Update README.md

README.md

@@ -21,13 +21,21 @@ tasks:
 - text-generation
 provider: OpenAI
 license_link: https://www.apache.org/licenses/LICENSE-2.0
+validated_on:
+  - RHOAI 2.25
+  - RHAIIS 3.2.2
 ---
 
-<p align="center">
-  <img alt="gpt-oss-20b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-20b.svg">
-</p>
+<h1 style="display: flex; align-items: center; gap: 10px; margin: 0;">
+  gpt-oss-20b
+  <img src="https://www.redhat.com/rhdc/managed-files/Catalog-Validated_model_0.png" alt="Model Icon" width="40" style="margin: 0; padding: 0;" />
+</h1>
+  
+<a href="https://www.redhat.com/en/products/ai/validated-models" target="_blank" style="margin: 0; padding: 0;">
+<img src="https://www.redhat.com/rhdc/managed-files/Validated_badge-Dark.png" alt="Validated Badge" width="250" style="margin: 0; padding: 0;" />
+</a>
 
-<p align="center">
+<p>
   <a href="https://gpt-oss.com"><strong>Try gpt-oss</strong></a> ·
   <a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Guides</strong></a> ·
   <a href="https://arxiv.org/abs/2508.10925"><strong>Model card</strong></a> ·
@@ -46,7 +54,7 @@ Both models were trained on our [harmony response format](https://github.com/ope
 
 
 > [!NOTE]
-> This model card is dedicated to the smaller `gpt-oss-20b` model. Check out [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) for the larger model.
+> This model card is dedicated to the smaller `gpt-oss-20b` model. Check out [`gpt-oss-120b`](https://huggingface.co/RedHatAI/gpt-oss-120b) for the larger model.
 
 # Highlights
 
@@ -121,6 +129,142 @@ vllm serve openai/gpt-oss-20b
 
 [Learn more about how to use gpt-oss with vLLM.](https://cookbook.openai.com/articles/gpt-oss/run-vllm)
 
+<details>
+  <summary>Deploy on <strong>Red Hat AI Inference Server</strong></summary>
+  
+```bash
+podman run --rm -it --device nvidia.com/gpu=all -p 8000:8000 \
+ --ipc=host \
+--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
+--env "HF_HUB_OFFLINE=0" -v ~/.cache/vllm:/home/vllm/.cache \
+--name=vllm \
+registry.access.redhat.com/rhaiis/rh-vllm-cuda \
+vllm serve \
+--tensor-parallel-size 8 \
+--max-model-len 32768  \
+--enforce-eager --model RedHatAI/gpt-oss-20b
+```
+</details>
+
+
+<details>
+  <summary>Deploy on <strong>Red Hat Openshift AI</strong></summary>
+  
+```python
+# Setting up vllm server with ServingRuntime
+# Save as: vllm-servingruntime.yaml
+apiVersion: serving.kserve.io/v1alpha1
+kind: ServingRuntime
+metadata:
+ name: vllm-cuda-runtime # OPTIONAL CHANGE: set a unique name
+ annotations:
+   openshift.io/display-name: vLLM NVIDIA GPU ServingRuntime for KServe
+   opendatahub.io/recommended-accelerators: '["nvidia.com/gpu"]'
+ labels:
+   opendatahub.io/dashboard: 'true'
+spec:
+ annotations:
+   prometheus.io/port: '8080'
+   prometheus.io/path: '/metrics'
+ multiModel: false
+ supportedModelFormats:
+   - autoSelect: true
+     name: vLLM
+ containers:
+   - name: kserve-container
+     image: quay.io/modh/vllm:rhoai-2.24-cuda # CHANGE if needed. If AMD: quay.io/modh/vllm:rhoai-2.24-rocm
+     command:
+       - python
+       - -m
+       - vllm.entrypoints.openai.api_server
+     args:
+       - "--port=8080"
+       - "--model=/mnt/models"
+       - "--served-model-name={{.Name}}"
+     env:
+       - name: HF_HOME
+         value: /tmp/hf_home
+     ports:
+       - containerPort: 8080
+         protocol: TCP
+```
+
+```python
+# Attach model to vllm server. This is an NVIDIA template
+# Save as: inferenceservice.yaml
+apiVersion: serving.kserve.io/v1beta1
+kind: InferenceService
+metadata:
+  annotations:
+    openshift.io/display-name: gpt-oss-20b # OPTIONAL CHANGE
+    serving.kserve.io/deploymentMode: RawDeployment
+  name: gpt-oss-20b         # specify model name. This value will be used to invoke the model in the payload
+  labels:
+    opendatahub.io/dashboard: 'true'
+spec:
+  predictor:
+    maxReplicas: 1
+    minReplicas: 1
+    model:
+      modelFormat:
+        name: vLLM
+      name: ''
+      resources:
+        limits:
+          cpu: '2'			# this is model specific
+          memory: 8Gi		# this is model specific
+          nvidia.com/gpu: '1'	# this is accelerator specific
+        requests:			# same comment for this block
+          cpu: '1'
+          memory: 4Gi
+          nvidia.com/gpu: '1'
+      runtime: vllm-cuda-runtime	# must match the ServingRuntime name above
+      storageUri: oci://registry.redhat.io/rhelai1/gpt-oss-20b:1.5
+    tolerations:
+    - effect: NoSchedule
+      key: nvidia.com/gpu
+      operator: Exists
+```
+
+```bash
+# make sure first to be in the project where you want to deploy the model
+# oc project <project-name>
+
+# apply both resources to run model
+
+# Apply the ServingRuntime
+oc apply -f vllm-servingruntime.yaml
+
+```
+
+```python
+# Replace <inference-service-name> and <cluster-ingress-domain> below:
+# - Run `oc get inferenceservice` to find your URL if unsure.
+
+# Call the server using curl:
+curl https://<inference-service-name>-predictor-default.<domain>/v1/chat/completions
+        -H "Content-Type: application/json" \
+        -d '{
+    "model": "gpt-oss-20b",
+    "stream": true,
+    "stream_options": {
+        "include_usage": true
+    },
+    "max_tokens": 1,
+    "messages": [
+        {
+            "role": "user",
+            "content": "How can a bee fly when its wings are so small?"
+        }
+    ]
+}'
+
+```
+
+See [Red Hat Openshift AI documentation](https://docs.redhat.com/en/documentation/red_hat_openshift_ai/2025) for more details.
+</details>
+
+
 ## PyTorch / Triton
 
 To learn about how to use this model with PyTorch and Triton, check out our [reference implementations in the gpt-oss repository](https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation).