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 <p>This is enabled by LLM model compression technique: <a href="https://github.com/mit-han-lab/smoothquant">SmoothQuant</a> and <a href="https://github.com/mit-han-lab/llm-awq">AWQ (Activation-aware Weight Quantization)</a>, co-designed with TinyChatEngine that implements the compressed low-precision model.</p>
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 Demo on an NVIDIA GeForce RTX 4070 laptop:</h3>
-<p><img src="assets/figures/chat_demo_gpu.gif" alt="chat_demo_gpu" width="49%" class="inline"/> <img src="assets/figures/coding_demo_gpu.gif" alt="coding_demo_gpu" width="49%" class="inline"/> </p><div class="fragment"><div class="line">LLaMA Chat                                                    Code LLaMA</div>
-</div><!-- fragment --><h3><a class="anchor" id="autotoc_md2"></a>
+<p><img src="assets/figures/chat_demo_gpu.gif" alt="chat_demo_gpu" width="49%" class="inline"/> <img src="assets/figures/coding_demo_gpu.gif" alt="coding_demo_gpu" width="49%" class="inline"/></p>
+<h3><a class="anchor" id="autotoc_md2"></a>
 Demo on an Apple MacBook Pro (M1, 2021):</h3>
-<p><img src="assets/figures/chat_demo_m1.gif" alt="chat_demo_m1" width="49%" class="inline"/> <img src="assets/figures/coding_demo_m1.gif" alt="coding_demo_m1" width="49%" class="inline"/> </p><div class="fragment"><div class="line">LLaMA Chat                                                    Code LLaMA</div>
-</div><!-- fragment --><p>Feel free to check out our <a href="assets/slides.pdf">slides</a> for more details!</p>
+<p><img src="assets/figures/chat_demo_m1.gif" alt="chat_demo_m1" width="49%" class="inline"/> <img src="assets/figures/coding_demo_m1.gif" alt="coding_demo_m1" width="49%" class="inline"/></p>
+<p>Feel free to check out our <a href="assets/slides.pdf">slides</a> for more details!</p>
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 Overview</h2>
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