---
license: cc-by-nc-4.0
base_model:
- Qwen/Qwen3.5-4B-Base
- unsloth/Qwen3.5-4B-Base
library_name: peft
pipeline_tag: text-generation
tags:
- lora
- sft
- hscm
- quantum-inspired
- guarded-generation
---

# 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

```python
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.