Instructions to use simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard:Q4_K_M

Use Docker

docker model run hf.co/simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard:Q4_K_M

LM Studio
Jan
Ollama
How to use simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard with Ollama:
```
ollama run hf.co/simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard:Q4_K_M
```

Unsloth Studio

How to use simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard to start chatting

Docker Model Runner
How to use simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard with Docker Model Runner:
```
docker model run hf.co/simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard:Q4_K_M
```

Lemonade

How to use simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull simoneschiavoi/VisionPsy-Nano-DomCal-EmbGuard:Q4_K_M

Run and chat with the model

lemonade run user.VisionPsy-Nano-DomCal-EmbGuard-Q4_K_M

List all available models

lemonade list

Atomic Chat

VisionPsy-Nano-DomCal-EmbGuard / README.md

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	---
	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>.