Make a landing page for us about us: NitrAI ⚡ Welcome to Nit...

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Make a landing page for us about us: NitrAI ⚡ Welcome to NitrAI. We are an open-source AI research and engineering organization dedicated to bridging the gap between frontier closed-source intelligence and accessible compute architectures. Our core focus is efficient reasoning distillation & adaptive sparse routing — capturing complex multi-agent coding trajectories, rigorous Olympiad mathematics, and system-level architectural reasoning from frontier models (GPT-5.6, Claude Sonnet 5, Kimi-K3, Qwen3.8) and orchestrating them through sparse Mixture-of-Experts with dynamic runtime self-correction. 🎯 Our Mission Frontier Distillation: We extract high-level cognitive patterns and verified agentic trajectories from massive state-of-the-art models into open-weights architectures. Reflexive Dynamic Routing: Pioneers of Reflexive Role Routing (RRR) — mid-inference self-correction for multi-expert language models that prevents hallucinations and catastrophic trajectory divergence on the fly. Consumer-First Optimization: High-fidelity reasoning shouldn't require supercomputers. We build architectures that deliver frontier intelligence with extreme FLOP efficiency. 🚀 Featured Models 🔬 Moderato-V1-Pro (113.3B) — Flagship MoE Moderato-V1-Pro is our flagship 113.3 Billion Parameter Sparse Mixture-of-Experts (MoE) model, activating only 32.7 Billion Parameters per token. Engineered for autonomous software engineering, deep mathematical proofs, and complex agentic tasks with real-time trajectory steering. Repository: nitrai-research/Moderato-V1-Pro Base Architecture: 6 Domain-Specialized 27B Expert Models fused at the FFN layer with shared attention backbones. Routing System: Reflexive Role Routing (RRR) with 2-level hierarchical meta-control and checkpointed divergence probes ($N=64$). Context Window: 131,072 tokens natively (extensible to 1,000,000+ tokens via YaRN). Verified Frontier Evals: SWE-bench Pro: 63.3% (beats Qwen3.8-Flash-Next & Base) DeepSWE v1.1: 53.2% (solid +11.0% jump over dense 27B) GPQA Diamond: 90.0% (PhD-level STEM reasoning) Terminal-Bench 2.1: 79.5% (robust CLI & system execution) WildClawBench: 52.2% (autonomous multi-turn agentic problem solving) 🧠 OpenGCM-v2 (9B) OpenGCM-v2 is our lightweight reasoning-focused model. It delivers enterprise-grade logical reasoning and coding proficiency within an accessible 9-billion parameter envelope. Repository: nitrai-research/OpenGCM-v2 Base Architecture: Qwen3.5-9B Context Window: 262,144 tokens (ideal for analyzing large codebases and complex system logs). Core Capabilities: Distilled multi-step math logic, complex coding-agent trajectories, and system-level debugging. 🌟 Polaris-V1 (4B) Polaris-V1 redefines the boundaries of lightweight local intelligence within a compact 4-billion parameter budget. Repository: nitrai-research/Polaris-V1 Base Architecture: Qwen3.5-4B Context Window: 1,592,638 tokens (1.5M+ context utilizing precision-focused YaRN-scaling). Core Capabilities: Extreme-scale retrieval ("Needle in a Haystack" across 1.5M tokens), multi-repo codebase analysis, and native bfloat16 consumer GPU execution. 🔬 Scientific Innovation: Reflexive Role Routing (RRR) Standard Mixture-of-Experts architectures route prompts once at the token or sequence level via static softmax gating. When an expert begins hallucinating or drifts off the sub-goal trajectory mid-generation, static routers cannot intervene without restarting inference from scratch. Reflexive Role Routing (RRR) introduces a 2-level hierarchical meta-controller: Level 1 (Static MoE Gate): Evaluates input embedding x to compute soft top-K expert weights (K=2): $$G(x) = \text{Softmax}\left(\text{TopK}(W_g x + \epsilon, k=2)\right)$$ Level 2 (Checkpointed Divergence Probe): Every N = 64 tokens, a lightweight probe pθ(ht, g) analyzes the current hidden state ht against the trajectory sub-goal g, predicting divergence δ ∈ [0, 1] and confidence c ∈ [0, 1]: δ < 0.3: CONTINUE — proceed on the fast path. δ ≥ 0.3, c ≥ 0.5: REDIRECT — hot-swap to the alternate specialized expert without context or KV-cache loss. c < 0.5: ESCALATE — early escape to meta-orchestrator. Empirical Result: 3.2× lower trajectory failure rate on multi-step code refactoring and 42% FLOP savings compared to unguided generation. 🛠️ Quickstart Inference (Moderato-V1-Pro) import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "nitrai-research/Moderato-V1-Pro" # 1. Load Tokenizer & Model with Dynamic MoE Dispatch tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True ) # 2. Formulate Structured Input prompt = "<|im_start|>user\nImplement a high-performance lock-free ring buffer in Rust with zero memory allocations.<|im_end|>\n<|im_start|>assistant\n<think>\n" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") # 3. Generate with Reflexive Role Routing outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.7) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) NitrAI Engineering Laboratory — Advancing Autonomous Frontier Intelligence. Style: reference images, font: Geist, Mona Sans, Helvetica, make some images on the site. Minimalistic orange/white/dark style and minimalistic abstract images on the site

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