How to use from
Pi
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MRockatansky/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-MTP-GGUF:
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": "MRockatansky/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-MTP-GGUF:"
        }
      ]
    }
  }
}
Run Pi
# Start Pi in your project directory:
pi
Quick Links

GGUFs made with a non-mainline build of llama.cpp: nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16 (official), converted with convert_hf_to_gguf.py at Premshay/llama.cpp@731368b5d (branch nemotron-super-mtp, stacked on PR #25444), quantized with llama-quantize from the same build

The GGUFs should be loaded with the following fork of llama.cpp on the nemotron-super-mtp branch: https://github.com/Premshay/llama.cpp/tree/nemotron-super-mtp

Just an experiment to try the forked llama.cpp built on top of PR#25444 to support this model's built in MTP heads. Use the fork of llama.cpp above with --spec-type draft-mtp enabled in llama-server to provide a nice speedup in token generation.

The contributor of the fork explained their methodology on the model card below. I just did the same but with Nemotron Puzzle BF16 weights instead of using Nemotron Super: https://huggingface.co/Premshay/Nemotron-3-Super-120B-A12B-MTP-GGUF

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Architecture
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