title
Introduction to large language models

description
Enroll in this course on Google Cloud Skills Boost → https://goo.gle/3nXSmLs Large Language Models (LLMs) and Generative AI intersect and they are both part of deep learning. Watch this video to learn about LLMs, including use cases, Prompt Tuning, and GenAI development tools. Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech

detail
{'title': 'Introduction to large language models', 'heatmap': [{'end': 39.12, 'start': 24.471, 'weight': 0.799}, {'end': 153.909, 'start': 132.82, 'weight': 0.714}, {'end': 199.94, 'start': 182.854, 'weight': 0.704}, {'end': 616.59, 'start': 594.85, 'weight': 0.789}, {'end': 668.474, 'start': 617.531, 'weight': 0.768}, {'end': 724.863, 'start': 696.851, 'weight': 0.798}], 'summary': "Introduces large language models, discussing their use cases, intersection with generative ai, and google's genai development tools. it also covers pre-training, fine-tuning, and the example of palm with 540 billion parameters, the role of transformer models, prompt design, domain knowledge for question answering models, nlp prompt design, task-specific tuning, generative ai studio & app builder, task-specific foundation models in vertex ai, and parameter-efficient tuning methods for llms.", 'chapters': [{'end': 49.827, 'segs': [{'end': 49.827, 'src': 'heatmap', 'start': 24.471, 'weight': 0, 'content': [{'end': 28.834, 'text': 'To find out more about deep learning, see our Introduction to Generative AI course video.', 'start': 24.471, 'duration': 4.363}, {'end': 33.617, 'text': 'LLMs and generative AI intersect, and they are both a part of deep learning.', 'start': 29.775, 'duration': 3.842}, {'end': 39.12, 'text': 'Another area of AI you may be hearing a lot about is generative AI.', 'start': 35.458, 'duration': 3.662}, {'end': 46.505, 'text': 'This is a type of artificial intelligence that can produce new content, including text, images, audio, and synthetic data.', 'start': 39.841, 'duration': 6.664}, {'end': 49.827, 'text': 'So what are large language models?', 'start': 48.366, 'duration': 1.461}], 'summary': 'Generative ai can produce new content, including text, images, audio, and synthetic data.', 'duration': 49.257, 'max_score': 24.471, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs24471.jpg'}], 'start': 0.57, 'title': 'Large language models', 'summary': "Introduces large language models, discussing their use cases and intersection with generative ai, and describes google's genai development tools.", 'chapters': [{'end': 49.827, 'start': 0.57, 'title': 'Introduction to large language models', 'summary': "Introduces large language models (llms), explains their use cases, and their intersection with generative ai, and it describes google's genai development tools.", 'duration': 49.257, 'highlights': ['Large language models, or LLMs, are a subset of deep learning, intersect with generative AI, and are used to produce new content including text, images, audio, and synthetic data.', "The course covers defining LLMs, LLM use cases, prompt tuning, and Google's GenAI development tools.", 'Generative AI is a type of artificial intelligence that can produce new content, including text, images, audio, and synthetic data.']}], 'duration': 49.257, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs570.jpg', 'highlights': ['LLMs intersect with generative AI, used to produce new content. (3)', 'Course covers defining LLMs, use cases, prompt tuning, and GenAI tools. (2)', 'Generative AI produces new content, including text, images, audio, and data. (1)']}, {'end': 523.496, 'segs': [{'end': 161.233, 'src': 'heatmap', 'start': 132.82, 'weight': 0.714, 'content': [{'end': 134.981, 'text': 'Second, it refers to the parameter count.', 'start': 132.82, 'duration': 2.161}, {'end': 138.103, 'text': 'In ML, parameters are often called hyperparameters.', 'start': 135.421, 'duration': 2.682}, {'end': 143.666, 'text': 'Parameters are basically the memories and the knowledge that the machine learned from the model training.', 'start': 138.983, 'duration': 4.683}, {'end': 148.849, 'text': 'Parameters define the skill of a model in solving a problem, such as predicting text.', 'start': 144.406, 'duration': 4.443}, {'end': 153.909, 'text': 'General purpose means that the models are sufficient to solve common problems.', 'start': 150.106, 'duration': 3.803}, {'end': 156.01, 'text': 'Two reasons lead to this idea.', 'start': 154.569, 'duration': 1.441}, {'end': 161.233, 'text': 'First is the commonality of a human language, regardless of the specific tasks.', 'start': 156.97, 'duration': 4.263}], 'summary': "Ml models' parameters define their skill in solving problems; general purpose models are sufficient for common tasks.", 'duration': 28.413, 'max_score': 132.82, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs132820.jpg'}, {'end': 211.217, 'src': 'heatmap', 'start': 182.854, 'weight': 0.704, 'content': [{'end': 192.017, 'text': 'meaning to pre-train a large language model for a general purpose with a large dataset and then fine-tune it for specific aims with a much smaller dataset.', 'start': 182.854, 'duration': 9.163}, {'end': 196.719, 'text': 'The benefits of using large language models are straightforward.', 'start': 193.738, 'duration': 2.981}, {'end': 199.94, 'text': 'First, a single model can be used for different tasks.', 'start': 197.259, 'duration': 2.681}, {'end': 202.154, 'text': 'This is a dream come true.', 'start': 200.834, 'duration': 1.32}, {'end': 211.217, 'text': 'These large language models that are trained with petabytes of data and generate billions of parameters are smart enough to solve different tasks,', 'start': 202.695, 'duration': 8.522}], 'summary': 'Pre-training large language models benefits versatility and efficiency.', 'duration': 28.363, 'max_score': 182.854, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs182854.jpg'}, {'end': 225.362, 'src': 'embed', 'start': 202.695, 'weight': 1, 'content': [{'end': 211.217, 'text': 'These large language models that are trained with petabytes of data and generate billions of parameters are smart enough to solve different tasks,', 'start': 202.695, 'duration': 8.522}, {'end': 217.819, 'text': 'including language translation, sentence completion, text classification, question answering and more.', 'start': 211.217, 'duration': 6.602}, {'end': 225.362, 'text': 'Second, large language models require minimal field training data when you tailor them to solve your specific problem.', 'start': 218.98, 'duration': 6.382}], 'summary': 'Large language models can solve various tasks with minimal training data.', 'duration': 22.667, 'max_score': 202.695, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs202695.jpg'}, {'end': 275.516, 'src': 'embed', 'start': 247.151, 'weight': 0, 'content': [{'end': 253.594, 'text': 'Third, the performance of large language models is continuously growing when you add more data and parameters.', 'start': 247.151, 'duration': 6.443}, {'end': 257.35, 'text': "Let's take Palm as an example.", 'start': 255.629, 'duration': 1.721}, {'end': 264.432, 'text': 'In April 2022, Google released Palm short for Pathways Language Model,', 'start': 258.089, 'duration': 6.343}, {'end': 270.615, 'text': 'a 540 billion parameter model that achieves a state-of-the-art performance across multiple language tasks.', 'start': 264.432, 'duration': 6.183}, {'end': 275.516, 'text': 'Palm is a dense decoder-only transformer model.', 'start': 271.735, 'duration': 3.781}], 'summary': 'Palm, a 540 billion parameter model, achieves state-of-the-art performance across multiple language tasks.', 'duration': 28.365, 'max_score': 247.151, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs247151.jpg'}, {'end': 333.21, 'src': 'embed', 'start': 304.726, 'weight': 2, 'content': [{'end': 307.326, 'text': 'We previously mentioned that Palm is a transformer model.', 'start': 304.726, 'duration': 2.6}, {'end': 310.907, 'text': 'A transformer model consists of encoder and decoder.', 'start': 308.006, 'duration': 2.901}, {'end': 319.169, 'text': 'The encoder encodes the input sequence and passes it to the decoder, which learns how to decode the representations for a relevant task.', 'start': 311.467, 'duration': 7.702}, {'end': 326.291, 'text': "We've come a long way from traditional programming to neural networks to generative models.", 'start': 321.15, 'duration': 5.141}, {'end': 331.649, 'text': 'In traditional programming, we used to have to hard code the rules for distinguishing a cat.', 'start': 327.205, 'duration': 4.444}, {'end': 333.21, 'text': 'Type, animal.', 'start': 332.249, 'duration': 0.961}], 'summary': 'Palm is a transformer model with encoder and decoder, representing a shift from traditional programming to generative models', 'duration': 28.484, 'max_score': 304.726, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs304726.jpg'}, {'end': 404.209, 'src': 'embed', 'start': 373.012, 'weight': 5, 'content': [{'end': 376.194, 'text': 'whether typing it into a prompt or verbally talking into the prompt.', 'start': 373.012, 'duration': 3.182}, {'end': 381.603, 'text': "So when you ask it, what's a cat? It can give you everything it has learned about a cat.", 'start': 377.421, 'duration': 4.182}, {'end': 388.267, 'text': "Let's compare LLM development using pre-trained models with traditional ML development.", 'start': 383.584, 'duration': 4.683}, {'end': 392.627, 'text': "First, with LLM development, you don't need to be an expert.", 'start': 389.366, 'duration': 3.261}, {'end': 396.567, 'text': "You don't need training examples, and there is no need to train a model.", 'start': 393.107, 'duration': 3.46}, {'end': 404.209, 'text': 'All you need to do is think about prompt design, which is the process of creating a prompt that is clear, concise, and informative.', 'start': 397.328, 'duration': 6.881}], 'summary': 'Llm development simplifies prompt-based learning, requiring no expertise, training data, or model training.', 'duration': 31.197, 'max_score': 373.012, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs373012.jpg'}, {'end': 473.167, 'src': 'embed', 'start': 443.728, 'weight': 4, 'content': [{'end': 447.852, 'text': 'The key here is that you need domain knowledge to develop these question answering models.', 'start': 443.728, 'duration': 4.124}, {'end': 457.202, 'text': 'For example, domain knowledge is required to develop a question answering model for customer IT support or healthcare or supply chain.', 'start': 449.619, 'duration': 7.583}, {'end': 463.164, 'text': 'Using generative QA, the model generates free text directly based on the context.', 'start': 458.362, 'duration': 4.802}, {'end': 465.724, 'text': 'There is no need for domain knowledge.', 'start': 463.524, 'duration': 2.2}, {'end': 473.167, 'text': "Let's look at three questions given to Bard, a large language model chatbot developed by Google AI.", 'start': 467.425, 'duration': 5.742}], 'summary': 'Domain knowledge is essential for question answering models, but generative qa bypasses this requirement.', 'duration': 29.439, 'max_score': 443.728, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs443728.jpg'}, {'end': 527.435, 'src': 'embed', 'start': 499, 'weight': 6, 'content': [{'end': 501.101, 'text': 'A new order requires 8, 000 units.', 'start': 499, 'duration': 2.101}, {'end': 508.706, 'text': 'How many units do I need to fill to complete the order? Again, BART answers the question by performing the calculation.', 'start': 501.882, 'duration': 6.824}, {'end': 514.37, 'text': 'And our last example, we have 1, 000 sensors in 10 geographic regions.', 'start': 509.907, 'duration': 4.463}, {'end': 517.493, 'text': 'How many sensors do we have on average in each region?', 'start': 514.751, 'duration': 2.742}, {'end': 523.496, 'text': 'BART answers the question with an example on how to solve the problem and some additional context.', 'start': 518.712, 'duration': 4.784}, {'end': 527.435, 'text': 'In each of our questions, a desired response was obtained.', 'start': 524.554, 'duration': 2.881}], 'summary': 'Bart solves 8,000 units order, 1,000 sensors in 10 regions effectively.', 'duration': 28.435, 'max_score': 499, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs499000.jpg'}], 'start': 50.697, 'title': 'Large language models and generative models', 'summary': 'Discusses large language models, their pre-training, fine-tuning process, and the example of palm with 540 billion parameters. it also covers the role of transformer models like palm in generating content, the comparison between traditional ml and llm development, and the advantages of prompt design and domain knowledge for question answering models.', 'chapters': [{'end': 303.025, 'start': 50.697, 'title': 'Large language models: pre-training and fine-tuning', 'summary': 'Discusses the concept of large language models, highlighting their pre-training and fine-tuning process, their general purpose, benefits, and the example of palm, a 540 billion parameter model achieving state-of-the-art performance.', 'duration': 252.328, 'highlights': ['The concept of large language models is explained, emphasizing their pre-training for general purposes with a large dataset and then fine-tuning for specific aims with a much smaller dataset, enabling them to be used for different tasks, even with minimal field training data.', 'The benefits of using large language models, including their ability to solve different tasks, minimal field training data requirement, and continuous performance growth with the addition of more data and parameters, are outlined.', 'The example of Palm, a 540 billion parameter model released by Google in April 2022, achieving state-of-the-art performance across multiple language tasks, is presented, along with its leveraging of the new Pathways system for efficient training and orchestration of distributed computation for accelerators.']}, {'end': 523.496, 'start': 304.726, 'title': 'Generative models in language processing', 'summary': 'Discusses the evolution from traditional programming to neural networks to generative models, particularly focusing on the role of transformer models like palm in generating content and the comparison between traditional ml development and llm development, highlighting the advantages of prompt design and the domain knowledge requirement for question answering models.', 'duration': 218.77, 'highlights': ['Transformer model consists of encoder and decoder', 'Comparison between traditional ML development and LLM development', 'QA systems are typically trained on a large amount of text and code', 'Domain knowledge is required to develop question answering models', 'BART answers the question by performing the calculation']}], 'duration': 472.799, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs50697.jpg', 'highlights': ['Palm, a 540 billion parameter model, achieves state-of-the-art performance across multiple language tasks', 'Large language models enable solving different tasks with minimal field training data', 'Transformer model consists of encoder and decoder', 'Benefits of large language models include continuous performance growth with more data and parameters', 'Domain knowledge is required to develop question answering models', 'Comparison between traditional ML and LLM development', 'BART answers questions by performing the calculation', 'QA systems are typically trained on a large amount of text and code']}, {'end': 945.203, 'segs': [{'end': 616.59, 'src': 'heatmap', 'start': 585.887, 'weight': 5, 'content': [{'end': 592.969, 'text': 'Prompt design is essential, while prompt engineering is only necessary for systems that require a high degree of accuracy or performance.', 'start': 585.887, 'duration': 7.082}, {'end': 601.212, 'text': 'There are three kinds of large language models, generic language models, instruction-tuned, and dialogue-tuned.', 'start': 594.85, 'duration': 6.362}, {'end': 603.853, 'text': 'Each needs prompting in a different way.', 'start': 601.973, 'duration': 1.88}, {'end': 608.875, 'text': 'Generic language models predict the next word based on the language in the training data.', 'start': 604.834, 'duration': 4.041}, {'end': 612.649, 'text': 'This is an example of a generic language model.', 'start': 610.148, 'duration': 2.501}, {'end': 616.59, 'text': 'The next word is a token based on the language in the training data.', 'start': 613.229, 'duration': 3.361}], 'summary': 'Prompt design is crucial; prompt engineering is essential for high accuracy systems. three types of large language models: generic, instruction-tuned, and dialogue-tuned.', 'duration': 22.988, 'max_score': 585.887, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs585887.jpg'}, {'end': 668.474, 'src': 'heatmap', 'start': 617.531, 'weight': 0.768, 'content': [{'end': 625.213, 'text': 'In this example, the cat sat on, the next word should be the, and you can see that the is the most likely next word.', 'start': 617.531, 'duration': 7.682}, {'end': 629.155, 'text': 'Think of this type as an autocomplete in search.', 'start': 626.634, 'duration': 2.521}, {'end': 636.257, 'text': 'In instruction tuned, the model is trained to predict a response to the instructions given in the input.', 'start': 631.135, 'duration': 5.122}, {'end': 648.811, 'text': 'For example, summarize a text of X, generate a poem in the style of X, give me a list of keywords based on semantic similarity for X.', 'start': 637.807, 'duration': 11.004}, {'end': 653.353, 'text': 'And in this example, classify the text into neutral, negative, or positive.', 'start': 648.811, 'duration': 4.542}, {'end': 659.535, 'text': 'In dialogue-tuned, the model is trained to have a dialogue by the next response.', 'start': 655.093, 'duration': 4.442}, {'end': 668.474, 'text': 'Dialogue-tuned models are a special case of instruction-tuned, where requests are typically framed as questions to a chatbot.', 'start': 661.112, 'duration': 7.362}], 'summary': 'Models trained for autocomplete, instruction prediction, and dialogue generation based on input.', 'duration': 50.943, 'max_score': 617.531, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs617531.jpg'}, {'end': 743.811, 'src': 'heatmap', 'start': 696.851, 'weight': 0, 'content': [{'end': 702.213, 'text': 'How many tennis balls does he have now? This question is posed initially with no response.', 'start': 696.851, 'duration': 5.362}, {'end': 705.874, 'text': 'The model is less likely to get the correct answer directly.', 'start': 702.933, 'duration': 2.941}, {'end': 711.836, 'text': 'However, by the time the second question is asked, the output is more likely to end with the correct answer.', 'start': 706.414, 'duration': 5.422}, {'end': 716.534, 'text': 'A model that can do everything has practical limitations.', 'start': 713.911, 'duration': 2.623}, {'end': 720.218, 'text': 'Task-specific tuning can make LLMs more reliable.', 'start': 717.195, 'duration': 3.023}, {'end': 724.863, 'text': 'Vertex AI provides task-specific foundation models.', 'start': 721.379, 'duration': 3.484}, {'end': 731.11, 'text': "Let's say you have a use case where you need to gather sentiments, or how your customers are feeling about your product or service.", 'start': 725.424, 'duration': 5.686}, {'end': 735.795, 'text': 'You can use the classification task Sentiment Analysis Task Model.', 'start': 731.971, 'duration': 3.824}, {'end': 738.229, 'text': 'Same for vision tasks.', 'start': 737.168, 'duration': 1.061}, {'end': 743.811, 'text': 'If you need to perform occupancy analytics, there is a task specific model for your use case.', 'start': 738.649, 'duration': 5.162}], 'summary': 'Llms have practical limitations, but task-specific tuning in vertex ai can make them more reliable for specific use cases.', 'duration': 64.534, 'max_score': 696.851, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs696851.jpg'}, {'end': 870.015, 'src': 'embed', 'start': 792.819, 'weight': 1, 'content': [{'end': 801.942, 'text': 'Fine-tuning is expensive and not realistic in many cases, So are there more efficient methods of tuning? Yes.', 'start': 792.819, 'duration': 9.123}, {'end': 811.105, 'text': 'Parameter-efficient tuning methods, or PETM, are methods for tuning a large language model on your own custom data without duplicating the model.', 'start': 802.402, 'duration': 8.703}, {'end': 814.187, 'text': 'The base model itself is not altered.', 'start': 811.966, 'duration': 2.221}, {'end': 819.849, 'text': 'Instead, a small number of add-on layers are tuned, which can be swapped in and out at inference time.', 'start': 814.827, 'duration': 5.022}, {'end': 829.785, 'text': 'Generative AI Studio lets you quickly explore and customize generative AI models that you can leverage in your applications on Google Cloud.', 'start': 821.587, 'duration': 8.198}, {'end': 839.608, 'text': 'Generative AI Studio helps developers create and deploy generative AI models by providing a variety of tools and resources that make it easy to get started.', 'start': 831.061, 'duration': 8.547}, {'end': 846.013, 'text': "For example, there's a library of pre-trained models, a tool for fine-tuning models,", 'start': 840.168, 'duration': 5.845}, {'end': 851.918, 'text': 'a tool for deploying models to production and a community forum for developers to share ideas and collaborate.', 'start': 846.013, 'duration': 5.905}, {'end': 858.543, 'text': 'Generative AI App Builder lets you create GenAI apps without having to write any code.', 'start': 853.419, 'duration': 5.124}, {'end': 865.693, 'text': 'GenAI App Builder has a drag and drop interface that makes it easy to design and build apps,', 'start': 859.71, 'duration': 5.983}, {'end': 870.015, 'text': 'a visual editor that makes it easy to create and edit app content,', 'start': 865.693, 'duration': 4.322}], 'summary': 'Parameter-efficient tuning methods enable custom data tuning without altering the base model, enhancing ai model efficiency.', 'duration': 77.196, 'max_score': 792.819, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs792819.jpg'}, {'end': 941.32, 'src': 'embed', 'start': 890.08, 'weight': 4, 'content': [{'end': 895.924, 'text': "Palm API lets you test and experiment with Google's large language models and Gen AI tools.", 'start': 890.08, 'duration': 5.844}, {'end': 900.008, 'text': 'To make prototyping quick and more accessible,', 'start': 897.125, 'duration': 2.883}, {'end': 907.693, 'text': 'developers can integrate Palm API with Makersuite and use it to access the API using a graphical user interface.', 'start': 900.008, 'duration': 7.685}, {'end': 915.599, 'text': 'The suite includes a number of different tools, such as a model training tool, a model deployment tool, and a model monitoring tool.', 'start': 908.414, 'duration': 7.185}, {'end': 921.364, 'text': 'The Model Training tool helps developers train ML models on their data using different algorithms.', 'start': 916.46, 'duration': 4.904}, {'end': 928.49, 'text': 'The Model Deployment tool helps developers deploy ML models to production with a number of different deployment options.', 'start': 922.044, 'duration': 6.446}, {'end': 938.437, 'text': 'And the Model Monitoring tool helps developers monitor the performance of their ML models in production using a dashboard and a number of different metrics.', 'start': 929.871, 'duration': 8.566}, {'end': 941.32, 'text': "That's all for now.", 'start': 940.539, 'duration': 0.781}], 'summary': 'Palm api enables quick prototyping with model training, deployment, and monitoring tools.', 'duration': 51.24, 'max_score': 890.08, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs890080.jpg'}], 'start': 524.554, 'title': 'Nlp prompt design, task-specific tuning, and generative ai studio & app builder', 'summary': 'Explores the significance of prompt design and engineering in nlp, the advantages of task-specific tuning for reliable llms, and introduces generative ai studio & app builder, along with examples and practical limitations. it also discusses the availability of task-specific foundation models in vertex ai and parameter-efficient tuning methods (petm) for llms.', 'chapters': [{'end': 702.213, 'start': 524.554, 'title': 'Prompt design and engineering in nlp', 'summary': 'Explains the importance of prompt design and engineering in natural language processing, detailing their differences and their relevance to different kinds of large language models, along with examples.', 'duration': 177.659, 'highlights': ['Prompt design is essential for creating tailored prompts, while prompt engineering is necessary for improving performance in systems that require high accuracy.', 'Different large language models, such as generic, instruction-tuned, and dialogue-tuned, require different prompting methods for prediction and response to instructions or dialogue.', 'Chain-of-thought reasoning demonstrates the importance of initial text output for models to provide accurate answers.']}, {'end': 819.849, 'start': 702.933, 'title': 'Task-specific tuning for reliable llms', 'summary': 'Discusses the practical limitations of llms, the benefits of task-specific tuning to make llms more reliable, and the availability of task-specific foundation models in vertex ai, including sentiment analysis and vision tasks, as well as the concept of parameter-efficient tuning methods (petm).', 'duration': 116.916, 'highlights': ['Task-specific tuning can make LLMs more reliable, enabling customization of the model response based on examples of the task, and practical limitations of a model that can do everything (e.g., gathering sentiments, occupancy analytics).', 'Availability of task-specific foundation models in Vertex AI for various use cases such as sentiment analysis, vision tasks, and domain-specific tasks like legal or medical, including fine-tuning by bringing your own dataset and retraining the model.', 'Parameter-efficient tuning methods (PETM) allow tuning a large language model on custom data without altering the base model, making fine-tuning more efficient and realistic in many cases.']}, {'end': 945.203, 'start': 821.587, 'title': 'Generative ai studio & app builder', 'summary': "Introduces generative ai studio and app builder on google cloud, offering tools to create and deploy generative ai models and apps without coding, along with the palm api for testing google's large language models and gen ai tools.", 'duration': 123.616, 'highlights': ['Generative AI Studio provides pre-trained models, fine-tuning and deployment tools, and a community forum for developers.', 'Generative AI App Builder enables the creation of GenAI apps without coding through a drag and drop interface, visual editor, built-in search engine, and conversational AI engine.', "Palm API facilitates testing and experimentation with Google's large language models and Gen AI tools, integrated with Makersuite for quick prototyping and access through a graphical user interface.", 'The suite includes a model training tool, a model deployment tool, and a model monitoring tool, aiding developers in training, deploying, and monitoring ML models on their data with different algorithms and deployment options.']}], 'duration': 420.649, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zizonToFXDs/pics/zizonToFXDs524554.jpg', 'highlights': ['Availability of task-specific foundation models in Vertex AI for various use cases such as sentiment analysis, vision tasks, and domain-specific tasks like legal or medical, including fine-tuning by bringing your own dataset and retraining the model.', 'Parameter-efficient tuning methods (PETM) allow tuning a large language model on custom data without altering the base model, making fine-tuning more efficient and realistic in many cases.', 'Task-specific tuning can make LLMs more reliable, enabling customization of the model response based on examples of the task, and practical limitations of a model that can do everything (e.g., gathering sentiments, occupancy analytics).', 'Generative AI App Builder enables the creation of GenAI apps without coding through a drag and drop interface, visual editor, built-in search engine, and conversational AI engine.', "Palm API facilitates testing and experimentation with Google's large language models and Gen AI tools, integrated with Makersuite for quick prototyping and access through a graphical user interface.", 'Prompt design is essential for creating tailored prompts, while prompt engineering is necessary for improving performance in systems that require high accuracy.', 'Different large language models, such as generic, instruction-tuned, and dialogue-tuned, require different prompting methods for prediction and response to instructions or dialogue.', 'Chain-of-thought reasoning demonstrates the importance of initial text output for models to provide accurate answers.', 'Generative AI Studio provides pre-trained models, fine-tuning and deployment tools, and a community forum for developers.', 'The suite includes a model training tool, a model deployment tool, and a model monitoring tool, aiding developers in training, deploying, and monitoring ML models on their data with different algorithms and deployment options.']}], 'highlights': ['Palm, a 540 billion parameter model, achieves state-of-the-art performance across multiple language tasks', 'LLMs intersect with generative AI, used to produce new content', 'Large language models enable solving different tasks with minimal field training data', 'Availability of task-specific foundation models in Vertex AI for various use cases', 'Parameter-efficient tuning methods (PETM) allow tuning a large language model on custom data without altering the base model', 'Generative AI App Builder enables the creation of GenAI apps without coding through a drag and drop interface', 'Prompt design is essential for creating tailored prompts, while prompt engineering is necessary for improving performance in systems that require high accuracy', 'Generative AI Studio provides pre-trained models, fine-tuning and deployment tools, and a community forum for developers', 'Task-specific tuning can make LLMs more reliable, enabling customization of the model response based on examples of the task', 'Comparison between traditional ML and LLM development', 'Domain knowledge is required to develop question answering models', 'BART answers questions by performing the calculation', 'Different large language models, such as generic, instruction-tuned, and dialogue-tuned, require different prompting methods for prediction and response to instructions or dialogue', 'Course covers defining LLMs, use cases, prompt tuning, and GenAI tools', 'QA systems are typically trained on a large amount of text and code', 'Benefits of large language models include continuous performance growth with more data and parameters', 'Chain-of-thought reasoning demonstrates the importance of initial text output for models to provide accurate answers', "Palm API facilitates testing and experimentation with Google's large language models and Gen AI tools, integrated with Makersuite for quick prototyping and access through a graphical user interface", 'Transformer model consists of encoder and decoder', 'Generative AI produces new content, including text, images, audio, and data']}