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cld2labs/Granite-3.2-2b-Instruct (#97)
* feat: add Granite-3.2-2b-Instruct model card and deployment guide for Dell EI Signed-off-by: arpannookala-12 <ganesh.arpan.nookala@cloud2labs.com> * update granite 3.2 2b instruct deployment.md * Remove README.md from model-deployment folder --------- Signed-off-by: arpannookala-12 <ganesh.arpan.nookala@cloud2labs.com> Co-authored-by: Harika <codewith3@gmail.com>
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‎third_party/Dell/model-deployment/Granite-3.2-2b-Instruct/deployment.md‎

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## Step 1: Prerequisites to Deploy Granite-3.2-2b-Instruct Model on Xeon with Keycloak
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Ensure the Enterprise Inference stack with Keycloak is already deployed before proceeding.
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Edit `core/scripts/generate-token.sh` and set your values before sourcing it:
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| Variable | Description |
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| ------------------------- | ------------------------------------------------------------------------ |
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| `BASE_URL` | Hostname of your cluster (e.g. `api.example.com`), without `https://` |
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| `KEYCLOAK_ADMIN_USERNAME` | Keycloak admin username |
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| `KEYCLOAK_PASSWORD` | Keycloak admin password |
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| `KEYCLOAK_CLIENT_ID` | Keycloak client ID configured during EI deployment |
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Then run:
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```bash
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export HUGGING_FACE_HUB_TOKEN="your_token_here"
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cd ~/Enterprise-Inference
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source core/scripts/generate-token.sh
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```
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This exports: `BASE_URL`, `KEYCLOAK_CLIENT_ID`, `KEYCLOAK_CLIENT_SECRET`, and `TOKEN`.
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## Step 2: Deploy Granite-3.2-2b-Instruct Model
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```bash
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helm install vllm-granite-3-2-instruct ./core/helm-charts/vllm \
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--values ./core/helm-charts/vllm/xeon-values.yaml \
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--set LLM_MODEL_ID="ibm-granite/granite-3.2-2b-instruct" \
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--set global.HUGGINGFACEHUB_API_TOKEN="$HUGGING_FACE_HUB_TOKEN" \
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--set ingress.enabled=true \
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--set ingress.secretname="${BASE_URL}" \
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--set ingress.host="${BASE_URL}" \
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--set oidc.client_id="$KEYCLOAK_CLIENT_ID" \
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--set oidc.client_secret="$KEYCLOAK_CLIENT_SECRET" \
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--set apisix.enabled=true \
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--set tensor_parallel_size="1" \
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--set pipeline_parallel_size="1"
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```
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## Step 3: Verify the Deployment
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```bash
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kubectl get pods
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kubectl get apisixroutes
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```
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Expected Output:
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```
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NAME READY STATUS RESTARTS
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keycloak-0 1/1 Running 0
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keycloak-postgresql-0 1/1 Running 0
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vllm-granite-3-2-instruct-<hash>-<hash> 1/1 Running 0
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```
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> Note: The pod name suffix `<hash>-<hash>` is auto-generated by Kubernetes and will differ on each deployment. Ensure all pods show `1/1 Running`.
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```
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NAME HOSTS
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vllm-granite-3-2-instruct-apisixroute api.example.com
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```
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## Step 4: Test the Deployed Model
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```bash
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curl -k https://${BASE_URL}/granite-3.2-2b-instruct-vllmcpu/v1/completions \
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-X POST \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer $TOKEN" \
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-d '{
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"model": "ibm-granite/granite-3.2-2b-instruct",
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"prompt": "What is Deep Learning?",
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"max_tokens": 25,
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"temperature": 0
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}'
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```
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If successful, the model will return a completion response.
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## To undeploy the model
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```bash
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helm uninstall vllm-granite-3-2-instruct
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```
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## Parameters
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| Parameter | Description |
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| ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------- |
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| `--set LLM_MODEL_ID="ibm-granite/granite-3.2-2b-instruct"` | Defines the target model from **Hugging Face** to deploy. |
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| `--set global.HUGGINGFACEHUB_API_TOKEN="..."` | Authenticates access to gated or private Hugging Face models. Replace with your own secure token. |
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| `--set ingress.enabled=true` | Enables Kubernetes **Ingress** to expose the model service externally. |
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| `--set ingress.host="${BASE_URL}"` | Public hostname or FQDN for the inference endpoint (maps to your Ingress controller IP). |
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| `--set ingress.secretname="${BASE_URL}"` | Kubernetes **TLS Secret** used for HTTPS termination at the ingress layer. |
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| `--set oidc.client_id="..."` | Keycloak OIDC client ID used for token-based authentication. |
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| `--set oidc.client_secret="..."` | Keycloak OIDC client secret corresponding to the client ID. |
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| `--set apisix.enabled=true` | Enables **APISIX** as the API gateway for routing and authentication. |
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| `--set tensor_parallel_size="1"` | Number of tensor parallel workers. Set to the number of available CPUs/GPUs per node. |
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| `--set pipeline_parallel_size="1"` | Number of pipeline parallel stages. Typically `1` for single-node deployments. |

‎third_party/Dell/model-deployment/Granite-3.2-2b-Instruct/model-card.md‎

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# granite-3.2-2b-instruct
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This model uses ibm-granite/granite-3.2-2b-instruct, a modern, lightweight instruction-tuned large language model developed by IBM Granite Team. It is designed for efficient reasoning, instruction-following, and enterprise-grade AI workloads such as summarization, problem solving, structured response generation, and conversational AI.
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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**
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https://huggingface.co/ibm-granite/granite-3.2-2b-instruct
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This model provides inference services using IBM’s open-weight Granite architecture and is distributed under the Apache 2.0 license.
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### Model Attribution
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**Developer:** IBM (Granite Team)
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**purpose:** General-purpose instruction-following, reasoning, and enterprise AI workloads
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**Sizes/Variants:** Granite 3.2 family – includes 2B, 8B, and larger variants optimized for different deployment scales
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**Modalities:** Text → Text (natural language, reasoning, structured responses, code-related logic)
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**Parameter Size:** ~2 billion parameters (dense)
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**Max Context:** Up to ~128K tokens (depending on backend and serving configuration)
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**License:** Apache 2.0 (open-weight, commercially usable)
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### Usage Notice
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**By using this model, you agree that:**
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- Inputs and outputs are processed by the IBM Granite 3.2 2B Instruct model.
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- You accept and comply with the Apache 2.0 License.
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- Generated outputs must be reviewed for accuracy, safety, and compliance prior to production use.
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- The model must not be used for malicious activities or violation of applicable laws or policies.
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- Deployment in high-risk or regulated environments should include appropriate validation and guardrails.
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### Intended Applications
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- Conversational AI and enterprise assistants
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- Instruction-following automation
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- Reasoning and decision-support systems
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- Long-document summarization and analysis
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- Retrieval-Augmented Generation (RAG) systems
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- Classification, extraction, and knowledge workflows
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- Code-related reasoning and structured logic explanation
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- Multilingual AI applications
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### Limitations
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- May still produce factual inaccuracies or hallucinated responses
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- Performance may vary depending on prompt quality and domain complexity
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- Not a replacement for expert decision-making in regulated environments
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- Requires human validation in sensitive or critical applications
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- Smaller size may reduce performance in highly complex multimodal tasks compared to very large models
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### References
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IBM Granite 3.2 Model Documentation
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https://www.ibm.com/architectures/product-guides/granite-32
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Hugging Face Model Page – IBM Granite 3.2 2B Instruct
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https://huggingface.co/ibm-granite/granite-3.2-2b-instruct
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IBM Granite Announcement Blog
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https://www.ibm.com/new/announcements/ibm-granite-3-2-open-source-reasoning-and-vision
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