An MOE LLM that follows instructions, completes requests, and generates creative text.
Follow the steps below to download and run the NVIDIA NIM inference microservice for this model on your infrastructure of choice.
Install the NVIDIA GPU Operator
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia \ && helm repo update helm install nim-operator nvidia/k8s-nim-operator --create-namespace -n nim-operator
kubectl create ns nim-service kubectl create secret -n nim-service docker-registry ngc-secret \ --docker-server=nvcr.io \ --docker-username='$oauthtoken' \ --docker-password=<PASTE_API_KEY_HERE> kubectl create secret -n nim-service generic ngc-api-secret \ --from-literal=NGC_API_KEY=<PASTE_API_KEY_HERE>
Ensure that a default StorageClass exists in the cluster. If none is present, create an appropriate StorageClass before proceeding.
NOTE:
model-size
based on the model and GPU type as described here.nvidia.com/gpu: 1
based on the model and number of GPU requirementsapiVersion: apps.nvidia.com/v1alpha1 kind: NIMService metadata: name: mixtral-8x7b-instruct-v01 namespace: nim-service spec: image: repository: nvcr.io/nim/mistralai/mixtral-8x7b-instruct-v01 tag: latest pullPolicy: IfNotPresent pullSecrets: - ngc-secret authSecret: ngc-api-secret storage: pvc: create: true size: "model-size" volumeAccessMode: "ReadWriteOnce" replicas: 1 resources: limits: nvidia.com/gpu: 1 expose: service: type: ClusterIP port: 8000
kubectl run --rm -it -n default curl --image=curlimages/curl:latest -- ash
curl -X "POST" \ 'http://mixtral-8x7b-instruct-v01.nim-service:8000/v1/chat/completions' \ -H 'Accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "model": "mistralai/mixtral-8x7b-instruct-v0-1", "messages": [ { "content":"What should I do for a 4 day vacation at Cape Hatteras National Seashore?", "role": "user" }], "top_p": 1, "n": 1, "max_tokens": 1024, "stream": false, "frequency_penalty": 0.0, "stop": ["STOP"] }'
For more details on getting started with this NIM, visit the NVIDIA NIM Operator Docs.