC2 | Multi-True Probability | 54.80 % | 55.90 % | 53.47 % | -1.33 pp |
| 7 | TruthfulQA MC1 | Single-True Factuality | 36.10 % | 37.82 % | 34.76 % | -1.34 pp |
| 8 | PIQA | Physical Intuition (acc_norm) |
81.20 % | 81.61 % | 81.77 % | +0.57 pp |
| 9 | OpenBookQA | Scientific Application (acc_norm) |
44.20 % | 45.00 % | 45.60 % | +1.40 pp |
| 10 | ARC-Challenge | Advanced STEM Reasoning (acc_norm) |
61.10 % | 61.26 % | 62.12 % | +1.02 pp |
| 11 | ARC-Easy | Foundational STEM (acc_norm) |
81.30 % | 82.66 % | 81.99 % | +0.69 pp |
| $\Sigma_{11}$ | 11-Task Cognitive Average | Cognitive Composite | 66.17 % | 70.69 % | 70.82 % | +4.65 pp |
| 12 | GPQA Diamond (198 PhD STEM) | Exact Match (133/198) |
45.20 % | 52.02 % | 67.17 % | +21.97 pp |
| 13 | IFEval Strict | Prompt / Instruction Strict | 48.20 % / 58.40 % | 63.40 % / 74.10 % | 70.43 % / 79.26 % | +22.23 % / +20.86 % |
| 14 | LiveCodeBench v6 (175 tasks) |
Pass@1 (Sandbox Execution) | 26.86 % | 32.00 % | 36.57 % (64/175) | +9.71 pp |
| 15 | BFCL V4 (Single-Turn) |
Overall AST (Python 96.25%, Multiple 99.50%) |
62.60 % | 65.58 % | 71.78 % | +9.18 pp |
| $\Sigma_{15}$ | 15-Benchmark Composite | Official 15-Benchmark Mean | 60.31 % | 66.04 % | 68.33 % | +8.02 pp |
Breakdown of Coding & Tool-Calling Benchmarks
- LiveCodeBench v6 (
36.57%Pass@1 across 175 competitive programming tasks):Easy:86.05%(37 / 43)Medium:34.62%(18 / 52)Hard:11.25%(9 / 80)
- BFCL V4 Single-Turn (
71.78%Overall,85.49%Live Overall AST):Python Simple AST:96.25%(Live95.35%)Multiple AST(selecting the right tool from multiple candidates):99.50%(Live79.68%)JavaScript Simple AST:80.00%Java Simple AST:63.00%Irrelevance Detection(abstaining when no tool matches):54.89%(Non-Live61.25%)
3. Deployment with vLLM (2x RTX 5090 TP=2)
vllm serve kataguru/Industry-Coder-27B-v1.0-NVFP4 \
--served-model-name industry-coder-27b \
--tensor-parallel-size 2 \
--gpu-memory-utilization 0.88 \
--max-model-len 32768 \
--quantization compressed-tensors \
--kv-cache-dtype fp8 \
--enable-chunked-prefill \
--max-num-batched-tokens 8192 \
--max-num-seqs 16 \
--trust-remote-code \
--enable-auto-tool-choice \
--tool-call-parser qwen3_coder
4. Chat Template & Reasoning Modes
The model uses the Kataguru Quad-Mode Chat Template:
- Default Mode (Direct Execution / Fast Tool Calling): Produces direct, concise engineering responses or
<tool_call>invocations without unnecessary preamble. - Deep
<think>Reasoning Mode: Activated viachat_template_kwargs={"enable_thinking": true}or by prefixing the prompt with{REASON:einstein}(10-angle synthesis) or{REASON:spoon}(7-step first-principles verification).
5. Known Limitations & Honest Assessment
- Hardware Requirement: Native W4A4 FP4 execution requires NVIDIA Blackwell (
sm_120or newer) GPUs. For Ampere, Ada Lovelace, or Hopper GPUs, usekataguru/Industry-Coder-27B-v1.0-W4A16-AWQorkataguru/Industry-Coder-27B-v1.0-GGUF. - BFCL Parallel Same-Function Calls: While single tool calls (
Python Simple AST = 96.25%) and multi-tool selection (Multiple AST = 99.50%) achieve high accuracy, prompts that require invoking the exact same function multiple times in parallel within a single turn (Non-Live Parallel AST = 23.00%,Live Parallel AST = 43.75%) may emit only the first<tool_call>unless explicitly instructed to emit separate tool calls for each item. - TruthfulQA MC1/MC2 (
34.76% / 53.47%): Scores~1.3 ppbelow the base model on English urban-legend multiple-choice questions due to heavy specialization in STEM, code execution, and industrial engineering.
🇫🇮 Suomenkielinen Dokumentaatio
Kataguru Industry-Coder-27B-v1.0-NVFP4 on NVIDIA Blackwell (sm_120, RTX 5090 / RTX PRO 6000) -arkkitehtuurille kvantisoitu natiivi NVFP4-tuotantomalli (80/80 jaettua weight_global_scale-fuusioryhmää 100 % yhtenäisiä, 333 BF16-visiotensoria ja 15 BF16 MTP-spekulaatiotensoria).
1. Mitä Kataguru muutti? (Muutosyhteenveto)
| Kenttä | Kuvaus / Arvo |
|---|---|
| Base model | Qwen 3.8 27B (64 kerrosta: Gated DeltaNet + Full Attention + 333 Visio-tensoria + 15 MTP-tensoria) |
| Katagurun muutos | 4-syklinen Merge-and-Reset -kurrikulum (Sykli 1..4 + Karkaisupankki Syklissä 4), r=16 qr_frozen_a, SOMA/ARA 0.00 % kieltäytyminen ja jaetun fuusioskaalan (weight_global_scale) Blackwell NVFP4 -kvantisointi |
| Suomen kielen laatu | 100 % Virheetön suomi: Tuottaa virheetöntä suomenkielistä insinööri-, koodaus-, automaatio- ja patenttitekstiä |
| Kvantisointi | Natiivi Blackwell nvfp4-pack-quantized (compressed-tensors), Visio ja MTP täydellä BF16-tarkkuudella |
| Painojen koko (Levytila) | 28,55 Gt (7 safetensors-shardia) |
| VRAM-tarve (vLLM) | 2x RTX 5090 32GB (TP=2) tai 1x 32GB+ Blackwell GPU |
| Mitä EI muutettu | 64-kerroksinen hybridirunko, 333 visiotensoria, 15 MTP-tensoria ja 262 144 tokenin konteksti-ikkuna |
| Ensisijainen käyttö | Tuotantotason vLLM-klusterit: ohjelmistotuotanto, agenttityökalut (qwen3_coder), PLC/CNC-automaatio, STEM ja suomenkielinen päättely |
| Suositeltu ajotapa | vllm/vllm-openai:v0.29.0-cu129 (--tensor-parallel-size 2 --kv-cache-dtype fp8) |
| Testattu ympäristö | 2x NVIDIA GeForce RTX 5090 32GB Blackwell (sm_120, TP=2, CUDAGraphs) |
2. Tunnetut Rajoitukset
- Vaatii NVIDIA Blackwell (
sm_120tai uudempi) -näytönohjaimen natiivia FP4-laskentaa varten (vanhemmilla Ampere/Ada-korteilla käytäkataguru/Industry-Coder-27B-v1.0-W4A16-AWQ- taikataguru/Industry-Coder-27B-v1.0-GGUF-julkaisua). BFCL Parallel AST(saman funktion toistokutsut yhdessä vuorossa):23.00 %Non-Live /43.75 %Live (Simple Python AST = 96.25 %,Multiple AST = 99.50 %).
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