Follow the steps below to download and run the NVIDIA NIM inference microservice for this model on your infrastructure of choice.
$ docker login nvcr.io
Username: $oauthtoken
Password: <PASTE_API_KEY_HERE>
Pull and run the NVIDIA NIM with the command below. This will download the optimized model for your infrastructure.
export NGC_API_KEY=<PASTE_API_KEY_HERE>
export LOCAL_NIM_CACHE=~/.cache/nim
mkdir -p "$LOCAL_NIM_CACHE"
docker run -it --rm \
--gpus all \
--shm-size=16GB \
-e NGC_API_KEY \
-v "$LOCAL_NIM_CACHE:/opt/nim/.cache" \
-u $(id -u) \
-p 8000:8000 \
nvcr.io/nim/nvidia/nv-rerankqa-mistral-4b-v3:latest
You can now make a local API call using this curl command:
curl -X "POST" \
"http://localhost:8000/v1/ranking" \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"model": "nvidia/nv-rerankqa-mistral-4b-v3",
"query": {"text": "which way should i go?"},
"passages": [
{"text": "two roads diverged in a yellow wood, and sorry i could not travel both and be one traveler, long i stood and looked down one as far as i could to where it bent in the undergrowth;"},
{"text": "then took the other, as just as fair, and having perhaps the better claim because it was grassy and wanted wear, though as for that the passing there had worn them really about the same,"},
{"text": "and both that morning equally lay in leaves no step had trodden black. oh, i marked the first for another day! yet knowing how way leads on to way i doubted if i should ever come back."},
{"text": "i shall be telling this with a sigh somewhere ages and ages hense: two roads diverged in a wood, and i, i took the one less traveled by, and that has made all the difference."}
],
"truncate": "END"
}'
For more details on getting started with this NIM, visit the NVIDIA NIM Docs.