nexus-os-v2-dualmode

specimba/nexus-os-v2-dualmode

nexus-os-v2-dualmode is a machine learning model from specimba. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

Landau-Ginzburg phase transition hypothesis - empirically confirmed. No universal Tc.

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Updated May 17, 2026

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Access: Public
Created: May 17, 2026
Updated: May 17, 2026
SHA: a6abb4a6

Model card

From the Hugging Face model README

NEXUS OS v2.1 -- Dual-Mode Token-Level Hallucination Control

Landau-Ginzburg phase transition hypothesis -> empirically confirmed. No universal T_c.

What This Is

NEXUS OS is an autonomous agent operating system that routes LLM inference across local hardware (8GB consumer GPUs) and cloud APIs. Its core is:

ChimeraRouter: 4-tier automatic model selection
TWAVE: Token-level wavefront expansion with Landau-Ginzburg thermodynamic tracking
QWAVE: Quality budget allocator mapping task -> resource budget
CK-PLUG: Token-level retrieval coupling (Confidence Gain from evidence entropy)
SafetyHead: Dual-mode safety detection (strict toxic-token / frontier bifurcation)

Quick Start

from nexus_os_v2 import RealityBridge, Mode, ChimeraRouter, QWAVEScorer

# STRICT mode (production -- proven methods only)
bridge = RealityBridge(mode=Mode.STRICT_REALITY)

# Classify intent -> quality budget
qs = QWAVEScorer()
budget = qs.classify_and_score("what is the capital of france",
                                 intent_model=my_real_classifier,
                                 mode="strict")

# Route to best model given VRAM + budget
router = ChimeraRouter(mode="strict")
decision = router.route(budget.task_type, budget.budget, local_vram=8192)
print(decision.model_name, decision.tier)
# -> "Qwen3-7B-Q4_K_M" tier=2

Dual-Mode Architecture (v2.1 Core Feature)

Every component exposes both proven (strict) and experimental (frontier) paths:

Mode	Uncertainty	Use When
STRICT	0.0-0.20	Production, reproducibility required, third-party validation exists
FRONTIER	0.4-0.65	Research, prototyping, exploring invention space

Promote frontier -> strict: bridge.promote("my_method", evidence_score=0.88) requires score >= 0.85.

Files

File	Purpose
`reality_bridge.py`	Central dual-mode gatekeeper, audit logging, promotion API
`twave_tracker.py`	Token entropy tracking + Landau-Ginzburg field evolution
`ckplug_retriever.py`	BM25 retrieval + CK-PLUG Confidence Gain (mu_ret coupling)
`model_registry.py`	ChimeraRouter: 4-tier model routing with VRAM probe
`qwave.py`	Quality budget allocator: task -> Q in [0,1]
`safety_head.py`	Dual-mode safety (strict toxic tokens / frontier bifurcation SSA)
`visualizations/phase_transition_plots.py`	Publication-quality 6-panel LG phase diagrams

Version

2.1.0 -- Dual-Mode Architecture release.

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

Try ML Intern: https://smolagents-ml-intern.hf.space
Source code: https://github.com/huggingface/ml-intern

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'specimba/nexus-os-v2-dualmode'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.

Created: May 17, 2026
Updated: May 17, 2026