Instructions to use simoneschiavoi/VisionPsy-Nano-TriStack 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-TriStack 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-TriStack:Q4_0
# Run inference directly in the terminal:
llama cli -hf simoneschiavoi/VisionPsy-Nano-TriStack:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf simoneschiavoi/VisionPsy-Nano-TriStack:Q4_0
# Run inference directly in the terminal:
llama cli -hf simoneschiavoi/VisionPsy-Nano-TriStack:Q4_0

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-TriStack:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf simoneschiavoi/VisionPsy-Nano-TriStack:Q4_0

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-TriStack:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf simoneschiavoi/VisionPsy-Nano-TriStack:Q4_0

Use Docker

docker model run hf.co/simoneschiavoi/VisionPsy-Nano-TriStack:Q4_0

LM Studio
Jan
Ollama
How to use simoneschiavoi/VisionPsy-Nano-TriStack with Ollama:
```
ollama run hf.co/simoneschiavoi/VisionPsy-Nano-TriStack:Q4_0
```

Unsloth Studio

How to use simoneschiavoi/VisionPsy-Nano-TriStack 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-TriStack 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-TriStack 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-TriStack to start chatting

Docker Model Runner
How to use simoneschiavoi/VisionPsy-Nano-TriStack with Docker Model Runner:
```
docker model run hf.co/simoneschiavoi/VisionPsy-Nano-TriStack:Q4_0
```

Lemonade

How to use simoneschiavoi/VisionPsy-Nano-TriStack with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull simoneschiavoi/VisionPsy-Nano-TriStack:Q4_0

Run and chat with the model

lemonade run user.VisionPsy-Nano-TriStack-Q4_0

List all available models

lemonade list

Atomic Chat

VisionPsy-Nano-TriStack / README.md

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metadata

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 TriStack

A flagship challenger at a smaller package: 9W/7L/1T over 17 displayed rows vs QVAC q4_k_m-imat (same-harness re-run) at −5.4 MiB (−1.4%) — the cleanest measured balance of size, quality and speed in the release, all three techniques stacked.

A GGUF quantized derivative of QVAC's VisionPsy-Nano-460M built with P1 (domcal) × P4 (embguard) × P10 (projcomp) — all three surviving quantization-side techniques stacked in one package. 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	`MY_q4_0-domcal-mmproj-q6.gguf`	85355072

Package size: 387.4 MiB. Use only this LM/mmproj mapping. SHA-256 checksums for both files ship in this repository as 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; P10 (projcomp) — multimodal projector compression q8 → q6_K
Quantization	q4_k_m LM (byte-identical to the DomCal-EmbGuard artifact) + q6_K mmproj (byte-identical to the DomCal-Slim one) — a composition of two already-verified artifacts, no new quantization step
Calibration data	VQAv2-train + TextVQA-train, ChatML-formatted; training splits only — never benchmark test data, no evaluation images
Total size	387.4 MiB = 306.0 MiB LM + 81.4 MiB mmproj — −5.4 MiB (−1.4%) vs QVAC q4_k_m-imat (392.8)
Headline	9W/7L/1T over 17 displayed rows vs QVAC q4_k_m-imat (same-harness re-run); normalized 61.64 vs 61.54 at a smaller package; best measured POPE (87.89) and ScienceQA (85.13) across all six ledger models; 13W/4L/0T vs QVAC q4_0 (same-harness)
Observed speed	795.4 ms/item — 2nd fastest observed in this 200-item solo sweep, faster than every QVAC configuration (q4_0 803.5, imat 813.2)

Why this build exists

The study's conclusion: the same trained weights become meaningfully different deployment tools once calibration and precision are assigned deliberately. TriStack is the balanced assignment — the only package that stacks all three surviving techniques. The projector sits at its measured q6_K floor (−21.5% projector, quality-neutral at pooled p = 0.49), the LM carries the DomCal-EmbGuard configuration, and the composition was verified as its own artifact: against its EmbGuard parent (identical LM, q8 projector) it measured parity — mean delta ≈ −0.36 point over 16 common cells — at −5.4% package. The result is a build that beats the flagship's score at a smaller size and a lower observed latency, with the best ScienceQA of the study (85.13).

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	9W/7L/1T	13W/4L/0T
Package size Δ (387.4 MiB)	−5.4 MiB (−1.4%)	+39.7 MiB (+11.4%)
Speed Δ (795.4 ms/item)	−17.8 ms/item (−2.2%)	−8.1 ms/item (−1.0%)

Measured results

Wins land on reasoning and knowledge: ScienceQA 85.13 (highest measured), MathVista 47.8 vs 47.1, MMVet 33.5 vs 32.8, MMMU 31.3 vs 29.3, plus MM-IFEval@2048 41.5 vs 41.1 under the identical 2048-token protocol. The named losses stay visible: QVAC q4_k_m-imat keeps OCRBench, DocVQA, ChartQA and InfoVQA; QVAC q4_0 keeps RealWorldQA, MME, MMMU dev and MMVet. The nine wins against the flagship come at −1.4% package and −2.2% observed latency — the balanced point of the release.

Limitations

Exploratory scope: one seed (17), one harness, one GPU. Counts are descriptive rows, not universal-superiority claims.
Composition note: the LM and projector are byte-identical to two sibling artifacts; the package was still benchmarked as its own hash-pinned artifact across the full 17-benchmark suite.
Judged rows: qwen3.6-27b via OpenRouter — an attempted same-model reconstruction of QVAC's judge, not their exact serving protocol.
Parity with the DomCal-EmbGuard parent is a measured mean (−0.36 pt); the largest single-cell delta is MMVet −2.11. DomCal-EmbGuard holds the maximum-quality end.

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.