Instructions to use vigneshk0702/qanimator-rig-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use vigneshk0702/qanimator-rig-7b 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 vigneshk0702/qanimator-rig-7b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vigneshk0702/qanimator-rig-7b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vigneshk0702/qanimator-rig-7b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vigneshk0702/qanimator-rig-7b:Q4_K_M

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 vigneshk0702/qanimator-rig-7b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf vigneshk0702/qanimator-rig-7b:Q4_K_M

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 vigneshk0702/qanimator-rig-7b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vigneshk0702/qanimator-rig-7b:Q4_K_M

Use Docker

docker model run hf.co/vigneshk0702/qanimator-rig-7b:Q4_K_M

LM Studio
Jan
Ollama
How to use vigneshk0702/qanimator-rig-7b with Ollama:
```
ollama run hf.co/vigneshk0702/qanimator-rig-7b:Q4_K_M
```
Unsloth Desktop

How to use vigneshk0702/qanimator-rig-7b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vigneshk0702/qanimator-rig-7b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "vigneshk0702/qanimator-rig-7b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use vigneshk0702/qanimator-rig-7b with Docker Model Runner:
```
docker model run hf.co/vigneshk0702/qanimator-rig-7b:Q4_K_M
```

Lemonade

How to use vigneshk0702/qanimator-rig-7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vigneshk0702/qanimator-rig-7b:Q4_K_M

Run and chat with the model

lemonade run user.qanimator-rig-7b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use vigneshk0702/qanimator-rig-7b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vigneshk0702/qanimator-rig-7b:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default vigneshk0702/qanimator-rig-7b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vigneshk0702/qanimator-rig-7b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vigneshk0702/qanimator-rig-7b:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "vigneshk0702/qanimator-rig-7b:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

QAnimator rig model (Qwen2.5-Coder-7B-Instruct fine-tune)

Status: v1, experimental. It reliably writes valid rig files (correct meta/draw structure, lint-clean, ~50 tokens/s on an 8 GB laptop GPU), but its drawings are still weak, especially for characters. Use it with "fallback":"claude-code" so rigs that lint but draw too little are handed to a larger model. Inference needs repeat_penalty ≈ 1.15 (set in the Modelfile) or it can loop.

Writes QAnimator rigs: one JavaScript module per character, object, environment or effect that draws itself in a hand-drawn ballpoint-pen style (meta + draw(ctx,p) on the @qa/engine pen API: stroke, hatch, fillShape, path, palette tokens, draw-on stages, puppet params). It is the fast local rig author for QAnimator Studio; it does not generate images.

Use

ollama create qa-rig -f Modelfile      # FROM ./qa-rig-q4_k_m.gguf

In qanimator.config.json:

{"providers":{"qa-rig":{"type":"ollama","model":"qa-rig","rigPrompt":"compact","fallback":"claude-code","numCtx":12288}},
 "tasks":{"rig":"qa-rig"}}

Prompt format (system + user) is buildCompactRigPrompt in author/rigfast.mjs: the rig id, kind, a description and, optionally, reference geometry traced from an image by tools/trace.mjs.

Training

QLoRA (r=32) with Unsloth, 2 epochs, max length 8192, on 914 examples (49 held out).
Data: the hand-made QAnimator rigs, rigs approved in the app, and SVG / Lottie items from MMSVG-Illustration and MMLottie-2M converted to rigs by a deterministic converter (tools/import-dataset.mjs) and kept only if they pass the rig linter.
Final train loss 0.3333, eval loss 0.3368.

Licence

Part of the training data is CC BY-NC-SA 4.0 (MMSVG-Illustration, MMLottie-2M), so this model is released under CC BY-NC-SA 4.0: non-commercial use, share-alike. The base model's own licence also applies.

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GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

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Model tree for vigneshk0702/qanimator-rig-7b

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Finetuned

unsloth/Qwen2.5-Coder-7B-Instruct

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this model

vigneshk0702
/

qanimator-rig-7b

QAnimator rig model (Qwen2.5-Coder-7B-Instruct fine-tune)

Use

Training

Licence

Model tree for vigneshk0702/qanimator-rig-7b

Datasets used to train vigneshk0702/qanimator-rig-7b