How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MRockatansky/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-MTP-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MRockatansky/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/MRockatansky/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-MTP-GGUF:
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

Downloads last month
562
GGUF
Model size
78B params
Architecture
nemotron_h_moe
Hardware compatibility
Log In to add your hardware

4-bit

5-bit

6-bit

16-bit

Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for MRockatansky/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-MTP-GGUF