Instructions to use LocalAI-io/GEM-X-GGUF 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 LocalAI-io/GEM-X-GGUF 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 LocalAI-io/GEM-X-GGUF:F32 # Run inference directly in the terminal: llama cli -hf LocalAI-io/GEM-X-GGUF:F32
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf LocalAI-io/GEM-X-GGUF:F32 # Run inference directly in the terminal: llama cli -hf LocalAI-io/GEM-X-GGUF:F32
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 LocalAI-io/GEM-X-GGUF:F32 # Run inference directly in the terminal: ./llama-cli -hf LocalAI-io/GEM-X-GGUF:F32
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 LocalAI-io/GEM-X-GGUF:F32 # Run inference directly in the terminal: ./build/bin/llama-cli -hf LocalAI-io/GEM-X-GGUF:F32
Use Docker
docker model run hf.co/LocalAI-io/GEM-X-GGUF:F32
- LM Studio
- Jan
- Ollama
How to use LocalAI-io/GEM-X-GGUF with Ollama:
ollama run hf.co/LocalAI-io/GEM-X-GGUF:F32
- Unsloth Desktop
- Docker Model Runner
How to use LocalAI-io/GEM-X-GGUF with Docker Model Runner:
docker model run hf.co/LocalAI-io/GEM-X-GGUF:F32
- Lemonade
How to use LocalAI-io/GEM-X-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LocalAI-io/GEM-X-GGUF:F32
Run and chat with the model
lemonade run user.GEM-X-GGUF-F32
List all available models
lemonade list
- Atomic Chat
Download USAGE.md from LocalAI-io/GEM-X-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 1.38 kB
-
https://huggingface.co/LocalAI-io/GEM-X-GGUF/resolve/main/USAGE.md
- Command line
-
hf download hf://LocalAI-io/GEM-X-GGUF/USAGE.md
-
curl -L -o USAGE.md https://huggingface.co/LocalAI-io/GEM-X-GGUF/resolve/main/USAGE.md
Use these models with gem-x.cpp
These custom GGUF models require the gem-x.cpp runtime. They do not run in llama.cpp. Build the runtime and browser demo using your source checkout's README instructions, then download the models from its repository root:
hf download LocalAI-io/GEM-X-GGUF gem-x-contact-f32.gguf vitpose-f32.gguf yolox-f32.gguf --local-dir generated/reference
./demo/gemx-demo --threads 8
Open http://localhost:8098 and choose Live webcam, then Start live. Keep the camera still and your full body in view.
Offline video additionally requires the SAM3D submodule's Body worker and three model files. Build the worker as described in the runtime's demo guide, then download its models to the default locations:
hf download LocalAI-io/sam-3d-body-dinov3-GGUF body-dinov3-f32.gguf body-pose-branch-f32.gguf --local-dir sam3d.cpp/generated/models/sam-3d-body-dinov3
hf download LocalAI-io/sam-3d-body-dinov3-GGUF mhr-lod1-f32.gguf --local-dir sam3d.cpp/generated/models/mhr-public
Choose Offline video, select a clip and press Build motion. Completed motion can be downloaded as an animated skeleton GLB.
hf is supplied by the optional huggingface_hub Python package. Python is
only needed to download or convert models, not for the native runtime. All
components retain the licenses documented in their model repositories.