Instructions to use fernandotonon/QtMeshEditor-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 fernandotonon/QtMeshEditor-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 fernandotonon/QtMeshEditor-models:Q8_0
# Run inference directly in the terminal:
llama cli -hf fernandotonon/QtMeshEditor-models:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fernandotonon/QtMeshEditor-models:Q8_0
# Run inference directly in the terminal:
llama cli -hf fernandotonon/QtMeshEditor-models:Q8_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 fernandotonon/QtMeshEditor-models:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf fernandotonon/QtMeshEditor-models:Q8_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 fernandotonon/QtMeshEditor-models:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fernandotonon/QtMeshEditor-models:Q8_0

Use Docker

docker model run hf.co/fernandotonon/QtMeshEditor-models:Q8_0

LM Studio
Jan
Ollama
How to use fernandotonon/QtMeshEditor-models with Ollama:
```
ollama run hf.co/fernandotonon/QtMeshEditor-models:Q8_0
```

Unsloth Studio

How to use fernandotonon/QtMeshEditor-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 fernandotonon/QtMeshEditor-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 fernandotonon/QtMeshEditor-models to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use fernandotonon/QtMeshEditor-models with Docker Model Runner:
```
docker model run hf.co/fernandotonon/QtMeshEditor-models:Q8_0
```

Lemonade

How to use fernandotonon/QtMeshEditor-models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fernandotonon/QtMeshEditor-models:Q8_0

Run and chat with the model

lemonade run user.QtMeshEditor-models-Q8_0

List all available models

lemonade list

Atomic Chat

QtMeshEditor-models / facerig /face_landmarks.onnx

fernandotonon

Upload facerig/face_landmarks.onnx with huggingface_hub

79483df verified 27 days ago

Download

History Blame Contribute Delete

4.92 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 7f32b677aafebf602cf7ac4d0b56b92a1c341e3c93f470aacf88d68fe3698f17
Size of remote file:: 4.92 MB
SHA256:: d16e5a55e6a480284d468ee32469692464049518c311662ab3956681de31e3e9

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.