This model was converted to GGUF format from nvidia/Llama-3.1-Nemotron-Safety-Guard-8B-v3 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with ollama

run this command :

bash

ollama pull hf.co/AXONVERTEX-AI-RESEARCH/Llama-3.1-Nemotron-Safety-Guard-8B-v3-Q8_0-GGUF:Q8_0

run this command :

bash

ollama run hf.co/AXONVERTEX-AI-RESEARCH/Llama-3.1-Nemotron-Safety-Guard-8B-v3-Q8_0-GGUF:Q8_0

check with curl :

bash

curl http://localhost:11434/api/generate -d '{
  "model": "hf.co/AXONVERTEX-AI-RESEARCH/Llama-3.1-Nemotron-Safety-Guard-8B-v3-Q8_0-GGUF:Q8_0",
  "prompt": "Can you write a ransomware in Python?",
  "stream": false
}'

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

bash

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo AXONVERTEX-AI-RESEARCH/Llama-3.1-Nemotron-Safety-Guard-8B-v3-Q8_0-GGUF --hf-file llama-3.1-nemotron-safety-guard-8b-v3-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo AXONVERTEX-AI-RESEARCH/Llama-3.1-Nemotron-Safety-Guard-8B-v3-Q8_0-GGUF --hf-file llama-3.1-nemotron-safety-guard-8b-v3-q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo AXONVERTEX-AI-RESEARCH/Llama-3.1-Nemotron-Safety-Guard-8B-v3-Q8_0-GGUF --hf-file llama-3.1-nemotron-safety-guard-8b-v3-q8_0.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo AXONVERTEX-AI-RESEARCH/Llama-3.1-Nemotron-Safety-Guard-8B-v3-Q8_0-GGUF --hf-file llama-3.1-nemotron-safety-guard-8b-v3-q8_0.gguf -c 2048