BPM RED Academy: Human-Centred Health & Performance Digital Twin | Fine-Tuning on Hyperstack + NIM Validation

AI & Data Science NVIDIA NIM NIM on RTX AI PCs and Workstations
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Hi NVIDIA Team and Community,

We’re working on a research-grade integration between Hyperstack fine-tuned inference models and NVIDIA NIM, leveraging the Llama-3.3-70B-Instruct base model.

Our project — BPM RED Academy (Baza Psihofizičke Moći) — is an AI-driven human-performance ecosystem combining health, readiness, and effort-based credentialing.

We’ve achieved:

  • 220 fine-tuning logs processed via Hyperstack
  • deterministic Pass/Fail readiness inference
  • initial NIM validation pipeline (inference ready)
  • integration design for Omniverse-based Digital Twin (health & performance dashboard)

We’d like to:

  • validate and benchmark our fine-tuned checkpoints through NIM
  • understand optimal inference setup for health-specific deterministic loops
  • explore enterprise collaboration (AI Workbench / Omniverse / NIM Stack)

Any NVIDIA guidance or collaboration opportunities would be highly appreciated.

Founder / Innovation Leader: Edin Vučelj

Project: BPM RED Academy | Human-Centred AI & Digital Twin System

Base Model: Meta Llama-3.3-70B-Instruct

Fine-Tuning Platform: Hyperstack

Validation: NVIDIA NIM

Thank you for your time and feedback.

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