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.
- 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
- Llama new
- LM Studio
- Jan
- Ollama
How to use fernandotonon/QtMeshEditor-models with Ollama:
ollama run hf.co/fernandotonon/QtMeshEditor-models:Q8_0
- Unsloth Desktop
- 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
Commit History
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motion library: vertical descent + spine guard + drop bad clips (#838), 46 clips
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t2m: curate — drop arm-broken Quaternius packs, 74 clips (#838)
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t2m: curate — drop Mixamo/Fox/dog + first-frame fix, 96 clips (#838)
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t2m: locomotion cull gate, 109 clean clips (#838)
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t2m v5 corpus CC-BY attribution (#838)
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t2m v5 corpus: 115 clips / 23 actions (#838)
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v6.1 t2m vocab
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v6.1 t2m: stride-gated retrain fixes sideways walk (fwd/side 1.2->2.29) (#837)
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v6 t2m vocab (21 actions)
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v6 t2m: curation-grade 21.7M-param retrain (58972 posture-gated windows, mirror+speed aug, 400ep) + posture best-of-16 scorer (#837)
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t2m v5.1: bake guidance 1.0 — CFG at 2.0 extrapolated past the posture-filtered data into a backward arch (#837)
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t2m v5.1 vocab
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t2m v5.1: retrained on posture-filtered windows (spine/neck-up + hanging-arms gates, 841 windows dropped) — fixes the hunched head-down model walk (#837)
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motion library v6: signed-arm/neck-up/unresolved-arm curation gates (#837 quality follow-up)
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t2m v5: vocab + canonical reference triple (#840/#858)
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t2m v5: flow-matching model, canonical reference triple (#840/#858)
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t2m: preserve v4 CVAE as motion/t2m-vocab-v4.json before the v5 flow model (#858)
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t2m: preserve v4 CVAE as motion/t2m-v4.onnx before the v5 flow model (#858)
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motion library curation: quality scoring + non-biped drop (#855)
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motion library v5 attribution (CC-BY credits)
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motion library v5: 114 bind-referenced clips (23 actions) from the CC0/CC-BY corpus (#839)
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t2m: retrain with corrected 6D rotation packing (#411, PR #792 review)
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t2m v4 vocab: 13 actions, frame=world, fps=30 (#411)
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t2m v4: 30fps world-frame CVAE, absolute decoder, derived-local supervision (#411)
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motion library v4: 47 active-window clips / 15 actions with per-action variety (#411)
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Add t2m vocab (#411)
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Add experimental t2m model (#411)
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Add text-to-motion template clip library (#411 MVP) — 10 CMU-derived actions
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