From 1b3b1a894006f9f815ee7af7df665d3823ebbbbd Mon Sep 17 00:00:00 2001
From: Enderfga <qq2639135175@gmail.com>
Date: Wed, 26 Jul 2023 19:22:23 +0800
Subject: [PATCH] gorilla

---
 assets/gorilla.png                | Bin 0 -> 7159 bytes
 assets/prism.css                  |   3 +
 assets/prism.js                   |   4 +
 examples/finetune/sg/alpaca.html  |   2 +-
 examples/finetune/sg/gorilla.html | 159 ++++++++++++++++++++++++++++++
 5 files changed, 167 insertions(+), 1 deletion(-)
 create mode 100644 assets/gorilla.png
 create mode 100644 assets/prism.css
 create mode 100644 assets/prism.js
 create mode 100644 examples/finetune/sg/gorilla.html

diff --git a/assets/gorilla.png b/assets/gorilla.png
new file mode 100644
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diff --git a/assets/prism.css b/assets/prism.css
new file mode 100644
index 0000000..7e1b368
--- /dev/null
+++ b/assets/prism.css
@@ -0,0 +1,3 @@
+/* PrismJS 1.29.0
+https://prismjs.com/download.html#themes=prism-solarizedlight&languages=python */
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diff --git a/assets/prism.js b/assets/prism.js
new file mode 100644
index 0000000..8eeeca3
--- /dev/null
+++ b/assets/prism.js
@@ -0,0 +1,4 @@
+/* PrismJS 1.29.0
+https://prismjs.com/download.html#themes=prism-solarizedlight&languages=python */
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diff --git a/examples/finetune/sg/alpaca.html b/examples/finetune/sg/alpaca.html
index daebb08..65f53db 100644
--- a/examples/finetune/sg/alpaca.html
+++ b/examples/finetune/sg/alpaca.html
@@ -74,7 +74,7 @@
 </head>
 <body>
     <div class="chat-container">
-        <h2>Demos of Instruction-Following LLaMA2.</h2>
+        <h2>Demos of Instruction-Following LLaMA2.<br>Using the alpaca dataset.</h2>
         <h3>Instruction 1:</h3>
         <div class="chat-group">
             <div class="chat-entry human">
diff --git a/examples/finetune/sg/gorilla.html b/examples/finetune/sg/gorilla.html
new file mode 100644
index 0000000..45bd5da
--- /dev/null
+++ b/examples/finetune/sg/gorilla.html
@@ -0,0 +1,159 @@
+<!DOCTYPE html>
+<html>
+<head>
+      <link rel="icon" href="../../../assets/icon.ico" type="image/x-icon">
+  <title>gorilla</title>
+    <style>
+    body {
+        background-color: #f9f9f9;
+        font-family: 'Microsoft YaHei', sans-serif;
+        padding: 20px;
+    }
+
+    h2 {
+        color: #333;
+        text-align: center;
+    }
+
+    h3 {
+        color: #666;
+    }
+
+    p {
+        color: #333;
+        line-height: 1.5;
+    }
+
+    .chat-container {
+        max-width: 800px;
+        margin: auto;
+        padding: 20px;
+        border-radius: 10px;
+        background-color: white;
+        box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+    }
+    img.human {
+        width: 40px;
+        height: 40px;
+    }
+    .chat-group {
+        border: 1px solid #ddd;
+        border-radius: 10px;
+        padding: 20px;
+        margin-bottom: 20px;
+    }
+
+    .chat-entry {
+        display: flex;
+        margin-bottom: 20px;
+    }
+
+    .chat-entry .image {
+        width: 50px;
+        height: 50px;
+    }
+
+    .chat-entry .message {
+        margin-left: 10px;
+        padding: 10px;
+        border-radius: 10px;
+    }
+
+    .chat-entry.human .message {
+        background-color: lightblue;
+    }
+
+    .chat-entry.assistant {
+        flex-direction: row-reverse;
+    }
+
+    .chat-entry.assistant .message {
+        background-color: lavender;
+        max-width: 90%;
+    }
+    </style>
+    <link href="../../../assets/prism.css" rel="stylesheet" />
+</head>
+<body>
+    <div class="chat-container">
+        <h2>Demos of Instruction-Following LLaMA2.<br>Using the gorilla tensorflow dataset.</h2>
+        <h3>Instruction 1:</h3>
+        <div class="chat-group">
+            <div class="chat-entry human">
+                <img class="human" src="../../../assets/human.png">
+                <div class="message">I am working on a project where I need to cluster similar images of street art. How can I get the relevant feature vectors from these images for clustering?</div>
+            </div>
+            <div class="chat-entry assistant">
+                <img class="image" src="../../../assets/gorilla.png">
+                <div class="message">
+                    <strong>&lt;&lt;&lt;domain&gt;&gt;&gt;: </strong>Image feature vector, <br><br><strong>&lt;&lt;&lt;api_call&gt;&gt;&gt;: 
+
+                    </strong>hub.KerasLayer<br>('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4'), <br><br><strong>&lt;&lt;&lt;api_provider&gt;&gt;&gt;: </strong>TensorFlow Hub, <br><br><strong>&lt;&lt;&lt;explanation&gt;&gt;&gt;: </strong>This API call loads a pre-trained MobileNetV2 model from TensorFlow Hub, which has been trained on the ImageNet dataset. This model is used for extracting feature vectors from images. You can feed your street art images into this model to obtain feature vectors, which can then be used for clustering. 
+                        <br><br>
+                    <strong>&lt;&lt;&lt;code&gt;&gt;&gt;: </strong>
+                    <pre><code class="language-python">import tensorflow as tf
+import tensorflow_hub as hub
+model = tf.keras.Sequential([
+hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',
+                input_shape=(224, 224, 3),
+                trainable=False)
+])
+
+image = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(224, 224))
+image_array = tf.keras.preprocessing.image.img_to_array(image)
+image_array = tf.expand_dims(image_array, 0)
+
+features = model.predict(image_array)
+                        </code></pre>
+                        
+            </div>
+            <!-- You can continue the conversation here -->
+        </div>
+    </div>
+        <h3>Instruction 2:</h3>
+        <div class="chat-group">
+            <div class="chat-entry human">
+                <img class="human" src="../../../assets/human.png">
+                <div class="message">My dog is always getting into things that can be dangerous. Can you help me identify the object my dog is about to eat?</div>
+            </div>
+            <div class="chat-entry assistant">
+                <img class="image" src="../../../assets/gorilla.png">
+                <div class="message">
+                    <strong>&lt;&lt;&lt;domain&gt;&gt;&gt;: </strong>Image object detection, <br><br><strong>&lt;&lt;&lt;api_call&gt;&gt;&gt;: 
+
+                    </strong>detector = hub.load('https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2'), <br><br><strong>&lt;&lt;&lt;api_provider&gt;&gt;&gt;: </strong>TensorFlow Hub, <br><br><strong>&lt;&lt;&lt;explanation&gt;&gt;&gt;: </strong> This API call loads a pre-trained TensorFlow Hub model for object detection in images using the SSD MobileNet V2 architecture. Once the model is loaded, it can be used to detect objects in images, allowing you to identify the object your dog is about to eat. The model is trained on the COCO dataset, which includes a variety of common objects. 
+
+                    <br><br><strong>&lt;&lt;&lt;code&gt;&gt;&gt;: </strong>
+                        <pre><code class="language-python">import tensorflow_hub as hub
+detector = hub.load('https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2')</code>
+                </div>
+                
+            </div>
+        </div> 
+            <h3>Instruction 3:</h3>
+        <div class="chat-group">
+            <div class="chat-entry human">
+                <img class="human" src="../../../assets/human.png">
+                <div class="message">For arranging my documents, I want to compute the semantic similarity between a set of sentences.</div>
+            </div>
+            <div class="chat-entry assistant">
+                <img class="image" src="../../../assets/gorilla.png">
+                <div class="message">
+                    <strong>&lt;&lt;&lt;domain&gt;&gt;&gt;: </strong>: Text embedding, <br><br><strong>&lt;&lt;&lt;api_call&gt;&gt;&gt;: 
+
+                    </strong>hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'), <br><br><strong>&lt;&lt;&lt;api_provider&gt;&gt;&gt;: </strong>TensorFlow Hub, <br><br><strong>&lt;&lt;&lt;explanation&gt;&gt;&gt;: </strong> The API call loads the Universal Sentence Encoder model from TensorFlow Hub. This model is designed to encode text into high-dimensional vectors, which can be used for various natural language tasks, including text classification, semantic similarity, clustering, etc. By using this model, you can compute the semantic similarity between a set of sentences, which can help you arrange your documents based on their content. 
+
+                    <br><br><strong>&lt;&lt;&lt;code&gt;&gt;&gt;: </strong> <pre><code class="language-python">import tensorflow_hub as hub
+embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')</code>
+                </div>
+            </div>
+        </div>
+          
+            <!-- You can continue the conversation here -->
+       
+        
+        <!-- Conversations go here -->
+    </div>
+    <script src="../../../assets/prism.js"></script>
+</body>
+</html>