HALION-AI/helionx-core-v1.1

TEXT GENERATIONConcurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jan 25, 2026License:mitArchitecture:Transformer Open Weights Featherless Exclusive Cold

HALION-AI/helionx-core-v1.1 is a 7.6 billion parameter governed, non-autonomous cognitive and defensive intelligence system. Designed with deterministic reasoning and explicit capability boundaries, it focuses on architecture correctness, safety, and auditability. This model is intended for research, defensive security reasoning, and architectural study in controlled environments, rather than general chatbot applications. Its primary differentiator is its emphasis on safety, governance, and defensive-only cybersecurity analysis.

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HelionX-Core v1.1: A Governed Defensive Intelligence System

HelionX-Core v1.1, developed by HALION AI, is a 7.6 billion parameter system designed as a governed, non-autonomous cognitive and defensive intelligence framework. This release focuses on establishing a robust architecture for safety, auditability, and deterministic reasoning, rather than being a general-purpose LLM. It incorporates explicit capability boundaries and is specifically engineered for defensive-only cybersecurity analysis.

Key Architectural Features

  • Governed and Non-Autonomous: Designed with strict policy enforcement and safety agents, lacking self-learning or autonomous capabilities.
  • Deterministic Reasoning: Emphasizes predictable and auditable outputs.
  • Defensive Cybersecurity Focus: Intended for analysis in defensive security contexts, not operational use.
  • System Release: Currently provides the full system architecture, runtime, and inference interface, including memory abstractions (non-learning) and a vision fusion adapter.

Intended Use Cases

  • Research: Ideal for studying advanced AI architectures with a strong emphasis on safety and control.
  • Defensive Security Reasoning: Suitable for non-operational analysis in cybersecurity.
  • Architecture Study: Valuable for understanding governed AI system design.
  • Controlled Deployments: Designed for environments where strict control and auditability are paramount.

Note: As of this release, the trained model weights and tokenizer files are not yet included but will be added in a future update. This repository serves as a system release for architectural review and development.

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