[Workers AI] Models update (cloudflare#24953)

* [Workers AI] Models update

* fix logic

* fix deprecation logic #2

* Update changelog for EmbeddingGemma

* Update src/content/changelog/workers-ai/2025-09-05-embeddinggemma.mdx

Co-authored-by: Anni Wang <[email protected]>

---------

Co-authored-by: kodster28 <[email protected]>
Co-authored-by: Anni Wang <[email protected]>

Author: 3 people

src/components/models/ModelBadges.tsx

@@ -38,7 +38,7 @@ const ModelBadges = ({ model }: { model: WorkersAIModelsSchema }) => {
 		}
 
 		if (property_id === "planned_deprecation_date") {
-			const timestamp = Math.floor(new Date(value as string).getTime() / 1000);
+			const timestamp = Math.floor(new Date(value as string).getTime());
 
 			if (Date.now() > timestamp) {
 				return { variant: "danger", text: "Deprecated" };

src/components/models/ModelFeatures.tsx

@@ -31,8 +31,7 @@ const ModelFeatures = ({ model }: { model: WorkersAIModelsSchema }) => {
 									<td>
 										{Date.now() >
 										Math.floor(
-											new Date(properties.planned_deprecation_date).getTime() /
-												1000,
+											new Date(properties.planned_deprecation_date).getTime(),
 										)
 											? "Deprecated"
 											: "Planned Deprecation"}

src/content/changelog/workers-ai/2025-09-05-embeddinggemma.mdx

@@ -0,0 +1,20 @@
+---
+title: Introducing EmbeddingGemma from Google on Workers AI
+description: Partnering with Google to bring to you EmbeddingGemma best-in-class embedding performance for RAG and semantic search
+date: 2025-09-05
+---
+
+We're excited to be a launch partner alongside [Google](https://developers.googleblog.com/en/introducing-embeddinggemma/) to bring their newest embedding model, **EmbeddingGemma**, to Workers AI that delivers best-in-class performance for its size, enabling RAG and semantic search use cases.
+
+[`@cf/google/embeddinggemma-300m`](/workers-ai/models/embeddinggemma-300m/) is a 300M parameter embedding model from Google, built from Gemma 3 and the same research used to create Gemini models. This multilingual model supports 100+ languages, making it ideal for RAG systems, semantic search, content classification, and clustering tasks.
+
+**Using EmbeddingGemma in AutoRAG:**
+Now you can leverage EmbeddingGemma directly through AutoRAG for your RAG pipelines. EmbeddingGemma's multilingual capabilities make it perfect for global applications that need to understand and retrieve content across different languages with exceptional accuracy.
+
+To use EmbeddingGemma for your AutoRAG projects:
+1. Go to **Create** in the [AutoRAG dashboard](https://dash.cloudflare.com/?to=/:account/ai/autorag)
+2. Follow the setup flow for your new RAG instance
+3. In the **Generate Index** step, open up **More embedding models** and select `@cf/google/embeddinggemma-300m` as your embedding model
+4. Complete the setup to create an AutoRAG
+
+Try it out and let us know what you think!

src/content/release-notes/workers-ai.yaml

@@ -3,6 +3,10 @@ link: "/workers-ai/changelog/"
 productName: Workers AI
 productLink: "/workers-ai/"
 entries:
+  - publish_date: "2025-09-05"
+    title: Introducing EmbeddingGemma from Google
+    description: |-
+      - We’re excited to be a launch partner alongside Google to bring their newest embedding model to Workers AI. We're excited to introduce EmbeddingGemma delivers best-in-class performance for its size, enabling RAG and semantic search use cases. Take a look at [`@cf/google/embeddinggemma-300m`](/workers-ai/models/embeddinggemma-300m) for more details. Now available to use for embedding in AutoRAG too.
   - publish_date: "2025-08-27"
     title: Introducing Partner models to the Workers AI catalog
     description: |-

src/content/workers-ai-models/deepseek-coder-6.7b-base-awq.json

@@ -19,6 +19,10 @@
             "property_id": "context_window",
             "value": "4096"
         },
+        {
+            "property_id": "planned_deprecation_date",
+            "value": "2025-10-01"
+        },
         {
             "property_id": "terms",
             "value": "https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-AWQ"

src/content/workers-ai-models/deepseek-coder-6.7b-instruct-awq.json

@@ -19,6 +19,10 @@
             "property_id": "context_window",
             "value": "4096"
         },
+        {
+            "property_id": "planned_deprecation_date",
+            "value": "2025-10-01"
+        },
         {
             "property_id": "terms",
             "value": "https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-AWQ"

src/content/workers-ai-models/deepseek-math-7b-instruct.json

@@ -23,6 +23,10 @@
             "property_id": "info",
             "value": "https://huggingface.co/deepseek-ai/deepseek-math-7b-instruct"
         },
+        {
+            "property_id": "planned_deprecation_date",
+            "value": "2025-10-01"
+        },
         {
             "property_id": "terms",
             "value": "https://github.com/deepseek-ai/DeepSeek-Math/blob/main/LICENSE-MODEL"

src/content/workers-ai-models/discolm-german-7b-v1-awq.json

@@ -22,6 +22,10 @@
         {
             "property_id": "info",
             "value": "https://huggingface.co/TheBloke/DiscoLM_German_7b_v1-AWQ"
+        },
+        {
+            "property_id": "planned_deprecation_date",
+            "value": "2025-10-01"
         }
     ],
     "schema": {

src/content/workers-ai-models/falcon-7b-instruct.json

@@ -22,6 +22,10 @@
         {
             "property_id": "info",
             "value": "https://huggingface.co/tiiuae/falcon-7b-instruct"
+        },
+        {
+            "property_id": "planned_deprecation_date",
+            "value": "2025-10-01"
         }
     ],
     "schema": {

src/content/workers-ai-models/llama-2-13b-chat-awq.json

@@ -22,6 +22,10 @@
         {
             "property_id": "info",
             "value": "https://huggingface.co/TheBloke/Llama-2-13B-chat-AWQ"
+        },
+        {
+            "property_id": "planned_deprecation_date",
+            "value": "2025-10-01"
         }
     ],
     "schema": {