title
7. Building Generative AI Applications with Gradio | Andrew Ng | DeepLearning.ai - Full Course

description
The course comes from [https://learn.deeplearning.ai/huggingface-gradio](https://learn.deeplearning.ai/huggingface-gradio) created by Andrew Ng Learn to use Gradio to build Generative AI Applications with Gradio through the Building Generative AI Applications with Gradio course on YouTube. The course introduces Gradio's fast and easy way to demonstrate machine learning models through a friendly Python web interface without writing front-end or JavaScript code. Specific examples include text summaries, named entity recognition, image captioning, diffusion model-generated images, and LLM-based chatbots. Get free course notes: https://t.me/NoteForYoutubeCourse

detail
{'title': '7. Building Generative AI Applications with Gradio | Andrew Ng | DeepLearning.ai - Full Course', 'heatmap': [], 'summary': 'Tutorial covers building generative ai and nlp apps using gradio, accessing models via api calls, creating image captioning and generation apps, introducing gradio blocks for complex user interfaces, simplifying ui using gradio accordion, combining models for a game application, and building chatbot apps powered by open-source language models, highlighting cost, speed, and performance advantages.', 'chapters': [{'end': 266.075, 'segs': [{'end': 65.433, 'src': 'embed', 'start': 41.252, 'weight': 0, 'content': [{'end': 48.197, 'text': 'Gradio lets you do that quickly via a Python interface without needing to write any front-end or web or JavaScript code.', 'start': 41.252, 'duration': 6.945}, {'end': 55.704, 'text': 'In this course, you see examples including text summarization, name entity recognition, image captioning,', 'start': 49.137, 'duration': 6.567}, {'end': 59.708, 'text': 'image generation using a diffusion model and an LLM-based chatbot.', 'start': 55.704, 'duration': 4.004}, {'end': 61.79, 'text': 'For each of these applications.', 'start': 59.968, 'duration': 1.822}, {'end': 65.433, 'text': "Polly will show how, after you've built the machine learning part of it,", 'start': 61.79, 'duration': 3.643}], 'summary': 'Gradio enables quick development via python interface for various applications including text summarization, name entity recognition, image captioning, and chatbot development.', 'duration': 24.181, 'max_score': 41.252, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs41252.jpg'}, {'end': 155.497, 'src': 'embed', 'start': 128.692, 'weight': 2, 'content': [{'end': 136.614, 'text': "To get feedback from your team or community, it can be very helpful to give them a user interface that doesn't require them to run any code.", 'start': 128.692, 'duration': 7.922}, {'end': 142.425, 'text': 'Gradio lets you build that user interface quickly and without writing much code.', 'start': 138.001, 'duration': 4.424}, {'end': 147.469, 'text': 'When you have a specific task in mind, such as summarizing text,', 'start': 143.226, 'duration': 4.243}, {'end': 155.497, 'text': 'a small specialist model that is designed for that specific task can perform just as well as a general purpose, large language model.', 'start': 147.469, 'duration': 8.028}], 'summary': 'Gradio enables quick ui creation without code. specialist models perform well for specific tasks.', 'duration': 26.805, 'max_score': 128.692, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs128692.jpg'}, {'end': 228.858, 'src': 'embed', 'start': 198.537, 'weight': 3, 'content': [{'end': 202.6, 'text': "So we're importing the pipeline function from the Hugging Face Transformers library.", 'start': 198.537, 'duration': 4.063}, {'end': 210.385, 'text': "We're choosing the model DistilledBartCNN for the text summarization because it is one of the state-of-the-art models for text summarization.", 'start': 203.24, 'duration': 7.145}, {'end': 218.67, 'text': 'In fact, if you use the Transformers pipeline function for the text summarization without specifying the model explicitly,', 'start': 211.205, 'duration': 7.465}, {'end': 220.592, 'text': 'it will default to DistilledBartCNN.', 'start': 218.67, 'duration': 1.922}, {'end': 228.858, 'text': 'Since this model is built specifically for summarization, for any text that you feed into the model, it will output a summary of it.', 'start': 221.696, 'duration': 7.162}], 'summary': 'Using distilledbartcnn model for text summarization, providing state-of-the-art results.', 'duration': 30.321, 'max_score': 198.537, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs198537.jpg'}, {'end': 271.72, 'src': 'embed', 'start': 242.982, 'weight': 4, 'content': [{'end': 248.783, 'text': 'Another way to improve cost and speed is to create a smaller version of the model that has a very similar performance.', 'start': 242.982, 'duration': 5.801}, {'end': 251.464, 'text': 'This is a process called desolation.', 'start': 249.463, 'duration': 2.001}, {'end': 256.708, 'text': 'Destillation uses the predictions of a large model to train a smaller model.', 'start': 252.205, 'duration': 4.503}, {'end': 266.075, 'text': "So the model we're using, Distilled BART-CNN, is actually a distilled model based on the larger model trained by Facebook called BART-Large-CNN.", 'start': 257.649, 'duration': 8.426}, {'end': 271.72, 'text': "For this course, we're running these models on a server and accessing them through API calls.", 'start': 266.936, 'duration': 4.784}], 'summary': 'Distillation process creates smaller model with similar performance for cost and speed improvement.', 'duration': 28.738, 'max_score': 242.982, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs242982.jpg'}], 'start': 0.376, 'title': 'Building generative ai and nlp apps with gradio', 'summary': 'Introduces gradio, a tool for building user interfaces for generative ai applications without writing front-end or javascript code. it includes examples of text summarization, named entity recognition, image captioning, and generation. additionally, it covers the creation of nlp apps for text summarization and named entity recognition using gradio, highlighting the advantages of specialist models in terms of cost, speed, and performance.', 'chapters': [{'end': 107.803, 'start': 0.376, 'title': 'Building generative ai with gradio', 'summary': 'Introduces gradio, a tool to quickly build user interfaces for generative ai applications without needing to write any front-end or web or javascript code, with examples including text summarization, name entity recognition, image captioning, and image generation.', 'duration': 107.427, 'highlights': ['Gradio is a fast and convenient way to demo machine learning models with a friendly web interface directly in Python, allowing quick demos without needing to write any front-end or web or JavaScript code.', 'Examples of applications for Gradio include text summarization, name entity recognition, image captioning, and image generation using a diffusion model and an LLM-based chatbot.', 'Polly will demonstrate how to use Gradio to quickly build cool demos after building the machine learning part, allowing others to interact with and experience the system.', 'Acknowledgment given to the contributors from Hugging Face and deeplearning.ai for their hard work in developing the course.']}, {'end': 266.075, 'start': 108.848, 'title': 'Building nlp apps with gradio', 'summary': 'Covers building two nlp apps for text summarization and named entity recognition using gradio, emphasizing the benefits of specialist models in terms of cost, speed, and performance.', 'duration': 157.227, 'highlights': ["Gradio allows for quick user interface creation without extensive coding, benefiting from specialist models' cost-effectiveness and speed.", 'The course focuses on utilizing the DistilledBartCNN model for text summarization due to its state-of-the-art performance and cost-effectiveness.', 'Desolation, the process of creating a smaller model with similar performance as a larger model, is utilized to improve cost and response time.']}], 'duration': 265.699, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs376.jpg', 'highlights': ['Gradio is a fast way to demo ML models with a friendly web interface in Python.', 'Examples of applications for Gradio include text summarization and named entity recognition.', 'Gradio allows for quick user interface creation without extensive coding.', 'The course focuses on utilizing the DistilledBartCNN model for text summarization.', 'Desolation is utilized to improve cost and response time by creating a smaller model.']}, {'end': 904.843, 'segs': [{'end': 330.366, 'src': 'embed', 'start': 266.936, 'weight': 0, 'content': [{'end': 271.72, 'text': "For this course, we're running these models on a server and accessing them through API calls.", 'start': 266.936, 'duration': 4.784}, {'end': 276.884, 'text': 'If you were running the model locally on your own machine, this is the code you would use.', 'start': 272.4, 'duration': 4.484}, {'end': 281.387, 'text': "But since we're not running the models directly in this classroom, I'll delete this code.", 'start': 277.424, 'duration': 3.963}, {'end': 287.252, 'text': 'Okay, so here we have a little text about the Eiffel Tower and the history of its construction.', 'start': 282.29, 'duration': 4.962}, {'end': 289.612, 'text': "It's actually quite a bit of text.", 'start': 288.092, 'duration': 1.52}, {'end': 294.054, 'text': "I myself haven't read it all, but that's why we have a summarization task for us.", 'start': 290.272, 'duration': 3.782}, {'end': 296.814, 'text': 'So we run the code and we can see a summary.', 'start': 294.634, 'duration': 2.18}, {'end': 298.955, 'text': 'So the Eiffel Tower is tall.', 'start': 297.515, 'duration': 1.44}, {'end': 301.776, 'text': 'It was the tallest, but it was surpassed.', 'start': 299.435, 'duration': 2.341}, {'end': 303.957, 'text': 'It was the first structure in the world.', 'start': 302.396, 'duration': 1.561}, {'end': 305.057, 'text': "It's so cool.", 'start': 304.457, 'duration': 0.6}, {'end': 306.958, 'text': 'It gives us a little description.', 'start': 305.497, 'duration': 1.461}, {'end': 308.378, 'text': "So that's what we wanted.", 'start': 307.338, 'duration': 1.04}, {'end': 314.796, 'text': 'But if you wanted people on your team or a beta testing community to try out your model,', 'start': 309.793, 'duration': 5.003}, {'end': 321.281, 'text': "maybe giving them this code to run isn't the best user experience, especially if your users aren't familiar with coding.", 'start': 314.796, 'duration': 6.485}, {'end': 328.305, 'text': "Or maybe, as you'll see later, your model has some options that would make it hard to try out even if your users are coders.", 'start': 321.981, 'duration': 6.324}, {'end': 330.366, 'text': "That's why Gradio can help.", 'start': 329.106, 'duration': 1.26}], 'summary': 'Models run on server, accessed via api. eiffel tower: tall, historic, surpassed in height. gradio aids user experience.', 'duration': 63.43, 'max_score': 266.936, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs266936.jpg'}, {'end': 443.624, 'src': 'embed', 'start': 411.874, 'weight': 1, 'content': [{'end': 414.396, 'text': 'We can go ahead and try to customize the user interface.', 'start': 411.874, 'duration': 2.522}, {'end': 418.217, 'text': 'For example, right now, it just says input and output.', 'start': 414.896, 'duration': 3.321}, {'end': 424.4, 'text': 'We can customize these labels if we replace the input and output with the Gradle component text box.', 'start': 418.718, 'duration': 5.682}, {'end': 427.962, 'text': 'The gr.textbox lets us put some labels on it.', 'start': 425.08, 'duration': 2.882}, {'end': 431.919, 'text': 'so we can label the input as text to summarize.', 'start': 428.897, 'duration': 3.022}, {'end': 439.002, 'text': "And let's label the output as result.", 'start': 436.661, 'duration': 2.341}, {'end': 443.624, 'text': "Let's see how this looks.", 'start': 442.524, 'duration': 1.1}], 'summary': 'Customize user interface with gradle component text box to label input as text and output as result.', 'duration': 31.75, 'max_score': 411.874, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs411874.jpg'}, {'end': 593.08, 'src': 'embed', 'start': 565.079, 'weight': 2, 'content': [{'end': 571.043, 'text': 'By that, I mean the model would take a text and label certain words as persons, institutions, or places.', 'start': 565.079, 'duration': 5.964}, {'end': 574.606, 'text': "We'll be using a BERT-based name density recognition model.", 'start': 571.504, 'duration': 3.102}, {'end': 579.71, 'text': 'BERT is a general-purpose language model that can perform many NLP tasks,', 'start': 574.986, 'duration': 4.724}, {'end': 586.455, 'text': "but the one we're using has been specifically fine-tuned to have a state-of-the-art performance on name density recognition tasks.", 'start': 579.71, 'duration': 6.745}, {'end': 593.08, 'text': 'It recognizes four types of entities, location, organizations, persons, and miscellaneous.', 'start': 587.099, 'duration': 5.981}], 'summary': 'Using bert-based model to recognize persons, institutions, and places, achieving state-of-the-art performance.', 'duration': 28.001, 'max_score': 565.079, 'thumbnail': ''}, {'end': 874.973, 'src': 'embed', 'start': 845.544, 'weight': 5, 'content': [{'end': 850.585, 'text': 'While it may sometimes be helpful to see the individual tokens for a user facing application,', 'start': 845.544, 'duration': 5.041}, {'end': 854.626, 'text': 'you probably want to just show the organization hugging face as a single word.', 'start': 850.585, 'duration': 4.041}, {'end': 857.732, 'text': 'we can write a bit of code to merge these tokens.', 'start': 855.609, 'duration': 2.123}, {'end': 861.736, 'text': 'So here, what you can see is that we have our merge tokens function.', 'start': 858.793, 'duration': 2.943}, {'end': 867.403, 'text': 'So to have each token visually as one word, we can use this function merge tokens here.', 'start': 862.377, 'duration': 5.026}, {'end': 870.227, 'text': "So let's run our code and see what's going on.", 'start': 868.064, 'duration': 2.163}, {'end': 874.973, 'text': 'So now, here I added some more entities.', 'start': 872.169, 'duration': 2.804}], 'summary': 'Code can merge individual tokens into a single word for user-facing applications.', 'duration': 29.429, 'max_score': 845.544, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs845544.jpg'}], 'start': 266.936, 'title': 'Accessing models via api and using gradio for model testing', 'summary': 'Covers accessing models via api calls, including a task on text summarization, and introduces gradio for user-friendly model testing with examples of text summarization and named entity recognition, customization options, and sharing capabilities.', 'chapters': [{'end': 308.378, 'start': 266.936, 'title': 'Using models via api calls', 'summary': "Explains how models are run on a server and accessed through api calls, with a code demonstration, followed by a task on text summarization resulting in a concise overview of the eiffel tower's history and features.", 'duration': 41.442, 'highlights': ['The chapter emphasizes the use of API calls to access models running on a server, demonstrating the practical application of the concept. Using models on a server and accessing them through API calls is highlighted as a key concept, showcasing the practical implementation for the learners.', "The text summarization task results in a concise summary of the Eiffel Tower's history and features, facilitating understanding and comprehension. The summarization task effectively condenses the information about the Eiffel Tower, providing a clear and concise overview for improved understanding.", 'The code demonstration for running models locally on a machine is mentioned, although not directly applicable in the current context. Although the code for running models locally is presented, its relevance is explained in the context of accessing models through API calls on a server.']}, {'end': 904.843, 'start': 309.793, 'title': 'Gradio for user-friendly model testing', 'summary': 'Introduces gradio, a tool for creating user-friendly interfaces to test machine learning models, demonstrated through examples of text summarization and named entity recognition, offering customization options and sharing capabilities, and explaining the process of merging tokens for a user-friendly display.', 'duration': 595.05, 'highlights': ['Gradio simplifies model testing by providing user-friendly interfaces, demonstrated through examples of text summarization and named entity recognition. Gradio facilitates user-friendly model testing with examples of text summarization and named entity recognition.', 'Customization options are available for the user interface, such as labeling inputs and outputs, adjusting text box sizes, adding titles and descriptions, and enabling web sharing. Customization options include labeling inputs and outputs, adjusting text box sizes, adding titles and descriptions, and enabling web sharing.', "The BERT-based named entity recognition model is showcased, along with its capability to recognize various entities and the process of fine-tuning for specific use cases. The BERT-based named entity recognition model's capability to recognize various entities and the process of fine-tuning for specific use cases are demonstrated.", 'Merging tokens is explained as a method to present entities as single words for user-friendly display, demonstrating the merge tokens function for this purpose. The process of merging tokens to present entities as single words for user-friendly display is explained, along with the demonstration of the merge tokens function.']}], 'duration': 637.907, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs266936.jpg', 'highlights': ['Gradio facilitates user-friendly model testing with examples of text summarization and named entity recognition.', 'Customization options include labeling inputs and outputs, adjusting text box sizes, adding titles and descriptions, and enabling web sharing.', "The BERT-based named entity recognition model's capability to recognize various entities and the process of fine-tuning for specific use cases are demonstrated.", 'The summarization task effectively condenses the information about the Eiffel Tower, providing a clear and concise overview for improved understanding.', 'Using models on a server and accessing them through API calls is highlighted as a key concept, showcasing the practical implementation for the learners.', 'The process of merging tokens to present entities as single words for user-friendly display is explained, along with the demonstration of the merge tokens function.', 'Although the code for running models locally is presented, its relevance is explained in the context of accessing models through API calls on a server.']}, {'end': 1502.229, 'segs': [{'end': 1040.642, 'src': 'embed', 'start': 1011.08, 'weight': 0, 'content': [{'end': 1020.003, 'text': 'So you can imagine there is a data set that has like, for example, a photo of a dog in the park, and then the caption say a dog in the park.', 'start': 1011.08, 'duration': 8.923}, {'end': 1031.5, 'text': 'And the way this works is that this model is trained on millions of those images and text caption pairs in a way that they learn,', 'start': 1020.923, 'duration': 10.577}, {'end': 1036.241, 'text': 'with the objective of predicting what is the caption if it sees a new image.', 'start': 1031.5, 'duration': 4.741}, {'end': 1040.642, 'text': "Okay, so let's test out our function.", 'start': 1037.502, 'duration': 3.14}], 'summary': 'A model trained on millions of image-text pairs learns to predict captions for new images.', 'duration': 29.562, 'max_score': 1011.08, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs1011080.jpg'}, {'end': 1240.329, 'src': 'embed', 'start': 1214.639, 'weight': 3, 'content': [{'end': 1219.524, 'text': "In this lesson, we're going to build an image generation app with an open source text to image model.", 'start': 1214.639, 'duration': 4.885}, {'end': 1222.687, 'text': 'It takes a description and generates an image with it.', 'start': 1219.985, 'duration': 2.702}, {'end': 1223.848, 'text': "Let's do that.", 'start': 1223.288, 'duration': 0.56}, {'end': 1228.093, 'text': 'The image generation model is a diffusion model called stable diffusion.', 'start': 1224.529, 'duration': 3.564}, {'end': 1232.317, 'text': "And we're connecting to this model on a server using an API URL.", 'start': 1228.393, 'duration': 3.924}, {'end': 1234.82, 'text': 'So here we set up our API key.', 'start': 1233.038, 'duration': 1.782}, {'end': 1238.028, 'text': 'Here we have our getCompletion function.', 'start': 1236.587, 'duration': 1.441}, {'end': 1240.329, 'text': 'This time it is a text to image endpoint.', 'start': 1238.048, 'duration': 2.281}], 'summary': 'Building an image generation app with a text to image model using stable diffusion and connecting to a server via api url.', 'duration': 25.69, 'max_score': 1214.639, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs1214639.jpg'}, {'end': 1286.867, 'src': 'embed', 'start': 1257.216, 'weight': 2, 'content': [{'end': 1262.559, 'text': 'So it was trained with an image and the text that describes it, but this time with the reverse objective.', 'start': 1257.216, 'duration': 5.343}, {'end': 1271.524, 'text': 'So it was trained to get a description or a text and generate the image that would be associated with it.', 'start': 1262.639, 'duration': 8.885}, {'end': 1275.708, 'text': "so let's test it out all right, it works.", 'start': 1271.524, 'duration': 4.184}, {'end': 1281.112, 'text': 'so you can see from our text a nice image was generated.', 'start': 1275.708, 'duration': 5.404}, {'end': 1284.646, 'text': "so Let's build a Gradio app for this.", 'start': 1281.112, 'duration': 3.534}, {'end': 1286.867, 'text': 'So here we have our Gradio app.', 'start': 1285.587, 'duration': 1.28}], 'summary': 'Model trained to generate images from text. gradio app created.', 'duration': 29.651, 'max_score': 1257.216, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs1257216.jpg'}, {'end': 1371.554, 'src': 'embed', 'start': 1346.963, 'weight': 6, 'content': [{'end': 1352.926, 'text': 'So you can kind of see like this little Tamagotchi and then the background, you see the city of Vienna.', 'start': 1346.963, 'duration': 5.963}, {'end': 1359.068, 'text': 'And every time you run the function to generate an image, it generates a new image.', 'start': 1353.686, 'duration': 5.382}, {'end': 1364.071, 'text': 'So with the same prompt, you already have infinite fun if you want to keep generating images.', 'start': 1359.168, 'duration': 4.903}, {'end': 1371.554, 'text': 'And of course you can also vary, here you have a very weird ghost, but we ask for a spirit anyways.', 'start': 1364.531, 'duration': 7.023}], 'summary': 'A function generates infinite images with a city backdrop and varied characters.', 'duration': 24.591, 'max_score': 1346.963, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs1346963.jpg'}, {'end': 1448.452, 'src': 'embed', 'start': 1423.388, 'weight': 5, 'content': [{'end': 1432.26, 'text': "And once you're done, we will move on to the next step, which is let's make this app a bit more fun.", 'start': 1423.388, 'duration': 8.872}, {'end': 1437.424, 'text': 'Stable diffusion is a powerful model, and it has a few more parameters.', 'start': 1432.66, 'duration': 4.764}, {'end': 1445.29, 'text': 'So we have our negative prompt, the inference steps, our guidance scale, and the width and height of this image.', 'start': 1439.566, 'duration': 5.724}, {'end': 1448.452, 'text': "So let's go back to our UI to understand what's going on.", 'start': 1445.75, 'duration': 2.702}], 'summary': 'Enhancing app with stable diffusion model and additional parameters.', 'duration': 25.064, 'max_score': 1423.388, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs1423388.jpg'}, {'end': 1502.229, 'src': 'embed', 'start': 1473.889, 'weight': 7, 'content': [{'end': 1479.233, 'text': 'And, surprisingly enough, if you type low quality as a negative prompt, it actually increases the quality,', 'start': 1473.889, 'duration': 5.344}, {'end': 1483.857, 'text': 'because it will try to drive away from images that had low quality in their captions.', 'start': 1479.233, 'duration': 4.624}, {'end': 1491.743, 'text': 'I will increase a bit the number of different steps, and then this will also increase the quality, but it will take a bit more time.', 'start': 1484.297, 'duration': 7.446}, {'end': 1493.665, 'text': "And let's do this.", 'start': 1492.644, 'duration': 1.021}, {'end': 1502.229, 'text': 'Here we have a dog drawn in anime style.', 'start': 1499.428, 'duration': 2.801}], 'summary': "Using a negative prompt 'low quality' increases image quality by driving away from low-quality captions.", 'duration': 28.34, 'max_score': 1473.889, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs1473889.jpg'}], 'start': 905.384, 'title': 'Building image captioning and generation app', 'summary': 'Covers building an image captioning app using an open source image to text model, with details on training, testing, api key setup, and gradio interface. it also discusses creating an image generation app using a text to image model, connecting to the model via api, and generating diverse images using a stable diffusion model by experimenting with prompts and parameters, including the impact of negative prompts and inference steps on image quality.', 'chapters': [{'end': 1189.99, 'start': 905.384, 'title': 'Building image captioning app', 'summary': "Covers building an image captioning app using an open source image to text model, including details on the model's training and testing, setting up the api key, and using gradio to create the app interface.", 'duration': 284.606, 'highlights': ['The model is trained on millions of images and text caption pairs, aiming to predict captions for new images. The model is trained on millions of image and text caption pairs, learning to predict captions for new images.', "The app successfully generates captions for images, including identifying objects and their actions. The app successfully generates captions for images, identifying objects and their actions, such as 'a dog wearing a Santa hat and a scarf' or 'there is a bird that is flying in the air.'", 'Gradio is used to create the app interface, allowing users to upload images and receive captions, demonstrating its simplicity and versatility. Gradio is used to create the app interface, enabling users to upload images and receive captions, showcasing its simplicity and versatility.']}, {'end': 1327.634, 'start': 1191.152, 'title': 'Image generation with text', 'summary': 'Covers building an image generation app using an open source text to image model called stable diffusion, connecting to the model via api, and creating a gradio app that generates images from text inputs.', 'duration': 136.482, 'highlights': ['We learn to build an image generation app using a stable diffusion model and connect to it via an API URL.', 'The model was trained to generate an image from a given text description, and it successfully generates images from text inputs in the Gradio app.', "The app's functionality is similar to the previous lesson's app, but in reverse, with text inputs generating images in the output component.", 'The lesson also involves setting up API keys and helper functions for interacting with the API.']}, {'end': 1502.229, 'start': 1327.674, 'title': 'Generating diverse images with a stable diffusion model', 'summary': 'Discusses using a stable diffusion model to generate diverse images by experimenting with prompts and parameters, including the example of generating images based on the spirit of a tamagotchi in the city of vienna and exploring the impact of negative prompts and inference steps on image quality.', 'duration': 174.555, 'highlights': ['The chapter discusses using a stable diffusion model to generate diverse images by experimenting with prompts and parameters The focus is on utilizing a stable diffusion model to create various images through prompt and parameter experimentation.', 'Example of generating images based on the spirit of a Tamagotchi in the city of Vienna An example is given where images are generated based on the spirit of a Tamagotchi in the city of Vienna, a city known for its livability and great public transportation.', "Exploring the impact of negative prompts and inference steps on image quality The impact of negative prompts and inference steps on image quality is explored, such as how typing 'low quality' as a negative prompt surprisingly increases the image quality."]}], 'duration': 596.845, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs905384.jpg', 'highlights': ['The model is trained on millions of image and text caption pairs, learning to predict captions for new images.', 'The app successfully generates captions for images, identifying objects and their actions.', 'Gradio is used to create the app interface, enabling users to upload images and receive captions, showcasing its simplicity and versatility.', 'We learn to build an image generation app using a stable diffusion model and connect to it via an API URL.', 'The model was trained to generate an image from a given text description, and it successfully generates images from text inputs in the Gradio app.', 'The chapter discusses using a stable diffusion model to generate diverse images by experimenting with prompts and parameters.', 'Example of generating images based on the spirit of a Tamagotchi in the city of Vienna.', 'Exploring the impact of negative prompts and inference steps on image quality.']}, {'end': 1793.653, 'segs': [{'end': 1560.213, 'src': 'embed', 'start': 1535.129, 'weight': 0, 'content': [{'end': 1543.054, 'text': 'which essentially is a very streamlined way where we can do a function the inputs, outputs, title description and it generates an app for us.', 'start': 1535.129, 'duration': 7.925}, {'end': 1556.97, 'text': 'While Gradial Blocks enable us to build a bit more complex user interfaces by essentially letting you build yourself your rows and columns and where to position elements in your app.', 'start': 1543.914, 'duration': 13.056}, {'end': 1560.213, 'text': 'So Gradial Interface makes it very easy to build.', 'start': 1557.59, 'duration': 2.623}], 'summary': 'Gradial interface simplifies app building with streamlined functions and allows for complex ui creation.', 'duration': 25.084, 'max_score': 1535.129, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs1535129.jpg'}, {'end': 1762.771, 'src': 'embed', 'start': 1734.099, 'weight': 1, 'content': [{'end': 1739.524, 'text': "This is a bit of the trade-off that we have with Gradio blocks that if you'd like to build a more simple UI,", 'start': 1734.099, 'duration': 5.425}, {'end': 1743.208, 'text': 'you may have to build a bit of more complex code and vice versa.', 'start': 1739.524, 'duration': 3.684}, {'end': 1748.913, 'text': "So with Gradio interface, it's the simplest possible code, but the UI can get a bit complex and cluttered.", 'start': 1743.228, 'duration': 5.685}, {'end': 1750.655, 'text': "So let's see what's going on here.", 'start': 1749.173, 'duration': 1.482}, {'end': 1755.966, 'text': 'So here we set a Gradial Markdown for the title, and this is where we have.', 'start': 1751.843, 'duration': 4.123}, {'end': 1762.771, 'text': 'We have a Gradial Row with two columns, one being the prompt and the other being the button right by it.', 'start': 1756.687, 'duration': 6.084}], 'summary': 'Trade-off with gradio blocks: simple ui requires complex code, resulting in cluttered ui.', 'duration': 28.672, 'max_score': 1734.099, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs1734099.jpg'}], 'start': 1504.19, 'title': 'Gradio blocks for ui', 'summary': 'Introduces gradial blocks for building complex user interfaces, enabling users to create rows, columns, and position elements, and defining button actions, showcasing simplicity and flexibility in creating different ui elements, and exploring the trade-off between code simplicity and ui complexity.', 'chapters': [{'end': 1657.614, 'start': 1504.19, 'title': 'Introduction to gradial blocks', 'summary': 'Introduces gradial blocks as a way to build more complex user interfaces, enabling users to create their own rows, columns, and position elements, and also explicitly define button actions, thus allowing for a more customizable user experience.', 'duration': 153.424, 'highlights': ['Gradial Blocks enable users to build more complex user interfaces by creating their own rows, columns, and positioning elements within the app, offering a more customizable user experience.', 'In Gradial Blocks, users can explicitly define the actions of buttons, allowing for greater control over user interactions.', 'Gradial Interface provides a streamlined way to generate apps based on function inputs, outputs, title, and description.']}, {'end': 1793.653, 'start': 1657.614, 'title': 'Building ui with gradio blocks', 'summary': 'Illustrates building a ui using gradio blocks, showcasing the simplicity and flexibility of creating different ui elements, such as prompts, buttons, advanced options, and outputs, and the trade-off between code simplicity and ui complexity within the gradio interface.', 'duration': 136.039, 'highlights': ['Gradio blocks allow building a variety of UI elements, such as prompts, buttons, and advanced options, providing simplicity and flexibility in creating different layouts. The chapter demonstrates the use of Gradio blocks to create prompts, buttons, and advanced options, showcasing the simplicity and flexibility in building different UI layouts.', 'The trade-off between code simplicity and UI complexity is discussed, where building a more simple UI may require more complex code and vice versa within the Gradio interface. The chapter highlights the trade-off between code simplicity and UI complexity within Gradio interface, emphasizing the relationship between code complexity and UI simplicity.', 'The use of Gradio blocks allows for defining UI elements such as scale elements and setting minimum width, providing granular control over the layout. Gradio blocks enable the definition of UI elements like scale elements and minimum width, offering granular control over the layout and design of the UI.']}], 'duration': 289.463, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs1504190.jpg', 'highlights': ['Gradial Blocks enable users to build more complex user interfaces by creating their own rows, columns, and positioning elements within the app, offering a more customizable user experience.', 'Gradio blocks allow building a variety of UI elements, such as prompts, buttons, and advanced options, providing simplicity and flexibility in creating different layouts.', 'The use of Gradio blocks allows for defining UI elements such as scale elements and setting minimum width, providing granular control over the layout.', 'In Gradial Blocks, users can explicitly define the actions of buttons, allowing for greater control over user interactions.', 'Gradial Interface provides a streamlined way to generate apps based on function inputs, outputs, title, and description.', 'The trade-off between code simplicity and UI complexity is discussed, where building a more simple UI may require more complex code and vice versa within the Gradio interface.']}, {'end': 2112.472, 'segs': [{'end': 1856.54, 'src': 'embed', 'start': 1795.305, 'weight': 0, 'content': [{'end': 1800.947, 'text': 'And here we have another element that can simplify our UI and that is the Gradial Accordion.', 'start': 1795.305, 'duration': 5.642}, {'end': 1805.769, 'text': "So if you have something like advanced options that we don't want to expose to every user,", 'start': 1800.987, 'duration': 4.782}, {'end': 1812.331, 'text': 'we can have an accordion that will essentially hide some information and that can be prompted when opened.', 'start': 1805.769, 'duration': 6.562}, {'end': 1816.652, 'text': 'So here by default, the accordions are already open.', 'start': 1812.951, 'duration': 3.701}, {'end': 1819.865, 'text': 'So we have to define that they Start closed.', 'start': 1816.672, 'duration': 3.193}, {'end': 1826.349, 'text': 'And here we have a structure that inside of the accordion, we can also customize the elements in the way we would like.', 'start': 1820.606, 'duration': 5.743}, {'end': 1832.331, 'text': 'So here we have the negative prompt because we want to give it a bit more real estate.', 'start': 1826.949, 'duration': 5.382}, {'end': 1835.973, 'text': 'So we put the negative prompt taking this entire row.', 'start': 1832.412, 'duration': 3.561}, {'end': 1839.015, 'text': 'And then below it we have a row with two columns,', 'start': 1836.373, 'duration': 2.642}, {'end': 1843.437, 'text': 'being the inference steps and the guidance scale in one side and the width and height on the other side.', 'start': 1839.015, 'duration': 4.422}, {'end': 1846.655, 'text': 'And after it all, we have the result.', 'start': 1844.494, 'duration': 2.161}, {'end': 1856.54, 'text': 'So we close the accordion and we have a simple UI using radial blocks that can make your image generation application really easy to use.', 'start': 1847.395, 'duration': 9.145}], 'summary': 'The radial accordion simplifies ui, hides advanced options, and allows for easy customization, resulting in a user-friendly image generation application.', 'duration': 61.235, 'max_score': 1795.305, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs1795305.jpg'}, {'end': 1906.204, 'src': 'embed', 'start': 1878.822, 'weight': 3, 'content': [{'end': 1885.273, 'text': 'text to image and image to text and put them together in a single fun app that we can play with.', 'start': 1878.822, 'duration': 6.451}, {'end': 1889.952, 'text': 'In the previous lessons we learned how to build a Gradle app for NLP applications,', 'start': 1886.169, 'duration': 3.783}, {'end': 1893.495, 'text': 'how to build a captioning app and how to build the text-to-image app.', 'start': 1889.952, 'duration': 3.543}, {'end': 1899.819, 'text': "So now, let's join all the knowledge that we got in the other lessons, and let's build a cool game.", 'start': 1894.095, 'duration': 5.724}, {'end': 1906.204, 'text': "In this game, we're going to start with the captioning, and then from that caption, we're going to generate a new image.", 'start': 1900.3, 'duration': 5.904}], 'summary': 'Combine text-to-image and image-to-text in a fun app, leveraging nlp and gradle, as well as previous lesson knowledge.', 'duration': 27.382, 'max_score': 1878.822, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs1878822.jpg'}, {'end': 2091.71, 'src': 'embed', 'start': 2061.627, 'weight': 2, 'content': [{'end': 2069.112, 'text': 'where you would have a single function that is called caption and generate that essentially calls the captioner and the generation.', 'start': 2061.627, 'duration': 7.485}, {'end': 2074.938, 'text': 'And then we do our game in a more streamlined way, where you simply upload an image,', 'start': 2069.734, 'duration': 5.204}, {'end': 2082.184, 'text': 'press the caption and generate button and then get the caption and the generated image all at once.', 'start': 2074.938, 'duration': 7.246}, {'end': 2084.106, 'text': "So let's see how that would look like.", 'start': 2082.685, 'duration': 1.421}, {'end': 2091.71, 'text': 'So let me upload this image of this llama keychain that I have here in the office.', 'start': 2085.426, 'duration': 6.284}], 'summary': "Function 'caption' streamlines image upload, captioning, and image generation for a more efficient game experience.", 'duration': 30.083, 'max_score': 2061.627, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs2061627.jpg'}], 'start': 1795.305, 'title': 'Ui simplification and model combination', 'summary': 'Introduces the gradial accordion for ui simplification, hiding advanced options, customizable elements, and prompts, and demonstrates combining text-to-image and image-to-text models to build a game application, using radial blocks to streamline the ui and encouraging user interaction. it also showcases a streamlined version of the application and its outputs.', 'chapters': [{'end': 1839.015, 'start': 1795.305, 'title': 'Using gradial accordion for ui simplification', 'summary': 'Introduces the gradial accordion, which simplifies ui by hiding advanced options, customizable elements, and prompts, ensuring they start closed and providing a structure for layout customization.', 'duration': 43.71, 'highlights': ['The Gradial Accordion simplifies UI by hiding advanced options, ensuring they start closed, and providing a customizable structure for layout, thus improving user experience and reducing clutter.', 'It allows the customization of elements within the accordion, such as utilizing negative prompts to maximize real estate and arranging content in rows with columns, enhancing the visual layout and user interaction.']}, {'end': 2112.472, 'start': 1839.015, 'title': 'Combining text-to-image and image-to-text models', 'summary': 'Demonstrates combining text-to-image and image-to-text models to build a game application, using radial blocks to streamline the ui and encouraging user interaction. it also showcases a streamlined version of the application and its outputs.', 'duration': 273.457, 'highlights': ['The chapter demonstrates combining text-to-image and image-to-text models to build a game application. The chapter discusses combining the text-to-image and image-to-text models to create a game application, showcasing the integration of knowledge gained from previous lessons.', 'Using radial blocks to streamline the UI and encourage user interaction. The use of radial blocks is demonstrated to streamline the UI and encourage user interaction, making image generation applications easier to use.', 'Showcasing a streamlined version of the application and its outputs. A streamlined version of the application is presented, allowing users to upload an image, press a caption and generate button, and receive the caption and generated image all at once.']}], 'duration': 317.167, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs1795305.jpg', 'highlights': ['The Gradial Accordion simplifies UI by hiding advanced options, ensuring they start closed, and providing a customizable structure for layout, thus improving user experience and reducing clutter.', 'It allows the customization of elements within the accordion, such as utilizing negative prompts to maximize real estate and arranging content in rows with columns, enhancing the visual layout and user interaction.', 'Showcasing a streamlined version of the application and its outputs. A streamlined version of the application is presented, allowing users to upload an image, press a caption and generate button, and receive the caption and generated image all at once.', 'The chapter demonstrates combining text-to-image and image-to-text models to build a game application. The chapter discusses combining the text-to-image and image-to-text models to create a game application, showcasing the integration of knowledge gained from previous lessons.', 'Using radial blocks to streamline the UI and encourage user interaction. The use of radial blocks is demonstrated to streamline the UI and encourage user interaction, making image generation applications easier to use.']}, {'end': 2947.328, 'segs': [{'end': 2185.907, 'src': 'embed', 'start': 2158.888, 'weight': 0, 'content': [{'end': 2166.84, 'text': "In our final lesson, we're building an app to chat with an open source LLM, Falcon 40B, one of the best open source models out there.", 'start': 2158.888, 'duration': 7.952}, {'end': 2168.663, 'text': "I'm excited and I hope you are too.", 'start': 2167.161, 'duration': 1.502}, {'end': 2174.62, 'text': "In our final lesson, we'll build a chatbot app powered by an open-source LLM.", 'start': 2169.417, 'duration': 5.203}, {'end': 2180.204, 'text': "You've probably already chatted with ChatGPT, but running it can be costly and rigid.", 'start': 2175.181, 'duration': 5.023}, {'end': 2185.907, 'text': 'Custom LLMs can run locally, be fine-tuned in your own data, or run cheaper on the cloud.', 'start': 2180.624, 'duration': 5.283}], 'summary': 'Building a chatbot app powered by open-source llm falcon 40b, offering cost-effective, flexible, and fine-tuned options.', 'duration': 27.019, 'max_score': 2158.888, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs2158888.jpg'}, {'end': 2244.469, 'src': 'embed', 'start': 2197.884, 'weight': 1, 'content': [{'end': 2203.786, 'text': 'And of course, you can also use Gradio to create interfaces for API only based LLM, so not only open source.', 'start': 2197.884, 'duration': 5.902}, {'end': 2207.928, 'text': 'You can use Gradio as well to build UIs for ChatGPT or Cloud.', 'start': 2203.846, 'duration': 4.082}, {'end': 2212.47, 'text': "But in this course, we're going to be focusing on the open source LLM Falcon 4 TB.", 'start': 2208.308, 'duration': 4.162}, {'end': 2215.351, 'text': "So here we're setting up our token.", 'start': 2213.61, 'duration': 1.741}, {'end': 2217.378, 'text': 'and our helper function.', 'start': 2216.297, 'duration': 1.081}, {'end': 2220.379, 'text': "And you can see that here we're using a different library.", 'start': 2217.558, 'duration': 2.821}, {'end': 2227.022, 'text': "So we're using the text generation library, which is a streamlined library for dealing with the open source LLMs.", 'start': 2220.399, 'duration': 6.623}, {'end': 2232.625, 'text': "that enables you to load both APIs, like we're doing here, but also to run locally your own LLM.", 'start': 2227.022, 'duration': 5.603}, {'end': 2240.089, 'text': "So here we have asked the model, has math been invented or discovered? Here's the completion of the model.", 'start': 2234.086, 'duration': 6.003}, {'end': 2244.469, 'text': 'where we asked it to have a maximum of 256 tokens.', 'start': 2241.067, 'duration': 3.402}], 'summary': 'Using gradio to build interfaces for llm falcon 4 tb, with focus on open source llms.', 'duration': 46.585, 'max_score': 2197.884, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs2197884.jpg'}, {'end': 2383.674, 'src': 'embed', 'start': 2354.205, 'weight': 3, 'content': [{'end': 2360.691, 'text': "And that's where the Gradient Chatbot component comes, because it allows us to streamline the process of sending the model,", 'start': 2354.205, 'duration': 6.486}, {'end': 2362.092, 'text': 'the history of the conversation.', 'start': 2360.691, 'duration': 1.401}, {'end': 2365.508, 'text': 'So we actually want to fix that.', 'start': 2362.847, 'duration': 2.661}, {'end': 2371.13, 'text': "And for that, we'll introduce a new Gradle component that is Gradle Chatbot.", 'start': 2366.088, 'duration': 5.042}, {'end': 2374.531, 'text': "So let's get started with the Gradle Chatbot component.", 'start': 2372.25, 'duration': 2.281}, {'end': 2383.674, 'text': 'I instantiated a Gradle Chatbot component with a text box prompt and a submit button.', 'start': 2376.451, 'duration': 7.223}], 'summary': 'Introducing the gradle chatbot component to streamline sending models and conversation history.', 'duration': 29.469, 'max_score': 2354.205, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs2354205.jpg'}, {'end': 2634.558, 'src': 'embed', 'start': 2606.896, 'weight': 4, 'content': [{'end': 2615.486, 'text': 'In order to get the most out of the model, we can come here and set our max new tokens to 124,', 'start': 2606.896, 'duration': 8.59}, {'end': 2620.109, 'text': "which is the maximum that this model can take in the hardware that we're running in the API.", 'start': 2615.486, 'duration': 4.623}, {'end': 2627.674, 'text': 'And that allows for a conversation with a couple of follow-up questions, but at some point it will load up our context window.', 'start': 2620.609, 'duration': 7.065}, {'end': 2632.397, 'text': 'So now we have our chat with 1, 024 tokens.', 'start': 2629.255, 'duration': 3.142}, {'end': 2634.558, 'text': "So let's start a conversation.", 'start': 2633.037, 'duration': 1.521}], 'summary': 'To optimize the model, set max new tokens to 124, allowing for a conversation with 1024 tokens.', 'duration': 27.662, 'max_score': 2606.896, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs2606896.jpg'}, {'end': 2685.568, 'src': 'embed', 'start': 2663.251, 'weight': 5, 'content': [{'end': 2677.241, 'text': "So a stop sequence guarantees that when a new line user which is essentially what would indicate that it's a message from us and not from the model is trying to be generated,", 'start': 2663.251, 'duration': 13.99}, {'end': 2678.162, 'text': 'the model will stop.', 'start': 2677.241, 'duration': 0.921}, {'end': 2685.568, 'text': 'So then we can guarantee that the model would stop in this example and that the strongest animal in the savannah is the elephant.', 'start': 2678.923, 'duration': 6.645}], 'summary': 'Stop sequence ensures model stops; strongest animal in savannah is elephant', 'duration': 22.317, 'max_score': 2663.251, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs2663251.jpg'}, {'end': 2734.464, 'src': 'embed', 'start': 2705.154, 'weight': 6, 'content': [{'end': 2710.856, 'text': 'But if you want to grab the best that Gradio can offer, you can build a UI that contains a few more features.', 'start': 2705.154, 'duration': 5.702}, {'end': 2717.198, 'text': 'So here we have advanced options, including a system message that can set a mode to which the LLM will chat with you.', 'start': 2711.376, 'duration': 5.822}, {'end': 2725.761, 'text': 'So in the system message, you can say, for example, you are a helpful assistant, or you can give it a particular tone, a particular intonation.', 'start': 2717.578, 'duration': 8.183}, {'end': 2728.422, 'text': 'You want it to be a bit more funny, a bit more serious.', 'start': 2725.781, 'duration': 2.641}, {'end': 2734.464, 'text': 'And really, you can play with the system message prompt and see how it affects your messages.', 'start': 2729.323, 'duration': 5.141}], 'summary': 'Gradio ui offers advanced options for customizing system message and chat tone.', 'duration': 29.31, 'max_score': 2705.154, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs2705154.jpg'}], 'start': 2112.472, 'title': 'Building chatbot apps', 'summary': 'Explores building chatbot apps powered by open-source language models, including falcon 4b and gradio, allowing local running, fine-tuning, and cheaper cloud usage. it also details the challenges of context retention and showcases advanced options like temperature control and streaming responses.', 'chapters': [{'end': 2292.311, 'start': 2112.472, 'title': 'Building chatbot app with open source llm', 'summary': 'Demonstrates the process of building a chatbot app powered by an open-source large language model, falcon 4b, allowing local running, fine-tuning, and cheaper cloud usage, using gradio to create interfaces for api-based llms, and using the text generation library for dealing with the open-source llms.', 'duration': 179.839, 'highlights': ['The chapter demonstrates the process of building a chatbot app powered by an open-source large language model, Falcon 4B. The lesson focuses on building a chatbot app powered by an open-source large language model, Falcon 4B, which allows local running, fine-tuning, and cheaper cloud usage.', 'Using Gradio to create interfaces for API-based LLMs. Gradio can be used to create interfaces for API-based large language models, including open source models like Falcon 4B and others, as demonstrated in the lesson.', 'Using the text generation library for dealing with the open-source LLMs. The lesson showcases the use of the text generation library, a streamlined library for dealing with open-source large language models, enabling the loading of APIs and local running of LLMs, as exemplified in the session.']}, {'end': 2947.328, 'start': 2294.312, 'title': 'Building a streamlined chatbot with gradio', 'summary': 'Details the process of building a chatbot using gradio and a language model, highlighting the challenges of context retention and showcasing advanced options like temperature control and streaming responses.', 'duration': 653.016, 'highlights': ['Gradio Chatbot component allows streamlined process of sending model the history of the conversation, easing the contextualization process.', 'Setting max new tokens to 124 enables a conversation with follow-up questions, optimizing model usage.', 'Stop sequence prevents model from generating its own messages, ensuring user-led follow-up questions.', 'Advanced options in Gradio UI include system messages, temperature control, and streaming responses, enhancing user interaction and model behavior.']}], 'duration': 834.856, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FJazOh2Tngs/pics/FJazOh2Tngs2112472.jpg', 'highlights': ['The chapter demonstrates building a chatbot app powered by Falcon 4B, enabling local running and fine-tuning.', 'Using Gradio to create interfaces for API-based large language models, including Falcon 4B and others.', 'The lesson showcases the use of the text generation library for dealing with open-source large language models.', 'Gradio Chatbot component streamlines sending the model the history of the conversation for contextualization.', 'Setting max new tokens to 124 optimizes model usage for conversations with follow-up questions.', 'Stop sequence prevents the model from generating its own messages, ensuring user-led follow-up questions.', 'Advanced options in Gradio UI include system messages, temperature control, and streaming responses.']}], 'highlights': ['Gradio allows for quick user interface creation without extensive coding.', 'Gradio is a fast way to demo ML models with a friendly web interface in Python.', 'The model is trained on millions of image and text caption pairs, learning to predict captions for new images.', 'The app successfully generates captions for images, identifying objects and their actions.', 'The course focuses on utilizing the DistilledBartCNN model for text summarization.', "The BERT-based named entity recognition model's capability to recognize various entities and the process of fine-tuning for specific use cases are demonstrated.", 'The chapter demonstrates combining text-to-image and image-to-text models to build a game application.', 'Gradio blocks allow building a variety of UI elements, such as prompts, buttons, and advanced options, providing simplicity and flexibility in creating different layouts.', 'The Gradial Accordion simplifies UI by hiding advanced options, ensuring they start closed, and providing a customizable structure for layout, thus improving user experience and reducing clutter.', 'The lesson showcases the use of the text generation library for dealing with open-source large language models.']}