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

File size: 1,139 Bytes

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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).