nvidia

llama-3.2-nv-embedqa-1b-v2

Deprecation in 82dDownloadable

Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.

embedding nemo retriever Retrieval Augmented Generation Text-to-Embedding

Accelerated by DGX Cloud

Select your target environment

Follow the steps below to download and run the NVIDIA NIM inference microservice for this model on your infrastructure of choice.

Step 1
Generate API Key

Step 2
Pull and Run the NIM

$ 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/llama-3.2-nv-embedqa-1b-v2:latest

Step 3
Test the NIM

You can now make a local API call using this curl command:

curl -X "POST" \
  "http://localhost:8000/v1/embeddings" \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "input": ["Hello world"],
    "model": "nvidia/llama-3.2-nv-embedqa-1b-v2",
    "input_type": "query"
}'

For more details on getting started with this NIM, visit the NVIDIA NIM Docs.