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

The models used by QtMeshEditor's AI-assisted authoring features. This repo is what the app downloads from at runtime (each model on first use, then it runs locally/offline).

Licenses are per model — see the table and each dedicated repo. The dedicated repos carry the full model cards (I/O contracts, provenance, reproduction scripts) for anyone who wants the converted weights standalone.

folder / files	feature	dedicated repo (full card)	license
`1x-PBRify_*.onnx`	PBR maps from albedo	QtMeshEditor-pbrify-onnx	CC0-1.0
`RealESRGAN_x{2,4}plus.onnx`	texture upscaling	QtMeshEditor-realesrgan-onnx	BSD-3-Clause
`unirig/`	auto-rig skeleton prediction	QtMeshEditor-unirig-onnx	MIT
`skintokens/`	ML skin-weight prediction	QtMeshEditor-skintokens-onnx	MIT
`triposr/`	image → 3D (triplane)	QtMeshEditor-triposr-onnx	MIT
`triposg/`	image → 3D (rectified-flow DiT)	QtMeshEditor-triposg-onnx	MIT
`inbetween/rmib.onnx`	animation in-betweening (ours)	QtMeshEditor-rmib-inbetween	CC-BY-4.0
`motion/`	text-to-motion + clip library (ours)	QtMeshEditor-t2m	CC0-1.0
`segment/meshseg.onnx`	mesh part segmentation (ours)	QtMeshEditor-mesh-segmentation	CC-BY-4.0
`rembg/u2net.onnx`	background removal	QtMeshEditor-u2net-onnx	Apache-2.0
`caption/SmolVLM-500M-*.gguf`	image captioning	QtMeshEditor-smolvlm-gguf	Apache-2.0

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 texture. ONNX re-exports of the CC0 SPAN models from Kim2091/PBRify_Remix — all credit to Kim2091. I/O: 1×3×H×W float [0,1] → 1×3×H×W, dynamic H/W.

Texture upscaling

RealESRGAN_x2plus.onnx, RealESRGAN_x4plus.onnx — 2×/4× super-resolution. ONNX re-exports of Real-ESRGAN (xinntao, BSD-3-Clause). Credit: xinntao.

Auto-rig skeleton prediction (UniRig)

unirig/{encoder,decoder,embed}.onnx — autoregressive skeleton prediction for unrigged meshes. ONNX re-export of the skeleton stage of VAST-AI/UniRig (SIGGRAPH 2025, MIT code + weights). Credit: VAST-AI-Research.

ML skin weights (SkinTokens / TokenRig)

skintokens/ — five ONNX graphs + manifest; QtMeshEditor's default skinner. ONNX re-export of VAST-AI SkinTokens/TokenRig (MIT code + weights, Qwen3-0.6B backbone). decoder.onnx.data holds the LM weights as external data (ORT can't parse the >1.6 GB single-file proto). Credit: VAST-AI-Research.

Image → 3D

triposr/ — TripoSR (Tripo AI + Stability AI, MIT): triplane encoder (fp32 + int8 tiers) + per-point density/colour decoder.
triposg/ — TripoSG (VAST-AI, SIGGRAPH 2025, MIT): DINOv2 image encoder, rectified-flow DiT step graph (fp32 external weights; the int8 tier here is deprecated — it degrades to blobs over the CFG flow loop), VAE latent + field-decoder graphs. Geometry-only; colour comes from TripoSR's colour field.
rembg/u2net.onnx — U²-Net saliency for background removal (Apache-2.0, the rembg model).

Animation in-betweening (RMIB) — trained by us

inbetween/rmib.onnx — fills the gap between two keyframes. Trained from scratch on the permissive CMU MoCap database; beats slerp by >2× on held-out CMU motion. License: CC-BY-4.0.

Text-to-motion — trained by us (experimental)

motion/t2m.onnx + motion/t2m-vocab.json — CVAE transformer, text keyword → 22-joint world-frame clip; motion/motion-library.json — the curated CMU template-clip library that is the shipped default. License: CC0-1.0.

Mesh part segmentation — trained by us

segment/meshseg.onnx — per-point head/torso/arm/leg labels (PointNet++-style). Trained on synthetic bodies we own + CC0 rigged characters (Quaternius). 94.7% per-vertex accuracy on rig-truth eval. License: CC-BY-4.0.

Image captioning

caption/SmolVLM-500M-Instruct-Q8_0.gguf + mmproj — quantized SmolVLM-500M-Instruct (HuggingFaceTB, Apache-2.0) for llama.cpp-based captioning. Credit: Hugging Face TB.

These models power the AI-assisted authoring features in QtMeshEditor and its companion QtMesh Cloud (qtmesh.dev). Provenance and licensing decisions are documented in the QtMeshEditor repo's THIRD_PARTY_AI_MODELS.md.

Downloads last month: 24

GGUF

Model size

0.4B params

Architecture

llama

Hardware compatibility

8-bit

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