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
- llama.cpp
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
| { | |
| "schema_version": 2, | |
| "updated_at": "2026-04-09T16:00:00Z", | |
| "models": [ | |
| { | |
| "id": "phi4-mini", | |
| "version": "1.0.0", | |
| "tier": "Fast", | |
| "filename": "phi-4-mini-Q4_K_M.gguf", | |
| "url": "https://huggingface.co/VisualArchiveSystem/vas-models/resolve/main/phi-4-mini-Q4_K_M.gguf", | |
| "sha256": "01999f17d9bdcf1a13f40f75c5d4fef23bd1ae87ae1ad4e93cbdb0f9f7d1c0c2", | |
| "size_bytes": 2491874688, | |
| "min_ram_gb": 4, | |
| "required": true, | |
| "changelog": "Initial release" | |
| }, | |
| { | |
| "id": "gemma4", | |
| "version": "1.0.0", | |
| "tier": "Standard", | |
| "filename": "gemma-4-4b-it-Q4_K_M.gguf", | |
| "url": "https://huggingface.co/VisualArchiveSystem/vas-models/resolve/main/gemma-4-4b-it-Q4_K_M.gguf", | |
| "sha256": "49960302edb17b95d7d84bb7e0e2e6915e98ac52dfb2fa24d53d5af5a268ae94", | |
| "size_bytes": 2489758112, | |
| "min_ram_gb": 6, | |
| "required": true, | |
| "changelog": "Initial release - Gemma 3 4B (Gemma 4 GGUF pending)" | |
| }, | |
| { | |
| "id": "qwen3.5-vision", | |
| "version": "1.0.0", | |
| "tier": "Vision", | |
| "filename": "qwen3.5-9b-vision-Q4_K_M.gguf", | |
| "url": "https://huggingface.co/VisualArchiveSystem/vas-models/resolve/main/qwen3.5-9b-vision-Q4_K_M.gguf", | |
| "sha256": "d16776dc5bea79ad0f2d3fdc3e6f2d96cdee924d63acac9ea7e9d2e8d36ec503", | |
| "size_bytes": 4683072384, | |
| "min_ram_gb": 8, | |
| "required": true, | |
| "changelog": "Initial release - Qwen 2.5 VL 7B (Qwen 3.5 GGUF pending)" | |
| }, | |
| { | |
| "id": "mxbai-embed", | |
| "version": "1.0.0", | |
| "tier": "Embedding", | |
| "filename": "mxbai-embed-large-v1-f16.gguf", | |
| "url": "https://huggingface.co/VisualArchiveSystem/vas-models/resolve/main/mxbai-embed-large-v1-f16.gguf", | |
| "sha256": "819c2adf65ea72bb16af37a4a86ce0d6b2c5e99ac19f1e4db8c731f4b3a79c3d", | |
| "size_bytes": 669603712, | |
| "min_ram_gb": 2, | |
| "required": true, | |
| "changelog": "Initial release" | |
| } | |
| ] | |
| } | |