README.md

---
base_model: qvac/VisionPsy-Nano-460M
base_model_relation: quantized
license: apache-2.0
language:
  - en
tags:
  - vision-language-model
  - gguf
  - quantization
  - llama.cpp
---

# VisionPsy-Nano DomCal-EmbGuard

**The flagship challenger of this release: 8W/5L/4T over 17 displayed rows vs QVAC q4_k_m-imat (same-harness re-run; MM-IFEval@2048 42.2 > 41.1) — the highest normalized score of all six ledger models (61.97) and the fastest observed in this 200-item solo sweep (786.7 ms/item).**

A GGUF quantized derivative of QVAC's [VisionPsy-Nano-460M](https://huggingface.co/qvac/VisionPsy-Nano-460M) built with **P1 (domcal) × P4 (embguard)** — domain-calibrated imatrix quantization with embedding-protected precision (input embedding tensor held at Q8_0) on a q4_k_m base. This is not a new trained checkpoint: the ~460M-parameter architecture is unchanged. Part of the VisionPsy-Nano release collection — see the Links section below.

## Exact runtime pair

| Component | File | Bytes |
|---|---|---:|
| LM | `visionpsy-nano-460m-q4_k_m-domcal-embguard.gguf` | 320862016 |
| mmproj | `mmproj-visionpsy-nano-460m-q8.gguf` | 108782144 |

Package size: **409.7 MiB**. Use only this LM/mmproj mapping. SHA-256 checksums for both files ship in this repository as [SHA256SUMS](https://huggingface.co/simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard/blob/main/SHA256SUMS) — verify after download with `sha256sum -c SHA256SUMS`.

## Model at a glance

| | |
|---|---|
| Base model | QVAC VisionPsy-Nano-460M (~460M parameters; SigLIP2 vision encoder + SmolLM2-360M backbone) |
| Techniques | **P1 (domcal)** — domain-calibrated imatrix quantization; **P4 (embguard)** — input embedding tensor (`token_embd`) held at Q8_0 |
| Quantization | q4_k_m LM + q8 mmproj (QVAC's own projector artifact) |
| Calibration data | VQAv2-train + TextVQA-train, ChatML-formatted; training splits only — never benchmark test data, no evaluation images |
| Total size | **409.7 MiB** = 306.0 MiB LM + 103.7 MiB mmproj — +16.9 MiB (+4.3%) vs QVAC q4_k_m-imat (392.8) |
| Headline | **8W/5L/4T over 17 displayed rows vs QVAC q4_k_m-imat (same-harness re-run)**; normalized **61.97**, best of all six ledger models; best measured MMStar (47.13), MMBench (61.46), MathVista (48.60), MMVet (35.60); MM-IFEval@2048 42.24 > 41.11; **14W/3L/0T vs QVAC q4_0 (same-harness)** |
| Observed speed | **786.7 ms/item — fastest observed in this 200-item solo sweep**, ahead of all 15 measured configurations (next: TriStack 795.4; q4_0 803.5; imat 813.2) |

## Why this build exists

The study's conclusion frames each package as a deliberate assignment of calibration and precision — and this is the maximum-quality assignment. Domain calibration repairs the importance information; embedding protection keeps the token-embedding tensor at Q8_0, where the study found quantization drift most expensive. The measured outcome: the reasoning capability area scores **52.3 — above even QVAC's fp32 published card (52.2)** — from a package roughly 4× smaller than fp32. Mechanism honesty: the incremental EmbGuard step at q4_k_m precision was not separately significant against its domain-calibrated parent (+0.27 pooled point, p = 0.38), so the complete result is attributed to the configuration as shipped, not to embedding protection alone.

## Vs the two rulers

The primary ruler is QVAC q4_k_m-imat, QVAC's flagship build — re-run in this same harness with a hash-pinned full-17 record (judged rows scored with a qwen3.6-27b API judge, a reconstruction of QVAC's judging protocol validated within ±1 pt of their published card on 6/8 judged benchmarks; QVAC's own card numbers remain labeled context, never medaled). QVAC q4_0 is the secondary same-harness ruler. Negative deltas = smaller/faster.

| | QVAC q4_k_m-imat (same-harness re-run) | QVAC q4_0 (same-harness) |
|---|---:|---:|
| W/L/T over 17 displayed rows | **8W/5L/4T** | **14W/3L/0T** |
| Package size Δ (409.7 MiB) | +16.9 MiB (+4.3%) | +62.0 MiB |
| Speed Δ (786.7 ms/item) | −26.5 ms/item (−3.3%) | −16.8 ms/item |

## Measured results

Row-golds across the six-model ledger: MMStar **47.13**, MMBench **61.46**, MathVista **48.60**, MMVet **35.60**, AI2D **66.03**, MM-IFEval@2048 **42.24**. The named losses stay visible: QVAC q4_k_m-imat keeps OCRBench (777 vs 766), DocVQA, ChartQA and InfoVQA — the document/OCR cluster stays with the imatrix build; QVAC q4_0 keeps RealWorldQA, MME and MMMU dev. The fastest-observed latency and the highest normalized score sit in the same package: the quality gains cost nothing in measured speed.

## Limitations

- **Exploratory scope:** one seed (17), one harness, one GPU. Counts are descriptive rows, not universal-superiority claims; "fastest observed" is scoped to this 200-item solo sweep.
- **Mechanism attribution:** the EmbGuard increment was not separately significant against its calibrated parent (p = 0.38); the package is evaluated as a whole.
- **Judged rows:** qwen3.6-27b via OpenRouter — an attempted same-model reconstruction of QVAC's judge, not their exact serving protocol.
- +4.3% package vs the flagship imat build is the price of the Q8_0 embedding; DomCal-Slim holds the small-size end.

## Links

This model is one of four verified VisionPsy-Nano GGUF packages released together under the [simoneschiavoi Hugging Face namespace](https://huggingface.co/simoneschiavoi).

- Project website (full interactive research write-up): <https://simoneschiavoi.github.io/visionpsy-optimization/>
- Benchmarks (full same-harness ledger, all models × 17 benchmarks): <https://simoneschiavoi.github.io/visionpsy-optimization/#benchmarks>
- Hugging Face namespace (all four packages): <https://huggingface.co/simoneschiavoi>
- Sibling models:
  - [VisionPsy-Nano DomCal](https://huggingface.co/simoneschiavoi/VisionPsy-Nano-DomCal)
  - [VisionPsy-Nano DomCal-Slim](https://huggingface.co/simoneschiavoi/VisionPsy-Nano-DomCal-Slim)
  - [VisionPsy-Nano TriStack](https://huggingface.co/simoneschiavoi/VisionPsy-Nano-TriStack)

## License and attribution

Apache-2.0 derivative. The Apache-2.0 NOTICE distributed with the artifact must be retained, and QVAC's VisionPsy-Nano-460M must be attributed as the base model. Build evidence, evaluation ledger, and reproduction scripts: <https://github.com/simoneschiavoi/visionpsy-optimization>. All four release packages: <https://huggingface.co/simoneschiavoi>.