---
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
**The q4_0 envelope, repaired at zero measured cost: 12W/4L/1T over 17 descriptive rows vs same-harness QVAC q4_0 — pooled +2.2 points (p = 4.7×10⁻⁵) — and 2W/15L/0T over 17 displayed rows vs QVAC q4_k_m-imat (same-harness re-run) at −44.6 MiB (−11.4%).**
A GGUF quantized derivative of QVAC's [VisionPsy-Nano-460M](https://huggingface.co/qvac/VisionPsy-Nano-460M) built with **P1 (domcal)** — domain-calibrated imatrix quantization. This is not a new trained checkpoint: the ~460M-parameter architecture (SigLIP2 vision encoder + SmolLM2-360M backbone) is unchanged; only the quantization calibration was modified. Part of the VisionPsy-Nano release collection — see the Links section below.
## Exact runtime pair
| Component | File | Bytes |
|---|---|---:|
| LM | `visionpsy-nano-460m-q4_0-domcal.gguf` | 256379776 |
| mmproj | `mmproj-visionpsy-nano-460m-q8.gguf` | 108782144 |
Package size: **348.2 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/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) |
| Technique | **P1 (domcal)** — importance matrix computed on a domain-matched corpus, steering q4_0 rounding toward the tensors that matter for visual question answering |
| Quantization | q4_0 LM (same format as QVAC's baseline) + QVAC's own q8 mmproj — same size class as the base |
| Calibration data | VQAv2-train + TextVQA-train, ChatML-formatted; training splits only — never benchmark test data, no evaluation images |
| Total size | **348.2 MiB** = 244.5 MiB LM + 103.7 MiB mmproj — +0.5 MiB vs QVAC q4_0 (347.7), −44.6 MiB (−11.4%) vs QVAC q4_k_m-imat (392.8) |
| Headline | **12W/4L/1T over 17 descriptive rows vs same-harness QVAC q4_0**; 9W/4L/1T over 14 non-selection rows; normalized **60.31** vs q4_0's 59.48; **2W/15L/0T over 17 displayed rows vs QVAC q4_k_m-imat (same-harness re-run)** |
| Observed speed | 802.2 ms/item vs q4_0's 803.5 — repair at zero measured latency cost (−1.3 ms/item) |
## Why this build exists
The study's core finding: a label like "4-bit" says how many bits are available, not whether those bits protect the tensors that matter for the model's actual job. **Domain calibration is a targeted repair, not a universal upgrade.** Applied to QVAC's uncalibrated q4_0 build, it recovered a pooled +2.2 points (p = 4.7×10⁻⁵) — the largest single-technique gain measured in the campaign. The same idea applied to QVAC's already-calibrated q4_k_m did not stack, and at 5-bit it actively hurt (strict OCR −2.2, p = 0.003): calibration matters most where importance information is missing. DomCal is that finding, shipped: the repair applied exactly where it helps.
## 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 | 2W/15L/0T | **12W/4L/1T** (17 descriptive rows) |
| Package size Δ (348.2 MiB) | −44.6 MiB (−11.4%) | +0.5 MiB |
| Speed Δ (802.2 ms/item) | −11.0 ms/item (−1.4%) | −1.3 ms/item |
## Measured results
Strongest gains land exactly where the uncalibrated build was weakest: MMStar **45.8 vs 42.3**, MMBench **58.1 vs 55.0**, MathVista **43.9 vs 41.7** against the q4_0 control. The named losses stay visible: QVAC q4_0 keeps MME, POPE and RealWorldQA; MMMU is tied. Against the flagship imat build, DomCal posts an honest 2W/15L/0T — imat keeps OCRBench, DocVQA, ChartQA and InfoVQA. This is the same-format repair of the q4_0 baseline and a smaller, faster alternative to the flagship — not an imat-beater.
## Limitations
- **Exploratory scope:** one seed (17), one harness, one GPU. Counts are descriptive rows, not statistical proof or universal-superiority claims; the pooled +2.2-point repair against q4_0 is the pre-registered significance claim (p = 4.7×10⁻⁵).
- **Judged rows:** qwen3.6-27b via OpenRouter — an attempted same-model reconstruction of QVAC's judge, not their exact serving protocol.
- **Calibration scope:** benefits demonstrated on the uncalibrated q4_0 base; the technique is not additive on already-calibrated builds.
- A completed post-hoc contamination audit of the calibration corpus is summarized in the release ledger ([release repository](https://github.com/simoneschiavoi/visionpsy-optimization)).
## 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):
- Benchmarks (full same-harness ledger, all models × 17 benchmarks):
- Hugging Face namespace (all four packages):
- Sibling models:
- [VisionPsy-Nano DomCal-Slim](https://huggingface.co/simoneschiavoi/VisionPsy-Nano-DomCal-Slim)
- [VisionPsy-Nano DomCal-EmbGuard](https://huggingface.co/simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard)
- [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: . All four release packages: .