The dataset viewer is not available because its heuristics could not detect any supported data files. You can try uploading some data files, or configuring the data files location manually.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Koinic Labs: Central Compliance & Transparency Report Date: April 2026
Status: SME Provider (Research & Development Phase)
Copyright Policy (EU 2019/790) Koinic Labs respects the rights of content creators. In accordance with Article 4(3) of Directive (EU) 2019/790, we honor all machine-readable reservations of rights (TDM opt-outs). Our training pipelines are designed to exclude data from sources that have explicitly opted out of AI training.
Training Data Summary (Synthetic-First) The AXL Architecture models are trained using a Synthetic-First Methodology.
Source: Data is primarily generated through high-fidelity AI-driven instruction sets and code-generation pipelines.
Categories: Programming logic (Python, C++, Rust, Go), multi-scale reasoning, and cybersecurity defense patterns.
Curation: Automated filters and human-in-the-loop (HITL) checks are used to ensure data quality and architectural alignment.
- Intended Use & Boundaries (Liability Protection) To ensure safety and compliance, use of Koinic Labs models is subject to the following boundaries:
AXL-Secure & AXL-Debugger Series: Intended Use: Defensive cybersecurity augmentation, code auditing, and vulnerability patching assistance.
Human-in-the-Loop: These models are designed to assist human experts. They are NOT intended for autonomous deployment in critical infrastructure (e.g., power grids, healthcare, transport) without human verification.
Forbidden Use: Any offensive cyber-operations or unauthorized intrusion testing.
- Environmental Impact Koinic Labs prioritizes sustainability. By optimizing for CPU-first inference, our models significantly reduce the carbon footprint compared to standard GPU-intensive LLMs.
Training Efficiency: Typical runs average 0.0070 kg CO2.
- Downloads last month
- 32