Experimental_QSystem

Experimental QSystem

An experimental adapter/system bundle for Qwen3.5-4B-Base. It combines a language-layer LoRA verbalizer with portable NumPy field and complex-wave rerankers. The full persistent HSCM memory engine is external to these weights.

Accuracy boundary

• This is not a physically quantum LLM. The transformer, neural weights, and KV cache are classical.
• Complex amplitudes, phase, interference, attractive/repellent signals, and the two-qubit field are routing analogues over HSCM candidates.
• The earlier IBM QPU candidate was rejected by held-out gates and is not the active artifact included here.
• The raw adapter failed one missing-evidence generation probe by inventing a number. It must be used with the supplied evidence-boundary prompt and fail-closed output guard.

Data status

Training used 182 examples: 150 Hope bridge candidates and 32 grounding-repair examples. None were human-approved. The source package explicitly labelled them HUMAN_REVIEW_REQUIRED; this bounded user-requested experiment does not promote them to authentic or production-reviewed persona data. No training rows, private memories, credentials, or source text are included in this repository.

Training and evaluation

• BF16, rank-16 LoRA, language attention/MLP projections only
• 21,233,664 trainable parameters (0.4656% of the loaded model)
• validation loss: 3.4935 -> 2.7221
• test loss: 3.5912 -> 2.7644
• guarded Windows end-to-end gate: 11/11 checks
• local project regression at export: 760 passed, 1 optional skip

The raw adapter remains quarantined; only the guarded composition passed.

Portable use

from portable_qsystem import PortableQSystem

system = PortableQSystem("o0Hailey-DSynth0o/Experimental_QSystem")
result = system.generate(
    "What exact number was in the sealed result?",
    ["The notes mention a sealed result but do not give its value."],
    unmet_need=True,
)
print(result["text"])

unmet_need must come from a trusted retrieval/controller layer. If you do not have that layer, treat this as an experimental LoRA—not a grounded system.

The default 8 GiB GPU / 96 GiB CPU memory limits allow Accelerate to offload overflow to RAM. Override them with QSYSTEM_GPU_MEMORY and QSYSTEM_CPU_MEMORY.

Included artifacts

• PEFT adapter and authoritative Qwen tokenizer/template
• portable_qsystem.py: official-template inference plus fail-closed guard
• runtime/field_reranker.py: NumPy two-qubit field evaluator
• runtime/wave_reranker.py: NumPy complex-wave controller
• artifacts/: hash-gated active scalar/wave parameters
• sanitized training and validation summaries

License

This repository is shared under CC BY-NC 4.0. The referenced base models retain their own licenses. Users are responsible for checking compatibility for their use case.