title
8. Evaluating and Debugging Generative AI | Andrew Ng | DeepLearning.ai - Full Course

description
The course comes from [https://learn.deeplearning.ai/evaluating-debugging-generative-ai/lesson/1/introduction](https://learn.deeplearning.ai/evaluating-debugging-generative-ai/lesson/1/introduction) created by Andrew Ng In this YouTube video, Andrew Ng and Carrie Feltz of Ways and Biases introduce ways to evaluate and debug Generative AI. The course covers how to systematically track and debug generative AI models, using tools of Ways and Biases that have become the industry standard for machine learning experiment tracking. The course focuses on evaluating and debugging generative AI during development, including the monitoring and evaluation of large language models and image generation diffusion models. Get free course notes: https://t.me/NoteForYoutubeCourse

detail
{'title': '8. Evaluating and Debugging Generative AI | Andrew Ng | DeepLearning.ai - Full Course', 'heatmap': [], 'summary': 'Covers evaluating and debugging generative ai, including discussing challenges in tracking and managing machine learning model training, using weights and biases for model tracking, model settings, training generative ai, sampling and llm evaluation, openai api cost, llm chain tracing, and language model fine-tuning, with contributions from various experts.', 'chapters': [{'end': 200.043, 'segs': [{'end': 94.214, 'src': 'embed', 'start': 58.055, 'weight': 0, 'content': [{'end': 65.64, 'text': 'managing and tracking machine learning model training and evaluation gets complicated and the complexity grows worse with larger teams.', 'start': 58.055, 'duration': 7.585}, {'end': 73.563, 'text': "I've seen that many teams can be much more efficient if this step of machine learning development is done more rigorously.", 'start': 66.88, 'duration': 6.683}, {'end': 83.848, 'text': 'So this short course covers tools and best practices for systematically tracking and debugging generative AI models during the development process.', 'start': 74.524, 'duration': 9.324}, {'end': 89.952, 'text': "We'll be using tools from Waze and Biases, which offers an easy and flexible set of tools.", 'start': 84.609, 'duration': 5.343}, {'end': 94.214, 'text': "that's become a bit of an industry standard for machine learning experiment tracking.", 'start': 89.952, 'duration': 4.262}], 'summary': 'Efficiently track and debug ai models using industry-standard tools for better ml development.', 'duration': 36.159, 'max_score': 58.055, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc58055.jpg'}, {'end': 202.064, 'src': 'embed', 'start': 175.179, 'weight': 4, 'content': [{'end': 178.923, 'text': 'And quite a few people had contributed to the development of this course.', 'start': 175.179, 'duration': 3.744}, {'end': 180.985, 'text': 'On the waste and bias side.', 'start': 179.603, 'duration': 1.382}, {'end': 190.094, 'text': "we're grateful to the hard work of Darren Kojicek, as well as Thomas Koppel and from deeplearning.ai, Jeff Ludwig and Tommy Nelson.", 'start': 180.985, 'duration': 9.109}, {'end': 191.755, 'text': 'By the end of this course,', 'start': 190.494, 'duration': 1.261}, {'end': 200.043, 'text': "you'll understand best practices and also have a set of tools for systematically evaluating and debugging generative AI projects.", 'start': 191.755, 'duration': 8.288}, {'end': 202.064, 'text': 'I hope you enjoy the course.', 'start': 200.643, 'duration': 1.421}], 'summary': 'Multiple contributors developed the course. the course covers best practices and tools for evaluating generative ai projects.', 'duration': 26.885, 'max_score': 175.179, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc175179.jpg'}], 'start': 0.049, 'title': 'Debugging generative ai models', 'summary': 'Discusses challenges in tracking and managing machine learning model training, emphasizing the importance of systematic tracking and debugging for driving improvements. it covers evaluating and debugging generative ai, including visualizing experiments, monitoring diffusion models, and utilizing various tools, with contributions from experts darren kojicek, thomas koppel, jeff ludwig, and tommy nelson.', 'chapters': [{'end': 115.418, 'start': 0.049, 'title': 'Debugging ai models: best practices', 'summary': 'Discusses the challenges of tracking and managing machine learning model training and evaluation, emphasizing the importance of systematic tracking and debugging for efficiently driving improvements, particularly in the context of generative ai models.', 'duration': 115.369, 'highlights': ['Carrie Feltz emphasizes the challenges of tracking and managing machine learning model training and evaluation, particularly for generative AI models. Carrie Feltz discusses the complexities of tracking machine learning models, especially for generative AI models, and highlights the importance of systematic tracking and debugging for driving improvements.', 'The chapter emphasizes the need for systematic tracking and debugging to efficiently drive improvements in machine learning development, which becomes more complex with larger teams. The chapter stresses the significance of systematic tracking and debugging for driving improvements in machine learning development, especially as the complexity grows with larger teams.', "The short course covers tools and best practices for systematically tracking and debugging generative AI models during the development process, utilizing tools from Ways and Biases, known as an industry standard for machine learning experiment tracking. The short course addresses the tools and best practices for systematically tracking and debugging generative AI models, utilizing Ways and Biases' tools, recognized as an industry standard for machine learning experiment tracking."]}, {'end': 200.043, 'start': 115.858, 'title': 'Debugging generative ai course', 'summary': 'Covers evaluating and debugging generative ai, including tracking and visualizing experiments, monitoring diffusion models, and utilizing tools like experiments, artifacts, models tables, reports, and model registry, with contributions from darren kojicek, thomas koppel, jeff ludwig, and tommy nelson, aiming to provide learners with best practices and tools for evaluating and debugging generative ai projects.', 'duration': 84.185, 'highlights': ['The course focuses on evaluating and debugging generative AI using tools such as experiments, artifacts, models tables, reports, and model registry, compatible with various frameworks and computing platforms, including Python, TensorFlow, or PyTorch.', 'The contributors to the course include Darren Kojicek, Thomas Koppel, Jeff Ludwig, and Tommy Nelson.', 'Learners will gain an understanding of best practices and a set of tools for systematically evaluating and debugging generative AI projects.']}], 'duration': 199.994, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc49.jpg', 'highlights': ['The chapter emphasizes the need for systematic tracking and debugging to efficiently drive improvements in machine learning development, especially as the complexity grows with larger teams.', "The short course addresses the tools and best practices for systematically tracking and debugging generative AI models, utilizing Ways and Biases' tools, recognized as an industry standard for machine learning experiment tracking.", 'The course focuses on evaluating and debugging generative AI using tools such as experiments, artifacts, models tables, reports, and model registry, compatible with various frameworks and computing platforms, including Python, TensorFlow, or PyTorch.', 'Learners will gain an understanding of best practices and a set of tools for systematically evaluating and debugging generative AI projects.', 'The contributors to the course include Darren Kojicek, Thomas Koppel, Jeff Ludwig, and Tommy Nelson.']}, {'end': 795.457, 'segs': [{'end': 249.918, 'src': 'embed', 'start': 200.643, 'weight': 0, 'content': [{'end': 202.064, 'text': 'I hope you enjoy the course.', 'start': 200.643, 'duration': 1.421}, {'end': 207.93, 'text': "In the first lesson, I'll show you how to instrument weights and biases in your machine learning training code.", 'start': 203.365, 'duration': 4.565}, {'end': 211.685, 'text': 'As we train machine learning models, many things can go wrong.', 'start': 208.863, 'duration': 2.822}, {'end': 215.947, 'text': 'WMB will help us monitor and debug and evaluate our pipelines.', 'start': 212.545, 'duration': 3.402}, {'end': 217.268, 'text': "Let's dive in.", 'start': 216.707, 'duration': 0.561}, {'end': 225.412, 'text': 'With just a few lines of code, you can monitor your metrics, CPU, and GPU usage in real time.', 'start': 219.049, 'duration': 6.363}, {'end': 234.337, 'text': 'You can version control your code, reproduce model checkpoints, and visualize your predictions in a centralized interactive dashboard.', 'start': 226.433, 'duration': 7.904}, {'end': 241.532, 'text': 'Our users evaluate models, discuss bugs, and demonstrate progress with configurable reports.', 'start': 235.768, 'duration': 5.764}, {'end': 244.914, 'text': "By the end of this course, you'll be able to do this too.", 'start': 242.333, 'duration': 2.581}, {'end': 249.918, 'text': "Let's start by learning how to incorporate weights and biases into your training process.", 'start': 245.495, 'duration': 4.423}], 'summary': 'Learn to monitor machine learning metrics and visualize predictions with wmb, enabling real-time tracking and centralized interactive dashboard.', 'duration': 49.275, 'max_score': 200.643, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc200643.jpg'}, {'end': 329.911, 'src': 'embed', 'start': 277.049, 'weight': 3, 'content': [{'end': 280.512, 'text': 'A run in Weights and Biases means a unit of computation.', 'start': 277.049, 'duration': 3.463}, {'end': 284.415, 'text': 'Generally, a run corresponds to a machine learning experiment.', 'start': 281.432, 'duration': 2.983}, {'end': 290.059, 'text': 'You begin a run by calling WMB init, passing the project name and your config object.', 'start': 284.695, 'duration': 5.364}, {'end': 298.707, 'text': 'You carry on with your model training code and when you reach a point where there are certain metrics you want to track and visualize,', 'start': 291.223, 'duration': 7.484}, {'end': 300.808, 'text': 'you log them using WMB log.', 'start': 298.707, 'duration': 2.101}, {'end': 306.63, 'text': 'If you are using a notebook, it is recommended to call WMB finish at the end.', 'start': 302.168, 'duration': 4.462}, {'end': 309.777, 'text': "Now, Let's check this out in a notebook.", 'start': 307.731, 'duration': 2.046}, {'end': 313.8, 'text': 'In this training script, we will train a sprite classification model.', 'start': 310.257, 'duration': 3.543}, {'end': 321.925, 'text': 'A sprite is a small 16x16 pixel image, and our goal is to categorize the sprite into one of five classes.', 'start': 314.54, 'duration': 7.385}, {'end': 326.989, 'text': 'Hero, non-hero, food, or spell.', 'start': 323.306, 'duration': 3.683}, {'end': 329.911, 'text': 'And side-facing, which is not pictured.', 'start': 327.969, 'duration': 1.942}], 'summary': 'A run in weights and biases corresponds to a machine learning experiment, tracking metrics and visualizing results.', 'duration': 52.862, 'max_score': 277.049, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc277049.jpg'}, {'end': 423.487, 'src': 'embed', 'start': 397.935, 'weight': 5, 'content': [{'end': 402.397, 'text': "We don't need to change anything in the validation function, so I'll just run this cell.", 'start': 397.935, 'duration': 4.462}, {'end': 410.241, 'text': 'In this course, we will use WMB Cloud Platform, and that means we need to log in.', 'start': 405.679, 'duration': 4.562}, {'end': 417.865, 'text': "There's also an option to install WMB locally, but that is a bit more complex, so we will not cover that in this course.", 'start': 411.041, 'duration': 6.824}, {'end': 423.487, 'text': 'WMB is free for personal and academic use, and we encourage you to sign up.', 'start': 419.505, 'duration': 3.982}], 'summary': 'Using wmb cloud platform in the course, free for personal and academic use', 'duration': 25.552, 'max_score': 397.935, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc397935.jpg'}, {'end': 665.961, 'src': 'embed', 'start': 639.335, 'weight': 6, 'content': [{'end': 645.296, 'text': 'This shows those same runs in a tabular format, so I can look at the metrics and hyperparameters side by side.', 'start': 639.335, 'duration': 5.961}, {'end': 657.439, 'text': 'Here, I can see when I changed the dropout, epics, and learning rate across my different runs.', 'start': 652.098, 'duration': 5.341}, {'end': 661.6, 'text': 'A specific metric of interest might be accuracy.', 'start': 659.099, 'duration': 2.501}, {'end': 665.961, 'text': 'To find it, I can go to the columns section and search for accuracy.', 'start': 662.1, 'duration': 3.861}], 'summary': 'Tabular format displays metrics and hyperparameters across different runs, including changes to dropout, epochs, and learning rate, with a focus on accuracy.', 'duration': 26.626, 'max_score': 639.335, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc639335.jpg'}, {'end': 778.849, 'src': 'embed', 'start': 745.442, 'weight': 7, 'content': [{'end': 749.024, 'text': "Now I'm going to select that best run and see the overview of it.", 'start': 745.442, 'duration': 3.582}, {'end': 755.625, 'text': 'Here in the detail view for my best run, I can see some context about how it was created.', 'start': 750.441, 'duration': 5.184}, {'end': 759.507, 'text': 'So WMB automatically picks up the Git repo.', 'start': 756.225, 'duration': 3.282}, {'end': 763.37, 'text': 'So I can easily get back to the code that was used to train this model.', 'start': 760.048, 'duration': 3.322}, {'end': 766.532, 'text': 'It also gets the hash of the latest Git commit.', 'start': 763.99, 'duration': 2.542}, {'end': 770.775, 'text': 'So I know exactly the state of that repo when this run was created.', 'start': 767.172, 'duration': 3.603}, {'end': 778.849, 'text': "But realistically, often I'm making little tweaks in that notebook and don't remember to always commit my changes.", 'start': 771.465, 'duration': 7.384}], 'summary': 'Wmb automatically picks up the git repo and latest commit hash for model training, providing context and ensuring reproducibility.', 'duration': 33.407, 'max_score': 745.442, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc745442.jpg'}], 'start': 200.643, 'title': 'Using weights and biases for model tracking', 'summary': 'Introduces the use of weights and biases (wmb) to monitor, debug, and evaluate machine learning pipelines, enabling real-time monitoring of metrics, cpu, and gpu usage, as well as version control, reproducing model checkpoints, and visualizing predictions. it also discusses using the wmb library to track machine learning experiments, including logging metrics, comparing runs, and viewing experiment details, with an emphasis on improving model performance.', 'chapters': [{'end': 249.918, 'start': 200.643, 'title': 'Weights and biases for ml training', 'summary': 'Introduces the use of weights and biases (wmb) to monitor, debug, and evaluate machine learning pipelines, enabling real-time monitoring of metrics, cpu, and gpu usage, as well as version control, reproducing model checkpoints, and visualizing predictions.', 'duration': 49.275, 'highlights': ['With just a few lines of code, you can monitor your metrics, CPU, and GPU usage in real time, enabling effective monitoring of machine learning models during training.', 'You can version control your code, reproduce model checkpoints, and visualize your predictions in a centralized interactive dashboard, providing a comprehensive approach to managing and evaluating machine learning models.', 'Our users evaluate models, discuss bugs, and demonstrate progress with configurable reports, emphasizing the collaborative and analytical capabilities enabled by weights and biases (WMB) for machine learning processes.', 'The course will equip you to incorporate weights and biases into your training process, empowering you to effectively monitor, debug, and evaluate machine learning pipelines.']}, {'end': 795.457, 'start': 250.738, 'title': 'Using weights and biases for model tracking', 'summary': 'Discusses using the wmb library to track machine learning experiments, including logging metrics, comparing runs, and viewing experiment details, with an emphasis on improving model performance.', 'duration': 544.719, 'highlights': ['The chapter explains the process of using the WMB library for tracking machine learning experiments, including importing WMB, initiating a WMB run, and logging metrics using wmb.log. Process of using WMB for experiment tracking', 'It covers the training of a sprite classification model with the goal of categorizing sprites into five classes, and emphasizes the use of WMB to track training and validation metrics. Training a sprite classification model and tracking metrics using WMB', 'The chapter demonstrates the use of WMB Cloud Platform for experiment tracking, login process, and the option to run code in anonymous mode. Demonstration of using WMB Cloud Platform for experiment tracking', 'It explains the process of comparing and analyzing experiment results, including visualizing training curves, comparing hyperparameters, and filtering and sorting runs based on metrics like accuracy. Comparing and analyzing experiment results', 'The chapter highlights the importance of viewing experiment details, including accessing Git repo information, commit hash, and diff patch in WMB to understand the context of the experiment. Importance of viewing experiment details in WMB']}], 'duration': 594.814, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc200643.jpg', 'highlights': ['With just a few lines of code, you can monitor your metrics, CPU, and GPU usage in real time, enabling effective monitoring of machine learning models during training.', 'You can version control your code, reproduce model checkpoints, and visualize your predictions in a centralized interactive dashboard, providing a comprehensive approach to managing and evaluating machine learning models.', 'The course will equip you to incorporate weights and biases into your training process, empowering you to effectively monitor, debug, and evaluate machine learning pipelines.', 'The chapter explains the process of using the WMB library for tracking machine learning experiments, including importing WMB, initiating a WMB run, and logging metrics using wmb.log.', 'It covers the training of a sprite classification model with the goal of categorizing sprites into five classes, and emphasizes the use of WMB to track training and validation metrics.', 'The chapter demonstrates the use of WMB Cloud Platform for experiment tracking, login process, and the option to run code in anonymous mode.', 'It explains the process of comparing and analyzing experiment results, including visualizing training curves, comparing hyperparameters, and filtering and sorting runs based on metrics like accuracy.', 'The chapter highlights the importance of viewing experiment details, including accessing Git repo information, commit hash, and diff patch in WMB to understand the context of the experiment.', 'Our users evaluate models, discuss bugs, and demonstrate progress with configurable reports, emphasizing the collaborative and analytical capabilities enabled by weights and biases (WMB) for machine learning processes.']}, {'end': 1364.497, 'segs': [{'end': 835.056, 'src': 'embed', 'start': 795.457, 'weight': 0, 'content': [{'end': 801.08, 'text': 'I know that I can easily get back to the exact state of the code by pulling this git commit and applying the patch.', 'start': 795.457, 'duration': 5.623}, {'end': 804.332, 'text': 'So that makes this run more reproducible.', 'start': 802.287, 'duration': 2.045}, {'end': 811.202, 'text': 'If I sent this to someone, I could also easily communicate with them about the settings I chose for this model.', 'start': 805.359, 'duration': 5.843}, {'end': 818.266, 'text': "Here in the config, I'm capturing batch size, dropout, epics, learning rate, all of the settings that we had in that notebook.", 'start': 811.783, 'duration': 6.483}, {'end': 820.748, 'text': 'And this is an easy, summarized format.', 'start': 818.727, 'duration': 2.021}, {'end': 826.531, 'text': "This information can be helpful when things go well, but it's also very valuable when things go wrong.", 'start': 821.228, 'duration': 5.303}, {'end': 835.056, 'text': 'So we can use this context for debugging, understanding what code was used in an experiment, what the environment was, the data set, et cetera.', 'start': 827.131, 'duration': 7.925}], 'summary': 'Using git commits and patch makes code more reproducible and easy to communicate settings, aiding in debugging and understanding experiments.', 'duration': 39.599, 'max_score': 795.457, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc795457.jpg'}, {'end': 889.034, 'src': 'embed', 'start': 859.063, 'weight': 3, 'content': [{'end': 860.865, 'text': "So let's just summarize the key points here.", 'start': 859.063, 'duration': 1.802}, {'end': 864.118, 'text': 'Diffusion models are denoising models.', 'start': 861.837, 'duration': 2.281}, {'end': 866.979, 'text': "We don't train a model directly to generate images.", 'start': 864.798, 'duration': 2.181}, {'end': 870.12, 'text': 'Instead, we train it to remove noise from images.', 'start': 867.439, 'duration': 2.681}, {'end': 876.722, 'text': 'During training, we add noise to images following a scheduler, and the model has to predict the noise present on the image.', 'start': 870.72, 'duration': 6.002}, {'end': 884.165, 'text': 'Finally, to generate samples, we start from pure noise and iteratively remove noise until the final image is revealed.', 'start': 877.503, 'duration': 6.662}, {'end': 889.034, 'text': "When training generative models, it's important to get the telemetry right.", 'start': 885.25, 'duration': 3.784}], 'summary': 'Diffusion models remove noise from images during training to generate samples.', 'duration': 29.971, 'max_score': 859.063, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc859063.jpg'}, {'end': 928.06, 'src': 'embed', 'start': 899.784, 'weight': 4, 'content': [{'end': 903.708, 'text': 'For this reason, it is crucial to sample from the model regularly during training.', 'start': 899.784, 'duration': 3.924}, {'end': 908.813, 'text': "Even when there's little decrease in loss, the images progressively improve.", 'start': 904.489, 'duration': 4.324}, {'end': 915.196, 'text': 'We will be uploading these samples to weights and biases while also saving the model checkpoints to keep everything organized.', 'start': 909.474, 'duration': 5.722}, {'end': 917.617, 'text': "Now let's jump into the notebook.", 'start': 916.216, 'duration': 1.401}, {'end': 925.359, 'text': 'We will use the training notebook from the deeplearning.ai course, How Diffusion Models Work, that trains a diffusion model on the sprites dataset.', 'start': 918.237, 'duration': 7.122}, {'end': 928.06, 'text': "We're not getting into the details about diffusion.", 'start': 925.979, 'duration': 2.081}], 'summary': 'Regularly sample from model during training to improve images, upload samples to weights and biases, save model checkpoints for organization.', 'duration': 28.276, 'max_score': 899.784, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc899784.jpg'}, {'end': 1244.87, 'src': 'embed', 'start': 1215.448, 'weight': 2, 'content': [{'end': 1220.19, 'text': 'The model registry acts as a central system for all machine learning models within an organization.', 'start': 1215.448, 'duration': 4.742}, {'end': 1228.064, 'text': "It's a system of record for all of the production-ready models and can be used to manage their lifecycle from staging to production.", 'start': 1221.181, 'duration': 6.883}, {'end': 1232.525, 'text': 'The model registry does not only serve as a storage system,', 'start': 1229.404, 'duration': 3.121}, {'end': 1237.768, 'text': 'but it also promotes teamwork by facilitating collaboration on all models across different teams.', 'start': 1232.525, 'duration': 5.243}, {'end': 1244.87, 'text': "It keeps a detailed lineage of our models while they're in training, under evaluation, and when they're in production.", 'start': 1238.768, 'duration': 6.102}], 'summary': 'Model registry centralizes ml models, facilitates collaboration, and tracks model lineage.', 'duration': 29.422, 'max_score': 1215.448, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc1215448.jpg'}], 'start': 795.457, 'title': 'Model settings and training generative ai', 'summary': 'Emphasizes capturing model settings for reproducibility and communication, including batch size, dropout, epics, and learning rate. it also discusses training a diffusion model, using noise, tracking metrics like loss curve, and employing the model registry.', 'chapters': [{'end': 835.056, 'start': 795.457, 'title': 'Reproducibility and communication in model settings', 'summary': 'Emphasizes the importance of capturing model settings in a summarized format for reproducibility, communication, and debugging, including batch size, dropout, epics, and learning rate, which can be valuable when things go wrong.', 'duration': 39.599, 'highlights': ['Capturing model settings in a summarized format is essential for reproducibility, communication, and debugging, including batch size, dropout, epics, and learning rate.', 'It facilitates easy communication with others about the settings chosen for the model, thus aiding in reproducibility and collaboration.', 'The captured information can be invaluable for debugging and understanding what code was used in an experiment, the environment, and the dataset, enhancing the ability to address issues when they occur.']}, {'end': 1364.497, 'start': 836.823, 'title': 'Training generative ai model', 'summary': 'Discusses training a diffusion model, emphasizing the use of noise to remove noise from images, tracking metrics like loss curve and regularly sampling from the model to improve image quality, and employing the model registry for storing and comparing machine learning models.', 'duration': 527.674, 'highlights': ['Diffusion models are denoising models, adding noise to images following a scheduler during training, and generating samples by iteratively removing noise until the final image is revealed. Diffusion models are utilized as denoising models, where noise is added to images following a scheduler during training, and samples are generated by iteratively removing noise until the final image is revealed.', "Importance of regularly sampling from the model during training, even when there's little decrease in loss, as the images progressively improve. Regularly sampling from the model during training is crucial, even when there's little decrease in loss, as the images progressively improve.", 'Utilizing the model registry as a system of record for all production-ready models, managing their lifecycle from staging to production, and promoting collaboration across different teams. The model registry serves as a system of record for all production-ready models, managing their lifecycle from staging to production, and promoting collaboration across different teams.']}], 'duration': 569.04, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc795457.jpg', 'highlights': ['Capturing model settings is essential for reproducibility, communication, and debugging, including batch size, dropout, epics, and learning rate.', 'Facilitates easy communication with others about the settings chosen for the model, aiding in reproducibility and collaboration.', 'Utilizing the model registry as a system of record for all production-ready models, managing their lifecycle from staging to production, and promoting collaboration across different teams.', 'Diffusion models are denoising models, adding noise to images following a scheduler during training, and generating samples by iteratively removing noise until the final image is revealed.', "Importance of regularly sampling from the model during training, even when there's little decrease in loss, as the images progressively improve.", 'The captured information can be invaluable for debugging and understanding what code was used in an experiment, the environment, and the dataset, enhancing the ability to address issues when they occur.']}, {'end': 1865.126, 'segs': [{'end': 1441.88, 'src': 'embed', 'start': 1387.666, 'weight': 0, 'content': [{'end': 1395.012, 'text': 'To make our experiments more interesting, we will also be importing another sampler called ddim, also from the diffusion course material.', 'start': 1387.666, 'duration': 7.346}, {'end': 1399.115, 'text': 'This sampler operates faster but compromises on output quality.', 'start': 1395.572, 'duration': 3.543}, {'end': 1402.098, 'text': 'Our goal will be to compare the output of both samplers.', 'start': 1399.796, 'duration': 2.302}, {'end': 1404.92, 'text': 'To achieve this, we generate two sets of samples.', 'start': 1402.458, 'duration': 2.462}, {'end': 1411.292, 'text': "First, we're sampling from DDPM.", 'start': 1409.651, 'duration': 1.641}, {'end': 1417.313, 'text': "Now, this will take a little while, so we'll speed it up in the video.", 'start': 1414.653, 'duration': 2.66}, {'end': 1422.235, 'text': "Now, let's compare that to DDIM.", 'start': 1420.494, 'duration': 1.741}, {'end': 1431.978, 'text': "When I run the DDIM sampler, it only goes for 25 timestamps, so it's much faster than DDPM, which was 500 timestamps.", 'start': 1425.316, 'duration': 6.662}, {'end': 1436.537, 'text': "Now let's compare our results in a visual table.", 'start': 1433.976, 'duration': 2.561}, {'end': 1441.88, 'text': 'This table behaves like a data frame that can be rendered in the workspace of your WMB project.', 'start': 1437.178, 'duration': 4.702}], 'summary': 'Comparing ddpm and ddim samplers in terms of speed and output quality', 'duration': 54.214, 'max_score': 1387.666, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc1387666.jpg'}, {'end': 1515.912, 'src': 'embed', 'start': 1491.454, 'weight': 1, 'content': [{'end': 1497.058, 'text': 'Once the table is finished uploading, I can click the run to open that in a new tab and look at the resulting images.', 'start': 1491.454, 'duration': 5.604}, {'end': 1499.48, 'text': 'Here we find the samplers table.', 'start': 1498.079, 'duration': 1.401}, {'end': 1506.005, 'text': 'So we can see the input noise, the ddpm, and the ddim result, as well as the class.', 'start': 1499.98, 'duration': 6.025}, {'end': 1507.946, 'text': "So here's a row of heroes.", 'start': 1506.465, 'duration': 1.481}, {'end': 1515.912, 'text': 'To compare the two samplers, you can see that the same input noise actually generated two different results from the two samplers.', 'start': 1508.847, 'duration': 7.065}], 'summary': "Upon finishing table upload, run to view images and compare samplers' results.", 'duration': 24.458, 'max_score': 1491.454, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc1491454.jpg'}, {'end': 1687.501, 'src': 'embed', 'start': 1659.721, 'weight': 2, 'content': [{'end': 1663.263, 'text': 'The notebook will explore three examples, starting with the simplest one.', 'start': 1659.721, 'duration': 3.542}, {'end': 1670.307, 'text': "You'll call an LLM API, in this case, OpenAI, and examine the results from the API using WMB tables.", 'start': 1663.903, 'duration': 6.404}, {'end': 1676.79, 'text': 'This will show you how to use tables for evaluation, analysis, and gaining insights from your experimentation.', 'start': 1671.267, 'duration': 5.523}, {'end': 1682.894, 'text': "Moving on to the second example, you'll create a custom LLM chain and track it with a tool called Tracer.", 'start': 1677.211, 'duration': 5.683}, {'end': 1687.501, 'text': 'This will demonstrate the value of tracking and debugging more complex chains.', 'start': 1683.939, 'duration': 3.562}], 'summary': 'Explore 3 examples, including api call and table usage, then create custom llm chain for tracking and debugging.', 'duration': 27.78, 'max_score': 1659.721, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc1659721.jpg'}, {'end': 1800.887, 'src': 'embed', 'start': 1771.253, 'weight': 3, 'content': [{'end': 1776.157, 'text': "Then we'll define a function that takes a system prompt, user prompt, and a WMB table.", 'start': 1771.253, 'duration': 4.904}, {'end': 1780.18, 'text': "We'll use our completionWithBackoff function to collect responses.", 'start': 1777.078, 'duration': 3.102}, {'end': 1784.983, 'text': "Additionally, we're tracking the start time and elapsed time after each response.", 'start': 1780.82, 'duration': 4.163}, {'end': 1791.248, 'text': "For each generated response, we're printing out the result.", 'start': 1788.306, 'duration': 2.942}, {'end': 1800.887, 'text': "If you run more experiments, the printing out results here in the notebook won't be very efficient.", 'start': 1795.785, 'duration': 5.102}], 'summary': 'Developing a function to collect responses, tracking start and elapsed time, and considering efficiency in printing results.', 'duration': 29.634, 'max_score': 1771.253, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc1771253.jpg'}, {'end': 1865.126, 'src': 'embed', 'start': 1837.399, 'weight': 4, 'content': [{'end': 1840.86, 'text': "This name, Unity's Valor, really does sound like a hero.", 'start': 1837.399, 'duration': 3.461}, {'end': 1844.702, 'text': "Harmonic Champion, Unity's Chorus, Unity's Valor.", 'start': 1841.501, 'duration': 3.201}, {'end': 1845.682, 'text': 'I like these names.', 'start': 1845.042, 'duration': 0.64}, {'end': 1849.043, 'text': "Next, let's do an item from the game.", 'start': 1847.243, 'duration': 1.8}, {'end': 1851.184, 'text': "Let's do user prompt is Jewel.", 'start': 1849.583, 'duration': 1.601}, {'end': 1854.378, 'text': 'Nice Here are a few options.', 'start': 1852.717, 'duration': 1.661}, {'end': 1858.742, 'text': "Harmony Gems, Laughter's Gem, Gleaming Unity.", 'start': 1854.578, 'duration': 4.164}, {'end': 1861.243, 'text': 'It seems like Unity is a big theme here.', 'start': 1859.422, 'duration': 1.821}, {'end': 1865.126, 'text': "Now let's log this table and go look at it in WMB.", 'start': 1861.664, 'duration': 3.462}], 'summary': "Unity-themed names for game elements, e.g., 'unity's valor' and 'harmony gems', are being considered.", 'duration': 27.727, 'max_score': 1837.399, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc1837399.jpg'}], 'start': 1365.998, 'title': 'Sampling and llm evaluation', 'summary': "Compares ddpm and ddim diffusion samplers, noting that ddim operates faster but compromises on output quality, with ddpm taking 500 timestamps compared to ddim's 25. it also evaluates language models by tracking and analyzing generated samples in wmb tables.", 'chapters': [{'end': 1441.88, 'start': 1365.998, 'title': 'Comparing ddpm and ddim samplers', 'summary': "Discusses setting up the ddpm and ddim diffusion samplers for comparison, where ddim operates faster but compromises on output quality, with ddpm taking 500 timestamps compared to ddim's 25, and results being compared in a visual table.", 'duration': 75.882, 'highlights': ['DDPM operates slower, taking 500 timestamps, while DDIM operates faster with only 25 timestamps, compromising on output quality.', 'Comparison of results from both samplers is visualized in a table, offering a direct comparison of their performance.']}, {'end': 1865.126, 'start': 1443.38, 'title': 'Sampling comparison and llm evaluation', 'summary': "Explores sampling comparison and llm evaluation, including comparing two samplers' results and evaluating language models by tracking and analyzing generated samples in wmb tables.", 'duration': 421.746, 'highlights': ['By comparing the results of DDPM and DDIM samplers, it was observed that the same input noise generated different results, with DDPM performing better than DDIM in this case, indicating a conditioned effect on the results.', 'The process of evaluating language models involved calling an LLM API, examining the results using WMB tables, creating a custom LLM chain tracked with Tracer, and exploring integrations with Langchain to build and track simple agents.', 'The use of completionWithBackoff function to avoid rate limits, tracking start time and elapsed time for each response, and logging all outputs to a table demonstrated efficient tracking and debugging of language model generation.', "The WMB table showed generated names for game assets based on different user prompts, such as 'Unity's Valor' and 'Harmony Gems', indicating a consistent theme of 'Unity' in the generated names."]}], 'duration': 499.128, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc1365998.jpg', 'highlights': ['DDPM operates slower, taking 500 timestamps, while DDIM operates faster with only 25 timestamps, compromising on output quality.', 'Comparison of results from both samplers is visualized in a table, offering a direct comparison of their performance.', 'The process of evaluating language models involved calling an LLM API, examining the results using WMB tables, creating a custom LLM chain tracked with Tracer, and exploring integrations with Langchain to build and track simple agents.', 'The use of completionWithBackoff function to avoid rate limits, tracking start time and elapsed time for each response, and logging all outputs to a table demonstrated efficient tracking and debugging of language model generation.', "The WMB table showed generated names for game assets based on different user prompts, such as 'Unity's Valor' and 'Harmony Gems', indicating a consistent theme of 'Unity' in the generated names.", 'By comparing the results of DDPM and DDIM samplers, it was observed that the same input noise generated different results, with DDPM performing better than DDIM in this case, indicating a conditioned effect on the results.']}, {'end': 2500.063, 'segs': [{'end': 1979.182, 'src': 'embed', 'start': 1952.472, 'weight': 0, 'content': [{'end': 1958.017, 'text': "Great, now this table is something I could share with a colleague to show them what I've tried for generating memes.", 'start': 1952.472, 'duration': 5.545}, {'end': 1961.459, 'text': "In the second example, you'll create a simple chain.", 'start': 1959.117, 'duration': 2.342}, {'end': 1967.044, 'text': "Although it may seem like a toy example, you'll use it to demonstrate the concept of a tracer,", 'start': 1962.26, 'duration': 4.784}, {'end': 1970.667, 'text': 'which is very powerful for debugging LLM chains and workflows.', 'start': 1967.044, 'duration': 3.623}, {'end': 1974.055, 'text': 'your chain will consist of just two actions.', 'start': 1971.772, 'duration': 2.283}, {'end': 1979.182, 'text': "The first action involves selecting a virtual world, and you'll call it World Picker.", 'start': 1974.636, 'duration': 4.546}], 'summary': 'Demonstrating a simple chain with two actions for debugging llm chains and workflows', 'duration': 26.71, 'max_score': 1952.472, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc1952472.jpg'}, {'end': 2235.517, 'src': 'embed', 'start': 2189.442, 'weight': 1, 'content': [{'end': 2195.446, 'text': 'Now, for inputs to that LLM, we have the system prompt as well as the results from the world picker.', 'start': 2189.442, 'duration': 6.004}, {'end': 2202.532, 'text': 'This trace timeline helps us understand the execution of this chain and how these steps fit together.', 'start': 2197.148, 'duration': 5.384}, {'end': 2205.054, 'text': 'Now, this trace timeline is pretty simple.', 'start': 2202.932, 'duration': 2.122}, {'end': 2212.44, 'text': "There's only two steps, but this gets really useful when you have a lot of different steps in your chain, when it's longer and more complex.", 'start': 2205.334, 'duration': 7.106}, {'end': 2218.424, 'text': "This will allow you to debug and pinpoint any issues if the results aren't what you expect.", 'start': 2213.16, 'duration': 5.264}, {'end': 2222.007, 'text': 'So, for example, if the world picker failed, we could see that here.', 'start': 2219.105, 'duration': 2.902}, {'end': 2229.654, 'text': 'Although defining chains manually can be exhausting, there are libraries like linkchain that can speed up that process.', 'start': 2223.55, 'duration': 6.104}, {'end': 2232.415, 'text': "We'll discuss that in the following example.", 'start': 2230.354, 'duration': 2.061}, {'end': 2235.517, 'text': 'This final example uses a linkchain agent.', 'start': 2233.156, 'duration': 2.361}], 'summary': 'Trace timeline helps understand chain execution, linkchain speeds up defining process.', 'duration': 46.075, 'max_score': 2189.442, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc2189442.jpg'}], 'start': 1866.848, 'title': 'Openai api cost and llm chain tracing', 'summary': 'Demonstrates calculating openai api cost for data dashboard and introduces tracing and debugging in llm chains, including examples and tools for issue identification and resolution.', 'chapters': [{'end': 1947.457, 'start': 1866.848, 'title': 'Analyzing openai api cost in data dashboard', 'summary': 'Demonstrates how to calculate the cost of using the openai api to generate fund names for assets in a data dashboard, including the process of creating a new column and performing the cost calculation.', 'duration': 80.609, 'highlights': ['Creating a new column to calculate the cost of using the OpenAI API, which is 0.000015 per token, in the data dashboard.', 'Accessing the generated examples, system prompt, and user prompt in the data dashboard for reviewing and sharing the results.', 'Showing the process of updating the cell expression and multiplying the total tokens by the estimated cost to derive the cost column.']}, {'end': 2500.063, 'start': 1952.472, 'title': 'Tracing and debugging llm chains', 'summary': 'Introduces the concept of tracing and debugging in llm chains, with examples showing the use of tracer to debug and pinpoint issues in the chains, such as tracking inputs, outputs, and results, and how to identify and resolve problems, and the use of linkchain and linkchain agent to speed up the chain definition and make decisions, respectively.', 'duration': 547.591, 'highlights': ['The concept of tracing and debugging in LLM chains is introduced, showcasing the use of tracer to debug and pinpoint issues in the chains. Demonstrates the use of tracer to track various aspects in the chain, identifying and analyzing workflow, and pinpointing issues for debugging.', 'The examples illustrate the tracking of inputs, outputs, and results in the LLM chains and how to identify and resolve problems. Shows how to track inputs, outputs, start and end times, and final results of various steps in the chain, aiding in identifying, analyzing, and resolving issues.', "The use of linkchain and linkchain agent to speed up the chain definition and make decisions is discussed. Explains the use of linkchain and linkchain agent to streamline chain definition and enable decision-making, showcasing their role in expediting the chain's creation and making unpredictable decisions."]}], 'duration': 633.215, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc1866848.jpg', 'highlights': ['Introducing tracing and debugging in LLM chains, using tracer to identify and analyze workflow and pinpoint issues for debugging.', 'Demonstrating the tracking of inputs, outputs, start and end times, and final results in LLM chains for issue identification and resolution.', 'Explaining the use of linkchain and linkchain agent to streamline chain definition and enable decision-making.']}, {'end': 3005.005, 'segs': [{'end': 2528.549, 'src': 'embed', 'start': 2500.684, 'weight': 0, 'content': [{'end': 2505.627, 'text': 'In summary, this analysis gave us valuable insights into how and where the chain might have gone wrong.', 'start': 2500.684, 'duration': 4.943}, {'end': 2510.27, 'text': 'And we can use that information to improve the results and make our agent more successful.', 'start': 2506.027, 'duration': 4.243}, {'end': 2513.853, 'text': "Now in the next lesson, we'll talk about fine tuning LLMs.", 'start': 2510.711, 'duration': 3.142}, {'end': 2524.065, 'text': 'In the last lesson, we learned about using LLMs via API, but sometimes you might need to train a completely new model or fine-tune an existing one.', 'start': 2515.717, 'duration': 8.348}, {'end': 2528.549, 'text': "We'll talk about it in this lesson with a focus on debugging and evaluation.", 'start': 2524.646, 'duration': 3.903}], 'summary': 'Analysis provides insights to improve results and agent success. next, fine tuning llms and debugging will be discussed.', 'duration': 27.865, 'max_score': 2500.684, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc2500684.jpg'}, {'end': 2563.382, 'src': 'embed', 'start': 2539.241, 'weight': 1, 'content': [{'end': 2545.124, 'text': "So it's important to keep a close eye on the training process and use checkpoints to deal with any unexpected problems.", 'start': 2539.241, 'duration': 5.883}, {'end': 2548.785, 'text': 'You can get valuable information from the dashboard,', 'start': 2546.124, 'duration': 2.661}, {'end': 2553.387, 'text': 'which shows the training progress metrics and helps you get the model checkpoints if you need them.', 'start': 2548.785, 'duration': 4.602}, {'end': 2560.078, 'text': 'Fine-tuning methods let you refine an LLM more economically, even if we have limited computing power.', 'start': 2554.551, 'duration': 5.527}, {'end': 2563.382, 'text': 'But you still need to be careful during the evaluation process.', 'start': 2560.799, 'duration': 2.583}], 'summary': 'Utilize checkpoints and dashboard for monitoring training progress and model refinement, even with limited computing power.', 'duration': 24.141, 'max_score': 2539.241, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc2539241.jpg'}, {'end': 2639.397, 'src': 'embed', 'start': 2575.941, 'weight': 3, 'content': [{'end': 2583.367, 'text': "To do this efficiently on a CPU, we'll use a small language model called Tiny Stories, which has 33 million parameters.", 'start': 2575.941, 'duration': 7.426}, {'end': 2589.613, 'text': "We'll fine-tune this light model on a dataset of character backstories from the Dungeons & Dragons gaming world.", 'start': 2583.748, 'duration': 5.865}, {'end': 2593.196, 'text': 'As usual, start with imports and log in.', 'start': 2591.074, 'duration': 2.122}, {'end': 2603.7, 'text': "We'll pull a data set from the Hugging Face hub.", 'start': 2601.698, 'duration': 2.002}, {'end': 2610.147, 'text': 'Looking at this example, we can see the data set has two columns.', 'start': 2606.503, 'duration': 3.644}, {'end': 2618.756, 'text': 'This text column asks the model to generate a backstory, and this target column holds the generated backstory of the character.', 'start': 2610.927, 'duration': 7.829}, {'end': 2622.96, 'text': "We'll set up the data set split so we have validation sent.", 'start': 2619.937, 'duration': 3.023}, {'end': 2635.273, 'text': "And then before training the model, we'll combine and prepare the instructions and stories, making sure they're tokenized and padded.", 'start': 2628.686, 'duration': 6.587}, {'end': 2639.397, 'text': "We'll also create labels, which are identical to our inputs.", 'start': 2636.093, 'duration': 3.304}], 'summary': 'Using tiny stories model with 33m parameters, fine-tuning on d&d backstories dataset for character generation.', 'duration': 63.456, 'max_score': 2575.941, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc2575941.jpg'}], 'start': 2500.684, 'title': 'Language model fine-tuning', 'summary': 'Discusses insights from analyzing llms, importance of training and evaluating llms, and cost-effective fine-tuning methods. it also introduces the process of fine-tuning a small language model with 33 million parameters on character backstories from dungeons & dragons, using hugging face and weights and biases to train and evaluate the model, with a focus on training efficiency and qualitative evaluation.', 'chapters': [{'end': 2575.24, 'start': 2500.684, 'title': 'Fine-tuning language models', 'summary': 'Discusses the insights gained from analyzing llms, the importance of training and evaluating llms, and the cost-effective fine-tuning methods.', 'duration': 74.556, 'highlights': ['The chapter emphasizes the value of analyzing LLMs to improve results and agent success.', 'Training LLMs from scratch is time-consuming and expensive, while evaluating them is complex and resource-intensive.', 'Using checkpoints and monitoring training progress metrics from the dashboard can help in dealing with unexpected problems and obtaining valuable information.', 'Fine-tuning methods are highlighted for refining LLMs more economically, even with limited computing power.', 'Specific evaluation strategies are necessary depending on the desired outcomes of the LLM.']}, {'end': 3005.005, 'start': 2575.941, 'title': 'Tiny stories model training', 'summary': 'Introduces the process of fine-tuning a small language model with 33 million parameters on character backstories from dungeons & dragons, using hugging face and weights and biases to train and evaluate the model, with a focus on training efficiency and qualitative evaluation.', 'duration': 429.064, 'highlights': ['The chapter introduces fine-tuning a small language model with 33 million parameters on character backstories from Dungeons & Dragons, using Hugging Face and Weights and Biases to train and evaluate the model, focusing on training efficiency and qualitative evaluation.', 'The model uses a dataset with two columns, a text column for generating backstories and a target column for holding the generated backstories of characters.', 'The model is trained using the Transformers trainer from Hugging Face for causal language modeling, with a demonstration of simple integration with Weights and Biases for streaming metrics.', 'The training process includes defining training arguments such as the number of training epics, learning rate, weight decay, and setting report2 to W and B for streaming all results to a central dashboard.', 'The chapter emphasizes the importance of qualitative evaluation when training generative AI models and encourages the use of metrics for specific use cases, such as measuring the number of unique words in the generated outputs.']}], 'duration': 504.321, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/kOMGjjinQzc/pics/kOMGjjinQzc2500684.jpg', 'highlights': ['The chapter emphasizes the value of analyzing LLMs to improve results and agent success.', 'Fine-tuning methods are highlighted for refining LLMs more economically, even with limited computing power.', 'Using checkpoints and monitoring training progress metrics from the dashboard can help in dealing with unexpected problems and obtaining valuable information.', 'The model uses a dataset with two columns, a text column for generating backstories and a target column for holding the generated backstories of characters.', 'The chapter introduces fine-tuning a small language model with 33 million parameters on character backstories from Dungeons & Dragons, using Hugging Face and Weights and Biases to train and evaluate the model, focusing on training efficiency and qualitative evaluation.']}], 'highlights': ['The course focuses on evaluating and debugging generative AI using tools such as experiments, artifacts, models tables, reports, and model registry, compatible with various frameworks and computing platforms, including Python, TensorFlow, or PyTorch. 0.95', 'The chapter emphasizes the need for systematic tracking and debugging to efficiently drive improvements in machine learning development, especially as the complexity grows with larger teams. 0.9', "The short course addresses the tools and best practices for systematically tracking and debugging generative AI models, utilizing Ways and Biases' tools, recognized as an industry standard for machine learning experiment tracking. 0.85", 'The contributors to the course include Darren Kojicek, Thomas Koppel, Jeff Ludwig, and Tommy Nelson. 0.8', 'You can version control your code, reproduce model checkpoints, and visualize your predictions in a centralized interactive dashboard, providing a comprehensive approach to managing and evaluating machine learning models. 0.75', 'The course will equip you to incorporate weights and biases into your training process, empowering you to effectively monitor, debug, and evaluate machine learning pipelines. 0.7', 'Capturing model settings is essential for reproducibility, communication, and debugging, including batch size, dropout, epics, and learning rate. 0.65', 'The process of evaluating language models involved calling an LLM API, examining the results using WMB tables, creating a custom LLM chain tracked with Tracer, and exploring integrations with Langchain to build and track simple agents. 0.6', 'Introducing tracing and debugging in LLM chains, using tracer to identify and analyze workflow and pinpoint issues for debugging. 0.55', 'Fine-tuning methods are highlighted for refining LLMs more economically, even with limited computing power. 0.5']}