How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "S4MPL3BI4S/Nemotron-3-Nano-4B-Coding-Agent-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": "S4MPL3BI4S/Nemotron-3-Nano-4B-Coding-Agent-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/S4MPL3BI4S/Nemotron-3-Nano-4B-Coding-Agent-GGUF:Q4_K_M
Quick Links

Nemotron 3 Nano 4B Coding Agent — GGUF

This repository contains a Q4_K_M GGUF quantization of a coding and tool-use fine-tune based on unsloth/NVIDIA-Nemotron-3-Nano-4B.

Training

The model was fine-tuned with Unsloth using QLoRA on:

driaforall/pythonic-function-calling

Quantization

  • Format: GGUF
  • Quantization: Q4_K_M
  • Base architecture: Nemotron Hybrid
  • Intended runtime: llama.cpp-compatible applications

License

Use of this model is governed by the NVIDIA Nemotron Open Model License.

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GGUF
Model size
4B params
Architecture
nemotron_h
Hardware compatibility
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