Improve SEO metadata, JSON-LD structured data, robots.txt, and bundle sample media assets

docs/deployment/ai-edge-gallery.md

@@ -1,5 +1,5 @@
 ---
-title: AI Edge Gallery
+title: Try Gemma on your phone with AI Edge Gallery
 description: Run Gemma 4 on an Android or iOS phone with no code — chat, vision, audio transcription and benchmarking, fully offline.
 models: [gemma-4]
 frameworks: [litert, android, ios]

docs/deployment/gemini-api.md

@@ -1,5 +1,5 @@
 ---
-title: Gemini API
+title: Run Gemma with the Gemini API
 description: Call hosted Gemma 4 models through the Gemini API instead of running and serving your own instance.
 models: [gemma-4]
 frameworks: [gemini-api]

docs/deployment/google-cloud.md

@@ -1,5 +1,5 @@
 ---
-title: Google Cloud
+title: Gemma on Google Cloud
 description: "Deploy, serve, and fine-tune Gemma 4 on Google Cloud: Cloud Run, GKE, the Agent Development Kit, vLLM on TPUs, MaxText, and Sovereign Cloud."
 models: [gemma-4]
 frameworks: [google-cloud, vllm]

docs/deployment/huggingface.md

@@ -1,6 +1,6 @@
 ---
 title: Run Gemma with Hugging Face Transformers
-description: Generating text, summarizing, and analysing content are just some of the tasks you can accomplish with Gemma open models.
+description: "Run Gemma 4 with Hugging Face Transformers in Python for text, image, audio, and multi-turn chat generation."
 models: [gemma-4, gemma-3, gemma-3n]
 frameworks: [transformers]
 source_url: "https://ai.google.dev/gemma/docs/core/huggingface_inference.md.txt"
@@ -163,7 +163,7 @@ messages = [
         "role": "user",
         "content": [
             {"type": "text", "text": "Transcribe the following speech segment in its original language. Follow these specific instructions for formatting the answer:\n* Only output the transcription, with no newlines.\n* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three."},
-            {"type": "audio", "audio": "https://ai.google.dev/gemma/docs/audio/roses-are.wav"},
+            {"type": "audio", "audio": "https://gemma.ai/assets/capabilities/roses-are.wav"},
         ]
     }
 ]
@@ -176,7 +176,7 @@ pipe(text=messages, return_full_text=False, generate_kwargs=gen_kwargs)
     'content': [{'type': 'text',
       'text': 'Transcribe the following speech segment in its original language. Follow these specific instructions for formatting the answer:\n* Only output the transcription, with no newlines.\n* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three.'},
      {'type': 'audio',
-      'audio': 'https://ai.google.dev/gemma/docs/audio/roses-are.wav'}]}],
+      'audio': 'https://gemma.ai/assets/capabilities/roses-are.wav'}]}],
   'generated_text': 'Roses are red, violets are blue.<turn|>'}]
 ```
 

docs/deployment/keras.md

@@ -1,6 +1,6 @@
 ---
 title: Run Gemma with Keras
-description: Generating content, summarizing, and analysing content are just some of the tasks you can accomplish with Gemma open models.
+description: "Run Gemma 4 inference with Keras and KerasHub for text and multimodal image generation in Python."
 models: [gemma-4]
 frameworks: [keras]
 source_url: "https://ai.google.dev/gemma/docs/core/keras_inference.md.txt"
@@ -269,7 +269,7 @@ Load the image and format the data so the model can process it. Use `read_image(
 from matplotlib import pyplot as plt
 
 image = read_image(
-    "https://ai.google.dev/gemma/docs/images/thali-indian-plate.jpg"
+    "https://gemma.ai/assets/deployment/thali-indian-plate.jpg"
 )
 plt.imshow(image)
 ```

docs/deployment/litert.md

@@ -1,5 +1,5 @@
 ---
-title: LiteRT-LM
+title: Run Gemma on-device with LiteRT-LM
 description: Run Gemma 4 on-device with LiteRT-LM — CLI install, prebuilt .litertlm weights, and exporting your own fine-tune.
 models: [gemma-4]
 frameworks: [litert]

docs/deployment/llamacpp.md

@@ -1,5 +1,5 @@
 ---
-title: llama.cpp
+title: Run Gemma with llama.cpp
 description: Run Gemma 4 with llama.cpp — GGUF repos for every size, image/audio/video input, and the OpenAI-compatible server.
 models: [gemma-4]
 frameworks: [llamacpp]

docs/deployment/lmstudio.md

@@ -1,5 +1,5 @@
 ---
-title: LM Studio
+title: Run Gemma with LM Studio
 description: Run Gemma 4 with LM Studio — desktop app, lms CLI, and the OpenAI-compatible local server.
 models: [gemma-4]
 frameworks: [lmstudio]

docs/deployment/mlx.md

@@ -1,5 +1,5 @@
 ---
-title: Apple MLX
+title: Run Gemma with MLX
 description: Run Gemma 4 on Apple silicon with MLX — text and vision generation, model repos, and the OpenAI-compatible server.
 models: [gemma-4]
 frameworks: [mlx]

docs/deployment/ollama.md

@@ -1,5 +1,5 @@
 ---
-title: Ollama
+title: Run Gemma with Ollama
 description: Run Gemma 4 locally with Ollama — model tags, image input, and the OpenAI-compatible server.
 models: [gemma-4]
 frameworks: [ollama]

docs/deployment/sglang.md

@@ -1,5 +1,5 @@
 ---
-title: SGLang
+title: Serve Gemma with SGLang
 description: Serve Gemma 4 with SGLang — install, launch_server, model repositories, and the OpenAI-compatible API.
 models: [gemma-4]
 frameworks: [sglang]

docs/deployment/unsloth.md

@@ -1,5 +1,5 @@
 ---
-title: Unsloth
+title: Run Gemma with Unsloth
 description: Run Gemma 4 with Unsloth — the desktop Studio app, dynamic-quant GGUFs for llama.cpp, and 2x faster fine-tuning.
 models: [gemma-4]
 frameworks: [unsloth, llamacpp]

docs/deployment/vllm.md

@@ -1,5 +1,5 @@
 ---
-title: vLLM
+title: Serve Gemma with vLLM
 description: Serve Gemma 4 with vLLM — install, vllm serve, model repositories, and the OpenAI-compatible API.
 models: [gemma-4]
 frameworks: [vllm]

docs/get-started/capabilities/audio.md

@@ -1,6 +1,6 @@
 ---
 title: Audio understanding
-description: Starting with Gemma 3n, you can use audio directly into your prompts and workflows.
+description: "Process and transcribe speech, music, and sound inputs with Gemma 4 and Gemma 3n multimodal audio understanding."
 models: [gemma-4]
 requires: audio
 frameworks: [transformers]
@@ -185,7 +185,7 @@ messages = [
         "role": "user",
         "content": [
             {"type": "text", "text": "Transcribe the following speech segment in English, then translate it into Korean. When formatting the answer, first output the transcription in English, then one newline, then output the string 'Korean: ', then the translation in Korean."},
-            {"type": "audio", "audio": "https://ai.google.dev/gemma/docs/audio/roses-are.wav"},
+            {"type": "audio", "audio": "https://gemma.ai/assets/capabilities/roses-are.wav"},
         ]
     }
 ]

docs/get-started/capabilities/function-calling.md

@@ -1,6 +1,6 @@
 ---
 title: Tool calling
-description: When using a generative artificial intelligence (AI) model such as Gemma, you may want to use the model to operate programming interfaces in order to complete tasks or answer questions.
+description: "Build agentic workflows with Gemma 4 native tool calling and structured function schemas in Hugging Face Transformers."
 models: [gemma-4]
 frameworks: [transformers]
 colab: https://colab.research.google.com/github/google-gemma/docs/blob/main/docs/notebooks/function-calling-gemma-4-e2b.ipynb

docs/get-started/capabilities/image.md

@@ -1,6 +1,6 @@
 ---
 title: Image understanding
-description: You can use Gemma 3 and later models to analyze and understand the content of images.
+description: "Analyze images with Gemma 4 using variable aspect ratios, configurable vision token budgets, and multi-image prompts."
 models: [gemma-4]
 frameworks: [transformers]
 colab: https://colab.research.google.com/github/google-gemma/docs/blob/main/docs/notebooks/image-gemma-4-e2b.ipynb
@@ -103,8 +103,8 @@ config.max_new_tokens = 512
 gen_kwargs = dict(generation_config=config)
 
 img_urls = [
-    "https://ai.google.dev/gemma/docs/capabilities/vision/images/surprise.png",
-    "https://ai.google.dev/gemma/docs/capabilities/vision/images/kitchen.jpg",
+    "https://gemma.ai/assets/capabilities/surprise.png",
+    "https://gemma.ai/assets/capabilities/kitchen.jpg",
 ]
 for img in img_urls:
     display(Image.open(requests.get(img, stream=True).raw))
@@ -196,7 +196,7 @@ config = GenerationConfig.from_pretrained(MODEL_ID)
 config.max_new_tokens = 512
 gen_kwargs = dict(generation_config=config)
 
-img_url = "https://ai.google.dev/gemma/docs/capabilities/vision/images/cat.png"
+img_url = "https://gemma.ai/assets/capabilities/cat.png"
 input_image = Image.open(requests.get(img_url, stream=True).raw)
 display(input_image)
 

docs/get-started/capabilities/video.md

@@ -1,6 +1,6 @@
 ---
 title: Video understanding
-description: Video data is a rich source of information that can help you accomplish tasks and understand the world around you.
+description: "Analyze video clips and interleaved frame sequences with Gemma 4 multimodal vision-language models."
 models: [gemma-4]
 frameworks: [transformers]
 colab: https://colab.research.google.com/github/google-gemma/docs/blob/main/docs/notebooks/video-gemma-4-e2b.ipynb