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

Libraries

How to use fernandotonon/QtMeshEditor-models with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="fernandotonon/QtMeshEditor-models",
	filename="caption/SmolVLM-500M-Instruct-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Atomic Chat new
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

QtMeshEditor — AI models

ONNX models used by QtMeshEditor's AI-assisted authoring features.

PBR map synthesis

1x-PBRify_NormalV3.onnx, 1x-PBRify_RoughnessV2.onnx, 1x-PBRify_Height.onnx generate tangent-space normal / roughness / height maps from a single albedo (diffuse) texture.

These are ONNX re-exports of the CC0 SPAN models from Kim2091/PBRify_Remix (LICENSE: CC0-1.0), trained on CC0 content from ambientCG / Poly Haven. Converted with scripts/export-pbrify-onnx.py in the QtMeshEditor repo (spandrel + torch.onnx.export, opset 18). All credit for the weights goes to Kim2091.

License: CC0-1.0 (public domain), same as the source models.
I/O: 1×3×H×W float NCHW in [0,1] → 1×3×H×W out (normal as RGB; roughness/height as RGB, consumed as luminance). Dynamic H/W.

QtMeshEditor downloads these on first use into <AppData>/ai_models/pbr/.

More info in QtMesh Cloud website

Texture upscaling

RealESRGAN_x2plus.onnx, RealESRGAN_x4plus.onnx — 2×/4× super-resolution. ONNX re-exports of Real-ESRGAN (xinntao, BSD-3-Clause). Downloaded into <AppData>/ai_models/pbr/. Credit: xinntao.

Auto-rig skeleton prediction (UniRig)

unirig/encoder.onnx, unirig/decoder.onnx, unirig/embed.onnx — ML skeleton prediction for unrigged meshes. These are ONNX re-exports of VAST-AI/UniRig (SIGGRAPH 2025 — MIT code + MIT weights, trained on Articulation-XL2.0 / CC-BY-4.0). Converted with scripts/export-unirig-onnx.py in the QtMeshEditor repo. Downloaded into <AppData>/ai_models/unirig/. Credit for the weights: VAST-AI-Research.

Animation in-betweening (RMIB) — trained by us

inbetween/rmib.onnx — fills the gap between two keyframes with smooth intermediate motion. Trained from scratch by the QtMeshEditor project on the permissive CMU Graphics Lab Motion Capture Database. Beats spherical-linear interpolation by >2× on held-out CMU motion. Dedicated repo: fernandotonon/QtMeshEditor-rmib-inbetween. Downloaded into <AppData>/ai_models/inbetween/. License: CC-BY-4.0.

Mesh part segmentation — trained by us

segment/meshseg.onnx — predicts head / torso / left+right arm / left+right leg labels per point (PointNet++-style, two kNN aggregation blocks). Trained from scratch (v2) by the QtMeshEditor project on surface-sampled synthetic bodies we own (humanoid incl. chibi, quadruped, biped-with-tail — exact by-construction labels, CC0) mixed with CC0 rigged characters mined for exact rig-derived labels (Quaternius packs). 94.7% per-vertex accuracy on rig-truth eval (v1: 31.5%). Dedicated repo (full model card + eval data): fernandotonon/QtMeshEditor-mesh-segmentation. Downloaded into <AppData>/ai_models/segment/. License: CC-BY-4.0.

These models power the AI-assisted authoring features in QtMeshEditor and its companion QtMesh Cloud (qtmesh.dev). Each downloads on first use and runs locally (offline). Mixed licenses per model as noted above (CC0 / BSD-3 / MIT-derived / CC-BY-4.0); see each section + the QtMeshEditor THIRD_PARTY_AI_MODELS.md.

Downloads last month: -

GGUF

Model size

0.4B params

Architecture

llama

Hardware compatibility

8-bit

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