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 / NOTICE

simoneschiavoi

Add quantized LM + projector weights and NOTICE (Apache-2.0 derivative of QVAC VisionPsy-Nano-460M)

db0ef70 verified 2 days ago

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	NOTICE — upstream attributions

	Base model
	----------
	VisionPsy-Nano-460M and VisionPsy-Nano-460M-Flash by QVAC.
	HF: https://huggingface.co/qvac/VisionPsy-Nano-460M
	Code: https://github.com/tether-ai-research/qvac-visionpsy-nano
	Blog: https://huggingface.co/blog/qvac/visionpsy

	All MY_* GGUF artifacts in this repository's GitHub Releases are quantized
	derivatives of QVAC's model weights (domain-calibrated imatrix quantization
	and/or embedding-protected quantization). They inherit QVAC's license terms;
	consult the upstream repository before redistribution. QVAC's own artifact
	names appear in tables and charts only as within-harness references.

	Runtime
	-------
	llama.cpp with QVAC's patched mtmd fork (multimodal batch inference).
	https://github.com/ggml-org/llama.cpp

	Evaluation
	----------
	VLMEvalKit (strict judge-free scoring), MMStar, ScienceQA_TEST, OCRBench,
	PerceptionBench (MoonshotAI), MMMU, MMBench.
	https://github.com/open-compass/VLMEvalKit
	https://github.com/MoonshotAI/PerceptionBench

	Judges (via OpenRouter): qwen3.6-27b (QVAC's published judge protocol),
	gpt-oss-120b (PerceptionBench scoring).