cld2labs/Granite-3.3-8b-Instruct

third_party/Dell/model-deployment/Granite-3.3-8b-Instruct/deployment.md

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+## Step 1: Prerequisites to Deploy Granite-3.3-8b-Instruct Model on Xeon with Keycloak
+
+Ensure the Enterprise Inference stack with Keycloak is already deployed before proceeding.
+
+Edit `core/scripts/generate-token.sh` and set your values before sourcing it:
+
+| Variable                  | Description                                                              |
+| ------------------------- | ------------------------------------------------------------------------ |
+| `BASE_URL`                | Hostname of your cluster (e.g. `api.example.com`), without `https://`   |
+| `KEYCLOAK_ADMIN_USERNAME` | Keycloak admin username                                                  |
+| `KEYCLOAK_PASSWORD`       | Keycloak admin password                                                  |
+| `KEYCLOAK_CLIENT_ID`      | Keycloak client ID configured during EI deployment                       |
+
+Then run:
+
+```bash
+export HUGGING_FACE_HUB_TOKEN="your_token_here"
+
+cd ~/Enterprise-Inference
+source core/scripts/generate-token.sh
+```
+
+This exports: `BASE_URL`, `KEYCLOAK_CLIENT_ID`, `KEYCLOAK_CLIENT_SECRET`, and `TOKEN`.
+
+## Step 2: Deploy Granite-3.3-8b-Instruct Model
+
+```bash
+helm install vllm-granite-3-3-instruct-cpu ./core/helm-charts/vllm \
+  --values ./core/helm-charts/vllm/xeon-values.yaml \
+  --set LLM_MODEL_ID="ibm-granite/granite-3.3-8b-instruct" \
+  --set global.HUGGINGFACEHUB_API_TOKEN="$HUGGING_FACE_HUB_TOKEN" \
+  --set ingress.enabled=true \
+  --set ingress.secretname="${BASE_URL}" \
+  --set ingress.host="${BASE_URL}" \
+  --set oidc.client_id="$KEYCLOAK_CLIENT_ID" \
+  --set oidc.client_secret="$KEYCLOAK_CLIENT_SECRET" \
+  --set apisix.enabled=true \
+  --set tensor_parallel_size="1" \
+  --set pipeline_parallel_size="1"
+```
+
+## Step 3: Verify the Deployment
+
+```bash
+kubectl get pods
+kubectl get apisixroutes
+```
+
+Expected Output:
+
+```
+NAME                                              READY   STATUS    RESTARTS
+keycloak-0                                        1/1     Running   0
+keycloak-postgresql-0                             1/1     Running   0
+vllm-granite-3-3-instruct-cpu-<hash>-<hash>       1/1     Running   0
+```
+
+> Note: The pod name suffix `<hash>-<hash>` is auto-generated by Kubernetes and will differ on each deployment. Ensure all pods show `1/1 Running`.
+
+```
+NAME                                         HOSTS
+vllm-granite-3-3-instruct-cpu-apisixroute    api.example.com
+```
+
+## Step 4: Test the Deployed Model
+
+```bash
+curl -k https://${BASE_URL}/granite-3.3-8b-instruct-vllmcpu/v1/completions \
+  -X POST \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $TOKEN" \
+  -d '{
+    "model": "ibm-granite/granite-3.3-8b-instruct",
+    "prompt": "What is Deep Learning?",
+    "max_tokens": 25,
+    "temperature": 0
+  }'
+```
+
+If successful, the model will return a completion response.
+
+## To undeploy the model
+
+```bash
+helm uninstall vllm-granite-3-3-instruct-cpu
+```
+
+## Parameters
+
+| Parameter                                                   | Description                                                                                       |
+| ----------------------------------------------------------- | ------------------------------------------------------------------------------------------------- |
+| `--set LLM_MODEL_ID="ibm-granite/granite-3.3-8b-instruct"` | Defines the target model from **Hugging Face** to deploy.                                         |
+| `--set global.HUGGINGFACEHUB_API_TOKEN="..."`               | Authenticates access to gated or private Hugging Face models. Replace with your own secure token. |
+| `--set ingress.enabled=true`                                | Enables Kubernetes **Ingress** to expose the model service externally.                            |
+| `--set ingress.host="${BASE_URL}"`                          | Public hostname or FQDN for the inference endpoint (maps to your Ingress controller IP).          |
+| `--set ingress.secretname="${BASE_URL}"`                    | Kubernetes **TLS Secret** used for HTTPS termination at the ingress layer.                        |
+| `--set oidc.client_id="..."`                                | Keycloak OIDC client ID used for token-based authentication.                                      |
+| `--set oidc.client_secret="..."`                            | Keycloak OIDC client secret corresponding to the client ID.                                       |
+| `--set apisix.enabled=true`                                 | Enables **APISIX** as the API gateway for routing and authentication.                             |
+| `--set tensor_parallel_size="1"`                            | Number of tensor parallel workers. Set to the number of available CPUs/GPUs per node.            |
+| `--set pipeline_parallel_size="1"`                          | Number of pipeline parallel stages. Typically `1` for single-node deployments.                   |

third_party/Dell/model-deployment/Granite-3.3-8b-Instruct/model-card.md

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+# granite-3.3-8b-instruct
+
+This model uses granite-3.3-8b-instruct, a large-scale instruction-tuned language model developed by IBM as part of the Granite model family. It is optimized for enterprise-grade instruction following, reasoning, summarization, and code-aware natural language tasks, with a strong emphasis on safety, reliability, and governance.
+
+For full details including model specifications, licensing, intended use, safety guidance, and example prompts, please visit the official Hugging Face page: **Official Hugging Face Page**
+
+https://huggingface.co/ibm-granite/granite-3.3-8b-instruct
+
+This model provides inference services only; weights are hosted by Hugging Face under IBM’s open enterprise license.
+
+Ensure compliance with the applicable Granite license terms before using this model.
+
+### Model Attribution
+
+**Developer:**	IBM (Granite Team)
+
+**purpose:** Instruction-tuned enterprise reasoning and language understanding
+
+**Sizes/Variants:**	8B parameters
+
+**Modalities:**	Text → Natural Language + Code
+
+**Parameter Size:** 8 Billion
+
+**Max Context:**	Up to ~128K tokens (depending on backend integration)
+
+**License:** IBM Open License (enterprise-friendly, commercial use permitted with conditions)
+
+### Usage Notice
+
+**By using this model, you agree that:**
+
+- Inputs and outputs are processed by the granite-3.3-8b-instruct model under IBM’s license terms.
+- You are responsible for validating outputs before production deployment.
+- This model should not be used for generating malicious, deceptive, or unsafe content.
+- All enterprise, regulatory, and data-residency requirements must be respected during usage.
+
+### Intended Applications
+
+- Enterprise conversational AI and copilots
+- Retrieval-Augmented Generation (RAG) systems
+- Secure document summarization and classification
+- Knowledge base question answering
+- Business process automation and workflow agents
+- Policy, compliance, and governance assistants
+- Technical documentation analysis and generation
+
+### Limitations
+
+- Requires more compute and memory than lightweight (≤3B) models
+- Not intended for real-time ultra-low-latency edge devices
+- May hallucinate in low-context or ambiguous prompts
+- Should not be used as a fully autonomous decision engine
+- Long-context performance depends on backend configuration
+
+### References
+
+Hugging Face Model Page — https://huggingface.co/ibm-granite/granite-3.3-8b-instruct
+
+