Image-to-3D
ONNX
GGUF
pbr
texture
normal-map
3d
rigging
qtmesheditor
qtmesh
qtmesh-cloud
conversational
Instructions to use fernandotonon/QtMeshEditor-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
Document all models + link dedicated repos for the trained ones (RMIB, MeshSeg)
Browse files
README.md
CHANGED
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QtMeshEditor downloads these on first use into `<AppData>/ai_models/pbr/`.
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More info in [QtMesh Cloud website](https://qtmesh.dev)
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QtMeshEditor downloads these on first use into `<AppData>/ai_models/pbr/`.
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More info in [QtMesh Cloud website](https://qtmesh.dev)
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## Texture upscaling
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`RealESRGAN_x2plus.onnx`, `RealESRGAN_x4plus.onnx` — 2×/4× super-resolution.
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ONNX re-exports of **Real-ESRGAN** ([xinntao](https://github.com/xinntao/Real-ESRGAN),
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**BSD-3-Clause**). Downloaded into `<AppData>/ai_models/pbr/`. Credit: xinntao.
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## Auto-rig skeleton prediction (UniRig)
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`unirig/encoder.onnx`, `unirig/decoder.onnx`, `unirig/embed.onnx` — ML skeleton
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prediction for unrigged meshes. These are **ONNX re-exports** of
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**[VAST-AI/UniRig](https://huggingface.co/VAST-AI/UniRig)** (SIGGRAPH 2025 — MIT
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code + MIT weights, trained on Articulation-XL2.0 / CC-BY-4.0). Converted with
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`scripts/export-unirig-onnx.py` in the QtMeshEditor repo. Downloaded into
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`<AppData>/ai_models/unirig/`. Credit for the weights: VAST-AI-Research.
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## Animation in-betweening (RMIB) — trained by us
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`inbetween/rmib.onnx` — fills the gap between two keyframes with smooth
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intermediate motion. **Trained from scratch** by the QtMeshEditor project on the
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permissive [CMU Graphics Lab Motion Capture Database](http://mocap.cs.cmu.edu).
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Beats spherical-linear interpolation by >2× on held-out CMU motion.
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**Dedicated repo:** [fernandotonon/QtMeshEditor-rmib-inbetween](https://huggingface.co/fernandotonon/QtMeshEditor-rmib-inbetween).
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Downloaded into `<AppData>/ai_models/inbetween/`. License: CC-BY-4.0.
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## Mesh part segmentation — trained by us
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`segment/meshseg.onnx` — predicts head / torso / arm / leg labels per point
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(PointNet++-style). **Trained from scratch** by the QtMeshEditor project on
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**synthetic, permissively-derived** data (per-vertex labels from rigged-humanoid
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bone weights — CC0). Sidesteps the non-commercial ShapeNet-Part / PartNet
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datasets. **Dedicated repo:** [fernandotonon/QtMeshEditor-mesh-segmentation](https://huggingface.co/fernandotonon/QtMeshEditor-mesh-segmentation).
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Downloaded into `<AppData>/ai_models/segment/`. License: CC-BY-4.0.
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---
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These models power the AI-assisted authoring features in
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**[QtMeshEditor](https://github.com/fernandotonon/QtMeshEditor)** and its
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companion **QtMesh Cloud** ([qtmesh.dev](https://qtmesh.dev)). Each downloads on
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first use and runs locally (offline). Mixed licenses per model as noted above
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(CC0 / BSD-3 / MIT-derived / CC-BY-4.0); see each section + the QtMeshEditor
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`THIRD_PARTY_AI_MODELS.md`.
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