How to use from
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 andersonscode/Nemotron-3-Nano-4B-oss-20b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf andersonscode/Nemotron-3-Nano-4B-oss-20b:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf andersonscode/Nemotron-3-Nano-4B-oss-20b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf andersonscode/Nemotron-3-Nano-4B-oss-20b:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf andersonscode/Nemotron-3-Nano-4B-oss-20b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf andersonscode/Nemotron-3-Nano-4B-oss-20b:Q4_K_M
Use Docker
docker model run hf.co/andersonscode/Nemotron-3-Nano-4B-oss-20b:Q4_K_M
Quick Links

Configuration Parsing Warning:Invalid JSON for config file config.json

Nemotron-3-Nano-4B-oss-20b : GGUF

This model was finetuned and converted to GGUF format using Unsloth.

Example usage:

  • For text only LLMs: llama-cli -hf andersonscode/Nemotron-3-Nano-4B-oss-20b --jinja
  • For multimodal models: llama-mtmd-cli -hf andersonscode/Nemotron-3-Nano-4B-oss-20b --jinja

Available Model files:

  • NVIDIA-Nemotron-3-Nano-4B.Q8_0.gguf
  • NVIDIA-Nemotron-3-Nano-4B.Q4_K_M.gguf This was trained 2x faster with Unsloth
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