KnowLine-31B-Gen1

KnowLine-31B-Gen1 is an open-weight, 31.27B parameter System One decision model developed by PelaAI. Built on a dense Gemma 4 31B base (google/gemma-4-31B-it), the model departs from standard autoregressive text generation. Instead of generating text token by token to state a conclusion, it accepts an input state alongside up to 64 structured questions and outputs probability distributions across target options in a single forward pass.

Released under the permissive Apache-2.0 license, KnowLine-31B-Gen1 targets deterministic routing, agent control flows, classification, and rubric scoring. On standard evaluation suites, the model registers a score of 70.13 on the Decision Index 0.3 public suite and 90.5 on JevBench. It is positioned as an on-premise alternative to proprietary decision APIs like Jev 1.14 and closed classification layers, providing reproducible, low-latency decisions for production systems without reliance on third-party cloud infrastructure.

As an enterprise-grade local AI model 31.27B parameters 2026 choice, KnowLine-31B-Gen1 provides engineers with direct access to calibrated output probabilities. If your architecture relies on multi-agent task routing, policy verification, or high-throughput document classification, this model replaces fragile prompt-engineered chains with a dedicated, zero-generation classification forward pass.

KnowLine-31B-Gen1 uses a dense transformer architecture consisting of 31.27B parameters. The base foundation is Google's Gemma 4 31B instruction-tuned model. PelaAI trained the network using LoRA supervised fine-tuning (rank 32, alpha 64) applied strictly to the language model layers for one epoch. The resulting adapter was merged back into 16-bit brain floating-point (bf16) weights and then blended with the base model weights at a ratio of 0.75 fine-tuned to 0.25 base across every tensor. Tensors left untouched during LoRA training (such as token embeddings and the base vision tower) retain their original base configuration.

Unlike PelaAI's Mixture-of-Experts alternatives (such as the KnowLine-26B-A4B and KnowLine-35B-A3B architectures), the choice of a fully dense base model was made to resolve the out-of-domain performance penalty common to sparse models. In internal evals on 34 unseen decision tasks outside the training distribution, KnowLine-31B-Gen1 scored 74.9, significantly outpacing the 26B-A4B (66.4) and 35B-A3B (56.2) models.

Key architectural characteristics include:

• Single Forward-Pass Scoring: The model processes the state and queries, emitting the direct logit probabilities for each question label. There is no autoregressive decoding step or token generation loop.
• Read-Twice Mechanism: Served via SGLang and PelaAI's custom knowline_server.py, short context requests are read twice by default to stabilize internal hidden states before extracting final output distributions.
• Embedded Temperature Calibration: A readout temperature of T = 1.44 is folded directly into the final normalization weight and logit soft-capping. This provides calibrated probabilities out of the box (Expected Calibration Error between 0.012 and 0.021 on public validation sets) without post-processing steps.
• Context Handling: While specific maximum context window limits are not formally documented, the attention setup inherits Gemma 4's base structure, easily supporting lengthy conversation states or multi-page documentation alongside up to 64 simultaneous queries.

KnowLine-31B-Gen1 handles structured analytical questions mapped against complex input states. The architecture processes three primary question formats: binary decisions (yes/no), single-choice categorical selections (choose one of k), and numeric grading against defined rubrics.

Local agent pipelines require fast, deterministic routing to select tools, hand off tasks, or escalate queries. Instead of prompting an LLM to generate JSON that needs parsing, KnowLine-31B-Gen1 yields raw categorical probabilities over candidate routes in one step, slashing execution latency and eliminating JSON schema decode errors.

The training loop for KnowLine-31B-Gen1 included dedicated adversarial injection data. In planted instruction benchmarks, only 0.4% of answers shifted when an adversary embedded conflicting instructions within the evaluated state text. This robustness makes it an ideal safety layer to screen inputs before passing them to generative models.

Trained natively across English, Simplified Chinese, and Traditional Chinese, the model demonstrates high performance in cross-lingual settings. It achieves an 82.5 on C-Eval and scores 75.9 across PelaAI's internal Chinese benchmarks, making it suitable for international customer operations, policy enforcement, and compliance triage.

Although PelaAI trained KnowLine-31B-Gen1 on text data only, the base Gemma 4 vision tower remains functional. When supplied with visual inputs, the model executes zero-shot visual reasoning and bounding box localization without task-specific fine-tuning, hitting an 81.3 centre-hit rate on ScreenSpot.

To run KnowLine-31B-Gen1 locally, you must plan your infrastructure around VRAM capacity and compute throughput. Because this model calculates output distributions in a single forward pass rather than generating continuous text, hardware bottlenecks differ from standard conversational models.

The unquantized model occupies approximately 63 GB of disk and memory in bf16 precision. The memory requirements across different formats are:

• BF16 / FP16 (Unquantized): Requires 68 GB to 72 GB of VRAM to comfortably fit model weights alongside KV cache and runtime overhead.
• FP8 / Q8 Quantization: Requires 34 GB to 38 GB of VRAM.
• 4-bit Quantization (Q4_K_M): Requires 20 GB to 22 GB of VRAM.

For engineers wondering how to run 31.27B model on consumer GPU setups, 4-bit quantization is mandatory:

• Single Consumer Card (24 GB): An NVIDIA GeForce RTX 3090 or RTX 4090 can host the model using Q4_K_M quantization. With weights demanding roughly 20 GB, a 24 GB card provides sufficient headroom for moderate context lengths and multi-question queries.
• Dual Consumer Cards (2x 24 GB): A dual RTX 3090 or dual RTX 4090 rig running pipeline or tensor parallelism represents the best GPU for KnowLine-31B-Gen1 on a budget. This setup allows you to run high-precision FP8 or 8-bit quantized weights locally without offloading.
• Workstation & Datacenter Cards: A single 48 GB card (such as the NVIDIA RTX 6000 Ada or A40) runs the model smoothly under FP8. To run native bf16 weights without quantization loss, an 80 GB A100, H100, or a 96 GB H20 is required.
• Apple Silicon: Mac systems with unified memory (M3 Max, M4 Max, or M2 Ultra with 36 GB, 64 GB, or 128 GB configurations) handle quantized versions reliably via unified memory pooling.

The best quantization for KnowLine-31B-Gen1 depends on your precision tolerance:

• Q4_K_M (Recommended for 24 GB GPUs): Offers the ideal balance of footprint and accuracy. Because System One tasks measure probability separations rather than subtle prose styling, 4-bit medium quantization preserves relative ranking across categories with minimal margin degradation.
• FP8 (Recommended for Multi-GPU / 48 GB Systems): Retains near-native bf16 calibration boundaries, preserving exact probability metrics for critical rubric evaluation.

Because this model skips autoregressive decoding, traditional KnowLine-31B-Gen1 tokens per second metrics do not reflect standard generation speeds. Instead, performance is evaluated by prompt ingestion throughput and forward-pass latency:

• SGLang: The recommended production stack. SGLang supports the default serve_knowline.sh script, serving the model on bf16 or FP8 backends. A single 96 GB card processes complex 64-question batches in under 200 ms.
• Ollama and Llama.cpp: For local desktop deployment, conversion to GGUF format and serving via Ollama provides the quickest way to get started. While Ollama handles local execution seamlessly, ensure your front-end scripts extract classification logits properly rather than prompting for generation.

On an RTX 4090 using 4-bit weights, expect input processing speeds exceeding 150-250 tokens per second during the state ingestion phase, followed by immediate logit calculation for the attached questions.

Evaluating KnowLine-31B-Gen1 vs competitor architectures highlights distinct tradeoffs between general generative models and specialized decision networks.

Fastino GLiDE V1.2 edges out KnowLine slightly on the Decision Index 0.3 public suite (71.99 vs 70.13). However, GLiDE utilizes a proprietary architecture with narrower task adaptability. KnowLine-31B-Gen1 benefits directly from the Gemma 4 foundation, offering superior language coverage (particularly across Chinese dialects) and built-in vision support, while remaining entirely open under Apache-2.0.

Baselabs mammoth-1 is a strong dense contender scoring 75.34 on the DI 0.3 public suite. Mammoth-1 operates with slightly lower memory pressure due to its smaller 27B parameter footprint. However, KnowLine-31B-Gen1 demonstrates stronger resistance to prompt injections (only 0.4% vulnerability on planted inputs) and comes pre-calibrated with an internal temperature fold (T = 1.44), making it safer for untrusted automated pipeline inputs.

Compared to its Mixture-of-Experts sibling, the dense 31B-Gen1 model requires more raw VRAM during inference (the 35B-A3B activates fewer parameters per pass). However, the dense architecture provides markedly higher accuracy on tasks sourced from previously unseen domains (74.9 vs 56.2). Teams deploying systems across unpredictable, heterogeneous environments will find the dense 31.27B model significantly more reliable than sparse MoE alternatives.