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index.html
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<html>
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<head>
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<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
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<title>
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</head>
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<body></body>
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</html>
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import hljs from "https://cdn.skypack.dev/highlight.js";
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// models base url
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const MODELS = {
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base_url:
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"https://huggingface.co/lmz/candle-quantized-phi/resolve/main/",
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model: "model-q4k.gguf",
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seq_len: 2048,
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size: "800 MB",
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},
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base_url:
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"https://huggingface.co/lmz/candle-quantized-phi/resolve/main/",
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model: "model-q80.gguf",
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seq_len: 2048,
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size: "1.51 GB",
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},
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base_url:
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"https://huggingface.co/lmz/candle-quantized-phi/resolve/main/",
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model: "model-puffin-phi-v2-q4k.gguf",
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seq_len: 2048,
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size: "798 MB",
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},
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base_url:
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"https://huggingface.co/lmz/candle-quantized-phi/resolve/main/",
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model: "model-puffin-phi-v2-q80.gguf",
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@@ -320,43 +320,11 @@ Very polite review:`,
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<main class="grid grid-cols-1 gap-8 relative">
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<span class="absolute text-5xl -ml-[1em]"> 🕯️ </span>
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<div>
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<h1 class="text-5xl font-bold">
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<h2 class="text-2xl font-bold">Rust/WASM Demo</h2>
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<p class="max-w-lg">
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The
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href="https://huggingface.co/microsoft/phi-1_5"
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class="link"
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target="_blank"
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>Phi-1.5</a
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>
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model achieves state-of-the-art performance with only 1.3 billion
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parameters, compared to models with up to 10 billion. You can try the
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quantized version of the model here. Additional prompt examples are
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available in the
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<a
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href="https://arxiv.org/pdf/2309.05463.pdf#page=8"
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class="link"
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target="_blank"
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>
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technical report </a
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>.
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</p>
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<p class="max-w-lg">
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You can also try
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<a
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href="https://huggingface.co/teknium/Puffin-Phi-v2"
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class="link"
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target="_blank"
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>Puffin-Phi V2
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</a>
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quantized version model, a fine-tuned version of Phi-1.5 on the
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<a
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href="https://huggingface.co/datasets/LDJnr/Puffin"
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class="link"
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target="_blank"
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>Puffin dataset
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</a>
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</p>
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</div>
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<div>
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<html>
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<head>
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<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
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<title>EasyChat - 安风 Rust/Web ASSEMBLY</title>
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</head>
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<body></body>
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</html>
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import hljs from "https://cdn.skypack.dev/highlight.js";
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// models base url
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const MODELS = {
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vp_quantized: {
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base_url:
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"https://huggingface.co/lmz/candle-quantized-phi/resolve/main/",
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model: "model-q4k.gguf",
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seq_len: 2048,
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size: "800 MB",
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},
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vp_quantized_q8: {
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base_url:
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"https://huggingface.co/lmz/candle-quantized-phi/resolve/main/",
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model: "model-q80.gguf",
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seq_len: 2048,
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size: "1.51 GB",
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},
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long_v2_quantized: {
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base_url:
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"https://huggingface.co/lmz/candle-quantized-phi/resolve/main/",
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model: "model-puffin-phi-v2-q4k.gguf",
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seq_len: 2048,
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size: "798 MB",
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},
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long_v2_quantized_q8: {
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base_url:
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"https://huggingface.co/lmz/candle-quantized-phi/resolve/main/",
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model: "model-puffin-phi-v2-q80.gguf",
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<main class="grid grid-cols-1 gap-8 relative">
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<span class="absolute text-5xl -ml-[1em]"> 🕯️ </span>
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<div>
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<h1 class="text-5xl font-bold">EasyChat - 安风</h1>
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<h2 class="text-2xl font-bold">Rust/WASM Demo</h2>
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<p class="max-w-lg">
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The EC-VP model achieves state-of-the-art performance with only 1.3 billion
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parameters.
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</p>
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</div>
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<div>
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