B01-NUna is Helloblue’s assistant stack: orchestration in production, and a local GPU training pipeline we own as a long-term asset. We publish this overview so partners and researchers can see both layers — cloud chat today, and the LoRA training loop we run on our own hardware — without mystique.

It is not a single monolithic model trained end-to-end in this repository. What we do train locally is a PEFT LoRA adapter, versioned with operator discipline; what users hit on helloblue.ai by default is still Groq-backed routing.

Local GPU training — our asset

The durable Helloblue investment is the training host: when GPU_TRAINING_HOST is set on a designated machine, we run PyTorch LoRA fine-tuning (rank 8 by default) against a compact chat base. In our code paths the Ollama tag llama3.2:1b maps to TinyLlama/TinyLlama-1.1B-Chat-v1.0 for Hugging Face fine-tuning. Example hardware in the stack is RTX 4060-class (8GB VRAM). Runs are deliberate — configuration- and schedule-driven — not continuous self-training on every chat turn.

Trusted positive feedback and quality-filtered conversation snippets feed supervised LoRA data; trusted negatives are exported as preference/rejection pairs and audited separately. Successful runs produce named adapter releases (local manifests and data/adapters/latest.json), optional private Hub tags, and eval-gated promotion before we treat an upload as releasable.

The private Hub adapter (pejmantheory/B01-NUna) is R&D and backup today — weights and a Hub card we control — not a marketed public model SKU and not the default path for end users. Adapter serve and canary traffic are opt-in (off in production until eval and operator sign-off). That is intentional: we build and own the training loop first, then decide when our weights should take traffic.

• Operator surface: src/lib/gpu-training/, /api/training status, Models page GPU and data stats
• Method: LoRA (r=8), typical learning rate ~2e-4, base llama3.2:1b → TinyLlama HF path
• Data: quality thresholding + trust-scored feedback; positives for supervised fine-tuning, negatives as preference pairs
• Release discipline: adapter manifests, optional Hub tags, eval gate before strict upload
• Serve path: B01_NUNA_ADAPTER_ENABLED / canary % — off by default on helloblue.ai

Cloud production routing (today)

Default cloud chat routes general and reasoning-heavy turns to Groq-hosted GPT-OSS 120B (openai/gpt-oss-120b). Llama 3.3 70B remains available as a Groq fallback / rollback profile. Both slots are configurable via environment variables.

Routing is heuristic and score-based — query analysis, provider capabilities, latency and cost hints, circuit-breaker state, and trust-weighted feedback — not a jointly trained softmax router. The orchestrator analyzes each request, selects among configured providers, and composes tools behind one interface.

Benchmarks and honesty

• Homepage / NUna Features performance figures are Helloblue production golden-eval results on helloblue.ai (155/155, avg quality 0.91 as of Jul 15, 2026)
• The model card may still show Meta Llama 3.3 published scores as a comparative backbone reference
• End-to-end app performance depends on routing, provider load, prompt policy, and tool usage
• Availability targets in the card are operational goals, not contractual SLAs