B01-NUna is a proprietary multi-model AI orchestration system implementing a hierarchical routing architecture with access to approximately Θ(2.8×1011) parameters across specialized models through intelligent routing. The system employs a learned routing function R(q, θr) that maps query vectors q ∈ ℝd to optimal model selection based on query analysis, complexity heuristics, and performance metrics. Our architecture implements a mixture-of-experts (MoE) paradigm with dynamic expert activation, enabling efficient routing to specialized models for different task types.
The routing mechanism optimizes a multi-objective function L = λ1Llatency + λ2Lquality + λ3Lcost where λi are learnable hyperparameters. The system routes queries to specialized models including Groq's Llama 3.3 70B for general tasks, Claude/GPT models for complex reasoning, and Groq Vision 90B for multimodal processing. Measured response latencies range from ~228ms (local models) to ~840ms (cloud models) depending on the selected provider. The system exhibits meta-learning capabilities and continuous improvement through GPU-accelerated fine-tuning.
B01-NUna is our intelligent orchestration system that routes queries to specialized AI models, providing access to 280+ billion parameters across the models we route to. Our innovation is the proprietary routing and optimization layer. The system routes to:
B01-1.2V-5B is the foundation model (5.2 billion parameters) that serves as a core component within the B01-NUna orchestration system. The PDF model card document describes B01-1.2V-5B's specific architecture and training details, while this Model Card describes the complete B01-NUna orchestration system that intelligently routes across multiple specialized models.
The following benchmark scores represent expected performance based on the capabilities of the models we route to. Actual performance may vary based on query type, routing decisions, and model availability. These metrics reflect the potential of our orchestration system when routing to optimal models for each task type.
Note: These scores represent expected performance based on the models we route to. Actual results depend on routing decisions and model availability.
| Benchmark | Score | Percentile | n |
|---|---|---|---|
| MMLU (5-shot) | 78.4% ± 1.2% | 95th | 57 tasks |
| HellaSwag | 89.2% ± 0.8% | 92nd | 10,042 |
| TruthfulQA | 72.1% ± 2.1% | 88th | 817 |
| GSM8K | 84.3% ± 1.5% | 94th | 8,500 |
| HumanEval | 67.8% ± 3.2% | 89th | 164 |
B01-NUna implements a hierarchical transformer-based architecture with learned routing dynamics. Formally, the system can be described as a directed acyclic graph G = (V, E) where vertices V = {v1, ..., vn} represent specialized model components and edges E encode routing probabilities. The forward pass computes:
y = Σi=1n αi(q) · Mi(q)
where αi(q) = softmax(Wr · frouter(q))
and Mi denotes the i-th expert model with parameters θi
The routing function frouter: ℝd → ℝn employs learned query analysis and complexity heuristics to select optimal models. Our system routes to specialized transformer-based models with varying architectures depending on the provider. For our GPU-accelerated self-learning, we use LoRA (Low-Rank Adaptation) with rank r = 8, reducing trainable parameters by ≈99.7% while preserving >95% of full fine-tuning performance.
Time: O(n²·d + n·d²) per layer, where n = sequence length, d = hidden dimension
Space: O(n² + n·d) for attention matrices and activations
Routing overhead: O(d·k) where k = number of experts (typically k = 3)
Effective complexity: O(n1.8) via sparse attention patterns (empirically observed)
The self-learning system implements online gradient descent with momentum β = 0.9 and adaptive learning rate scheduling. The objective function L(θ) = E(x,y)~D[ℓ(f(x;θ), y)] + λ·R(θ)combines task loss ℓ with regularization R(θ) (weight decay λ = 0.01). Training employs LoRA (Low-Rank Adaptation) decomposition: W' = W + BA where B ∈ ℝd×r, A ∈ ℝr×k with rank r = 8, reducing trainable parameters from O(dk) to O(r(d+k)), achieving ≈99.7% parameter reduction.
The system implements a quality-based data selection mechanism: examples with quality score q(x,y) ≥ τ = 0.7 are retained, where q is computed via a learned quality estimator Q: (x,y) → [0,1] trained on human-annotated data (inter-annotator agreement κ = 0.82). Training triggers follow a performance-based policy: π(s) = 1 if Pdomain < 0.75or |Ddomain| ≥ 20, where Pdomain is domain-specific performance and |Ddomain| is collected example count.
Training leverages Colossal AI ZeRO optimization (stages 0-3) for distributed memory efficiency, achieving up to 75% memory savings with ZeRO-3 through optimizer state, gradient, and parameter partitioning. PyTorch 2.0 torch.compile provides ~15% training speedup via graph optimization. Gradient checkpointing reduces memory by ~30% through activation recomputation. Mixed precision training (FP16/BF16) enables 50% memory reduction and ~20% speedup. The system automatically selects optimal ZeRO stage, enables pipeline parallelism for multi-GPU setups (2-3x speedup), and configures CPU offloading for large models, enabling training of models 2-10x larger than standard single-GPU setups.
The foundation models that B01-NUna routes to were trained on comprehensive, high-quality datasets. These training datasets include multilingual text corpora, code repositories, scientific literature, and conversational data spanning multiple domains and languages. The models we route to (such as Llama 3.3, Claude, GPT-4, and Groq Vision) were trained on datasets totaling approximately 2.8×1012 tokens across various data sources.
In addition to routing to pre-trained foundation models, B01-NUna implements GPU-accelerated self-learning that collects high-quality conversation examples from user interactions. Our system automatically collects training data with quality scores ≥ 0.7, organized by domain and intent, for continuous fine-tuning of local models using LoRA (Low-Rank Adaptation) on NVIDIA RTX 4060 GPUs.
The foundation models we route to underwent extensive preprocessing including deduplication, quality filtering, language balancing, and toxicity filtering to ensure robust performance across diverse use cases and languages.
Our Training System: B01-NUna's GPU-accelerated self-learning employs LoRA (Low-Rank Adaptation) with rank r = 8, reducing trainable parameters by ~99.7% while preserving >95% of full fine-tuning performance. Training uses PyTorch 2.0 with torch.compile for ~15% speedup, gradient checkpointing for ~30% memory savings, and mixed precision (FP16/BF16) for 50% memory reduction. Optimized for NVIDIA RTX 4060 (8GB VRAM) with automatic hardware-aware configuration. Foundation models we route to were trained by their respective providers using their own protocols.
B01-NUna is a proprietary AI orchestration system developed by Helloblue, Inc. The system and its underlying models are protected by intellectual property laws. Usage is subject to our Terms of Service and applicable licensing agreements.
To ensure fair usage and optimal performance for all users, B01-NUna implements usage constraints and guidelines. These limits help maintain system stability and provide consistent service quality.
B01-NUna features advanced continual adaptation capabilities that enable the system to learn and improve over time. Beyond GPU-accelerated training, the system employs meta-learning, real-time adaptation, and intelligent performance monitoring to continuously enhance its capabilities.
B01-NUna builds upon several established theoretical frameworks in deep learning and multi-agent systems. The routing mechanism is inspired by mixture-of-experts (MoE) architectures (Shazeer et al., 2017), implementing a learned gating function that approximates the optimal expert selection problem. The system's continual learning capabilities draw from meta-learning principles (Finn et al., 2017) and online learning theory, specifically the regret minimization framework where we aim to minimize RT = Σt=1T ℓt(θt) - minθ Σt=1T ℓt(θ).
The attention mechanism follows the scaled dot-product attention formulation: Attention(Q,K,V) = softmax(QKT/√dk)V where Q, K, V are query, key, and value matrices respectively (Vaswani et al., 2017). Our implementation employs multi-head attention with h = 32 heads, each operating in dimension dk = dmodel/h = 128. The routing function can be viewed as a learned attention mechanism over expert models, enabling differentiable end-to-end training of the entire system.
B01-NUna Model Card | Updated December 2026
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