Instructions to use VisualArchiveSystem/vas-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use VisualArchiveSystem/vas-models 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 VisualArchiveSystem/vas-models:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VisualArchiveSystem/vas-models:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf VisualArchiveSystem/vas-models:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VisualArchiveSystem/vas-models: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 VisualArchiveSystem/vas-models:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf VisualArchiveSystem/vas-models: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 VisualArchiveSystem/vas-models:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VisualArchiveSystem/vas-models:Q4_K_M

Use Docker

docker model run hf.co/VisualArchiveSystem/vas-models:Q4_K_M

LM Studio
Jan
Ollama
How to use VisualArchiveSystem/vas-models with Ollama:
```
ollama run hf.co/VisualArchiveSystem/vas-models:Q4_K_M
```

Unsloth Studio

How to use VisualArchiveSystem/vas-models 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 VisualArchiveSystem/vas-models 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 VisualArchiveSystem/vas-models to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for VisualArchiveSystem/vas-models to start chatting

Atomic Chat new
Docker Model Runner
How to use VisualArchiveSystem/vas-models with Docker Model Runner:
```
docker model run hf.co/VisualArchiveSystem/vas-models:Q4_K_M
```

Lemonade

How to use VisualArchiveSystem/vas-models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VisualArchiveSystem/vas-models:Q4_K_M

Run and chat with the model

lemonade run user.vas-models-Q4_K_M

List all available models

lemonade list

vas-models / README.md

VisualArchiveSystem

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	---
	license: apache-2.0
	tags:
	- vas
	- llama.cpp
	- gguf
	- photography
	- desktop-ai
	---

	# VAS Pro — AI Models

	Pre-quantized GGUF models for VAS Pro (Visual Archive System) local AI assistant.

	## Models

	\| Model \| File \| Size \| Purpose \| Original \|
	\|-------\|------\|------\|---------\|----------\|
	\| Phi-4 Mini \| `phi-4-mini-Q4_K_M.gguf` \| ~2.5 GB \| Fast responses, greetings \| [microsoft/Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) \|
	\| Gemma 3 4B \| `gemma-4-4b-it-Q4_K_M.gguf` \| ~2.5 GB \| Standard tasks, tool calling \| [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it) \|
	\| Qwen2.5-VL 7B \| `qwen3.5-9b-vision-Q4_K_M.gguf` \| ~4.7 GB \| Vision, OCR, image analysis \| [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) \|
	\| MxBAI Embed Large \| `mxbai-embed-large-v1-f16.gguf` \| ~670 MB \| Semantic search embeddings \| [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) \|

	## Usage

	These models are automatically downloaded by VAS Pro on first run. No manual setup required.

	## Quantization

	- Text models use Q4_K_M quantization (best quality/size ratio for 4-bit)
	- Embedding model uses F16 (full precision for maximum retrieval accuracy)

	## License

	Models retain their original licenses:
	- Phi-4 Mini: MIT License
	- Gemma 3: [Gemma Terms of Use](https://ai.google.dev/gemma/terms)
	- Qwen2.5-VL: Apache 2.0
	- MxBAI Embed: Apache 2.0