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

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

Atomic Chat

VisualArchiveSystem commited on Apr 9

Commit

2d28770

verified ·

1 Parent(s): 255708b

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +39 -0

README.md ADDED Viewed

	@@ -0,0 +1,39 @@

+---
+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