How to use from
OpenClaw
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 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 "MRockatansky/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-MTP-GGUF:" \
  --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"
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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