Change order a bit

index.html

@@ -213,58 +213,6 @@ class BertModel(PreTrainedModel):
         </ul>
       </section>
 
-      <section>
-        <h2>Beyond Transformers: Ecosystem Reuse</h2>
-        <p><strong>Transformers</strong> makes modeling easy. <strong>vLLM</strong> makes inference fast.</p>
-      
-        <div style="display: flex; gap: 2rem; margin-top: 2rem;">
-          <div style="flex: 1;">
-            <p><strong>🔧 Prototype with Transformers:</strong></p>
-            <pre><code class="language-python" data-trim data-noescape>
-      from transformers import pipeline
-      
-      pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B")
-      print(pipe("The future of AI is")[0]["generated_text"])
-            </code></pre>
-          </div>
-          <div style="flex: 1;">
-            <img src="assets/vLLM-Full-Logo.png" alt="vLLM Illustration" style="border-radius: 1rem; box-shadow: 0 0 12px #000; width: 100%;" />
-          </div>
-        </div>
-      </section>
-      <section>
-        <h2>Deploy with vLLM — No Rewrite Needed</h2>
-        <p><strong>vLLM</strong> supports <code>transformers</code> models out of the box. </p>
-          
-        <p>Just specify <code>model_impl="transformers"</code> if needed:</p>
-      
-        <pre><code class="language-python" data-trim data-noescape>
-      from vllm import LLM, SamplingParams
-      
-      llm = LLM(model="meta-llama/Llama-3.2-1B", model_impl="transformers")
-      params = SamplingParams(max_tokens=20)
-      outputs = llm.generate("The future of AI is", sampling_params=params)
-      print(outputs[0].outputs[0].text)
-        </code></pre>
-        <p class="fragment">We also support SGLang now, along with thousands of other libraries! </p>
-
-      </section>
-      <section>
-        <h2 style="margin-bottom: 1rem;">
-          Transformers × PyTorch — Enabling the Community
-        </h2>
-        <img src="assets/transformers_as_ref.png" alt="Transformers as Reference"
-          style="
-            width: 120%;
-            height: 110%;
-            object-fit: cover;
-            margin-left: -2.5%;
-            margin-top: -2.5%;
-          " />
-      </section>
-      
-      
-
             <!-- 8 · Paradigms come at a cost -->
       <section>
         <h2>Paradigms Come at a Cost</h2>
@@ -625,6 +573,58 @@ model = AutoModelForConditionalGeneration.from_pretrained("Qwen/Qwen3-8B")
         <iframe src="assets/model_growth.html" width="80%" height="600" style="border:none;"></iframe>
       </section>
 
+
+      
+      <section>
+        <h2>Beyond Transformers: Ecosystem Reuse</h2>
+        <p><strong>Transformers</strong> makes modeling easy. <strong>vLLM</strong> makes inference fast.</p>
+      
+        <div style="display: flex; gap: 2rem; margin-top: 2rem;">
+          <div style="flex: 1;">
+            <p><strong>🔧 Prototype with Transformers:</strong></p>
+            <pre><code class="language-python" data-trim data-noescape>
+      from transformers import pipeline
+      
+      pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B")
+      print(pipe("The future of AI is")[0]["generated_text"])
+            </code></pre>
+          </div>
+          <div style="flex: 1;">
+            <img src="assets/vLLM-Full-Logo.png" alt="vLLM Illustration" style="border-radius: 1rem; box-shadow: 0 0 12px #000; width: 100%;" />
+          </div>
+        </div>
+      </section>
+      <section>
+        <h2>Deploy with vLLM — No Rewrite Needed</h2>
+        <p><strong>vLLM</strong> supports <code>transformers</code> models out of the box. </p>
+          
+        <p>Just specify <code>model_impl="transformers"</code> if needed:</p>
+      
+        <pre><code class="language-python" data-trim data-noescape>
+      from vllm import LLM, SamplingParams
+      
+      llm = LLM(model="meta-llama/Llama-3.2-1B", model_impl="transformers")
+      params = SamplingParams(max_tokens=20)
+      outputs = llm.generate("The future of AI is", sampling_params=params)
+      print(outputs[0].outputs[0].text)
+        </code></pre>
+        <p class="fragment">We also support SGLang now, along with thousands of other libraries! </p>
+
+      </section>
+      <section>
+        <h2 style="margin-bottom: 1rem;">
+          Transformers × PyTorch — Enabling the Community
+        </h2>
+        <img src="assets/transformers_as_ref.png" alt="Transformers as Reference"
+          style="
+            width: 120%;
+            height: 110%;
+            object-fit: cover;
+            margin-left: -2.5%;
+            margin-top: -2.5%;
+          " />
+      </section>
+      
       <section>
         <h2>Takeaways &amp; The Future</h2>
         <div style="display: grid; grid-template-columns: repeat(2, 1fr); gap: 1rem; margin-top: 1.5rem;">